From 2b5e5e92120603ac0f47b69bf498af84cb8fcac1 Mon Sep 17 00:00:00 2001
From: Aditya Gahlot <adityagahlot2002@gmail.com>
Date: Sat, 10 Aug 2024 20:25:05 +1000
Subject: [PATCH 1/5] Add Jupyter Notebook for sentiment analysis of EV news
 articles

---
 Sentiment_Analysis.ipynb | 1000 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 1000 insertions(+)
 create mode 100644 Sentiment_Analysis.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a2a1d701-1644-4a79-a16a-d5196eacb4e1",
+   "metadata": {},
+   "source": [
+    "# EV News Articles Sentiment Analysis\n",
+    "Performing news article sentiment analysis of EV vehicles in Australia involves several steps, from data collection to sentiment analysis and visualization. Although the sentiments of people vary across various regions in Australia, I have presented a broad analysis of the Electric Vehicle industry through four online articles. The articles for this project were taken from the following links:\n",
+    "\n",
+    "1. https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/\n",
+    "2. https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles\n",
+    "3. https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html\n",
+    "4. https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac0279aa-b72c-4ccc-8808-9664ad081343",
+   "metadata": {},
+   "source": [
+    "# Procedure\n",
+    "Performing news article sentiment analysis of EV vehicles in Australia involves several steps, from data collection to sentiment analysis and visualization. These steps are summarized further.\n",
+    "## Step 1: Data Collection\n",
+    "After gathering the news articles for EVs, I used the technique of **web scraping** to extract meaningful information from the articles like the title, the publication date of the article and its broad content. For this purpose, I used web scraping tools like BeautifulSoup which is imported in the first cell along with other important libraries. After that, I have initialized all the URLs as an array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "aea29846-81dc-468e-ac46-c98193afc97a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import pandas as pd\n",
+    "import time\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "57681cd9-a557-4283-a734-cf346329ee34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List of article URLs\n",
+    "urls = [\n",
+    "    'https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/',\n",
+    "    'https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles',\n",
+    "    'https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html',\n",
+    "    'https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment',\n",
+    "    \n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "ac8f969b-2142-4494-889b-4e8bfd13caf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a list to store article data\n",
+    "articles_data = []\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fff14daf-1cb0-4e21-a0f1-175818fd1ec4",
+   "metadata": {},
+   "source": [
+    "## Step 2: Scraping an Article with Improved Selectors\n",
+    "After initializing an empty list to store article data named **articles_data**, I have defined a function **scrape_article** to fetch and parse a single article. It returns the details of an article, including its title, author, publication date, and content. Within the function, **headers** represents a dictionary with the User-Agent header. This helps to mimic a request from a web browser, which can be useful to avoid blocks from some websites that restrict automated scraping. Then an HTTP GET request was sent to the specified URL with the custom headers.\n",
+    "\n",
+    "The **BeautifulSoup (response.content, 'html.parser')** function parses the HTML content of the web page, creating a BeautifulSoup object for further extraction.I manually checked the structure of the HTML for each website to determine the correct selectors. This step required viewing the source code of each of the webpages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "6320942d-9d3e-4299-aa52-af8866ccafe2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scrape_article(url):\n",
+    "    headers = {\n",
+    "        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n",
+    "    }\n",
+    "    \n",
+    "    try:\n",
+    "        # Fetch the web page\n",
+    "        response = requests.get(url, headers=headers)\n",
+    "        response.raise_for_status()  # Raise HTTPError for bad responses\n",
+    "\n",
+    "        # Parse the HTML content\n",
+    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
+    "\n",
+    "        # Initialize variables\n",
+    "        title = 'No title found'\n",
+    "        author = 'No author found'\n",
+    "        publication_date = 'No date found'\n",
+    "        content = 'No content found'\n",
+    "\n",
+    "        # Check URL and set selectors accordingly\n",
+    "        if 'thedriven.io' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            author_tag = soup.find('a', class_='url fn n')\n",
+    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
+    "            date_tag = soup.find('a', rel='bookmark')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        elif 'ey.com' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            authors = [a.get_text(strip=True) for a in soup.find_all('a', class_='surfaceProfile-author-link')]\n",
+    "            author = ', '.join(authors) if authors else 'No author found'\n",
+    "            date_tag = soup.select_one('#container4 > div > div:nth-child(2) > div > div > span:nth-child(2)')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        elif 'sydney.edu.au' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            author_tag = soup.find('h3', class_='b-contact-information__title')\n",
+    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
+    "            date_tag = soup.find('span')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        elif 'carexpert.com.au' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            author_tag = soup.find('div', class_='gubuy9f')\n",
+    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
+    "            date_tag = soup.find('time')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        # Store the data\n",
+    "        article_data = {\n",
+    "            'title': title,\n",
+    "            'author': author,\n",
+    "            'publication_date': publication_date,\n",
+    "            'content': content\n",
+    "        }\n",
+    "\n",
+    "        # Print success message\n",
+    "        print(f'Successfully scraped {url}')\n",
+    "        \n",
+    "        return article_data\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        # Handle errors (e.g., missing elements, request errors)\n",
+    "        print(f'Error fetching {url}: {e}')\n",
+    "        return None\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5d6a6e7-1f9d-4f92-8080-a56787f32bd5",
+   "metadata": {},
+   "source": [
+    "The below code iterates over a list of URLs, scraping article data from each URL using the **scrape_article** function, and then appends the collected data to a list. It also includes an optional delay of 2 seconds between requests to avoid overloading the server.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "e5bb5795-ee78-4fd2-b8cd-a254e46e7460",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully scraped https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/\n",
+      "Successfully scraped https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles\n",
+      "Successfully scraped https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html\n",
+      "Successfully scraped https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment\n"
+     ]
+    }
+   ],
+   "source": [
+    "for url in urls:\n",
+    "    article_data = scrape_article(url)\n",
+    "    if article_data:\n",
+    "        articles_data.append(article_data)\n",
+    "    \n",
+    "    # Optional: Delay between requests to avoid overwhelming the server\n",
+    "    time.sleep(2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3645339f-324d-4832-b072-9be6162c9ff3",
+   "metadata": {},
+   "source": [
+    "In the below cell, I have stored the collected articles in a structured format such as CSV or JSON for further processing. It creates a DataFrame from a list of article data, saves it as a CSV file named **ev_articles.csv**, and displays the DataFrame for review. The **pd.Dataframe()** function creates a DataFrame named **articles_df** from the list of dictionaries **articles_data**. Each dictionary in the list represents a row in the DataFrame, with dictionary keys becoming column names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "c6c2e563-874b-43c6-ad78-f8f7bc81bff8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>author</th>\n",
+       "      <th>publication_date</th>\n",
+       "      <th>content</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Most Australians think there are too few publi...</td>\n",
+       "      <td>Jennifer Dudley-Nicholson</td>\n",
+       "      <td>February 23, 2024</td>\n",
+       "      <td>Most Australians think the nation has too few ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Why Australian consumers are charging toward e...</td>\n",
+       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
+       "      <td>27 Jul. 2022</td>\n",
+       "      <td>The CEO Imperative: Is your strategy set for t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EVs face future challenges despite increasing ...</td>\n",
+       "      <td>Harrison Vesey</td>\n",
+       "      <td>10 April 2024</td>\n",
+       "      <td>Professor David Hensher One in three Australia...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
+       "      <td>Mike Costello</td>\n",
+       "      <td>19 August 2020, 1:56pm</td>\n",
+       "      <td>Guest User Australia's electric-vehicle penetr...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               title  \\\n",
+       "0  Most Australians think there are too few publi...   \n",
+       "1  Why Australian consumers are charging toward e...   \n",
+       "2  EVs face future challenges despite increasing ...   \n",
+       "3  EVs in Australia: Report outlines sales, and i...   \n",
+       "\n",
+       "                                              author        publication_date  \\\n",
+       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
+       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
+       "2                                     Harrison Vesey           10 April 2024   \n",
+       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
+       "\n",
+       "                                             content  \n",
+       "0  Most Australians think the nation has too few ...  \n",
+       "1  The CEO Imperative: Is your strategy set for t...  \n",
+       "2  Professor David Hensher One in three Australia...  \n",
+       "3  Guest User Australia's electric-vehicle penetr...  "
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert the articles data to a DataFrame\n",
+    "articles_df = pd.DataFrame(articles_data)\n",
+    "\n",
+    "# Save the DataFrame to a CSV file\n",
+    "articles_df.to_csv('ev_articles.csv', index=False)\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "articles_df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8532a3e-860a-4d5c-a594-b5528e60757d",
+   "metadata": {},
+   "source": [
+    "## Step 3: Data Preprocessing\n",
+    "To perform data preprocessing for each article, we need to clean and structure the data appropriately. This typically involves removing unwanted characters or HTML tags. Then we need to normalize text by converting it to lowercase, removing extra whitespace, and handling punctuation. After that, we need to break the text into tokens (words). All this is carried out in the further cells. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "fcb4e4c7-f9ce-41c2-8f4e-b1166dcd13f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "\n",
+    "# Download necessary NLTK data files\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b8887af-b132-4188-9dea-b1f4cd0687aa",
+   "metadata": {},
+   "source": [
+    "The **clean_text()** function removes unwanted characters and HTML tags from the text. It uses a regular expression to remove anything that looks like HTML tags. It is also used to remove special characters and numbers since we don' require them for sentiment analysis. It returns a cleaned version of the text with HTML tags, special characters, and extra whitespace removed. \n",
+    "\n",
+    "The **normalize_text()** function normalizes the text by converting it to lowercase and removing extra spaces. First, it converts all characters in the text to lowercase. Then it splits the text into words and then joins them back together with a single space between each word, effectively removing extra spaces. It returns the normalized text with all lowercase characters and consistent spacing.\n",
+    "\n",
+    "The **tokenize_text()** function tokenizes the text into individual words and a list of tokens (words) from the text.\n",
+    "\n",
+    "The **remove_stop_words()** function remove common stop words from the list of tokens. It returns a list of tokens with stop words removed.\n",
+    "\n",
+    "The **stem_tokens()** function apply stemming to the list of tokens. Stemming reduces words to their root form so that the kewords can be analysed easily. It returns a list of stemmed tokens (words reduced to their root form).\n",
+    "\n",
+    "Finally, the **lemmatize_tokens()** function applies lemmatization to the list of tokens. Lemmatization reduces words to their base or dictionary form, which is usually more meaningful than stemming. It returns a list of lemmatized tokens (words reduced to their base form)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "bdb471d4-e732-4363-a62d-b3e1a98275ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_text(text):\n",
+    "    \"\"\"\n",
+    "    Clean the text by removing unwanted characters and HTML tags.\n",
+    "    \"\"\"\n",
+    "    # Remove HTML tags\n",
+    "    text = re.sub(r'<[^>]+>', '', text)\n",
+    "    # Remove special characters and numbers\n",
+    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
+    "    # Remove extra whitespace\n",
+    "    text = text.strip()\n",
+    "    return text\n",
+    "\n",
+    "def normalize_text(text):\n",
+    "    \"\"\"\n",
+    "    Normalize the text by converting to lowercase and removing extra spaces.\n",
+    "    \"\"\"\n",
+    "    # Convert to lowercase\n",
+    "    text = text.lower()\n",
+    "    # Remove extra whitespace\n",
+    "    text = ' '.join(text.split())\n",
+    "    return text\n",
+    "\n",
+    "def tokenize_text(text):\n",
+    "    \"\"\"\n",
+    "    Tokenize the text into words.\n",
+    "    \"\"\"\n",
+    "    tokens = word_tokenize(text)\n",
+    "    return tokens\n",
+    "\n",
+    "def remove_stop_words(tokens):\n",
+    "    \"\"\"\n",
+    "    Remove stop words from the tokenized text.\n",
+    "    \"\"\"\n",
+    "    stop_words = set(stopwords.words('english'))\n",
+    "    filtered_tokens = [word for word in tokens if word not in stop_words]\n",
+    "    return filtered_tokens\n",
+    "\n",
+    "def stem_tokens(tokens):\n",
+    "    \"\"\"\n",
+    "    Apply stemming to tokens.\n",
+    "    \"\"\"\n",
+    "    stemmer = PorterStemmer()\n",
+    "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
+    "    return stemmed_tokens\n",
+    "\n",
+    "def lemmatize_tokens(tokens):\n",
+    "    \"\"\"\n",
+    "    Apply lemmatization to tokens.\n",
+    "    \"\"\"\n",
+    "    lemmatizer = WordNetLemmatizer()\n",
+    "    lemmatized_tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
+    "    return lemmatized_tokens\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "484d6bf9-826f-4b57-9f5c-7b737c81979a",
+   "metadata": {},
+   "source": [
+    "This cell applies preprocessing functions to each article’s content, then converts the preprocessed data into a DataFrame and saves it to a CSV file. The **preprocessed_articles** list is initialized as an empty list to store the preprocessed article data. By looping through each article, many steps of preprocessing are applied like removing HTML tags, special characters, and extra whitespace using **clean_text** function, converting the text to lowercase and removing extra spaces using the **normalize_text** function, splitting the text into individual words (tokens) using **tokenize_text** function, filtering out common stop words from the tokens using **remove_stop_words** function and reducing to their root form using **stem_tokens** function.\n",
+    "\n",
+    "After all this, the preprocessed text is joined back into a single string and stored in a dictionary along with the article’s title, author, and publication date which is added to the **preprocessed_articles** list. Finally, we convert the list of preprocessed articles into a DataFrame using **pd.DataFrame** function, save it to a CSV file and display the resulting DataFrame to verify the output.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "8f35cdf6-3bd8-49f9-956c-a0c587bd5b19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>author</th>\n",
+       "      <th>publication_date</th>\n",
+       "      <th>content</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Most Australians think there are too few publi...</td>\n",
+       "      <td>Jennifer Dudley-Nicholson</td>\n",
+       "      <td>February 23, 2024</td>\n",
+       "      <td>australian think nation public charg station s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Why Australian consumers are charging toward e...</td>\n",
+       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
+       "      <td>27 Jul. 2022</td>\n",
+       "      <td>ceo imper strategi set takeoff clever govern c...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EVs face future challenges despite increasing ...</td>\n",
+       "      <td>Harrison Vesey</td>\n",
+       "      <td>10 April 2024</td>\n",
+       "      <td>professor david hensher one three australian c...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
+       "      <td>Mike Costello</td>\n",
+       "      <td>19 August 2020, 1:56pm</td>\n",
+       "      <td>guest user australia electricvehicl penetr wel...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               title  \\\n",
+       "0  Most Australians think there are too few publi...   \n",
+       "1  Why Australian consumers are charging toward e...   \n",
+       "2  EVs face future challenges despite increasing ...   \n",
+       "3  EVs in Australia: Report outlines sales, and i...   \n",
+       "\n",
+       "                                              author        publication_date  \\\n",
+       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
+       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
+       "2                                     Harrison Vesey           10 April 2024   \n",
+       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
+       "\n",
+       "                                             content  \n",
+       "0  australian think nation public charg station s...  \n",
+       "1  ceo imper strategi set takeoff clever govern c...  \n",
+       "2  professor david hensher one three australian c...  \n",
+       "3  guest user australia electricvehicl penetr wel...  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Apply preprocessing to each article's content\n",
+    "preprocessed_articles = []\n",
+    "\n",
+    "for article in articles_data:\n",
+    "    # Clean the text\n",
+    "    cleaned_content = clean_text(article['content'])\n",
+    "    # Normalize the text\n",
+    "    normalized_content = normalize_text(cleaned_content)\n",
+    "    # Tokenize the text\n",
+    "    tokens = tokenize_text(normalized_content)\n",
+    "    # Remove stop words\n",
+    "    filtered_tokens = remove_stop_words(tokens)\n",
+    "    # Optionally apply stemming or lemmatization\n",
+    "    stemmed_tokens = stem_tokens(filtered_tokens)\n",
+    "    # lemmatized_tokens = lemmatize_tokens(filtered_tokens)\n",
+    "    \n",
+    "    # Store preprocessed data\n",
+    "    preprocessed_article = {\n",
+    "        'title': article['title'],\n",
+    "        'author': article['author'],\n",
+    "        'publication_date': article['publication_date'],\n",
+    "        'content': ' '.join(stemmed_tokens)  # Use lemmatized_tokens if preferred\n",
+    "    }\n",
+    "    preprocessed_articles.append(preprocessed_article)\n",
+    "    \n",
+    "# Convert the preprocessed articles data to a DataFrame\n",
+    "preprocessed_articles_df = pd.DataFrame(preprocessed_articles)\n",
+    "\n",
+    "# Save the DataFrame to a CSV file\n",
+    "preprocessed_articles_df.to_csv('preprocessed_ev_articles.csv', index=False)\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "preprocessed_articles_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "592c4d07-49e8-4f5d-8165-3057d18f48dc",
+   "metadata": {},
+   "source": [
+    "In sentiment analysis, **polarity** and **subjectivity** are two key metrics used to assess the sentiment of a text. \n",
+    "\n",
+    "Polarity measures the sentiment of the text on a scale from -1 to 1. Negative sentiment indicates that the text expresses a strong negative sentiment or emotion. Neutral sentiment indicates that the text is neutral and does not convey any strong positive or negative sentiment. Positive sentiment indicates that the text expresses a strong positive sentiment or emotion.  This can be useful for understanding overall attitudes or reactions.\n",
+    "\n",
+    "Subjectivity measures the degree to which the text expresses personal opinions, feelings, or beliefs, as opposed to objective facts. Zero subjectivity means the text is factual and does not include personal opinions or emotions. It is more about reporting facts.A subjectivity of 1 means the text is more personal and opinionated, including personal beliefs, emotions, or feelings. This can be useful for distinguishing between factual reports and personal opinions or feelings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "7a2419ad-a537-4396-999f-efe9ad8fb316",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16abc529-f4c0-42db-b793-624477ac2394",
+   "metadata": {},
+   "source": [
+    "This code defines a function **analyze_sentiment()** that uses the TextBlob library to analyze the sentiment of a given text. It takes a single argument **text**, which is a string of text that we want to analyze for sentiment. The TextBlob object **blob** has a sentiment property that returns a Sentiment namedtuple - polarity and subjectivity. The function returns the polarity and subjectivity scores as a tuple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "7a253b62-1ce6-42c2-986c-bba61766c17a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def analyze_sentiment(text):\n",
+    "    \"\"\"\n",
+    "    Analyze the sentiment of the given text using TextBlob.\n",
+    "    \"\"\"\n",
+    "    blob = TextBlob(text)\n",
+    "    # Return polarity and subjectivity\n",
+    "    return blob.sentiment.polarity, blob.sentiment.subjectivity\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c6d5f8-09c8-493b-b866-c0a2fd628a9d",
+   "metadata": {},
+   "source": [
+    "This code performs sentiment analysis on the preprocessed content of each article, stores the results, and saves them to a CSV file. The **sentiment_results[]** is an empty list initialized to store the sentiment analysis results for each article. In the 'for' loop, we iterate through each row in the preprocessed articles DataFrame (**preprocessed_articles_df**). Each row represents an article. For each article, the content is passed to the **analyze_sentiment()** function, which returns the polarity and subjectivity scores.\n",
+    "The results for each article, including its title, author, publication date, content, polarity, and subjectivity, are stored in a dictionary and appended to the above initialized list.\n",
+    "We then conver this list of dictionaries into pandas DataFrame. Finally, the DataFrame is displayed, showing the sentiment analysis results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "1d55944e-1875-47bf-a203-5f2b5a360a6a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>author</th>\n",
+       "      <th>publication_date</th>\n",
+       "      <th>content</th>\n",
+       "      <th>polarity</th>\n",
+       "      <th>subjectivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Most Australians think there are too few publi...</td>\n",
+       "      <td>Jennifer Dudley-Nicholson</td>\n",
+       "      <td>February 23, 2024</td>\n",
+       "      <td>australian think nation public charg station s...</td>\n",
+       "      <td>0.002210</td>\n",
+       "      <td>0.187689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Why Australian consumers are charging toward e...</td>\n",
+       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
+       "      <td>27 Jul. 2022</td>\n",
+       "      <td>ceo imper strategi set takeoff clever govern c...</td>\n",
+       "      <td>0.102000</td>\n",
+       "      <td>0.283466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EVs face future challenges despite increasing ...</td>\n",
+       "      <td>Harrison Vesey</td>\n",
+       "      <td>10 April 2024</td>\n",
+       "      <td>professor david hensher one three australian c...</td>\n",
+       "      <td>0.181563</td>\n",
+       "      <td>0.347917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
+       "      <td>Mike Costello</td>\n",
+       "      <td>19 August 2020, 1:56pm</td>\n",
+       "      <td>guest user australia electricvehicl penetr wel...</td>\n",
+       "      <td>0.011245</td>\n",
+       "      <td>0.306212</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               title  \\\n",
+       "0  Most Australians think there are too few publi...   \n",
+       "1  Why Australian consumers are charging toward e...   \n",
+       "2  EVs face future challenges despite increasing ...   \n",
+       "3  EVs in Australia: Report outlines sales, and i...   \n",
+       "\n",
+       "                                              author        publication_date  \\\n",
+       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
+       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
+       "2                                     Harrison Vesey           10 April 2024   \n",
+       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
+       "\n",
+       "                                             content  polarity  subjectivity  \n",
+       "0  australian think nation public charg station s...  0.002210      0.187689  \n",
+       "1  ceo imper strategi set takeoff clever govern c...  0.102000      0.283466  \n",
+       "2  professor david hensher one three australian c...  0.181563      0.347917  \n",
+       "3  guest user australia electricvehicl penetr wel...  0.011245      0.306212  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Apply sentiment analysis to each article's content\n",
+    "sentiment_results = []\n",
+    "\n",
+    "for index, article in preprocessed_articles_df.iterrows():\n",
+    "    # Analyze sentiment\n",
+    "    polarity, subjectivity = analyze_sentiment(article['content'])\n",
+    "    \n",
+    "    # Store the results\n",
+    "    sentiment_results.append({\n",
+    "        'title': article['title'],\n",
+    "        'author': article['author'],\n",
+    "        'publication_date': article['publication_date'],\n",
+    "        'content': article['content'],\n",
+    "        'polarity': polarity,\n",
+    "        'subjectivity': subjectivity\n",
+    "    })\n",
+    "\n",
+    "# Convert the results to a DataFrame\n",
+    "sentiment_df = pd.DataFrame(sentiment_results)\n",
+    "\n",
+    "# Save the sentiment analysis results to a CSV file\n",
+    "sentiment_df.to_csv('sentiment_analysis_results.csv', index=False)\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "sentiment_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37a82af6-129d-4a96-ac4e-f063316184b9",
+   "metadata": {},
+   "source": [
+    "This code creates a visual representation of the sentiment analysis results for a set of articles, focusing on both polarity and subjectivity. The plotting style is configured to \"whitegrid\" for a clean and readable background. A figure is setup with two subplots arranged vertically, each subplot having its own set of axes. this code generates two horizontal bar plots within a single figure, visually comparing the polarity and subjectivity of the sentiment analysis for each article."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "8dfd03d0-cab3-4de2-939f-97b4a64bc416",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nz56patWqunjxohFIHThwQCaTySy43LVrlypUqKDkyZNHe2ynTp3S3r17NW/ePE2fPl1HjhxRjx49oi0bk1u3bungwYOaP3++Zs6cqVOnThmBSGxev34tb29vXb58WTNmzNDKlSsVFhamdu3aRRuGPn78WC1atFBoaKgWLlyo+fPn6/r162bt/fXXX/X48WMtXbpUs2bN0m+//WbcM0JCQtS2bVsVK1ZMa9euVUBAgBwcHPTdd9+ZhVSBgYEqX7681qxZozp16mjnzp3q16+fGjZsqPXr18vT0zPGayVCbK/B58+fq3Xr1goLC9OyZcs0adIk+fv7G/eM6CxdulRbt27VpEmTFBgYqJYtW2rIkCE6evRotPeNRYsWyc/PTz4+PgoMDNT06dMVHBxsrA3k6+srR0dH1apVK9opkT/99JO+/vprValSRf7+/ho6dKg2b96sXr16mZUbM2aM6tevr02bNqlly5by9fXVkSNHJElTpkzRhQsXNHv2bG3evFmVKlVSz549ow1XHB0dza5/d3f3OH9WRedd7rcxuX//vnFfWrhwoX7//Xe1a9dOr1+/NsrMmDFDjo6OCggIUIsWLTRo0CBt3rxZUtw/YyMsXrxYAwYM0Ny5czVjxgx5e3sra9as2rt37wePWm7QoIG2bdtmFjzv2LFDz58/V7169SSFT/kdMGCAunXrpsDAQI0cOVLr1q3T3LlzP2jfAAAAsJxkid2ACLVq1ZKfn59CQkKUJUsW7du3TxkyZFCRIkXMys2bN092dnbGIqT58+fXxIkTVa9ePe3du1clSpTQgwcPlDlzZuXIkUM5c+bU5MmTjVEGGTJkUNKkSZUqVSqlS5cu2rb4+/srefLkqlatmiSpdu3aCgwM1Pbt21WrVq14HU9ISIhCQ0OVPXt25ciRQzly5NDMmTONX+AzZcokSUqbNq3Spk1rjIxq3ry5Pv/8c0nhbyS8vLzUokULY2pJ9+7dNXfuXF24cEGFCxeOtQ3FihVTzZo1VapUKUlSjhw5VL58eV28eNGsXOvWreXm5iZJ6tmzp5YsWaITJ04oZ86cun79ugoWLKicOXMqRYoU+u6771S3bt1ow54UKVIobdq0Zscnha/xNX78eKVJk0aS1LRpU02aNCnGdqdNm1bDhg2TtbW18ufPL3d3d+3evduszM2bN9W3b19VrlxZw4cPj3GdsJj6uUuXLsbIiZ49e2rZsmU6c+aMcufOrRkzZujrr782ws+cOXPq6dOnGjp0qL755huj7sjn6sCBA/rtt9908OBB47rq1auXjh8/rkWLFkW7qG7SpEk1cOBANW/e3Njm5eWl9u3b6969e8abuMKFC5uNFIqrfW+HNWnTplWKFClkbW2tTJky6enTp1q2bJl8fHyMevPkyaObN29q9uzZ+uqrr7R06VKlSpVK48ePl42NjSRp6tSpcnNz07p169SiRQuzfaRIkUK2traSwl9jadOmlZ+fn7JmzaoBAwYY5SZPnqxy5cpp69at+vLLL2Vra6t9+/bJw8ND+/fvl5ubm7Zv3667d+8qY8aM+uWXX2INlKysrDR58mRlyZJFUviHFrRv315XrlxRvnz5YnxeZMmTJ9ekSZOMNasGDRokb29vXbt2Tblz547xeQcOHNCFCxe0detW5c2bV5L0/fffa8GCBXr06FGUqXqbN2/WX3/9pYkTJxp99f3332vTpk1G4JgpUyYNHz5cSZIkUb58+eTu7q79+/dLkl6+fKlu3bqpbdu2xvXeoUMHBQQEKDg42Gi/ra2t2rVrZ+y3T58+qlmzptq2bStJyps3r4KDg2Nd9Du21+DmzZt1//59+fv7G9f6uHHjjGAgOtevX1eqVKn02WefKXPmzGrZsqXy5cunvHnzRnvfyJUrl8aMGaOqVatKCr931axZ0/gjRbp06WRtba0UKVJEOyVy9uzZql69ujp37mwcs8lkUpcuXXT58mXjdevh4WG0u1OnTvLz89Px48dVunRpXb9+XalTp1bOnDn1ySef6JtvvlHp0qWNcxeZjY2N2fWfIkWKOH9WVa5cOdp+iu/9NiYRo+js7e0lhZ8bd3d3HThwQBUrVpQkVahQQV27dpUk5cuXTydOnNDChQvl7u4e73ZXrlxZ5cuXN/abKlUqJU2a1OzeH5327dtHezxTpkxRpUqVJIWfl0mTJmnnzp2qWbOmpPDRTtWqVVP69Oklhf8MsLKyUo4cOZQ9e3Zlz55dfn5+xs8ZAAAA/PP8YwIoe3t75cyZU4GBgfLy8tLmzZujjH6SpIsXL8rFxcVsW6FChZQ2bVpduHBBlStXVrt27TR8+HBNnTpV5cqVU+XKleMdHL1+/Vrr169X5cqVjV9kq1SpotSpU2v58uXxrqdw4cKqU6eOOnXqpEyZMsnFxUVVqlRR9erVY31e5De8GTJkUPPmzbVx40adPXtW169f1/nz5yUpxulWkdWrV0/79+/X+PHjFRwcrCtXrujq1atGIBUhf/78xv8j3ghGjPbq2rWr+vTpo8DAQJUsWVIVKlRQnTp1YhyREp1PP/3U7E3BJ598YjYV5m25cuWStbW1WZveHn02ZMgQvXr1StmyZXuvReojAgNJxhvHly9f6v79+/rjjz80ceJETZkyxSgTFhamly9f6ubNm8axRz5XZ86ckclkMt4wRwgNDY12+pgUfo3Y2tpq9uzZunLliq5du2ac38gjDSLvJz7ti3w+o3PlyhW9evVKJUuWNNtepkwZLVy4UPfu3dPFixdlb29vhE9SeDiQN2/eKAFmTM6ePatLly4ZI+EivHz5UkFBQUqWLJkqVqyo/fv3y8PDQ/v27dOgQYN04sQJHTp0SPnz59eff/4Z7Rv1CHny5DHCJyl8+o4Ufp+IbwCVO3duswXTixcvLil8JGRsAdTFixdla2trdi1lyZJF/fr1i7F8njx5zAKMQoUKqVChQsb3uXLlUpIk/zcw1dbW1nit5MqVS56enlq0aJEuXrxodj+I6XqRwq/NL774wmxb6dKlYw2gYnsNnj17Vnnz5jUL8CPuwTFp0aKFtm/frsqVK6tw4cJycXFR7dq19emnn0Zb3tXVVSdOnNCUKVN09epVXb16VZcvXzY717G5ePFilJ8fZcqUMR6LCKDefq1EPs727durU6dOcnZ2loODg1xcXFS3bt1Yj/PtNsT1s+ptCXG/TZ06tRE+RRyjra2tLl68aARQZcuWNXuOo6Oj8cmw8W13bK+N2Hz//ffGayyyzJkzG//PmDGjKleurA0bNqhmzZq6d++e9u7dq1mzZhllKlasKEdHRzVs2FCfffaZMR068rEDAADgn+UfE0BJ/zcNr0mTJtqxY0e0Hxsf00KnJpPJeMPUu3dvNW/eXLt379aBAwc0fPhwzZ07VwEBAWZvqKOza9cu3b17Vz/99JPZ6Ks3b97o0KFDunr1qtkbzsgiT3GQpAkTJqhLly765ZdftH//fvXp00clS5bUwoULY9x/5IWA79y5oyZNmihDhgxydXVVhQoVVKxYsVjfkEc2aNAgBQYGysPDQ66ururSpYsxyiyy6Pokop+rV6+uPXv2aM+ePdq/f7/mz5+vadOmaeXKlSpQoEC82vEuf72PqT1vq1+/vgoWLKjRo0erevXqKliw4DvtI/Kb/Agmk8kI9vr372/21/0I2bJl059//inJ/FyFhYUpTZo00a6BE9PxHD58WG3btlWVKlVUsmRJ1a1bV8+fP1eXLl3Myr29n7jaF5eYXkMRdSdLlizWMpGDidiEhYWpXLlyGjx4cJTHIt7Eu7m5aeTIkbp+/bpCQkJUunRplS1bVocOHdLNmzdVsmRJY8RDdN5uS0QQE9M19/ZrNLqyEXXEdZzJkr3b7TM+5WN7rVy+fFnNmzdX0aJFVb58eX3xxRdKnz59lE8Ee3sx8WTJksUrsI4sttdg0qRJ37m+PHnyaNu2bTp8+LD27dunXbt2ac6cORo1apTq168fpfzs2bM1ffp01a9fX87OzmrdurV27NihTZs2xWt/0V2/ka/vCLHd+xwdHbV7927t27dPBw4cUEBAgH744QfNnTtXzs7O79WGiO0xXVvvc799+5qOru43b96YXVtvX4thYWHGPTG+7X7fReuzZMkSr/CqYcOG6t69ux49eqQNGzYoS5YsZve85MmTa9GiRTp79qzxSXqdOnWSh4fHO3+iIgAAAP4e/5g1oKTwAOr48eNas2aNcubMGe1IDjs7Ox07dsxs2/nz5/X06VPlz59fV65c0eDBg/Xpp5+qWbNmmjp1qubOnaugoCBjtEBs1qxZo/Tp0ysgIMDsa8aMGTKZTMZi5NbW1vrrr7/Mnht58eETJ05o5MiRypcvn1q3bq3Zs2dr5MiROnjwoNmiw7GJWIdl2bJl6ty5s6pXr25MIYvrE4cePHigFStWaPDgwerfv788PT1VuHBhXblyJd6fVhQaGqpRo0bpxo0bcnd31/fff6/t27crSZIkxl/L3/Y+o5HeR+3atdW8eXPZ29urf//+Ma6p8q4+/fRTZciQQTdu3FDu3LmNr5jWoYpQsGBBPX36VK9evTJ73pw5c7Rjx45onzNv3jyVLVtWvr6+at26tVxcXPT7779Livn8vm/7Ip+X/Pnzy9raOsrr6OjRo8qUKZNsbW1lZ2enU6dOma1FdffuXV27di3OEVYRChQooKCgIGXLls1op62trUaOHGmMoqpYsaIePnyoRYsWqXjx4kqVKpXKly+vgwcPaufOncbU0JhcvXpVT548Mb7/9ddfJckIj62trc3WrIpugfDg4GCzMseOHZOVlZUxSiYmn3/+uR49emRW5/3791W2bFn99ttv0ZYPDg42a++ZM2fk7Owc5RNAo7N8+XJ9+umnmj9/vtq3b6/KlSsbaz/F9pouVKiQTpw4YbYtop/eR6FChXTt2jWzhdqDgoLMjuttixYt0rZt24wPDNiwYYOcnZ2NdYfevm/MnDlTXbp00ZAhQ9SkSROVKFF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+      "text/plain": [
+       "<Figure size 1200x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set plot style\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "\n",
+    "# Create a figure and axes\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(12, 12))  # Increase figure height\n",
+    "\n",
+    "# Plot polarity (horizontal bars)\n",
+    "sns.barplot(x='polarity', y='title', data=sentiment_df, ax=ax[0], palette='viridis', hue='title', legend=False)\n",
+    "ax[0].set_title('Sentiment Polarity of Articles')\n",
+    "ax[0].set_xlabel('Polarity')\n",
+    "ax[0].set_ylabel('Article Title')\n",
+    "\n",
+    "# Plot subjectivity (horizontal bars)\n",
+    "sns.barplot(x='subjectivity', y='title', data=sentiment_df, ax=ax[1], palette='viridis', hue='title', legend=False)\n",
+    "ax[1].set_title('Sentiment Subjectivity of Articles')\n",
+    "ax[1].set_xlabel('Subjectivity')\n",
+    "ax[1].set_ylabel('Article Title')\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d6748a7-f128-40f2-b7ed-b11f6ddd8a60",
+   "metadata": {},
+   "source": [
+    "The overall goal of this code is to analyze sentiment scores (polarity and subjectivity) of articles and aggregate these scores by the year of publication. \n",
+    "It defines a dictionary data with sample data, including article titles, authors, publication dates, content, polarity, and subjectivity scores and converts the **data** dictionary into a pandas DataFrame named **sentiment_df**.\n",
+    "It defines a function **parse_date()** that attempts to parse date strings into datetime objects using dateutil.parser.parse with the fuzzy=True option to handle various date formats. It applies the **parse_date()** function to the **publication_date** column in the DataFrame to convert date strings to datetime objects. Then it extracts the year from the parsed **publication_date** column and stores it in a new column named **year**. Then, it prints the **publication_date** and **year** columns to check for any missing values in the year column. After printing the polarity and subjectivity columns, it groups the DataFrame by the year column and calculates the mean polarity and subjectivity scores for each year.\n",
+    "\n",
+    "Finally, it prints the  stored **yearly_sentiment** DataFrame to display the average polarity and subjectivity scores for each year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "a493e974-a1d9-4eb9-88d8-bb6fd1d39e54",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     publication_date  year\n",
+      "0 2024-02-23 00:00:00  2024\n",
+      "1 2022-07-27 00:00:00  2022\n",
+      "2 2024-04-10 00:00:00  2024\n",
+      "3 2020-08-19 13:56:00  2020\n",
+      "   polarity  subjectivity\n",
+      "0  0.002210      0.187689\n",
+      "1  0.102000      0.283466\n",
+      "2  0.181563      0.347917\n",
+      "3  0.011245      0.306212\n",
+      "   year  polarity  subjectivity\n",
+      "0  2020  0.011245      0.306212\n",
+      "1  2022  0.102000      0.283466\n",
+      "2  2024  0.091886      0.267803\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from dateutil import parser\n",
+    "\n",
+    "# Sample data as a dictionary\n",
+    "data = {\n",
+    "    'title': [\n",
+    "        'Most Australians think there are too few publi...',\n",
+    "        'Why Australian consumers are charging toward e...',\n",
+    "        'EVs face future challenges despite increasing ...',\n",
+    "        'EVs in Australia: Report outlines sales, and i...'\n",
+    "    ],\n",
+    "    'author': [\n",
+    "        'Jennifer Dudley-Nicholson',\n",
+    "        'Neal Johnston, Glenn Maris, Damien Smith, Neal...',\n",
+    "        'Harrison Vesey',\n",
+    "        'Mike Costello'\n",
+    "    ],\n",
+    "    'publication_date': [\n",
+    "        'February 23, 2024',\n",
+    "        '27 Jul. 2022',\n",
+    "        '10 April 2024',\n",
+    "        '19 August 2020, 1:56pm'\n",
+    "    ],\n",
+    "    'content': [\n",
+    "        'australian think nation public charg station s...',\n",
+    "        'ceo imper strategi set takeoff clever govern c...',\n",
+    "        'professor david hensher one three australian c...',\n",
+    "        'guest user australia electricvehicl penetr wel...'\n",
+    "    ],\n",
+    "    'polarity': [0.002210, 0.102000, 0.181563, 0.011245],\n",
+    "    'subjectivity': [0.187689, 0.283466, 0.347917, 0.306212]\n",
+    "}\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "sentiment_df = pd.DataFrame(data)\n",
+    "\n",
+    "# Define a function to parse dates with different formats\n",
+    "def parse_date(date_str):\n",
+    "    try:\n",
+    "        return parser.parse(date_str, fuzzy=True)\n",
+    "    except ValueError:\n",
+    "        return pd.NaT\n",
+    "\n",
+    "# Apply the parsing function\n",
+    "sentiment_df['publication_date'] = sentiment_df['publication_date'].apply(parse_date)\n",
+    "\n",
+    "# Extract year from publication_date\n",
+    "sentiment_df['year'] = sentiment_df['publication_date'].dt.year\n",
+    "\n",
+    "# Check for missing values in 'year'\n",
+    "print(sentiment_df[['publication_date', 'year']])\n",
+    "\n",
+    "# Check for missing values in sentiment scores\n",
+    "print(sentiment_df[['polarity', 'subjectivity']])\n",
+    "\n",
+    "# Aggregate sentiment scores by year\n",
+    "yearly_sentiment = sentiment_df.groupby('year').agg({'polarity': 'mean', 'subjectivity': 'mean'}).reset_index()\n",
+    "\n",
+    "# Display the aggregated sentiment scores\n",
+    "print(yearly_sentiment)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0c41a73-9458-46c3-9f0e-816db6f65144",
+   "metadata": {},
+   "source": [
+    "This code generates a line plot to visualize the average sentiment scores (polarity and subjectivity) of articles over time. It uses the **yearly_sentiment** DataFrame, which contains the aggregated sentiment scores by year, and plots these scores with markers for each year. The plot includes a title, axis labels, a legend to differentiate between polarity and subjectivity, and a grid for better readability. Finally, it displays the plot, allowing for a clear visual comparison of how sentiment has evolved over the years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "abc62267-805b-4255-bf29-5c054cb5b4c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot sentiment scores over time\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot Polarity\n",
+    "plt.plot(yearly_sentiment['year'], yearly_sentiment['polarity'], marker='o', linestyle='-', color='b', label='Polarity')\n",
+    "\n",
+    "# Plot Subjectivity\n",
+    "plt.plot(yearly_sentiment['year'], yearly_sentiment['subjectivity'], marker='o', linestyle='-', color='r', label='Subjectivity')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Average Sentiment Scores by Year')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Average Score')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02793131-4dd8-4278-8f71-cc56b2ad14ac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From fc84960db3a4fa12f62196b386d2115bf475d4b8 Mon Sep 17 00:00:00 2001
From: Aditya Gahlot <adityagahlot2002@gmail.com>
Date: Sat, 10 Aug 2024 20:37:06 +1000
Subject: [PATCH 2/5] Move Jupyter notebook to personal-work/aditya-gahlot

---
 .../aditya-gahlot/Sentiment_Analysis.ipynb    | 1000 +++++++++++++++++
 1 file changed, 1000 insertions(+)
 create mode 100644 personal-work/aditya-gahlot/Sentiment_Analysis.ipynb

diff --git a/personal-work/aditya-gahlot/Sentiment_Analysis.ipynb b/personal-work/aditya-gahlot/Sentiment_Analysis.ipynb
new file mode 100644
index 0000000..e919145
--- /dev/null
+++ b/personal-work/aditya-gahlot/Sentiment_Analysis.ipynb
@@ -0,0 +1,1000 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a2a1d701-1644-4a79-a16a-d5196eacb4e1",
+   "metadata": {},
+   "source": [
+    "# EV News Articles Sentiment Analysis\n",
+    "Performing news article sentiment analysis of EV vehicles in Australia involves several steps, from data collection to sentiment analysis and visualization. Although the sentiments of people vary across various regions in Australia, I have presented a broad analysis of the Electric Vehicle industry through four online articles. The articles for this project were taken from the following links:\n",
+    "\n",
+    "1. https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/\n",
+    "2. https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles\n",
+    "3. https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html\n",
+    "4. https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac0279aa-b72c-4ccc-8808-9664ad081343",
+   "metadata": {},
+   "source": [
+    "# Procedure\n",
+    "Performing news article sentiment analysis of EV vehicles in Australia involves several steps, from data collection to sentiment analysis and visualization. These steps are summarized further.\n",
+    "## Step 1: Data Collection\n",
+    "After gathering the news articles for EVs, I used the technique of **web scraping** to extract meaningful information from the articles like the title, the publication date of the article and its broad content. For this purpose, I used web scraping tools like BeautifulSoup which is imported in the first cell along with other important libraries. After that, I have initialized all the URLs as an array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "aea29846-81dc-468e-ac46-c98193afc97a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import pandas as pd\n",
+    "import time\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "57681cd9-a557-4283-a734-cf346329ee34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List of article URLs\n",
+    "urls = [\n",
+    "    'https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/',\n",
+    "    'https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles',\n",
+    "    'https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html',\n",
+    "    'https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment',\n",
+    "    \n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "ac8f969b-2142-4494-889b-4e8bfd13caf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a list to store article data\n",
+    "articles_data = []\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fff14daf-1cb0-4e21-a0f1-175818fd1ec4",
+   "metadata": {},
+   "source": [
+    "## Step 2: Scraping an Article with Improved Selectors\n",
+    "After initializing an empty list to store article data named **articles_data**, I have defined a function **scrape_article** to fetch and parse a single article. It returns the details of an article, including its title, author, publication date, and content. Within the function, **headers** represents a dictionary with the User-Agent header. This helps to mimic a request from a web browser, which can be useful to avoid blocks from some websites that restrict automated scraping. Then an HTTP GET request was sent to the specified URL with the custom headers.\n",
+    "\n",
+    "The **BeautifulSoup (response.content, 'html.parser')** function parses the HTML content of the web page, creating a BeautifulSoup object for further extraction.I manually checked the structure of the HTML for each website to determine the correct selectors. This step required viewing the source code of each of the webpages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "6320942d-9d3e-4299-aa52-af8866ccafe2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scrape_article(url):\n",
+    "    headers = {\n",
+    "        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n",
+    "    }\n",
+    "    \n",
+    "    try:\n",
+    "        # Fetch the web page\n",
+    "        response = requests.get(url, headers=headers)\n",
+    "        response.raise_for_status()  # Raise HTTPError for bad responses\n",
+    "\n",
+    "        # Parse the HTML content\n",
+    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
+    "\n",
+    "        # Initialize variables\n",
+    "        title = 'No title found'\n",
+    "        author = 'No author found'\n",
+    "        publication_date = 'No date found'\n",
+    "        content = 'No content found'\n",
+    "\n",
+    "        # Check URL and set selectors accordingly\n",
+    "        if 'thedriven.io' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            author_tag = soup.find('a', class_='url fn n')\n",
+    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
+    "            date_tag = soup.find('a', rel='bookmark')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        elif 'ey.com' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            authors = [a.get_text(strip=True) for a in soup.find_all('a', class_='surfaceProfile-author-link')]\n",
+    "            author = ', '.join(authors) if authors else 'No author found'\n",
+    "            date_tag = soup.select_one('#container4 > div > div:nth-child(2) > div > div > span:nth-child(2)')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        elif 'sydney.edu.au' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            author_tag = soup.find('h3', class_='b-contact-information__title')\n",
+    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
+    "            date_tag = soup.find('span')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        elif 'carexpert.com.au' in url:\n",
+    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
+    "            author_tag = soup.find('div', class_='gubuy9f')\n",
+    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
+    "            date_tag = soup.find('time')\n",
+    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
+    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
+    "\n",
+    "        # Store the data\n",
+    "        article_data = {\n",
+    "            'title': title,\n",
+    "            'author': author,\n",
+    "            'publication_date': publication_date,\n",
+    "            'content': content\n",
+    "        }\n",
+    "\n",
+    "        # Print success message\n",
+    "        print(f'Successfully scraped {url}')\n",
+    "        \n",
+    "        return article_data\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        # Handle errors (e.g., missing elements, request errors)\n",
+    "        print(f'Error fetching {url}: {e}')\n",
+    "        return None\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5d6a6e7-1f9d-4f92-8080-a56787f32bd5",
+   "metadata": {},
+   "source": [
+    "The below code iterates over a list of URLs, scraping article data from each URL using the **scrape_article** function, and then appends the collected data to a list. It also includes an optional delay of 2 seconds between requests to avoid overloading the server.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "e5bb5795-ee78-4fd2-b8cd-a254e46e7460",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully scraped https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/\n",
+      "Successfully scraped https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles\n",
+      "Successfully scraped https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html\n",
+      "Successfully scraped https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment\n"
+     ]
+    }
+   ],
+   "source": [
+    "for url in urls:\n",
+    "    article_data = scrape_article(url)\n",
+    "    if article_data:\n",
+    "        articles_data.append(article_data)\n",
+    "    \n",
+    "    # Optional: Delay between requests to avoid overwhelming the server\n",
+    "    time.sleep(2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3645339f-324d-4832-b072-9be6162c9ff3",
+   "metadata": {},
+   "source": [
+    "In the below cell, I have stored the collected articles in a structured format such as CSV or JSON for further processing. It creates a DataFrame from a list of article data, saves it as a CSV file named **ev_articles.csv**, and displays the DataFrame for review. The **pd.Dataframe()** function creates a DataFrame named **articles_df** from the list of dictionaries **articles_data**. Each dictionary in the list represents a row in the DataFrame, with dictionary keys becoming column names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "c6c2e563-874b-43c6-ad78-f8f7bc81bff8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>author</th>\n",
+       "      <th>publication_date</th>\n",
+       "      <th>content</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Most Australians think there are too few publi...</td>\n",
+       "      <td>Jennifer Dudley-Nicholson</td>\n",
+       "      <td>February 23, 2024</td>\n",
+       "      <td>Most Australians think the nation has too few ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Why Australian consumers are charging toward e...</td>\n",
+       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
+       "      <td>27 Jul. 2022</td>\n",
+       "      <td>The CEO Imperative: Is your strategy set for t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EVs face future challenges despite increasing ...</td>\n",
+       "      <td>Harrison Vesey</td>\n",
+       "      <td>10 April 2024</td>\n",
+       "      <td>Professor David Hensher One in three Australia...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
+       "      <td>Mike Costello</td>\n",
+       "      <td>19 August 2020, 1:56pm</td>\n",
+       "      <td>Guest User Australia's electric-vehicle penetr...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               title  \\\n",
+       "0  Most Australians think there are too few publi...   \n",
+       "1  Why Australian consumers are charging toward e...   \n",
+       "2  EVs face future challenges despite increasing ...   \n",
+       "3  EVs in Australia: Report outlines sales, and i...   \n",
+       "\n",
+       "                                              author        publication_date  \\\n",
+       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
+       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
+       "2                                     Harrison Vesey           10 April 2024   \n",
+       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
+       "\n",
+       "                                             content  \n",
+       "0  Most Australians think the nation has too few ...  \n",
+       "1  The CEO Imperative: Is your strategy set for t...  \n",
+       "2  Professor David Hensher One in three Australia...  \n",
+       "3  Guest User Australia's electric-vehicle penetr...  "
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert the articles data to a DataFrame\n",
+    "articles_df = pd.DataFrame(articles_data)\n",
+    "\n",
+    "# Save the DataFrame to a CSV file\n",
+    "articles_df.to_csv('ev_articles.csv', index=False)\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "articles_df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8532a3e-860a-4d5c-a594-b5528e60757d",
+   "metadata": {},
+   "source": [
+    "## Step 3: Data Preprocessing\n",
+    "To perform data preprocessing for each article, we need to clean and structure the data appropriately. This typically involves removing unwanted characters or HTML tags. Then we need to normalize text by converting it to lowercase, removing extra whitespace, and handling punctuation. After that, we need to break the text into tokens (words). All this is carried out in the further cells. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "fcb4e4c7-f9ce-41c2-8f4e-b1166dcd13f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "\n",
+    "# Download necessary NLTK data files\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b8887af-b132-4188-9dea-b1f4cd0687aa",
+   "metadata": {},
+   "source": [
+    "The **clean_text()** function removes unwanted characters and HTML tags from the text. It uses a regular expression to remove anything that looks like HTML tags. It is also used to remove special characters and numbers since we don' require them for sentiment analysis. It returns a cleaned version of the text with HTML tags, special characters, and extra whitespace removed. \n",
+    "\n",
+    "The **normalize_text()** function normalizes the text by converting it to lowercase and removing extra spaces. First, it converts all characters in the text to lowercase. Then it splits the text into words and then joins them back together with a single space between each word, effectively removing extra spaces. It returns the normalized text with all lowercase characters and consistent spacing.\n",
+    "\n",
+    "The **tokenize_text()** function tokenizes the text into individual words and a list of tokens (words) from the text.\n",
+    "\n",
+    "The **remove_stop_words()** function remove common stop words from the list of tokens. It returns a list of tokens with stop words removed.\n",
+    "\n",
+    "The **stem_tokens()** function apply stemming to the list of tokens. Stemming reduces words to their root form so that the kewords can be analysed easily. It returns a list of stemmed tokens (words reduced to their root form).\n",
+    "\n",
+    "Finally, the **lemmatize_tokens()** function applies lemmatization to the list of tokens. Lemmatization reduces words to their base or dictionary form, which is usually more meaningful than stemming. It returns a list of lemmatized tokens (words reduced to their base form)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "bdb471d4-e732-4363-a62d-b3e1a98275ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_text(text):\n",
+    "    \"\"\"\n",
+    "    Clean the text by removing unwanted characters and HTML tags.\n",
+    "    \"\"\"\n",
+    "    # Remove HTML tags\n",
+    "    text = re.sub(r'<[^>]+>', '', text)\n",
+    "    # Remove special characters and numbers\n",
+    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
+    "    # Remove extra whitespace\n",
+    "    text = text.strip()\n",
+    "    return text\n",
+    "\n",
+    "def normalize_text(text):\n",
+    "    \"\"\"\n",
+    "    Normalize the text by converting to lowercase and removing extra spaces.\n",
+    "    \"\"\"\n",
+    "    # Convert to lowercase\n",
+    "    text = text.lower()\n",
+    "    # Remove extra whitespace\n",
+    "    text = ' '.join(text.split())\n",
+    "    return text\n",
+    "\n",
+    "def tokenize_text(text):\n",
+    "    \"\"\"\n",
+    "    Tokenize the text into words.\n",
+    "    \"\"\"\n",
+    "    tokens = word_tokenize(text)\n",
+    "    return tokens\n",
+    "\n",
+    "def remove_stop_words(tokens):\n",
+    "    \"\"\"\n",
+    "    Remove stop words from the tokenized text.\n",
+    "    \"\"\"\n",
+    "    stop_words = set(stopwords.words('english'))\n",
+    "    filtered_tokens = [word for word in tokens if word not in stop_words]\n",
+    "    return filtered_tokens\n",
+    "\n",
+    "def stem_tokens(tokens):\n",
+    "    \"\"\"\n",
+    "    Apply stemming to tokens.\n",
+    "    \"\"\"\n",
+    "    stemmer = PorterStemmer()\n",
+    "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
+    "    return stemmed_tokens\n",
+    "\n",
+    "def lemmatize_tokens(tokens):\n",
+    "    \"\"\"\n",
+    "    Apply lemmatization to tokens.\n",
+    "    \"\"\"\n",
+    "    lemmatizer = WordNetLemmatizer()\n",
+    "    lemmatized_tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
+    "    return lemmatized_tokens\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "484d6bf9-826f-4b57-9f5c-7b737c81979a",
+   "metadata": {},
+   "source": [
+    "This cell applies preprocessing functions to each article’s content, then converts the preprocessed data into a DataFrame and saves it to a CSV file. The **preprocessed_articles** list is initialized as an empty list to store the preprocessed article data. By looping through each article, many steps of preprocessing are applied like removing HTML tags, special characters, and extra whitespace using **clean_text** function, converting the text to lowercase and removing extra spaces using the **normalize_text** function, splitting the text into individual words (tokens) using **tokenize_text** function, filtering out common stop words from the tokens using **remove_stop_words** function and reducing to their root form using **stem_tokens** function.\n",
+    "\n",
+    "After all this, the preprocessed text is joined back into a single string and stored in a dictionary along with the article’s title, author, and publication date which is added to the **preprocessed_articles** list. Finally, we convert the list of preprocessed articles into a DataFrame using **pd.DataFrame** function, save it to a CSV file and display the resulting DataFrame to verify the output.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "8f35cdf6-3bd8-49f9-956c-a0c587bd5b19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>author</th>\n",
+       "      <th>publication_date</th>\n",
+       "      <th>content</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Most Australians think there are too few publi...</td>\n",
+       "      <td>Jennifer Dudley-Nicholson</td>\n",
+       "      <td>February 23, 2024</td>\n",
+       "      <td>australian think nation public charg station s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Why Australian consumers are charging toward e...</td>\n",
+       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
+       "      <td>27 Jul. 2022</td>\n",
+       "      <td>ceo imper strategi set takeoff clever govern c...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EVs face future challenges despite increasing ...</td>\n",
+       "      <td>Harrison Vesey</td>\n",
+       "      <td>10 April 2024</td>\n",
+       "      <td>professor david hensher one three australian c...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
+       "      <td>Mike Costello</td>\n",
+       "      <td>19 August 2020, 1:56pm</td>\n",
+       "      <td>guest user australia electricvehicl penetr wel...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               title  \\\n",
+       "0  Most Australians think there are too few publi...   \n",
+       "1  Why Australian consumers are charging toward e...   \n",
+       "2  EVs face future challenges despite increasing ...   \n",
+       "3  EVs in Australia: Report outlines sales, and i...   \n",
+       "\n",
+       "                                              author        publication_date  \\\n",
+       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
+       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
+       "2                                     Harrison Vesey           10 April 2024   \n",
+       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
+       "\n",
+       "                                             content  \n",
+       "0  australian think nation public charg station s...  \n",
+       "1  ceo imper strategi set takeoff clever govern c...  \n",
+       "2  professor david hensher one three australian c...  \n",
+       "3  guest user australia electricvehicl penetr wel...  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Apply preprocessing to each article's content\n",
+    "preprocessed_articles = []\n",
+    "\n",
+    "for article in articles_data:\n",
+    "    # Clean the text\n",
+    "    cleaned_content = clean_text(article['content'])\n",
+    "    # Normalize the text\n",
+    "    normalized_content = normalize_text(cleaned_content)\n",
+    "    # Tokenize the text\n",
+    "    tokens = tokenize_text(normalized_content)\n",
+    "    # Remove stop words\n",
+    "    filtered_tokens = remove_stop_words(tokens)\n",
+    "    # Optionally apply stemming or lemmatization\n",
+    "    stemmed_tokens = stem_tokens(filtered_tokens)\n",
+    "    # lemmatized_tokens = lemmatize_tokens(filtered_tokens)\n",
+    "    \n",
+    "    # Store preprocessed data\n",
+    "    preprocessed_article = {\n",
+    "        'title': article['title'],\n",
+    "        'author': article['author'],\n",
+    "        'publication_date': article['publication_date'],\n",
+    "        'content': ' '.join(stemmed_tokens)  # Use lemmatized_tokens if preferred\n",
+    "    }\n",
+    "    preprocessed_articles.append(preprocessed_article)\n",
+    "    \n",
+    "# Convert the preprocessed articles data to a DataFrame\n",
+    "preprocessed_articles_df = pd.DataFrame(preprocessed_articles)\n",
+    "\n",
+    "# Save the DataFrame to a CSV file\n",
+    "preprocessed_articles_df.to_csv('preprocessed_ev_articles.csv', index=False)\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "preprocessed_articles_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "592c4d07-49e8-4f5d-8165-3057d18f48dc",
+   "metadata": {},
+   "source": [
+    "In sentiment analysis, **polarity** and **subjectivity** are two key metrics used to assess the sentiment of a text. \n",
+    "\n",
+    "Polarity measures the sentiment of the text on a scale from -1 to 1. Negative sentiment indicates that the text expresses a strong negative sentiment or emotion. Neutral sentiment indicates that the text is neutral and does not convey any strong positive or negative sentiment. Positive sentiment indicates that the text expresses a strong positive sentiment or emotion.  This can be useful for understanding overall attitudes or reactions.\n",
+    "\n",
+    "Subjectivity measures the degree to which the text expresses personal opinions, feelings, or beliefs, as opposed to objective facts. Zero subjectivity means the text is factual and does not include personal opinions or emotions. It is more about reporting facts.A subjectivity of 1 means the text is more personal and opinionated, including personal beliefs, emotions, or feelings. This can be useful for distinguishing between factual reports and personal opinions or feelings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "7a2419ad-a537-4396-999f-efe9ad8fb316",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16abc529-f4c0-42db-b793-624477ac2394",
+   "metadata": {},
+   "source": [
+    "This code defines a function **analyze_sentiment()** that uses the TextBlob library to analyze the sentiment of a given text. It takes a single argument **text**, which is a string of text that we want to analyze for sentiment. The TextBlob object **blob** has a sentiment property that returns a Sentiment namedtuple - polarity and subjectivity. The function returns the polarity and subjectivity scores as a tuple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "7a253b62-1ce6-42c2-986c-bba61766c17a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def analyze_sentiment(text):\n",
+    "    \"\"\"\n",
+    "    Analyze the sentiment of the given text using TextBlob.\n",
+    "    \"\"\"\n",
+    "    blob = TextBlob(text)\n",
+    "    # Return polarity and subjectivity\n",
+    "    return blob.sentiment.polarity, blob.sentiment.subjectivity\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c6d5f8-09c8-493b-b866-c0a2fd628a9d",
+   "metadata": {},
+   "source": [
+    "This code performs sentiment analysis on the preprocessed content of each article, stores the results, and saves them to a CSV file. The **sentiment_results[]** is an empty list initialized to store the sentiment analysis results for each article. In the 'for' loop, we iterate through each row in the preprocessed articles DataFrame (**preprocessed_articles_df**). Each row represents an article. For each article, the content is passed to the **analyze_sentiment()** function, which returns the polarity and subjectivity scores.\n",
+    "The results for each article, including its title, author, publication date, content, polarity, and subjectivity, are stored in a dictionary and appended to the above initialized list.\n",
+    "We then conver this list of dictionaries into pandas DataFrame. Finally, the DataFrame is displayed, showing the sentiment analysis results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "1d55944e-1875-47bf-a203-5f2b5a360a6a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>author</th>\n",
+       "      <th>publication_date</th>\n",
+       "      <th>content</th>\n",
+       "      <th>polarity</th>\n",
+       "      <th>subjectivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Most Australians think there are too few publi...</td>\n",
+       "      <td>Jennifer Dudley-Nicholson</td>\n",
+       "      <td>February 23, 2024</td>\n",
+       "      <td>australian think nation public charg station s...</td>\n",
+       "      <td>0.002210</td>\n",
+       "      <td>0.187689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Why Australian consumers are charging toward e...</td>\n",
+       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
+       "      <td>27 Jul. 2022</td>\n",
+       "      <td>ceo imper strategi set takeoff clever govern c...</td>\n",
+       "      <td>0.102000</td>\n",
+       "      <td>0.283466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EVs face future challenges despite increasing ...</td>\n",
+       "      <td>Harrison Vesey</td>\n",
+       "      <td>10 April 2024</td>\n",
+       "      <td>professor david hensher one three australian c...</td>\n",
+       "      <td>0.181563</td>\n",
+       "      <td>0.347917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
+       "      <td>Mike Costello</td>\n",
+       "      <td>19 August 2020, 1:56pm</td>\n",
+       "      <td>guest user australia electricvehicl penetr wel...</td>\n",
+       "      <td>0.011245</td>\n",
+       "      <td>0.306212</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               title  \\\n",
+       "0  Most Australians think there are too few publi...   \n",
+       "1  Why Australian consumers are charging toward e...   \n",
+       "2  EVs face future challenges despite increasing ...   \n",
+       "3  EVs in Australia: Report outlines sales, and i...   \n",
+       "\n",
+       "                                              author        publication_date  \\\n",
+       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
+       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
+       "2                                     Harrison Vesey           10 April 2024   \n",
+       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
+       "\n",
+       "                                             content  polarity  subjectivity  \n",
+       "0  australian think nation public charg station s...  0.002210      0.187689  \n",
+       "1  ceo imper strategi set takeoff clever govern c...  0.102000      0.283466  \n",
+       "2  professor david hensher one three australian c...  0.181563      0.347917  \n",
+       "3  guest user australia electricvehicl penetr wel...  0.011245      0.306212  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Apply sentiment analysis to each article's content\n",
+    "sentiment_results = []\n",
+    "\n",
+    "for index, article in preprocessed_articles_df.iterrows():\n",
+    "    # Analyze sentiment\n",
+    "    polarity, subjectivity = analyze_sentiment(article['content'])\n",
+    "    \n",
+    "    # Store the results\n",
+    "    sentiment_results.append({\n",
+    "        'title': article['title'],\n",
+    "        'author': article['author'],\n",
+    "        'publication_date': article['publication_date'],\n",
+    "        'content': article['content'],\n",
+    "        'polarity': polarity,\n",
+    "        'subjectivity': subjectivity\n",
+    "    })\n",
+    "\n",
+    "# Convert the results to a DataFrame\n",
+    "sentiment_df = pd.DataFrame(sentiment_results)\n",
+    "\n",
+    "# Save the sentiment analysis results to a CSV file\n",
+    "sentiment_df.to_csv('sentiment_analysis_results.csv', index=False)\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "sentiment_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37a82af6-129d-4a96-ac4e-f063316184b9",
+   "metadata": {},
+   "source": [
+    "This code creates a visual representation of the sentiment analysis results for a set of articles, focusing on both polarity and subjectivity. The plotting style is configured to \"whitegrid\" for a clean and readable background. A figure is setup with two subplots arranged vertically, each subplot having its own set of axes. this code generates two horizontal bar plots within a single figure, visually comparing the polarity and subjectivity of the sentiment analysis for each article."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "8dfd03d0-cab3-4de2-939f-97b4a64bc416",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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p0qXTwIEDTb5ErV69Wq6urlq2bJkqVqwY5/rCDR48WLVr15aDg4PRH+PHj9esWbPUvXt3+fr6qnXr1hozZoyxDk9QUJBatmypZMmSafHixVqwYIHu3LmjNm3amAQ84erUqWN8nlavXq3Bgwfr9u3bcnFxUY4cObRmzRrNnj1bT58+VcuWLfX8+XNVrVpVly5dMgKpQ4cOKSwszCS43L17typUqKDkyZNHeWxnzpzR/v37tWDBAs2cOVO///67evfuHWXb6AQFBenw4cNauHChZs+erTNnzhiBSExev34tNzc3Xb58WbNmzdKqVasUGhqqDh06RBmGPn78WK1bt1ZISIgWL16shQsX6vr16yb1njhxQo8fP9ayZcs0Z84cnTx50rhm3L59W+3bt1fRokW1bt06rV+/Xvb29ho8eLBJSLV9+3aVK1dOa9euVb169fTbb7/p+++/V7NmzbRx40Y1adIk2nMlXEyfweDgYLVr106hoaFavny5pk6dKh8fH+OaEZVly5Zp27Ztmjp1qrZv3642bdpoxIgROnbsWJTXjSVLlsjLy0vu7u7avn27Zs6cqYCAAGNtIE9PTzk4OKh27dpRTon85Zdf1KVLF1WpUkU+Pj4aOXKktmzZor59+5q0mzBhgho3bqzNmzerTZs28vT01O+//y5J8vDw0MWLFzV37lxt2bJFlSpVUp8+faIMVxwcHEzO/zp16sT6d1VU4nO9jc79+/eN69LixYv1119/qUOHDnr9+rXRZtasWXJwcND69evVunVrDRs2TFu2bJEU+9+x4by9vTVkyBDNnz9fs2bNkpubm7JkyaL9+/e/96jlpk2baseOHSbB865duxQcHKyGDRtKejvld8iQIerRo4e2b9+usWPHasOGDZo/f/577RsAAADmk/RDFxCudu3a8vLy0u3bt5U5c2YdOHBA6dKlU6FChUzaLViwQHZ2dsYipHny5NGPP/6ohg0bav/+/SpevLgePHigTJkyKVu2bMqePbumTZtmjDJIly6dLC0tZW1trTRp0kRZi4+Pj5InT67q1atLkurWravt27dr586dql27dpyO5/bt2woJCdHnn3+ubNmyKVu2bJo9e7bxD/iMGTNKkmxsbGRjY2OMjHJxcVHevHklvf0i4erqqtatWxtTS3r27Kn58+fr4sWLKliwYIw1FC1aVLVq1VKpUqUkSdmyZVO5cuV06dIlk3bt2rVTtWrVJEl9+vTR0qVLderUKWXPnl3Xr19X/vz5lT17dqVIkUKDBw9W/fr1owx7UqRIIRsbG5Pjk96u8TV58mSlSpVKkvT1119r6tSp0dZtY2OjUaNGycrKSnny5FGdOnW0Z88ekzaBgYEaOHCgKleurNGjR0e7Tlh0/dytWzdj5ESfPn20fPlynTt3Tjly5NCsWbPUpUsXI/zMnj27nj59qpEjR6pXr17GtiO+V4cOHdLJkyd1+PBh47zq27evjh8/riVLlkS5qK6lpaWGDh0qFxcX4zFXV1d17NhR9+7dM77EFSxY0GSkUGz1vRvW2NjYKEWKFLKyslLGjBn19OlTLV++XO7u7sZ2c+bMqcDAQM2dO1fffPONli1bJmtra02ePFnJkiWTJE2fPl3VqlXThg0b1Lp1a5N9pEiRQra2tpLefsZsbGzk5eWlLFmyaMiQIUa7adOmqWzZstq2bZsaNGggW1tbHThwQI0aNdLBgwdVrVo17dy5U3fv3lWGDBm0d+/eGAMlCwsLTZs2TZkzZ5b09qYFHTt21JUrV5Q7d+5oXxdR8uTJNXXqVGPNqmHDhsnNzU3Xrl1Tjhw5on3doUOHdPHiRW3btk25cuWSJP3www9atGiRHj16FGmq3pYtW/Ts2TP9+OOPRl/98MMP2rx5sxE4ZsyYUaNHj1aSJEmUO3du1alTRwcPHpQkvXz5Uj169FD79u2N8/27777T+vXrFRAQYNRva2urDh06GPsdMGCAatWqpfbt20uScuXKpYCAgBgX/Y7pM7hlyxbdv39fPj4+xrk+adIkIxiIyvXr12Vtba0vvvhCmTJlUps2bZQ7d27lypUryuvGl19+qQkTJqhq1aqS3l67atWqZfwnRZo0aWRlZaUUKVJEOSVy7ty5qlGjhrp27Wocc1hYmLp166bLly8bn9tGjRoZdXfu3FleXl46fvy4SpcurevXrytlypTKnj27UqdOrV69eql06dLGexdRsmTJTM7/FClSxPp3VeXKlaPsp7heb6MTPoquSJEikt6+N3Xq1NGhQ4dUsWJFSVKFChXUvXt3SVLu3Ll16tQpLV68WHXq1Ilz3ZUrV1a5cuWM/VpbW8vS0tLk2h+Vjh07Rnk8Hh4eqlSpkqS378vUqVP122+/qVatWpLejnaqXr260qZNK+nt3wEWFhbKli2bPv/8c33++efy8vIy/p4BAADAv8+/JoAqUqSIsmfPru3bt8vV1VVbtmyJNPpJki5duqTy5cubPFagQAHZ2Njo4sWLqly5sjp06KDRo0dr+vTpKlu2rCpXrhzn4Oj169fauHGjKleubPxDtkqVKkqZMqVWrFgR5+0ULFhQ9erVU+fOnZUxY0aVL19eVapUUY0aNWJ8XcQvvOnSpZOLi4s2bdokPz8/Xb9+XRcuXJCkaKdbRdSwYUMdPHhQkydPVkBAgK5cuaKrV68agVS4PHnyGH8O/yIYPtqre/fuGjBggLZv366SJUuqQoUKqlevXrQjUqKSPn16ky8FqVOnNpkK864vv/xSVlZWJjW9O/psxIgRevXqlbJmzZqgRerDAwNJxhfHly9f6v79+7p165Z+/PFHeXh4GG1CQ0P18uVLBQYGGsce8b06d+6cwsLCjC/M4UJCQqKcPia9PUdsbW01d+5cXblyRdeuXTPe34gjDSLuJy71RXw/o3LlyhW9evVKJUuWNHm8TJkyWrx4se7du6dLly6pSJEiRvgkvQ0HcuXKFSnAjI6fn5/+/PNPYyRcuJcvX8rf319JkyZVxYoVdfDgQTVq1EgHDhzQsGHDdOrUKR05ckR58uTR33//HeUX9XA5c+Y0wifp7fQd6e11Iq4BVI4cOUwWTC9WrJiktyMhYwqgLl26JFtbW5NzKXPmzPr++++jbZ8zZ06TAKNAgQIqUKCA8fuXX36pJEn+f2Cqra2t8Vn58ssv1aRJEy1ZskSXLl0yuR5Ed75Ib8/Nr776yuSx0qVLxxhAxfQZ9PPzU65cuUwC/PBrcHRat26tnTt3qnLlyipYsKDKly+vunXrKn369FG2d3Z21qlTp+Th4aGrV6/q6tWrunz5ssl7HZNLly5F+vujTJkyxnPhAdS7n5WIx9mxY0d17txZTk5Osre3V/ny5VW/fv0Yj/PdGmL7u+pdiXG9TZkypRE+hR+jra2tLl26ZARQjo6OJq9xcHAw7gwb17pj+mzE5IcffjA+YxFlypTJ+HOGDBlUuXJl+fr6qlatWrp3757279+vOXPmGG0qVqwoBwcHNWvWTF988YUxHTrisQMAAODf5V8TQEn/Pw2vZcuW2rVrV5S3jY9uodOwsDDjC1P//v3l4uKiPXv26NChQxo9erTmz5+v9evXm3yhjsru3bt19+5d/fLLLyajr968eaMjR47o6tWrJl84I4o4xUGSpkyZom7dumnv3r06ePCgBgwYoJIlS2rx4sXR7j/iQsB37txRy5YtlS5dOjk7O6tChQoqWrRojF/IIxo2bJi2b9+uRo0aydnZWd26dTNGmUUUVZ+E93ONGjW0b98+7du3TwcPHtTChQs1Y8YMrVq1Svny5YtTHfH53/vo6nlX48aNlT9/fo0fP141atRQ/vz547WPiF/yw4WFhRnB3qBBg0z+dz9c1qxZ9ffff0syfa9CQ0OVKlWqKNfAie54jh49qvbt26tKlSoqWbKk6tevr+DgYHXr1s2k3bv7ia2+2ET3GQrfdtKkSWNsEzGYiEloaKjKli2r4cOHR3ou/Et8tWrVNHbsWF2/fl23b99W6dKl5ejoqCNHjigwMFAlS5Y0RjxE5d1awoOY6M65dz+jUbUN30Zsx5k0afwun3FpH9Nn5fLly3JxcVHhwoVVrlw5ffXVV0qbNm2kO4K9u5h40qRJ4xRYRxTTZ9DS0jLe28uZM6d27Niho0eP6sCBA9q9e7fmzZuncePGqXHjxpHaz507VzNnzlTjxo3l5OSkdu3aadeuXdq8eXOc9hfV+Rvx/A4X07XPwcFBe/bs0YEDB3To0CGtX79eP/30k+bPny8nJ6cE1RD+eHTnVkKut++e01Ft+82bNybn1rvnYmhoqHFNjGvdCV20PnPmzHEKr5o1a6aePXvq0aNH8vX1VebMmU2uecmTJ9eSJUvk5+dn3Emvc+fOatSoUbzvqAgAAIB/xr9mDSjpbQB1/PhxrV27VtmzZ49yJIednZ3++OMPk8cuXLigp0+fKk+ePLpy5YqGDx+u9OnTq1WrVpo+fbrmz58vf39/Y7RATNauXau0adNq/fr1Jj+zZs1SWFiYsRi5lZWVnj17ZvLaiIsPnzp1SmPHjlXu3LnVrl07zZ07V2PHjtXhw4dNFh2OSfg6LMuXL1fXrl1Vo0YNYwpZbHccevDggVauXKnhw4dr0KBBatKkiQoWLKgrV67E+W5FISEhGjdunG7cuKE6derohx9+0M6dO5UkSRLjf8vflZDRSAlRt25dubi4qEiRIho0aFC0a6rEV/r06ZUuXTrduHFDOXLkMH6iW4cqXP78+fX06VO9evXK5HXz5s3Trl27onzNggUL5OjoKE9PT7Vr107ly5fXX3/9JSn69zeh9UV8X/LkySMrK6tIn6Njx44pY8aMsrW1lZ2dnc6cOWOyFtXdu3d17dq1WEdYhcuXL5/8/f2VNWtWo05bW1uNHTvWGEVVsWJFPXz4UEuWLFGxYsVkbW2tcuXK6fDhw/rtt9+MqaHRuXr1qp48eWL8fuLECUkywmMrKyuTNauiWiA8ICDApM0ff/whCwsLY5R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+      "text/plain": [
+       "<Figure size 1200x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set plot style\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "\n",
+    "# Create a figure and axes\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(12, 12))  # Increase figure height\n",
+    "\n",
+    "# Plot polarity (horizontal bars)\n",
+    "sns.barplot(x='polarity', y='title', data=sentiment_df, ax=ax[0], palette='viridis', hue='title', legend=False)\n",
+    "ax[0].set_title('Sentiment Polarity of Articles')\n",
+    "ax[0].set_xlabel('Polarity')\n",
+    "ax[0].set_ylabel('Article Title')\n",
+    "\n",
+    "# Plot subjectivity (horizontal bars)\n",
+    "sns.barplot(x='subjectivity', y='title', data=sentiment_df, ax=ax[1], palette='viridis', hue='title', legend=False)\n",
+    "ax[1].set_title('Sentiment Subjectivity of Articles')\n",
+    "ax[1].set_xlabel('Subjectivity')\n",
+    "ax[1].set_ylabel('Article Title')\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d6748a7-f128-40f2-b7ed-b11f6ddd8a60",
+   "metadata": {},
+   "source": [
+    "The overall goal of this code is to analyze sentiment scores (polarity and subjectivity) of articles and aggregate these scores by the year of publication. \n",
+    "It defines a dictionary data with sample data, including article titles, authors, publication dates, content, polarity, and subjectivity scores and converts the **data** dictionary into a pandas DataFrame named **sentiment_df**.\n",
+    "It defines a function **parse_date()** that attempts to parse date strings into datetime objects using dateutil.parser.parse with the fuzzy=True option to handle various date formats. It applies the **parse_date()** function to the **publication_date** column in the DataFrame to convert date strings to datetime objects. Then it extracts the year from the parsed **publication_date** column and stores it in a new column named **year**. Then, it prints the **publication_date** and **year** columns to check for any missing values in the year column. After printing the polarity and subjectivity columns, it groups the DataFrame by the year column and calculates the mean polarity and subjectivity scores for each year.\n",
+    "\n",
+    "Finally, it prints the  stored **yearly_sentiment** DataFrame to display the average polarity and subjectivity scores for each year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "a493e974-a1d9-4eb9-88d8-bb6fd1d39e54",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     publication_date  year\n",
+      "0 2024-02-23 00:00:00  2024\n",
+      "1 2022-07-27 00:00:00  2022\n",
+      "2 2024-04-10 00:00:00  2024\n",
+      "3 2020-08-19 13:56:00  2020\n",
+      "   polarity  subjectivity\n",
+      "0  0.002210      0.187689\n",
+      "1  0.102000      0.283466\n",
+      "2  0.181563      0.347917\n",
+      "3  0.011245      0.306212\n",
+      "   year  polarity  subjectivity\n",
+      "0  2020  0.011245      0.306212\n",
+      "1  2022  0.102000      0.283466\n",
+      "2  2024  0.091886      0.267803\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from dateutil import parser\n",
+    "\n",
+    "# Sample data as a dictionary\n",
+    "data = {\n",
+    "    'title': [\n",
+    "        'Most Australians think there are too few publi...',\n",
+    "        'Why Australian consumers are charging toward e...',\n",
+    "        'EVs face future challenges despite increasing ...',\n",
+    "        'EVs in Australia: Report outlines sales, and i...'\n",
+    "    ],\n",
+    "    'author': [\n",
+    "        'Jennifer Dudley-Nicholson',\n",
+    "        'Neal Johnston, Glenn Maris, Damien Smith, Neal...',\n",
+    "        'Harrison Vesey',\n",
+    "        'Mike Costello'\n",
+    "    ],\n",
+    "    'publication_date': [\n",
+    "        'February 23, 2024',\n",
+    "        '27 Jul. 2022',\n",
+    "        '10 April 2024',\n",
+    "        '19 August 2020, 1:56pm'\n",
+    "    ],\n",
+    "    'content': [\n",
+    "        'australian think nation public charg station s...',\n",
+    "        'ceo imper strategi set takeoff clever govern c...',\n",
+    "        'professor david hensher one three australian c...',\n",
+    "        'guest user australia electricvehicl penetr wel...'\n",
+    "    ],\n",
+    "    'polarity': [0.002210, 0.102000, 0.181563, 0.011245],\n",
+    "    'subjectivity': [0.187689, 0.283466, 0.347917, 0.306212]\n",
+    "}\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "sentiment_df = pd.DataFrame(data)\n",
+    "\n",
+    "# Define a function to parse dates with different formats\n",
+    "def parse_date(date_str):\n",
+    "    try:\n",
+    "        return parser.parse(date_str, fuzzy=True)\n",
+    "    except ValueError:\n",
+    "        return pd.NaT\n",
+    "\n",
+    "# Apply the parsing function\n",
+    "sentiment_df['publication_date'] = sentiment_df['publication_date'].apply(parse_date)\n",
+    "\n",
+    "# Extract year from publication_date\n",
+    "sentiment_df['year'] = sentiment_df['publication_date'].dt.year\n",
+    "\n",
+    "# Check for missing values in 'year'\n",
+    "print(sentiment_df[['publication_date', 'year']])\n",
+    "\n",
+    "# Check for missing values in sentiment scores\n",
+    "print(sentiment_df[['polarity', 'subjectivity']])\n",
+    "\n",
+    "# Aggregate sentiment scores by year\n",
+    "yearly_sentiment = sentiment_df.groupby('year').agg({'polarity': 'mean', 'subjectivity': 'mean'}).reset_index()\n",
+    "\n",
+    "# Display the aggregated sentiment scores\n",
+    "print(yearly_sentiment)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0c41a73-9458-46c3-9f0e-816db6f65144",
+   "metadata": {},
+   "source": [
+    "This code generates a line plot to visualize the average sentiment scores (polarity and subjectivity) of articles over time. It uses the **yearly_sentiment** DataFrame, which contains the aggregated sentiment scores by year, and plots these scores with markers for each year. The plot includes a title, axis labels, a legend to differentiate between polarity and subjectivity, and a grid for better readability. Finally, it displays the plot, allowing for a clear visual comparison of how sentiment has evolved over the years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "abc62267-805b-4255-bf29-5c054cb5b4c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot sentiment scores over time\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot Polarity\n",
+    "plt.plot(yearly_sentiment['year'], yearly_sentiment['polarity'], marker='o', linestyle='-', color='b', label='Polarity')\n",
+    "\n",
+    "# Plot Subjectivity\n",
+    "plt.plot(yearly_sentiment['year'], yearly_sentiment['subjectivity'], marker='o', linestyle='-', color='r', label='Subjectivity')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Average Sentiment Scores by Year')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Average Score')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02793131-4dd8-4278-8f71-cc56b2ad14ac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From e68361c4cc19fa1b917be718227ecb99db8e1abe Mon Sep 17 00:00:00 2001
From: Aditya Gahlot <adityagahlot2002@gmail.com>
Date: Sat, 10 Aug 2024 20:42:08 +1000
Subject: [PATCH 3/5] Remove Sentiment_Analysis.ipynb from root directory

---
 Sentiment_Analysis.ipynb | 1000 --------------------------------------
 1 file changed, 1000 deletions(-)
 delete mode 100644 Sentiment_Analysis.ipynb

diff --git a/Sentiment_Analysis.ipynb b/Sentiment_Analysis.ipynb
deleted file mode 100644
index e919145..0000000
--- a/Sentiment_Analysis.ipynb
+++ /dev/null
@@ -1,1000 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a2a1d701-1644-4a79-a16a-d5196eacb4e1",
-   "metadata": {},
-   "source": [
-    "# EV News Articles Sentiment Analysis\n",
-    "Performing news article sentiment analysis of EV vehicles in Australia involves several steps, from data collection to sentiment analysis and visualization. Although the sentiments of people vary across various regions in Australia, I have presented a broad analysis of the Electric Vehicle industry through four online articles. The articles for this project were taken from the following links:\n",
-    "\n",
-    "1. https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/\n",
-    "2. https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles\n",
-    "3. https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html\n",
-    "4. https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac0279aa-b72c-4ccc-8808-9664ad081343",
-   "metadata": {},
-   "source": [
-    "# Procedure\n",
-    "Performing news article sentiment analysis of EV vehicles in Australia involves several steps, from data collection to sentiment analysis and visualization. These steps are summarized further.\n",
-    "## Step 1: Data Collection\n",
-    "After gathering the news articles for EVs, I used the technique of **web scraping** to extract meaningful information from the articles like the title, the publication date of the article and its broad content. For this purpose, I used web scraping tools like BeautifulSoup which is imported in the first cell along with other important libraries. After that, I have initialized all the URLs as an array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "aea29846-81dc-468e-ac46-c98193afc97a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from bs4 import BeautifulSoup\n",
-    "import pandas as pd\n",
-    "import time\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "57681cd9-a557-4283-a734-cf346329ee34",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# List of article URLs\n",
-    "urls = [\n",
-    "    'https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/',\n",
-    "    'https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles',\n",
-    "    'https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html',\n",
-    "    'https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment',\n",
-    "    \n",
-    "]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "ac8f969b-2142-4494-889b-4e8bfd13caf8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize a list to store article data\n",
-    "articles_data = []\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fff14daf-1cb0-4e21-a0f1-175818fd1ec4",
-   "metadata": {},
-   "source": [
-    "## Step 2: Scraping an Article with Improved Selectors\n",
-    "After initializing an empty list to store article data named **articles_data**, I have defined a function **scrape_article** to fetch and parse a single article. It returns the details of an article, including its title, author, publication date, and content. Within the function, **headers** represents a dictionary with the User-Agent header. This helps to mimic a request from a web browser, which can be useful to avoid blocks from some websites that restrict automated scraping. Then an HTTP GET request was sent to the specified URL with the custom headers.\n",
-    "\n",
-    "The **BeautifulSoup (response.content, 'html.parser')** function parses the HTML content of the web page, creating a BeautifulSoup object for further extraction.I manually checked the structure of the HTML for each website to determine the correct selectors. This step required viewing the source code of each of the webpages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "6320942d-9d3e-4299-aa52-af8866ccafe2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def scrape_article(url):\n",
-    "    headers = {\n",
-    "        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n",
-    "    }\n",
-    "    \n",
-    "    try:\n",
-    "        # Fetch the web page\n",
-    "        response = requests.get(url, headers=headers)\n",
-    "        response.raise_for_status()  # Raise HTTPError for bad responses\n",
-    "\n",
-    "        # Parse the HTML content\n",
-    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
-    "\n",
-    "        # Initialize variables\n",
-    "        title = 'No title found'\n",
-    "        author = 'No author found'\n",
-    "        publication_date = 'No date found'\n",
-    "        content = 'No content found'\n",
-    "\n",
-    "        # Check URL and set selectors accordingly\n",
-    "        if 'thedriven.io' in url:\n",
-    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
-    "            author_tag = soup.find('a', class_='url fn n')\n",
-    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
-    "            date_tag = soup.find('a', rel='bookmark')\n",
-    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
-    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
-    "\n",
-    "        elif 'ey.com' in url:\n",
-    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
-    "            authors = [a.get_text(strip=True) for a in soup.find_all('a', class_='surfaceProfile-author-link')]\n",
-    "            author = ', '.join(authors) if authors else 'No author found'\n",
-    "            date_tag = soup.select_one('#container4 > div > div:nth-child(2) > div > div > span:nth-child(2)')\n",
-    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
-    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
-    "\n",
-    "        elif 'sydney.edu.au' in url:\n",
-    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
-    "            author_tag = soup.find('h3', class_='b-contact-information__title')\n",
-    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
-    "            date_tag = soup.find('span')\n",
-    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
-    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
-    "\n",
-    "        elif 'carexpert.com.au' in url:\n",
-    "            title = soup.find('h1').get_text(strip=True) if soup.find('h1') else 'No title found'\n",
-    "            author_tag = soup.find('div', class_='gubuy9f')\n",
-    "            author = author_tag.get_text(strip=True) if author_tag else 'No author found'\n",
-    "            date_tag = soup.find('time')\n",
-    "            publication_date = date_tag.get_text(strip=True) if date_tag else 'No date found'\n",
-    "            content = ' '.join([p.get_text(strip=True) for p in soup.find_all('p')])\n",
-    "\n",
-    "        # Store the data\n",
-    "        article_data = {\n",
-    "            'title': title,\n",
-    "            'author': author,\n",
-    "            'publication_date': publication_date,\n",
-    "            'content': content\n",
-    "        }\n",
-    "\n",
-    "        # Print success message\n",
-    "        print(f'Successfully scraped {url}')\n",
-    "        \n",
-    "        return article_data\n",
-    "    \n",
-    "    except Exception as e:\n",
-    "        # Handle errors (e.g., missing elements, request errors)\n",
-    "        print(f'Error fetching {url}: {e}')\n",
-    "        return None\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d5d6a6e7-1f9d-4f92-8080-a56787f32bd5",
-   "metadata": {},
-   "source": [
-    "The below code iterates over a list of URLs, scraping article data from each URL using the **scrape_article** function, and then appends the collected data to a list. It also includes an optional delay of 2 seconds between requests to avoid overloading the server.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "e5bb5795-ee78-4fd2-b8cd-a254e46e7460",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Successfully scraped https://thedriven.io/2024/02/23/most-australians-think-there-are-too-few-public-charging-stations-to-support-evs/\n",
-      "Successfully scraped https://www.ey.com/en_au/sustainability/why-consumers-are-charging-toward-electric-vehicles\n",
-      "Successfully scraped https://www.sydney.edu.au/news-opinion/news/2024/04/10/evs-face-future-challenges-despite-increasing-uptake-.html\n",
-      "Successfully scraped https://www.carexpert.com.au/car-news/evs-in-australia-report-outlines-sales-and-improving-consumer-sentiment\n"
-     ]
-    }
-   ],
-   "source": [
-    "for url in urls:\n",
-    "    article_data = scrape_article(url)\n",
-    "    if article_data:\n",
-    "        articles_data.append(article_data)\n",
-    "    \n",
-    "    # Optional: Delay between requests to avoid overwhelming the server\n",
-    "    time.sleep(2)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3645339f-324d-4832-b072-9be6162c9ff3",
-   "metadata": {},
-   "source": [
-    "In the below cell, I have stored the collected articles in a structured format such as CSV or JSON for further processing. It creates a DataFrame from a list of article data, saves it as a CSV file named **ev_articles.csv**, and displays the DataFrame for review. The **pd.Dataframe()** function creates a DataFrame named **articles_df** from the list of dictionaries **articles_data**. Each dictionary in the list represents a row in the DataFrame, with dictionary keys becoming column names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "c6c2e563-874b-43c6-ad78-f8f7bc81bff8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>title</th>\n",
-       "      <th>author</th>\n",
-       "      <th>publication_date</th>\n",
-       "      <th>content</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Most Australians think there are too few publi...</td>\n",
-       "      <td>Jennifer Dudley-Nicholson</td>\n",
-       "      <td>February 23, 2024</td>\n",
-       "      <td>Most Australians think the nation has too few ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Why Australian consumers are charging toward e...</td>\n",
-       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
-       "      <td>27 Jul. 2022</td>\n",
-       "      <td>The CEO Imperative: Is your strategy set for t...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>EVs face future challenges despite increasing ...</td>\n",
-       "      <td>Harrison Vesey</td>\n",
-       "      <td>10 April 2024</td>\n",
-       "      <td>Professor David Hensher One in three Australia...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
-       "      <td>Mike Costello</td>\n",
-       "      <td>19 August 2020, 1:56pm</td>\n",
-       "      <td>Guest User Australia's electric-vehicle penetr...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               title  \\\n",
-       "0  Most Australians think there are too few publi...   \n",
-       "1  Why Australian consumers are charging toward e...   \n",
-       "2  EVs face future challenges despite increasing ...   \n",
-       "3  EVs in Australia: Report outlines sales, and i...   \n",
-       "\n",
-       "                                              author        publication_date  \\\n",
-       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
-       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
-       "2                                     Harrison Vesey           10 April 2024   \n",
-       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
-       "\n",
-       "                                             content  \n",
-       "0  Most Australians think the nation has too few ...  \n",
-       "1  The CEO Imperative: Is your strategy set for t...  \n",
-       "2  Professor David Hensher One in three Australia...  \n",
-       "3  Guest User Australia's electric-vehicle penetr...  "
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Convert the articles data to a DataFrame\n",
-    "articles_df = pd.DataFrame(articles_data)\n",
-    "\n",
-    "# Save the DataFrame to a CSV file\n",
-    "articles_df.to_csv('ev_articles.csv', index=False)\n",
-    "\n",
-    "# Display the DataFrame\n",
-    "articles_df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8532a3e-860a-4d5c-a594-b5528e60757d",
-   "metadata": {},
-   "source": [
-    "## Step 3: Data Preprocessing\n",
-    "To perform data preprocessing for each article, we need to clean and structure the data appropriately. This typically involves removing unwanted characters or HTML tags. Then we need to normalize text by converting it to lowercase, removing extra whitespace, and handling punctuation. After that, we need to break the text into tokens (words). All this is carried out in the further cells. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "fcb4e4c7-f9ce-41c2-8f4e-b1166dcd13f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to\n",
-      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n",
-      "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package stopwords is already up-to-date!\n",
-      "[nltk_data] Downloading package wordnet to\n",
-      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import re\n",
-    "import nltk\n",
-    "from nltk.corpus import stopwords\n",
-    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
-    "from nltk.tokenize import word_tokenize\n",
-    "\n",
-    "# Download necessary NLTK data files\n",
-    "nltk.download('punkt')\n",
-    "nltk.download('stopwords')\n",
-    "nltk.download('wordnet')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b8887af-b132-4188-9dea-b1f4cd0687aa",
-   "metadata": {},
-   "source": [
-    "The **clean_text()** function removes unwanted characters and HTML tags from the text. It uses a regular expression to remove anything that looks like HTML tags. It is also used to remove special characters and numbers since we don' require them for sentiment analysis. It returns a cleaned version of the text with HTML tags, special characters, and extra whitespace removed. \n",
-    "\n",
-    "The **normalize_text()** function normalizes the text by converting it to lowercase and removing extra spaces. First, it converts all characters in the text to lowercase. Then it splits the text into words and then joins them back together with a single space between each word, effectively removing extra spaces. It returns the normalized text with all lowercase characters and consistent spacing.\n",
-    "\n",
-    "The **tokenize_text()** function tokenizes the text into individual words and a list of tokens (words) from the text.\n",
-    "\n",
-    "The **remove_stop_words()** function remove common stop words from the list of tokens. It returns a list of tokens with stop words removed.\n",
-    "\n",
-    "The **stem_tokens()** function apply stemming to the list of tokens. Stemming reduces words to their root form so that the kewords can be analysed easily. It returns a list of stemmed tokens (words reduced to their root form).\n",
-    "\n",
-    "Finally, the **lemmatize_tokens()** function applies lemmatization to the list of tokens. Lemmatization reduces words to their base or dictionary form, which is usually more meaningful than stemming. It returns a list of lemmatized tokens (words reduced to their base form)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "bdb471d4-e732-4363-a62d-b3e1a98275ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def clean_text(text):\n",
-    "    \"\"\"\n",
-    "    Clean the text by removing unwanted characters and HTML tags.\n",
-    "    \"\"\"\n",
-    "    # Remove HTML tags\n",
-    "    text = re.sub(r'<[^>]+>', '', text)\n",
-    "    # Remove special characters and numbers\n",
-    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
-    "    # Remove extra whitespace\n",
-    "    text = text.strip()\n",
-    "    return text\n",
-    "\n",
-    "def normalize_text(text):\n",
-    "    \"\"\"\n",
-    "    Normalize the text by converting to lowercase and removing extra spaces.\n",
-    "    \"\"\"\n",
-    "    # Convert to lowercase\n",
-    "    text = text.lower()\n",
-    "    # Remove extra whitespace\n",
-    "    text = ' '.join(text.split())\n",
-    "    return text\n",
-    "\n",
-    "def tokenize_text(text):\n",
-    "    \"\"\"\n",
-    "    Tokenize the text into words.\n",
-    "    \"\"\"\n",
-    "    tokens = word_tokenize(text)\n",
-    "    return tokens\n",
-    "\n",
-    "def remove_stop_words(tokens):\n",
-    "    \"\"\"\n",
-    "    Remove stop words from the tokenized text.\n",
-    "    \"\"\"\n",
-    "    stop_words = set(stopwords.words('english'))\n",
-    "    filtered_tokens = [word for word in tokens if word not in stop_words]\n",
-    "    return filtered_tokens\n",
-    "\n",
-    "def stem_tokens(tokens):\n",
-    "    \"\"\"\n",
-    "    Apply stemming to tokens.\n",
-    "    \"\"\"\n",
-    "    stemmer = PorterStemmer()\n",
-    "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
-    "    return stemmed_tokens\n",
-    "\n",
-    "def lemmatize_tokens(tokens):\n",
-    "    \"\"\"\n",
-    "    Apply lemmatization to tokens.\n",
-    "    \"\"\"\n",
-    "    lemmatizer = WordNetLemmatizer()\n",
-    "    lemmatized_tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
-    "    return lemmatized_tokens\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "484d6bf9-826f-4b57-9f5c-7b737c81979a",
-   "metadata": {},
-   "source": [
-    "This cell applies preprocessing functions to each article’s content, then converts the preprocessed data into a DataFrame and saves it to a CSV file. The **preprocessed_articles** list is initialized as an empty list to store the preprocessed article data. By looping through each article, many steps of preprocessing are applied like removing HTML tags, special characters, and extra whitespace using **clean_text** function, converting the text to lowercase and removing extra spaces using the **normalize_text** function, splitting the text into individual words (tokens) using **tokenize_text** function, filtering out common stop words from the tokens using **remove_stop_words** function and reducing to their root form using **stem_tokens** function.\n",
-    "\n",
-    "After all this, the preprocessed text is joined back into a single string and stored in a dictionary along with the article’s title, author, and publication date which is added to the **preprocessed_articles** list. Finally, we convert the list of preprocessed articles into a DataFrame using **pd.DataFrame** function, save it to a CSV file and display the resulting DataFrame to verify the output.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "8f35cdf6-3bd8-49f9-956c-a0c587bd5b19",
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       "      <th>title</th>\n",
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-       "      <th>publication_date</th>\n",
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-       "      <td>Most Australians think there are too few publi...</td>\n",
-       "      <td>Jennifer Dudley-Nicholson</td>\n",
-       "      <td>February 23, 2024</td>\n",
-       "      <td>australian think nation public charg station s...</td>\n",
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-       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
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-       "      <td>Harrison Vesey</td>\n",
-       "      <td>10 April 2024</td>\n",
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-       "      <td>Mike Costello</td>\n",
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-       "                                               title  \\\n",
-       "0  Most Australians think there are too few publi...   \n",
-       "1  Why Australian consumers are charging toward e...   \n",
-       "2  EVs face future challenges despite increasing ...   \n",
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-       "                                              author        publication_date  \\\n",
-       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
-       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
-       "2                                     Harrison Vesey           10 April 2024   \n",
-       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
-       "\n",
-       "                                             content  \n",
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-       "2  professor david hensher one three australian c...  \n",
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-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Apply preprocessing to each article's content\n",
-    "preprocessed_articles = []\n",
-    "\n",
-    "for article in articles_data:\n",
-    "    # Clean the text\n",
-    "    cleaned_content = clean_text(article['content'])\n",
-    "    # Normalize the text\n",
-    "    normalized_content = normalize_text(cleaned_content)\n",
-    "    # Tokenize the text\n",
-    "    tokens = tokenize_text(normalized_content)\n",
-    "    # Remove stop words\n",
-    "    filtered_tokens = remove_stop_words(tokens)\n",
-    "    # Optionally apply stemming or lemmatization\n",
-    "    stemmed_tokens = stem_tokens(filtered_tokens)\n",
-    "    # lemmatized_tokens = lemmatize_tokens(filtered_tokens)\n",
-    "    \n",
-    "    # Store preprocessed data\n",
-    "    preprocessed_article = {\n",
-    "        'title': article['title'],\n",
-    "        'author': article['author'],\n",
-    "        'publication_date': article['publication_date'],\n",
-    "        'content': ' '.join(stemmed_tokens)  # Use lemmatized_tokens if preferred\n",
-    "    }\n",
-    "    preprocessed_articles.append(preprocessed_article)\n",
-    "    \n",
-    "# Convert the preprocessed articles data to a DataFrame\n",
-    "preprocessed_articles_df = pd.DataFrame(preprocessed_articles)\n",
-    "\n",
-    "# Save the DataFrame to a CSV file\n",
-    "preprocessed_articles_df.to_csv('preprocessed_ev_articles.csv', index=False)\n",
-    "\n",
-    "# Display the DataFrame\n",
-    "preprocessed_articles_df\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "592c4d07-49e8-4f5d-8165-3057d18f48dc",
-   "metadata": {},
-   "source": [
-    "In sentiment analysis, **polarity** and **subjectivity** are two key metrics used to assess the sentiment of a text. \n",
-    "\n",
-    "Polarity measures the sentiment of the text on a scale from -1 to 1. Negative sentiment indicates that the text expresses a strong negative sentiment or emotion. Neutral sentiment indicates that the text is neutral and does not convey any strong positive or negative sentiment. Positive sentiment indicates that the text expresses a strong positive sentiment or emotion.  This can be useful for understanding overall attitudes or reactions.\n",
-    "\n",
-    "Subjectivity measures the degree to which the text expresses personal opinions, feelings, or beliefs, as opposed to objective facts. Zero subjectivity means the text is factual and does not include personal opinions or emotions. It is more about reporting facts.A subjectivity of 1 means the text is more personal and opinionated, including personal beliefs, emotions, or feelings. This can be useful for distinguishing between factual reports and personal opinions or feelings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "7a2419ad-a537-4396-999f-efe9ad8fb316",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from textblob import TextBlob\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16abc529-f4c0-42db-b793-624477ac2394",
-   "metadata": {},
-   "source": [
-    "This code defines a function **analyze_sentiment()** that uses the TextBlob library to analyze the sentiment of a given text. It takes a single argument **text**, which is a string of text that we want to analyze for sentiment. The TextBlob object **blob** has a sentiment property that returns a Sentiment namedtuple - polarity and subjectivity. The function returns the polarity and subjectivity scores as a tuple."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "7a253b62-1ce6-42c2-986c-bba61766c17a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def analyze_sentiment(text):\n",
-    "    \"\"\"\n",
-    "    Analyze the sentiment of the given text using TextBlob.\n",
-    "    \"\"\"\n",
-    "    blob = TextBlob(text)\n",
-    "    # Return polarity and subjectivity\n",
-    "    return blob.sentiment.polarity, blob.sentiment.subjectivity\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3c6d5f8-09c8-493b-b866-c0a2fd628a9d",
-   "metadata": {},
-   "source": [
-    "This code performs sentiment analysis on the preprocessed content of each article, stores the results, and saves them to a CSV file. The **sentiment_results[]** is an empty list initialized to store the sentiment analysis results for each article. In the 'for' loop, we iterate through each row in the preprocessed articles DataFrame (**preprocessed_articles_df**). Each row represents an article. For each article, the content is passed to the **analyze_sentiment()** function, which returns the polarity and subjectivity scores.\n",
-    "The results for each article, including its title, author, publication date, content, polarity, and subjectivity, are stored in a dictionary and appended to the above initialized list.\n",
-    "We then conver this list of dictionaries into pandas DataFrame. Finally, the DataFrame is displayed, showing the sentiment analysis results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "1d55944e-1875-47bf-a203-5f2b5a360a6a",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
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-       "      <th>polarity</th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>Most Australians think there are too few publi...</td>\n",
-       "      <td>Jennifer Dudley-Nicholson</td>\n",
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-       "      <td>Why Australian consumers are charging toward e...</td>\n",
-       "      <td>Neal Johnston, Glenn Maris, Damien Smith, Neal...</td>\n",
-       "      <td>27 Jul. 2022</td>\n",
-       "      <td>ceo imper strategi set takeoff clever govern c...</td>\n",
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-       "      <td>EVs face future challenges despite increasing ...</td>\n",
-       "      <td>Harrison Vesey</td>\n",
-       "      <td>10 April 2024</td>\n",
-       "      <td>professor david hensher one three australian c...</td>\n",
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-       "      <td>EVs in Australia: Report outlines sales, and i...</td>\n",
-       "      <td>Mike Costello</td>\n",
-       "      <td>19 August 2020, 1:56pm</td>\n",
-       "      <td>guest user australia electricvehicl penetr wel...</td>\n",
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-       "      <td>0.306212</td>\n",
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-      "text/plain": [
-       "                                               title  \\\n",
-       "0  Most Australians think there are too few publi...   \n",
-       "1  Why Australian consumers are charging toward e...   \n",
-       "2  EVs face future challenges despite increasing ...   \n",
-       "3  EVs in Australia: Report outlines sales, and i...   \n",
-       "\n",
-       "                                              author        publication_date  \\\n",
-       "0                          Jennifer Dudley-Nicholson       February 23, 2024   \n",
-       "1  Neal Johnston, Glenn Maris, Damien Smith, Neal...            27 Jul. 2022   \n",
-       "2                                     Harrison Vesey           10 April 2024   \n",
-       "3                                      Mike Costello  19 August 2020, 1:56pm   \n",
-       "\n",
-       "                                             content  polarity  subjectivity  \n",
-       "0  australian think nation public charg station s...  0.002210      0.187689  \n",
-       "1  ceo imper strategi set takeoff clever govern c...  0.102000      0.283466  \n",
-       "2  professor david hensher one three australian c...  0.181563      0.347917  \n",
-       "3  guest user australia electricvehicl penetr wel...  0.011245      0.306212  "
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Apply sentiment analysis to each article's content\n",
-    "sentiment_results = []\n",
-    "\n",
-    "for index, article in preprocessed_articles_df.iterrows():\n",
-    "    # Analyze sentiment\n",
-    "    polarity, subjectivity = analyze_sentiment(article['content'])\n",
-    "    \n",
-    "    # Store the results\n",
-    "    sentiment_results.append({\n",
-    "        'title': article['title'],\n",
-    "        'author': article['author'],\n",
-    "        'publication_date': article['publication_date'],\n",
-    "        'content': article['content'],\n",
-    "        'polarity': polarity,\n",
-    "        'subjectivity': subjectivity\n",
-    "    })\n",
-    "\n",
-    "# Convert the results to a DataFrame\n",
-    "sentiment_df = pd.DataFrame(sentiment_results)\n",
-    "\n",
-    "# Save the sentiment analysis results to a CSV file\n",
-    "sentiment_df.to_csv('sentiment_analysis_results.csv', index=False)\n",
-    "\n",
-    "# Display the DataFrame\n",
-    "sentiment_df\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37a82af6-129d-4a96-ac4e-f063316184b9",
-   "metadata": {},
-   "source": [
-    "This code creates a visual representation of the sentiment analysis results for a set of articles, focusing on both polarity and subjectivity. The plotting style is configured to \"whitegrid\" for a clean and readable background. A figure is setup with two subplots arranged vertically, each subplot having its own set of axes. this code generates two horizontal bar plots within a single figure, visually comparing the polarity and subjectivity of the sentiment analysis for each article."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "8dfd03d0-cab3-4de2-939f-97b4a64bc416",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
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-      "text/plain": [
-       "<Figure size 1200x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set plot style\n",
-    "sns.set(style=\"whitegrid\")\n",
-    "\n",
-    "# Create a figure and axes\n",
-    "fig, ax = plt.subplots(2, 1, figsize=(12, 12))  # Increase figure height\n",
-    "\n",
-    "# Plot polarity (horizontal bars)\n",
-    "sns.barplot(x='polarity', y='title', data=sentiment_df, ax=ax[0], palette='viridis', hue='title', legend=False)\n",
-    "ax[0].set_title('Sentiment Polarity of Articles')\n",
-    "ax[0].set_xlabel('Polarity')\n",
-    "ax[0].set_ylabel('Article Title')\n",
-    "\n",
-    "# Plot subjectivity (horizontal bars)\n",
-    "sns.barplot(x='subjectivity', y='title', data=sentiment_df, ax=ax[1], palette='viridis', hue='title', legend=False)\n",
-    "ax[1].set_title('Sentiment Subjectivity of Articles')\n",
-    "ax[1].set_xlabel('Subjectivity')\n",
-    "ax[1].set_ylabel('Article Title')\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d6748a7-f128-40f2-b7ed-b11f6ddd8a60",
-   "metadata": {},
-   "source": [
-    "The overall goal of this code is to analyze sentiment scores (polarity and subjectivity) of articles and aggregate these scores by the year of publication. \n",
-    "It defines a dictionary data with sample data, including article titles, authors, publication dates, content, polarity, and subjectivity scores and converts the **data** dictionary into a pandas DataFrame named **sentiment_df**.\n",
-    "It defines a function **parse_date()** that attempts to parse date strings into datetime objects using dateutil.parser.parse with the fuzzy=True option to handle various date formats. It applies the **parse_date()** function to the **publication_date** column in the DataFrame to convert date strings to datetime objects. Then it extracts the year from the parsed **publication_date** column and stores it in a new column named **year**. Then, it prints the **publication_date** and **year** columns to check for any missing values in the year column. After printing the polarity and subjectivity columns, it groups the DataFrame by the year column and calculates the mean polarity and subjectivity scores for each year.\n",
-    "\n",
-    "Finally, it prints the  stored **yearly_sentiment** DataFrame to display the average polarity and subjectivity scores for each year."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "a493e974-a1d9-4eb9-88d8-bb6fd1d39e54",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     publication_date  year\n",
-      "0 2024-02-23 00:00:00  2024\n",
-      "1 2022-07-27 00:00:00  2022\n",
-      "2 2024-04-10 00:00:00  2024\n",
-      "3 2020-08-19 13:56:00  2020\n",
-      "   polarity  subjectivity\n",
-      "0  0.002210      0.187689\n",
-      "1  0.102000      0.283466\n",
-      "2  0.181563      0.347917\n",
-      "3  0.011245      0.306212\n",
-      "   year  polarity  subjectivity\n",
-      "0  2020  0.011245      0.306212\n",
-      "1  2022  0.102000      0.283466\n",
-      "2  2024  0.091886      0.267803\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from dateutil import parser\n",
-    "\n",
-    "# Sample data as a dictionary\n",
-    "data = {\n",
-    "    'title': [\n",
-    "        'Most Australians think there are too few publi...',\n",
-    "        'Why Australian consumers are charging toward e...',\n",
-    "        'EVs face future challenges despite increasing ...',\n",
-    "        'EVs in Australia: Report outlines sales, and i...'\n",
-    "    ],\n",
-    "    'author': [\n",
-    "        'Jennifer Dudley-Nicholson',\n",
-    "        'Neal Johnston, Glenn Maris, Damien Smith, Neal...',\n",
-    "        'Harrison Vesey',\n",
-    "        'Mike Costello'\n",
-    "    ],\n",
-    "    'publication_date': [\n",
-    "        'February 23, 2024',\n",
-    "        '27 Jul. 2022',\n",
-    "        '10 April 2024',\n",
-    "        '19 August 2020, 1:56pm'\n",
-    "    ],\n",
-    "    'content': [\n",
-    "        'australian think nation public charg station s...',\n",
-    "        'ceo imper strategi set takeoff clever govern c...',\n",
-    "        'professor david hensher one three australian c...',\n",
-    "        'guest user australia electricvehicl penetr wel...'\n",
-    "    ],\n",
-    "    'polarity': [0.002210, 0.102000, 0.181563, 0.011245],\n",
-    "    'subjectivity': [0.187689, 0.283466, 0.347917, 0.306212]\n",
-    "}\n",
-    "\n",
-    "# Create a DataFrame\n",
-    "sentiment_df = pd.DataFrame(data)\n",
-    "\n",
-    "# Define a function to parse dates with different formats\n",
-    "def parse_date(date_str):\n",
-    "    try:\n",
-    "        return parser.parse(date_str, fuzzy=True)\n",
-    "    except ValueError:\n",
-    "        return pd.NaT\n",
-    "\n",
-    "# Apply the parsing function\n",
-    "sentiment_df['publication_date'] = sentiment_df['publication_date'].apply(parse_date)\n",
-    "\n",
-    "# Extract year from publication_date\n",
-    "sentiment_df['year'] = sentiment_df['publication_date'].dt.year\n",
-    "\n",
-    "# Check for missing values in 'year'\n",
-    "print(sentiment_df[['publication_date', 'year']])\n",
-    "\n",
-    "# Check for missing values in sentiment scores\n",
-    "print(sentiment_df[['polarity', 'subjectivity']])\n",
-    "\n",
-    "# Aggregate sentiment scores by year\n",
-    "yearly_sentiment = sentiment_df.groupby('year').agg({'polarity': 'mean', 'subjectivity': 'mean'}).reset_index()\n",
-    "\n",
-    "# Display the aggregated sentiment scores\n",
-    "print(yearly_sentiment)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0c41a73-9458-46c3-9f0e-816db6f65144",
-   "metadata": {},
-   "source": [
-    "This code generates a line plot to visualize the average sentiment scores (polarity and subjectivity) of articles over time. It uses the **yearly_sentiment** DataFrame, which contains the aggregated sentiment scores by year, and plots these scores with markers for each year. The plot includes a title, axis labels, a legend to differentiate between polarity and subjectivity, and a grid for better readability. Finally, it displays the plot, allowing for a clear visual comparison of how sentiment has evolved over the years."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "abc62267-805b-4255-bf29-5c054cb5b4c6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Plot sentiment scores over time\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "# Plot Polarity\n",
-    "plt.plot(yearly_sentiment['year'], yearly_sentiment['polarity'], marker='o', linestyle='-', color='b', label='Polarity')\n",
-    "\n",
-    "# Plot Subjectivity\n",
-    "plt.plot(yearly_sentiment['year'], yearly_sentiment['subjectivity'], marker='o', linestyle='-', color='r', label='Subjectivity')\n",
-    "\n",
-    "# Add titles and labels\n",
-    "plt.title('Average Sentiment Scores by Year')\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('Average Score')\n",
-    "plt.legend()\n",
-    "plt.grid(True)\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "02793131-4dd8-4278-8f71-cc56b2ad14ac",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 600992f5359950f7c401e139f6a7ad0bae9fef1e Mon Sep 17 00:00:00 2001
From: Aditya Gahlot <adityagahlot2002@gmail.com>
Date: Fri, 30 Aug 2024 22:29:42 +1000
Subject: [PATCH 4/5] Social Media Sentiment analysis of electric vehicles
 .ipynb file

---
 .../SocialMedia_SentimentAnalysis.ipynb       | 1189 +++++++++++++++++
 1 file changed, 1189 insertions(+)
 create mode 100644 personal-work/aditya-gahlot/SocialMedia_SentimentAnalysis.ipynb

diff --git a/personal-work/aditya-gahlot/SocialMedia_SentimentAnalysis.ipynb b/personal-work/aditya-gahlot/SocialMedia_SentimentAnalysis.ipynb
new file mode 100644
index 0000000..a1e4f7c
--- /dev/null
+++ b/personal-work/aditya-gahlot/SocialMedia_SentimentAnalysis.ipynb
@@ -0,0 +1,1189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "df66e9ba-0317-4628-b617-3e705321d814",
+   "metadata": {},
+   "source": [
+    "# Social Media Sentiment Analysis\n",
+    "\n",
+    "To perform social media sentiment analysis on EVs, I took up a dataset from Kaggle which contained Youtube comments of people around the globe. Kaggle is a platform for data scientists which contains numerous datasets on any topic we wish to search for. For doing that, in the below cell, I imported several essential Python libraries required for data manipulation, natural language processing (NLP), and machine learning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e274c491-bc85-4279-a58e-115af15d8874",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import nltk\n",
+    "from textblob import TextBlob\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d51c9fa-d442-48e5-8bda-e9f93d3b503e",
+   "metadata": {},
+   "source": [
+    "For downloading the dataset, this cell uses the Kaggle API to download a dataset titled \"EV Talk YouTube Sentiments Unveiled\" from the Kaggle repository. The dataset is saved locally for further processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "164759de-eab8-4a14-9200-649dc89b2036",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset URL: https://www.kaggle.com/datasets/kanchana1990/ev-talk-youtube-sentiments-unveiled\n",
+      "License(s): other\n",
+      "ev-talk-youtube-sentiments-unveiled.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kaggle datasets download -d kanchana1990/ev-talk-youtube-sentiments-unveiled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47262fed-9e27-457d-bde6-be1d227aaffd",
+   "metadata": {},
+   "source": [
+    "For extracting the dataset, this cell takes out the contents of the downloaded zip file into a specified directory named **dataset-directory** using Python's built-in **zipfile** module. This allows us to access the dataset files directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fe45e097-95ab-48a2-b601-ad6749505fcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "with zipfile.ZipFile('archive.zip', 'r') as zip_ref:\n",
+    "    zip_ref.extractall('dataset-directory')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90fbf825-3420-4db7-a522-02b888556567",
+   "metadata": {},
+   "source": [
+    "In the next step, we load the dataset into a pandas DataFrame from the CSV file named **youtube_comments_full_anonymized.csv** located in the extracted directory. This DataFrame (df) will be used for all subsequent data manipulation and analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a356a6c7-047f-410d-830b-b790f9ce18ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('dataset-directory/youtube_comments_full_anonymized.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "717528d2-ced9-473f-b9a9-79d374b41f28",
+   "metadata": {},
+   "source": [
+    "Here, the **head()** method is used to display the first five rows of the DataFrame. This allows us to quickly inspect the data structure and contents to ensure it loaded correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "329b098b-32d5-44a0-898d-f48ae646fad0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Comment</th>\n",
+       "      <th>Anonymized Author</th>\n",
+       "      <th>Published At</th>\n",
+       "      <th>Likes</th>\n",
+       "      <th>Reply Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ya this is an good invention that we drive ev ...</td>\n",
+       "      <td>667e8d19fa9123ecadb71631917bbbc2</td>\n",
+       "      <td>2023-12-06T09:54:19Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Let car be charged while you may drink a cup o...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:17:25Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is.that the car stops charhing better than up ...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:08:28Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>😂how much of invetsment of.this instead of a n...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:04:52Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>what will happen if an accident happens and a ...</td>\n",
+       "      <td>e673dba59001bbeacd4ae6d6fa09f3f2</td>\n",
+       "      <td>2023-12-04T15:47:42Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Comment  \\\n",
+       "0  Ya this is an good invention that we drive ev ...   \n",
+       "1  Let car be charged while you may drink a cup o...   \n",
+       "2  Is.that the car stops charhing better than up ...   \n",
+       "3  😂how much of invetsment of.this instead of a n...   \n",
+       "4  what will happen if an accident happens and a ...   \n",
+       "\n",
+       "                  Anonymized Author          Published At  Likes  Reply Count  \n",
+       "0  667e8d19fa9123ecadb71631917bbbc2  2023-12-06T09:54:19Z      0            0  \n",
+       "1  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:17:25Z      0            0  \n",
+       "2  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:08:28Z      0            0  \n",
+       "3  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:04:52Z      0            0  \n",
+       "4  e673dba59001bbeacd4ae6d6fa09f3f2  2023-12-04T15:47:42Z      0            0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb565932-ad2e-4640-ade5-a7fb12f1bc71",
+   "metadata": {},
+   "source": [
+    "As we can see above, we can see the first few rows of the dataset rendered in a csv file. This dataset contains information on the comments made by a particular user and the likes and the reply count associated with each of them. In the below cell, we define and apply a function to clean the text in the **Comment** column. This function converts the text to lowercase, removes numbers, non-alphanumeric characters and extra spaces. After this, the cleaned text is stored in a new column called **cleaned_comment**. This new column is also displayed along with the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7995a71e-36e1-4922-88b8-3fe762522755",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Comment</th>\n",
+       "      <th>Anonymized Author</th>\n",
+       "      <th>Published At</th>\n",
+       "      <th>Likes</th>\n",
+       "      <th>Reply Count</th>\n",
+       "      <th>cleaned_comment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ya this is an good invention that we drive ev ...</td>\n",
+       "      <td>667e8d19fa9123ecadb71631917bbbc2</td>\n",
+       "      <td>2023-12-06T09:54:19Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ya this is an good invention that we drive ev ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Let car be charged while you may drink a cup o...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:17:25Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>let car be charged while you may drink a cup o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is.that the car stops charhing better than up ...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:08:28Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>is that the car stops charhing better than up ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>😂how much of invetsment of.this instead of a n...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:04:52Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>how much of invetsment of this instead of a n...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>what will happen if an accident happens and a ...</td>\n",
+       "      <td>e673dba59001bbeacd4ae6d6fa09f3f2</td>\n",
+       "      <td>2023-12-04T15:47:42Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>what will happen if an accident happens and a ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Comment  \\\n",
+       "0  Ya this is an good invention that we drive ev ...   \n",
+       "1  Let car be charged while you may drink a cup o...   \n",
+       "2  Is.that the car stops charhing better than up ...   \n",
+       "3  😂how much of invetsment of.this instead of a n...   \n",
+       "4  what will happen if an accident happens and a ...   \n",
+       "\n",
+       "                  Anonymized Author          Published At  Likes  Reply Count  \\\n",
+       "0  667e8d19fa9123ecadb71631917bbbc2  2023-12-06T09:54:19Z      0            0   \n",
+       "1  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:17:25Z      0            0   \n",
+       "2  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:08:28Z      0            0   \n",
+       "3  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:04:52Z      0            0   \n",
+       "4  e673dba59001bbeacd4ae6d6fa09f3f2  2023-12-04T15:47:42Z      0            0   \n",
+       "\n",
+       "                                     cleaned_comment  \n",
+       "0  ya this is an good invention that we drive ev ...  \n",
+       "1  let car be charged while you may drink a cup o...  \n",
+       "2  is that the car stops charhing better than up ...  \n",
+       "3   how much of invetsment of this instead of a n...  \n",
+       "4  what will happen if an accident happens and a ...  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Function to clean the text data\n",
+    "import re\n",
+    "\n",
+    "def clean_text(text):\n",
+    "    text = text.lower()  # Convert to lowercase\n",
+    "    text = re.sub(r'\\d+', '', text)  # Remove numbers\n",
+    "    text = re.sub(r'\\s+', ' ', text)  # Remove extra spaces\n",
+    "    text = re.sub(r'\\W', ' ', text)  # Remove non-alphanumeric characters\n",
+    "    return text\n",
+    "\n",
+    "# Apply the function to the Comment column\n",
+    "df['cleaned_comment'] = df['Comment'].apply(clean_text)\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80e559ae-71d7-48d3-8021-f6f785a6f934",
+   "metadata": {},
+   "source": [
+    "This cell converts the cleaned text data into numerical features using the TF-IDF vectorization method. The vectorized data is stored in 'X' variable. Additionally, for simplicity, we define the sentiment as positive if the number of likes is greater than zero (1 for positive, 0 for negative), and store these labels in y. This assumption can be replaced with actual sentiment labels if available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "28afe252-9a89-4cef-8d87-69ceb604a985",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Vectorize the text data using TF-IDF\n",
+    "tfidf_vectorizer = TfidfVectorizer(max_features=5000)\n",
+    "X = tfidf_vectorizer.fit_transform(df['cleaned_comment']).toarray()\n",
+    "\n",
+    "# For demonstration purposes, let's assume the sentiment is positive if likes are greater than 0\n",
+    "# Replace this with the actual sentiment column if available\n",
+    "df['sentiment'] = df['Likes'].apply(lambda x: 1 if x > 0 else 0)\n",
+    "y = df['sentiment']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "87fe3f6c-3f5f-41db-b1f1-20be82b8eb6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8f7c7479-9481-4eb2-80ed-7059ab83f0a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "MultinomialNB()"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train a Multinomial Naive Bayes model\n",
+    "model = MultinomialNB()\n",
+    "model.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ad773321-0c89-41c7-8ad0-50765de3accf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 75.29%\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.75      1.00      0.86        64\n",
+      "           1       0.00      0.00      0.00        21\n",
+      "\n",
+      "    accuracy                           0.75        85\n",
+      "   macro avg       0.38      0.50      0.43        85\n",
+      "weighted avg       0.57      0.75      0.65        85\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\envs\\labelme_env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "D:\\anaconda\\envs\\labelme_env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "D:\\anaconda\\envs\\labelme_env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict the sentiment on the test set\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "classification_rep = classification_report(y_test, y_pred)\n",
+    "\n",
+    "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
+    "print(\"Classification Report:\\n\", classification_rep)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f5095ccb-2345-42b6-871d-0d9b02d1fe64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Comment</th>\n",
+       "      <th>Anonymized Author</th>\n",
+       "      <th>Published At</th>\n",
+       "      <th>Likes</th>\n",
+       "      <th>Reply Count</th>\n",
+       "      <th>cleaned_comment</th>\n",
+       "      <th>sentiment</th>\n",
+       "      <th>polarity</th>\n",
+       "      <th>subjectivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ya this is an good invention that we drive ev ...</td>\n",
+       "      <td>667e8d19fa9123ecadb71631917bbbc2</td>\n",
+       "      <td>2023-12-06T09:54:19Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ya this is an good invention that we drive ev ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.041667</td>\n",
+       "      <td>0.610648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Let car be charged while you may drink a cup o...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:17:25Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>let car be charged while you may drink a cup o...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.278571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is.that the car stops charhing better than up ...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:08:28Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>is that the car stops charhing better than up ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>😂how much of invetsment of.this instead of a n...</td>\n",
+       "      <td>7ddfba69037c0d749088d184f0fab66a</td>\n",
+       "      <td>2023-12-05T03:04:52Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>how much of invetsment of this instead of a n...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.175000</td>\n",
+       "      <td>0.425000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>what will happen if an accident happens and a ...</td>\n",
+       "      <td>e673dba59001bbeacd4ae6d6fa09f3f2</td>\n",
+       "      <td>2023-12-04T15:47:42Z</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>what will happen if an accident happens and a ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.357143</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             Comment  \\\n",
+       "0  Ya this is an good invention that we drive ev ...   \n",
+       "1  Let car be charged while you may drink a cup o...   \n",
+       "2  Is.that the car stops charhing better than up ...   \n",
+       "3  😂how much of invetsment of.this instead of a n...   \n",
+       "4  what will happen if an accident happens and a ...   \n",
+       "\n",
+       "                  Anonymized Author          Published At  Likes  Reply Count  \\\n",
+       "0  667e8d19fa9123ecadb71631917bbbc2  2023-12-06T09:54:19Z      0            0   \n",
+       "1  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:17:25Z      0            0   \n",
+       "2  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:08:28Z      0            0   \n",
+       "3  7ddfba69037c0d749088d184f0fab66a  2023-12-05T03:04:52Z      0            0   \n",
+       "4  e673dba59001bbeacd4ae6d6fa09f3f2  2023-12-04T15:47:42Z      0            0   \n",
+       "\n",
+       "                                     cleaned_comment  sentiment  polarity  \\\n",
+       "0  ya this is an good invention that we drive ev ...          0  0.041667   \n",
+       "1  let car be charged while you may drink a cup o...          0  0.100000   \n",
+       "2  is that the car stops charhing better than up ...          0  0.500000   \n",
+       "3   how much of invetsment of this instead of a n...          0  0.175000   \n",
+       "4  what will happen if an accident happens and a ...          0  0.000000   \n",
+       "\n",
+       "   subjectivity  \n",
+       "0      0.610648  \n",
+       "1      0.278571  \n",
+       "2      0.500000  \n",
+       "3      0.425000  \n",
+       "4      0.357143  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Function to calculate polarity and subjectivity using TextBlob\n",
+    "def get_sentiment(text):\n",
+    "    blob = TextBlob(text)\n",
+    "    return blob.sentiment.polarity, blob.sentiment.subjectivity\n",
+    "\n",
+    "# Apply the function to the cleaned_comment column and create new columns for polarity and subjectivity\n",
+    "df['polarity'], df['subjectivity'] = zip(*df['cleaned_comment'].apply(get_sentiment))\n",
+    "\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d02a851e-9f13-4965-b3bb-3f61ac034c58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>polarity</th>\n",
+       "      <th>subjectivity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>424.000000</td>\n",
+       "      <td>424.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.090511</td>\n",
+       "      <td>0.365949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.281015</td>\n",
+       "      <td>0.312195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         polarity  subjectivity\n",
+       "count  424.000000    424.000000\n",
+       "mean     0.090511      0.365949\n",
+       "std      0.281015      0.312195\n",
+       "min     -1.000000      0.000000\n",
+       "25%      0.000000      0.000000\n",
+       "50%      0.000000      0.400000\n",
+       "75%      0.200000      0.600000\n",
+       "max      1.000000      1.000000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display summary statistics for polarity and subjectivity\n",
+    "df[['polarity', 'subjectivity']].describe()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bde9135b-521d-4d40-a52f-7a5426b67cc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Plot histogram for polarity\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.histplot(df['polarity'], bins=20, kde=True)\n",
+    "plt.title('Polarity Distribution')\n",
+    "plt.xlabel('Polarity')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot histogram for subjectivity\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.histplot(df['subjectivity'], bins=20, kde=True)\n",
+    "plt.title('Subjectivity Distribution')\n",
+    "plt.xlabel('Subjectivity')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31e4b7f7-293b-4611-9348-05e3b725a36f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 27502061568904eb72ad07b5995594b69c98208b Mon Sep 17 00:00:00 2001
From: Aditya Gahlot <adityagahlot2002@gmail.com>
Date: Sat, 31 Aug 2024 00:03:22 +1000
Subject: [PATCH 5/5] Analyzing user behaviour patterns .ipynb file

---
 .../UserBehaviourPatterns_EV.ipynb            | 372 ++++++++++++++++++
 1 file changed, 372 insertions(+)
 create mode 100644 personal-work/aditya-gahlot/UserBehaviourPatterns_EV.ipynb

diff --git a/personal-work/aditya-gahlot/UserBehaviourPatterns_EV.ipynb b/personal-work/aditya-gahlot/UserBehaviourPatterns_EV.ipynb
new file mode 100644
index 0000000..0158ffa
--- /dev/null
+++ b/personal-work/aditya-gahlot/UserBehaviourPatterns_EV.ipynb
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "543677b6-33cc-40aa-88fd-c68101983522",
+   "metadata": {},
+   "source": [
+    "# Analyzing User Behaviour Patterns\n",
+    "\n",
+    "For analyzing user behaviour patterns of EVs over a certain period of time, we can analyse the number of charging points across Australia. More concentration of charging points means more users are inclined to use EVs more often. First, we import the necessary libraries for data manipulation and visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "99feacbe-449d-4124-9808-27d8ac373c4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10625422-bcff-422b-9f2a-7a65ba48c42a",
+   "metadata": {},
+   "source": [
+    "The best source of EV charger data in Australia is Open Charge Map which contains data on location of each and every charging points all over the world. In this cell, we make an API request to the Open Charge Map to retrieve EV charging point data for Australia. We have defined various variables for making the API request, like **url**, which represents the endpoint for the API request. The **headers** variable contains the API key for authentication. The **response** variable represents the result of the API request. Then we print the first two records to verify the data retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "99a5b247-959f-46c0-961c-0c4bba1b36d4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'DataProvider': {'WebsiteURL': 'http://openchargemap.org', 'Comments': None, 'DataProviderStatusType': {'IsProviderEnabled': True, 'ID': 1, 'Title': 'Manual Data Entry'}, 'IsRestrictedEdit': False, 'IsOpenDataLicensed': True, 'IsApprovedImport': True, 'License': 'Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)', 'DateLastImported': None, 'ID': 1, 'Title': 'Open Charge Map Contributors'}, 'OperatorInfo': {'WebsiteURL': 'https://www.bp.com/en_au/australia/home/products-services/bppulse.html', 'Comments': None, 'PhonePrimaryContact': None, 'PhoneSecondaryContact': None, 'IsPrivateIndividual': False, 'AddressInfo': None, 'BookingURL': None, 'ContactEmail': None, 'FaultReportEmail': None, 'IsRestrictedEdit': False, 'ID': 3659, 'Title': 'BP Pulse (AU)'}, 'UsageType': {'IsPayAtLocation': False, 'IsMembershipRequired': True, 'IsAccessKeyRequired': True, 'ID': 4, 'Title': 'Public - Membership Required'}, 'StatusType': {'IsOperational': True, 'IsUserSelectable': True, 'ID': 50, 'Title': 'Operational'}, 'SubmissionStatus': {'IsLive': True, 'ID': 200, 'Title': 'Submission Published'}, 'UserComments': None, 'PercentageSimilarity': None, 'MediaItems': None, 'IsRecentlyVerified': True, 'DateLastVerified': '2024-08-14T03:03:00Z', 'ID': 302585, 'UUID': '2700862A-909F-48A0-93D8-83CFA5593669', 'ParentChargePointID': None, 'DataProviderID': 1, 'DataProvidersReference': None, 'OperatorID': 3659, 'OperatorsReference': None, 'UsageTypeID': 4, 'UsageCost': '65c/kWh', 'AddressInfo': {'ID': 302972, 'Title': 'BP South Bound', 'AddressLine1': 'Leary Road', 'AddressLine2': 'Baldivis', 'Town': None, 'StateOrProvince': 'Western Australia', 'Postcode': '6171', 'CountryID': 18, 'Country': {'ISOCode': 'AU', 'ContinentCode': 'OC', 'ID': 18, 'Title': 'Australia'}, 'Latitude': -32.30983528619279, 'Longitude': 115.83240066607163, 'ContactTelephone1': None, 'ContactTelephone2': None, 'ContactEmail': None, 'AccessComments': None, 'RelatedURL': None, 'Distance': None, 'DistanceUnit': 0}, 'Connections': [{'ID': 568719, 'ConnectionTypeID': 33, 'ConnectionType': {'FormalName': 'IEC 62196-3 Configuration FF', 'IsDiscontinued': False, 'IsObsolete': False, 'ID': 33, 'Title': 'CCS (Type 2)'}, 'Reference': None, 'StatusTypeID': 50, 'StatusType': {'IsOperational': True, 'IsUserSelectable': True, 'ID': 50, 'Title': 'Operational'}, 'LevelID': 3, 'Level': {'Comments': '40KW and Higher', 'IsFastChargeCapable': True, 'ID': 3, 'Title': 'Level 3:  High (Over 40kW)'}, 'Amps': None, 'Voltage': None, 'PowerKW': 150, 'CurrentTypeID': 30, 'CurrentType': {'Description': 'Direct Current', 'ID': 30, 'Title': 'DC'}, 'Quantity': 6, 'Comments': None}], 'NumberOfPoints': 6, 'GeneralComments': None, 'DatePlanned': None, 'DateLastConfirmed': None, 'StatusTypeID': 50, 'DateLastStatusUpdate': '2024-08-14T03:03:00Z', 'MetadataValues': None, 'DataQualityLevel': 1, 'DateCreated': '2024-08-14T03:03:00Z', 'SubmissionStatusTypeID': 200}, {'DataProvider': {'WebsiteURL': 'http://openchargemap.org', 'Comments': None, 'DataProviderStatusType': {'IsProviderEnabled': True, 'ID': 1, 'Title': 'Manual Data Entry'}, 'IsRestrictedEdit': False, 'IsOpenDataLicensed': True, 'IsApprovedImport': True, 'License': 'Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)', 'DateLastImported': None, 'ID': 1, 'Title': 'Open Charge Map Contributors'}, 'OperatorInfo': {'WebsiteURL': 'https://exploren.com.au/', 'Comments': None, 'PhonePrimaryContact': None, 'PhoneSecondaryContact': None, 'IsPrivateIndividual': False, 'AddressInfo': None, 'BookingURL': None, 'ContactEmail': None, 'FaultReportEmail': None, 'IsRestrictedEdit': False, 'ID': 3622, 'Title': 'Exploren'}, 'UsageType': {'IsPayAtLocation': False, 'IsMembershipRequired': True, 'IsAccessKeyRequired': True, 'ID': 4, 'Title': 'Public - Membership Required'}, 'StatusType': {'IsOperational': True, 'IsUserSelectable': True, 'ID': 50, 'Title': 'Operational'}, 'SubmissionStatus': {'IsLive': True, 'ID': 200, 'Title': 'Submission Published'}, 'UserComments': None, 'PercentageSimilarity': None, 'MediaItems': None, 'IsRecentlyVerified': True, 'DateLastVerified': '2024-08-06T13:19:00Z', 'ID': 302397, 'UUID': 'D3C20FB4-E2AE-4F1F-89D3-7FABA3C17762', 'ParentChargePointID': None, 'DataProviderID': 1, 'DataProvidersReference': None, 'OperatorID': 3622, 'OperatorsReference': None, 'UsageTypeID': 4, 'UsageCost': None, 'AddressInfo': {'ID': 302784, 'Title': 'Miller Grove', 'AddressLine1': 'Miller Grove', 'AddressLine2': 'Mount Waverley', 'Town': None, 'StateOrProvince': 'Victoria', 'Postcode': '3149', 'CountryID': 18, 'Country': {'ISOCode': 'AU', 'ContinentCode': 'OC', 'ID': 18, 'Title': 'Australia'}, 'Latitude': -37.875016711913474, 'Longitude': 145.128081929617, 'ContactTelephone1': None, 'ContactTelephone2': None, 'ContactEmail': None, 'AccessComments': None, 'RelatedURL': None, 'Distance': None, 'DistanceUnit': 0}, 'Connections': [{'ID': 567076, 'ConnectionTypeID': 25, 'ConnectionType': {'FormalName': 'IEC 62196-2 Type 2', 'IsDiscontinued': False, 'IsObsolete': False, 'ID': 25, 'Title': 'Type 2 (Socket Only)'}, 'Reference': None, 'StatusTypeID': 50, 'StatusType': {'IsOperational': True, 'IsUserSelectable': True, 'ID': 50, 'Title': 'Operational'}, 'LevelID': 2, 'Level': {'Comments': 'Over 2 kW, usually non-domestic socket type', 'IsFastChargeCapable': False, 'ID': 2, 'Title': 'Level 2 : Medium (Over 2kW)'}, 'Amps': 32, 'Voltage': 400, 'PowerKW': 22, 'CurrentTypeID': 20, 'CurrentType': {'Description': 'Alternating Current - Three Phase', 'ID': 20, 'Title': 'AC (Three-Phase)'}, 'Quantity': 1, 'Comments': None}], 'NumberOfPoints': 2, 'GeneralComments': None, 'DatePlanned': None, 'DateLastConfirmed': None, 'StatusTypeID': 50, 'DateLastStatusUpdate': '2024-08-06T13:19:00Z', 'MetadataValues': None, 'DataQualityLevel': 1, 'DateCreated': '2024-08-03T13:14:00Z', 'SubmissionStatusTypeID': 200}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# API Request to Open Charge Map\n",
+    "url = \"https://api.openchargemap.io/v3/poi/?output=json&countrycode=AU\"\n",
+    "\n",
+    "headers = {\n",
+    "    \"X-API-Key\": \"2f5df05b-4b9d-46a3-b38e-c138e919abea\"  # Replace 'YOUR_API_KEY' with your actual API key\n",
+    "}\n",
+    "response = requests.get(url, headers=headers)\n",
+    "\n",
+    "# Check if the request was successful\n",
+    "if response.status_code == 200:\n",
+    "    data = response.json()\n",
+    "    print(data[:2])\n",
+    "else:\n",
+    "    print(f\"Failed to retrieve data. Status code: {response.status_code}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ad3b3f2-cfac-490b-9396-3f59eeaa61ee",
+   "metadata": {},
+   "source": [
+    "In this cell, we calculate and print the total number of records (instances) in the dataset to get an overview of the data size using the **len()** function with the **data** list as parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "19f0bdb7-e947-4ed3-bad7-8906b8340f69",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of instances in the dataset: 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the total number of instances in the dataset\n",
+    "total_instances = len(data)\n",
+    "print(f\"Total number of instances in the dataset: {total_instances}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d263dcd6-b2bc-4736-b2c0-d7861f3bd283",
+   "metadata": {},
+   "source": [
+    "In the below cell, we create a DataFrame from the JSON data and print the column names to understand the structure of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "74c8f753-75ed-4b6d-9eee-93346058fb62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['DataProvider', 'OperatorInfo', 'UsageType', 'StatusType',\n",
+      "       'SubmissionStatus', 'UserComments', 'PercentageSimilarity',\n",
+      "       'MediaItems', 'IsRecentlyVerified', 'DateLastVerified', 'ID', 'UUID',\n",
+      "       'ParentChargePointID', 'DataProviderID', 'DataProvidersReference',\n",
+      "       'OperatorID', 'OperatorsReference', 'UsageTypeID', 'UsageCost',\n",
+      "       'AddressInfo', 'Connections', 'NumberOfPoints', 'GeneralComments',\n",
+      "       'DatePlanned', 'DateLastConfirmed', 'StatusTypeID',\n",
+      "       'DateLastStatusUpdate', 'MetadataValues', 'DataQualityLevel',\n",
+      "       'DateCreated', 'SubmissionStatusTypeID'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Assuming `data` is your JSON data\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Print the column names\n",
+    "print(df.columns)\n",
+    "data = []\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58299fa2-6582-44f1-ab3b-56b23a88fa2b",
+   "metadata": {},
+   "source": [
+    "In this cell, we extract relevant information about charger types, number of points, and power from the dataset for the purpose of data visualization. First of all, 3 empty lists are created, **charger_types**, **points**, and **power_kW**. The **for** loop is used for looping through each station in the **data** list, where **data** is expected to be a list of dictionaries and each dictionary represents a charging station. Inside each station dictionary, there is a key **'Connections'** which is a list of connection types available at that station. This loop iterates over each connection type. The **charger_types.append()** function extracts the title of the connection type and appends it to the **charger_types** list. The **points.append()** function gets the number of charging points available at the station. If this key is not present, it defaults to 0. The result is appended to the **points** list. Lastly, the **power_kW.append()** function retrieves the power output (in kW) of the connection.\n",
+    "\n",
+    "After that, we create a DataFrame using the pandas library. Each key in the dictionary passed to **pd.DataFrame()** becomes a column in the DataFrame. The print statements in the end help in verifying the extracted data before performing further analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "45c87183-6e23-4f1b-acd3-5f4b2b68a151",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['CCS (Type 2)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Tethered Connector) ', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'Type 2 (Tethered Connector) ', 'Type 2 (Tethered Connector) ', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'Unknown', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'CHAdeMO', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'Three Phase 5-Pin (AS/NZ 3123)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'Unknown', 'Three Phase 5-Pin (AS/NZ 3123)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'Type 2 (Socket Only)', 'Type 2 (Socket Only)', 'Type 2 (Tethered Connector) ', 'CCS (Type 2)', 'CHAdeMO', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Tethered Connector) ', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'Unknown', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'CHAdeMO', 'Type 2 (Socket Only)', 'Type 2 (Tethered Connector) ', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'CCS (Type 2)', 'Type 2 (Socket Only)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CCS (Type 2)', 'CHAdeMO', 'Type 2 (Socket Only)', 'Type 2 (Socket Only)', 'CCS (Type 2)', 'CHAdeMO', 'Type 2 (Tethered Connector) ', 'Type 2 (Socket Only)', 'CCS (Type 2)']\n",
+      "[6, 2, 6, 6, 6, 6, 6, 6, 4, 2, 2, 6, 2, 2, 1, 3, 1, 12, 6, 1, 1, 3, 3, 1, 1, 12, 3, 1, 1, 1, 1, 2, 2, 4, 1, 1, 2, 3, 3, 1, 1, 7, 7, 7, 3, 12, 8, 1, 2, 2, 1, 1, 1, 4, 4, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 4, 1, 1, 12, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 4, 1, 1, 1, 1, 2, 2, 1, 1, 3, 3, 4, 4, 2, 1, 1, 1, 2, 2, 4, 4, 4, 1, 1, 2, 2, 6, 6, 6, 2, 2, 2, 1, 1, 1, 1, 6, 2, 2, 1, 1, 1, 1, 6, 6, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 3, 1, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Extract information from the dataset\n",
+    "charger_types = []\n",
+    "points = []\n",
+    "power_kW = []\n",
+    "\n",
+    "for station in data:\n",
+    "    for connection in station['Connections']:\n",
+    "        charger_types.append(connection['ConnectionType']['Title'])\n",
+    "        points.append(station.get('NumberOfPoints', 0))\n",
+    "        power_kW.append(connection.get('PowerKW', 0))\n",
+    "\n",
+    "# Create a DataFrame for analysis\n",
+    "df = pd.DataFrame({\n",
+    "    'ChargerType': charger_types,\n",
+    "    'NumberOfPoints': points,\n",
+    "    'Power_kW': power_kW\n",
+    "})\n",
+    "print(charger_types)\n",
+    "print(points)\n",
+    "# Aggregate data by ChargerType\n",
+    "popularity_df = df.groupby('ChargerType').agg({\n",
+    "    'NumberOfPoints': 'sum',\n",
+    "    'Power_kW': 'sum'\n",
+    "}).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f005511-48cc-4515-b87e-73e469aae3e2",
+   "metadata": {},
+   "source": [
+    "In this cell, we create two bar plots to visualize the popularity of different charger types based on the total number of charging points and their power output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0cb5d438-52e3-452c-8224-7bce6ad7aadb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the popularity based on number of points\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(popularity_df['ChargerType'], popularity_df['NumberOfPoints'], color='skyblue')\n",
+    "plt.title('Popularity of Charger Types by Number of Points')\n",
+    "plt.xlabel('Charger Type')\n",
+    "plt.ylabel('Total Number of Points')\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Visualize the popularity based on total power output\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(popularity_df['ChargerType'], popularity_df['Power_kW'], color='orange')\n",
+    "plt.title('Popularity of Charger Types by Total Power Output (kW)')\n",
+    "plt.xlabel('Charger Type')\n",
+    "plt.ylabel('Total Power Output (kW)')\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28a5a9c4-c908-49c0-822a-71a38864142d",
+   "metadata": {},
+   "source": [
+    "The above plots indicate that CCS (Type 2) EV charger was used most by the consumers during the years 2023-24 as indicated by its high availability and high power and hence large efficiency. Hence, it was the most suitable type of EV charger.\n",
+    "\n",
+    "In this cell, we:\n",
+    "1. Define a function to fetch data from the API.\n",
+    "2. Fetch data and convert it into a DataFrame.\n",
+    "3. Convert **DateCreated** to a naive datetime format and extract year-month periods.\n",
+    "4. Filter the data for the years 2023 and 2024.\n",
+    "5. Count the number of charging stations per month and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "879bdff7-18b0-45e6-a528-a9ce17491e70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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p0qVVzjdhwgRREATxxo0b2m0AREtLyyrbzp49KwIQP/roI+22e5Xy1eUcNfnrr79EAOKOHTvuepzGwIEDRQcHhyq/37NmzRLlcnmNx7u7u4sPPfTQXc85Y8YMUS6Xi9euXau2LyIiQgSg/WnVqlWN5YA1uXr1qtisWTPR0tJS7Nmzp1hQUFCn+91LRkaG6OHhIfbq1avK9uDg4Gp/i0RRFC9evCgCED/99NNaz1lSUiK2bdtWDAgIqFYyunPnTtHCwkL79+xe5c+Wlpbis88+q8tDIiKie+CMKSIi0ovAwEA8+uij+Pzzz5GcnKy38z755JPaf8vlcnTt2hWiKGLGjBna7U5OTmjVqlWVsg+NadOmwd7eXnt7woQJ8PLywq5duwBUfMt+/fp1TJ06FRkZGUhPT0d6ejoKCgowYMAAHDp0qFoj4DubGN/Nrl27EBoaWqXcz87ODk899RTi4uJw6dKlug3CHXJzcwGgymOqi//G3KtXr2rjZW1trf13VlYWcnJy0KtXL5w6dara+fr06YO2bdtW2bZ7926Eh4ejY8eO2m0uLi7VZjzdzfTp02FpaVklTgDaWE+cOIGMjAzMnDmzSsnNww8/DGdn53ueXxAE/Pnnn1i6dCmcnZ3x448/YtasWfDz88PkyZPrPPPjzrHKyclBeno6+vTpg5iYGOTk5NTpHHc6ceIEbt++jeeeew5KpVK7fcSIEWjdunWNs9bu9Zw6OTmhoKCg2qpldTVmzJgqM55CQ0MRFham/d0BKn6/kpKScODAAe22jRs3wtrausZS09pMmjQJRUVF2LlzJ/Ly8rBz585ay/h27doFuVyOF154ocr2efPmQRRF/PHHH1W2Dxw4EEFBQdrbHTp0gIODQ41/L2pT33NoZlTV5bX5zjvv4K+//sKyZcvg5OSk3V5UVFTld+JOSqWyWlPzO/3www/46quvMG/ePLRo0aLa/rZt22Lv3r3Ytm2btvF3XVbly8nJwdChQxEWFoajR4/i7NmzGDt2LEpLS7XHvPvuu7CwsEBJSck9z6ehVqvx8MMPIzs7u9qqg0VFRTWWCWp+X+42DrNnz8alS5fw8ccfV/m7UVpaijlz5uCZZ56p9vesNs7OzkhPT6/TsUREVDdMTBERkd688cYbKC8v1+sKfc2aNaty29HREUqlslo5hqOjI7Kysqrd/78fxgRBQPPmzbX9Y65fvw4AeOyxx+Du7l7l58svv0RJSUm1RENAQECdYr958yZatWpVbXubNm20+3WlKWPJy8ur832USmWVsiSg4sPVf8dr586d6N69O5RKJVxcXODu7o5169bVmGipaQxu3ryJ5s2bV9te07ba/Pf51nyg18SqGbP/ntPCwkLbH+derKys8Prrr+Py5ctISkrCjz/+iO7du2PTpk2YPXt2nc7xzz//YODAgbC1tYWTkxPc3d3x2muvAUC9ElOax1XT66V169bVXit1eU6fe+45tGzZEsOGDYOPjw+eeOKJOvdFAqr/7gBAy5Ytq/ReGjRoELy8vLTlfGq1Gj/++CNGjx6tU/LU3d0dAwcOxA8//IBff/0VKpUKEyZMqPHYmzdvwtvbu9r5a/u9+u9rCqj59X8393sO8T99mP7r559/xhtvvIEZM2bg2WefrbLP2tq6SsLnTsXFxVWSpHc6fPgwZsyYgSFDhuDtt9+u8RgHBwcMHDgQo0ePxvLlyzFv3jyMHj0aZ8+evWu869atQ3x8PNasWYMuXbpg69atOHjwIKZMmaLtc3bhwgV07NhRp55Tzz//PHbv3o0vv/wSISEhVfZZW1vXmOQqLi7W7q/J+++/jy+++AL/+9//qpXqrVq1Cunp6ViyZEmdYxRF8a59AImISHdMTBERkd4EBgbikUceqXXWVG1v5v/bsPlOcrm8TtuAe3/4q4lmNtT777+PvXv31vhjZ2dX5T61fQAyhNatWwMAzp8/X+f71DZedzp8+DBGjRoFpVKJTz75BLt27cLevXsxderUGse1ocZAn89tXXh5eeGhhx7CoUOH0KJFC2zatOmuvaeAigbqAwYMQHp6OlauXInff/8de/fuxZw5cwCg2gy7hlCX59TDwwNnzpzB9u3bMWrUKBw4cADDhg3DY489ptc4pk6dii1btqC4uBgHDhxAUlISHnnkEZ3PNXXqVPzxxx/49NNPMWzYsCqzhu43xpro8pqq7zk0zfTvlsDau3cvpk2bhhEjRuDTTz+ttt/LywsqlQq3b9+usr20tBQZGRnw9vaudp+zZ89i1KhRaNeuHTZv3nzXht53GjduHICKpvl3c/ToUfj5+cHLywsAMGDAAHz33XfYtm0bnnjiCaSmpmLbtm06zZZcsmQJPvnkEyxbtqzGHnpeXl41/r+i2VbTOGzYsAHz58/HM888gzfeeKPKvpycHCxduhQzZ85Ebm4u4uLiEBcXh/z8fIiiiLi4uGpjDlQ0hq+pTxUREdUfE1NERKRXmllTy5cvr7ZPM/vlv+VS9Zk5VFeaGVEaoijixo0b2tk1mvIczcyBmn4UCkW9ru3n54erV69W237lyhXtfl2NHDkSAPD999/XK6babNmyBUqlEn/++SeeeOIJDBs2DAMHDtTpHH5+fjWuoFfTtvrSjNl/z1leXl5tFTVdKBQKdOjQAWVlZdoyndoSqTt27EBJSQm2b9+Op59+GsOHD8fAgQNrTNbVdWaF5nHV9Hq5evVqvV4rQMXqbyNHjsQnn3yC6OhoPP300/j222/r9Jz893cHAK5du1ZtZtq0adOQm5uLHTt2YOPGjXB3d8eQIUN0jnXs2LGQyWQ4duxYrWV8QMVYJSUlVZs1eD+/Vw01A0aTSI6Nja1xf2RkJMaOHYuuXbti06ZNNSaQNKWxJ06cqLL9xIkTUKvVVUpngYrE6dChQ+Hh4YFdu3ZVS6zfTUlJCdRq9T1n/QmCgOTk5CpJ3EmTJmHNmjX49ttv0b17dzg7O1dZQe9u1q5di8WLF+Oll17C/PnzazymY8eOuHbtmracWSMyMlK7/06//fYbnnzySYwbN67KogQaWVlZyM/Px3vvvYeAgADtz5YtW1BYWIiAgIBq8ScmJqK0tFQ7O4+IiPSDiSkiItKroKAgPPLII/jss8+0qyBpODg4wM3NrdrqeZ988kmDxfPtt99W+QC7efNmJCcnY9iwYQAqVlkKCgrCihUrauytkpaWVu9rDx8+HFFRUYiIiNBuKygowOeffw5/f/869zS5U3h4OIYOHYovv/wS27Ztq7a/tLQUL7/8ss7nlcvlEAShyuy1uLi4Gq9RmyFDhiAiIqLK6liZmZk1rtpWX127doWrqyu++OKLKh+KN27cWKeyquvXryM+Pr7a9uzsbERERMDZ2VlbImdra6vddyfN7Jk7Z8vk5ORg/fr11c5ra2tbp75VXbt2hYeHBz799NMq5Up//PEHLl++jBEjRtzzHP/13xXjZDIZOnToAAB16vuzbds2JCYmam9HRUUhMjJS+7uj0aFDB3To0AFffvkltmzZgoceeqjOM3TuZGdnh3Xr1mHx4sXaBGxNhg8fDpVKhY8//rjK9lWrVkEQhGrx1UVtz/X9atq0KXx9fasllQBon1d/f3/s3Lmz1lmI/fv3h4uLC9atW1dl+7p162BjY1PltZGSkoLBgwdDJpPhzz//rFbuqZGdnY2ysrJq27/88ksA1VcA/C/Nyqbvvvtule2zZ8/GkCFDEBcXh0GDBmnH9W5+/vlnvPDCC3j44YexcuXKWo+bMGECVCpVlZUnS0pKsH79eoSFhcHX11e7/dChQ3jooYfQu3dvbNy4ETJZ9Y88Hh4e2Lp1a7Wffv36QalUYuvWrViwYEGV+5w8eRIA0KNHj3s+LiIiqjvd3zUQERHdw+uvv47vvvsOV69eRXBwcJV9Tz75JJYtW4Ynn3wSXbt2xaFDh3Dt2rUGi8XFxQU9e/bE9OnTkZqaitWrV6N58+aYOXMmgIoP619++SWGDRuG4OBgTJ8+HU2bNkViYiIOHDgABwcH7Nixo17X/r//+z/8+OOPGDZsGF544QW4uLjgm2++QWxsLLZs2VLjh6W6+PbbbzF48GCMGzcOI0eOxIABA2Bra4vr16/jp59+QnJyMlasWKHTOUeMGIGVK1di6NChmDp1Km7fvo21a9eiefPmOHfuXJ3O8eqrr+L777/HoEGD8Pzzz8PW1hZffvklmjVrhszMTL3MSrG0tMTixYvx/PPPo3///pg0aRLi4uKwYcMGBAUF3fMaZ8+exdSpUzFs2DD06tULLi4uSExMxDfffIOkpCSsXr1am3jSLA3/+uuv46GHHoJCocDIkSMxePBg7Uykp59+Gvn5+fjiiy/g4eFRrdSoS5cuWLduHZYuXYrmzZvDw8MD/fv3rxaXQqHA8uXLMX36dPTp0wdTpkxBamoq1qxZA39/f22ZoC6efPJJZGZmon///vDx8cHNmzfx0UcfoWPHjnWa8dG8eXP07NkTzz77LEpKSrB69Wq4urri1VdfrXbstGnTtAnR+pTxadSlzHDkyJHo168fXn/9dcTFxSEkJAR79uzBb7/9hpdeeqlKk/K6qu25rkti5V5Gjx6NrVu3VulNlJeXhyFDhiArKwuvvPJKteb2QUFBCA8PB1BRNvu///0Ps2bNwsSJEzFkyBAcPnwY33//Pd5++224uLho7zd06FDExMTg1VdfxZEjR3DkyBHtviZNmmDQoEEAgIMHD+KFF17AhAkT0KJFC5SWluLw4cP49ddf0bVr13s+hzNnzsT333+PhQsX4sSJExg8eDDKy8uxbds2HD58GA888AA2bNiAXr164Yknnqj1PFFRUZg2bRpcXV0xYMCAaknsHj16IDAwEAAQFhaGiRMnYsGCBbh9+zaaN2+Ob775BnFxcfjqq6+097l58yZGjRoFQRAwYcIE/PLLL1XOqUmk2tjYYMyYMdVi2rZtG6Kiomrct3fvXjRr1gydOnW66/gQEZGOJFkLkIiIzMKdy73/l2ZJ++Dg4CrbCwsLxRkzZoiOjo6ivb29OGnSJPH27dsiAHHRokXa4xYtWiQCENPS0qqdt6alvPv06VPlWgcOHBABiD/++KO4YMEC0cPDQ7S2thZHjBgh3rx5s9r9T58+LY4bN050dXUVraysRD8/P3HSpEnivn377hnT3URHR4sTJkwQnZycRKVSKYaGhoo7d+6sdhwAcdasWXU+b2FhobhixQqxW7duop2dnWhpaSm2aNFCfP7556ssa1/beGkey52++uorsUWLFqKVlZXYunVrcf369TUed7dYT58+Lfbq1Uu0srISfXx8xHfffVf88MMPRQBiSkqK9rg+ffqIffr00d7WPF+//PJLlfPFxsaKAMT169dX2f7hhx+Kfn5+opWVlRgaGir+888/YpcuXcShQ4feddxSU1PFZcuWiX369BG9vLxECwsL0dnZWezfv7+4efPmasf/73//E5s2bSrKZDIRgBgbGyuKoihu375d7NChg6hUKkV/f39x+fLl4tdff13lGFEUxZSUFHHEiBGivb29CED7mDWP98CBA1Wu9/PPP4udOnUSraysRBcXF/Hhhx8WExISqhxT1+d08+bN4uDBg0UPDw/R0tJSbNasmfj000+LycnJdx0jzZi///774gcffCD6+vqKVlZWYq9evcSzZ8/WeJ/k5GRRLpeLLVu2vOu573S3vx938vPzE0eMGFFlW15enjhnzhzR29tbVCgUYosWLcT3339fVKvVVY6r7bXq5+cnPvbYY1W21fZc63KOmpw6dUoEIB4+fFi7TTPGtf3UdN7PP/9cbNWqlWhpaSkGBQWJq1atqvHx1vZz5+/bjRs3xGnTpomBgYGitbW1qFQqxeDgYHHRokVifn7+PR+TKIpiQUGB+Prrr4tBQUGiQqEQXV1dxXHjxolRUVFiWVmZ2Lt3b1GhUIh//fVXrefQvAZq+/nv731RUZH48ssvi56enqKVlZXYrVs3cffu3VWO0fxu1fZz5/8zNant90ulUoleXl7iG2+8UafxISKiuhNEsYG6iRIREREBeOmll/DZZ58hPz+/Tk2760OtVsPd3R3jxo3DF1980SDXoNqlp6fDy8sLCxcuxJtvvil1OEZnwIAB8Pb2xnfffSd1KFRP27Ztw9SpUxEdHa1t+k5ERPrBHlNERESkN0VFRVVuZ2Rk4LvvvkPPnj31lpQqLi6uthrat99+i8zMTPTt21cv1yDdbNiwASqVqsbV1Ah455138PPPPzfoQg/UsJYvX47Zs2czKUVE1AA4Y4qIiIj0pmPHjujbty/atGmD1NRUfPXVV0hKSsK+ffvQu3dvvVzj4MGDmDNnDiZOnAhXV1ecOnUKX331Fdq0aYOTJ0/C0tJSL9ehe9u/fz8uXbqEN998E/369cOvv/4qdUhERERkYpiYIiIiIr157bXXsHnzZiQkJEAQBHTu3BmLFi3CwIED9XaNuLg4vPDCC4iKikJmZiZcXFwwfPhwLFu2DB4eHnq7Dt1b3759cfToUTzwwAP4/vvv0bRpU6lDIiIiIhPDxBQREREREREREUmCPaaIiIiIiIiIiEgSTEwREREREREREZEkLKQOwBip1WokJSXB3t4egiBIHQ4RERERERERkckQRRF5eXnw9vaGTHb3OVFMTNUgKSkJvr6+UodBRERERERERGSybt26BR8fn7sew8RUDezt7QFUDKCDg4PE0dRfWVkZ9uzZg8GDB0OhUEgdjtnjeBsWx9uwON6Gw7E2LI63YXG8DYvjbTgca8PieBsWx9uwzGW8c3Nz4evrq82v3A0TUzXQlO85ODiYfGLKxsYGDg4OJv2CNhUcb8PieBsWx9twONaGxfE2LI63YXG8DYdjbVgcb8PieBuWuY13Xdojsfk5ERERERERERFJgokpIiIiIiIiIiKSBBNTREREREREREQkCSamiIiIiIiIiIhIEkxMERERERERERGRJJiYIiIiIiIiIiIiSTAxRUREREREREREkmBiioiIiIiIiIiIJMHEFBERERERERERSYKJKSIiIiIiIiIikgQTU0REREREREREJAkmpoiIiIiIiIiISBJMTBERERERERERkSSYmCIiIiIiIiIiIkkwMUVEREREDU6lFhEZm4mT6QIiYzOhUotSh0RERERGwELqAIiIiIjIvO2+kIwlOy4hOacYgBzfXj8BL0clFo1si6HtvKQOj4iIiCTEGVNERERE1GB2X0jGs9+fqkxK/SslpxjPfn8Kuy8kSxQZERERGQMmpoiIiIioQajUIpbsuISaivY025bsuMSyPiIiokaMiSkiIiIiahBRsZnVZkrdSQSQnFOMqNhMwwVFRERERoWJKSIiIiJqELfzak9K1ec4IiIiMj9MTBERERFRg/CwV+r1OCIiIjI/TEwRERERUYMIDXCBl6MSQi37BQBejkqEBrgYMiwiIiIyIkxMEREREVGDkMsELBrZtsbm55pk1aKRbSGX1Za6IiIiInPHxBQRERERNZih7bwwLdyv2nZXO0use6QzhrbzkiAqIiIiMhZMTBERERFRgyoqVQEARrb3hK+tGgDw+AP+TEoRERERE1NERERE1LCi4jIBAKM7eqGbe0VhX2RMppQhERERkZFgYoqIiIiIGkxqbjFuZhRCJgCdmzmhhWNFYup4XCZKy9USR0dERERSY2KKiIiIiBpMVGzFzKi23g6wVyrgZQ242CpQXKbGmVvZ0gZHREREkmNiioiIiIgajCYx1c3fBQAgCED3gIp/R0RnSBYXERERGQcmpoiIiIiowWgSU2GVyag7/300Ol2SmIiIiMh4MDFFRERERA0iu7AUV1PzAPw7YwoAwgMr/n06PhvFZSpJYiMiIiLjwMQUERERETWI43FZAIDmHnZwtbPSbvd3tYGngxKlKjVO3sySKjwiIiIyAkxMEREREVGDiIqt6CF152wpABAEAeFBrgBYzkdERNTYMTFFRERERA2ipv5SGprEFBugExERNW5MTBERERGR3hWUlONCUi4AILSmxFRgRWLqbEIO8kvKDRobERERGQ8mpoiIiIhI707FZ0GlFtHUyRreTtbV9vu62MDXxRoqtYjjcZkSREhERETGQNLE1Lvvvotu3brB3t4eHh4eGDNmDK5evVrlmOLiYsyaNQuurq6ws7PD+PHjkZqaetfziqKIhQsXwsvLC9bW1hg4cCCuX7/ekA+FiIiIiO5wtzI+jR6BbgBYzkdERNSYSZqY+vvvvzFr1iwcO3YMe/fuRVlZGQYPHoyCggLtMXPmzMGOHTvwyy+/4O+//0ZSUhLGjRt31/O+9957+PDDD/Hpp58iMjIStra2GDJkCIqLixv6IRERERERgMjKxFRNZXwabIBOREREFlJefPfu3VVub9iwAR4eHjh58iR69+6NnJwcfPXVV/jhhx/Qv39/AMD69evRpk0bHDt2DN27d692TlEUsXr1arzxxhsYPXo0AODbb79FkyZNsG3bNjz00EMN/8CIiIiIGrGSchXO3MoGULfE1MWkXOQUlsHRRmGI8IiIiMiIGFWPqZycHACAi0vFG5iTJ0+irKwMAwcO1B7TunVrNGvWDBERETWeIzY2FikpKVXu4+joiLCwsFrvQ0RERET6cy4hB6XlarjZWSLAzbbW45o4KBHobgtRBI7FspyPiIioMZJ0xtSd1Go1XnrpJTzwwANo164dACAlJQWWlpZwcnKqcmyTJk2QkpJS43k025s0aVLn+5SUlKCkpER7Oze3YgWZsrIylJWV1evxGANN7Kb8GEwJx9uwON6GxfE2HI61YXG8G0bEjTQAQFc/Z5SX/7viXk3j3T3AGTFpBfjnehr6t3Q1bKBmjq9vw+FYGxbH27A43oZlLuOtS/xGk5iaNWsWLly4gCNHjhj82u+++y6WLFlSbfuePXtgY2Nj8Hj0be/evVKH0KhwvA2L421YHG/D4VgbFsdbv/64LAMgg21BEnbtSqy2/87xtswWAMix9+xNdBFiDBdkI8LXt+FwrA2L421YHG/DMvXxLiwsrPOxRpGYmj17Nnbu3IlDhw7Bx8dHu93T0xOlpaXIzs6uMmsqNTUVnp6eNZ5Lsz01NRVeXl5V7tOxY8ca77NgwQLMnTtXezs3Nxe+vr4YPHgwHBwc7uORSausrAx79+7FoEGDoFCwZ0ND43gbFsfbsDjehsOxNiyOt/6Vq9R47dQBACo8Nrwn2njZa/fVNN5hBaXYsOwgkosEhPUeAFc7K4kiNz98fRsOx9qwON6GxfE2LHMZb00lWl1ImpgSRRHPP/88tm7dioMHDyIgIKDK/i5dukChUGDfvn0YP348AODq1auIj49HeHh4jecMCAiAp6cn9u3bp01E5ebmIjIyEs8++2yN97GysoKVVfU3QQqFwqRfCBrm8jhMBcfbsDjehsXxNhyOtWFxvPXnSmoOCkpUsFdaINjHGXKZUO2YO8fb00mB1p72uJKShxO3cvFgB29Dh2z2+Po2HI61YXG8DYvjbVimPt66xC5p8/NZs2bh+++/xw8//AB7e3ukpKQgJSUFRUVFACqals+YMQNz587FgQMHcPLkSUyfPh3h4eFVVuRr3bo1tm7dCgAQBAEvvfQSli5diu3bt+P8+fOYNm0avL29MWbMGCkeJhEREVGjEVnZxLybv0uNSamaaFbnOxrNBuhERESNjaQzptatWwcA6Nu3b5Xt69evx+OPPw4AWLVqFWQyGcaPH4+SkhIMGTIEn3zySZXjr169ql3RDwBeffVVFBQU4KmnnkJ2djZ69uyJ3bt3Q6lUNujjISIiImrsjsdlAgBCA1zqfJ8eQW5Y/08cjjExRURE1OhIXsp3L0qlEmvXrsXatWvrfB5BEPDWW2/hrbfeuu8YiYiIiKhuRFFEVGxFYqqbf90TU6EBLpAJQEx6AVJyiuHpyC8TiYiIGgtJS/mIiIiIyHzcuJ2PrMIyKBUytG/qWOf7OVor0K7y+IiY9IYKj4iIiIwQE1NEREREpBeRlbOlOjdzhqWFbm8ztX2mbrCcj4iIqDFhYoqIiIiI9KI+/aU0wgPZAJ2IiKgxYmKKiIiIiO6bKIqIjKlMTOnQX0qjm78LLGQCErOLcCuzUN/hERERkZFiYoqIiIiI7ltCVhFScothIRPQqZmzzve3tbJAiK8TAOBoNPtMERERNRZMTBERERHRfdOsxtfBxxHWlvJ6naNHZZ+pCJbzERERNRpMTBERERHRfdMkprrVo7+Uxp19pkRR1EtcREREZNyYmCIiIiKi+xZV2fg87D4SU539Klbzu51Xgpj0An2FRkREREaMiSkiIiIiui+384oRm14AQQC6+NU/MaVUyNGlsj8VV+cjIiJqHJiYIiIiIqL7cjw2CwDQxtMBjtaK+zpXuLbPFBugExERNQZMTBERERHRfYmKrZjdFHofZXwamgbox2IyoVazzxQREZG5Y2KKiIiIiO5LZGXjc30kpjr4OMFaIUdmQSmupubd9/mIiIjIuDExRURERET1llNYpk0gdfO//8SUpYVMu7JfBPtMERERmT0mpoiIiIio3k7czIQoAoHutnC3t9LLOTXlfGyATkREZP6YmCIiIiKieovSlPHpYbaURnhgRWIqMjYDKvaZIiIiMmtMTBERERFRvemzv5RGsLcD7JUWyCsux8WkHL2dl4iIiIwPE1NEREREVC+FpeW4kFiRONJnYspCLkNY5flYzkdERGTemJgiIiIiono5HZ+NcrUIb0clfJxt9Hru8CA3AGyATkREZO6YmCIiIiKiemmIMj4NTZ+p43GZKFOp9X5+IiIiMg5MTBERERFRvUTFVsxmCg1w1fu5W3vaw9lGgcJSFc4lZOv9/ERERGQcmJgiIiIiIp2VlqtxOj4bQMPMmJLJBIQHVSS8jt5gOR8REZG5YmKKiIiIiHR2PjEbJeVquNpaIsjdtkGuoSnnYwN0IiIi88XEFBERERHpTNNfqpu/CwRBaJBraBqgn4zPQnGZqkGuQURERNJiYoqIiIiIdHa8ARufawS528Ld3gql5Wqcis9qsOsQERGRdJiYIiIiIiKdqNQiTsRVJIoaMjElCAJ6VPaZOsZyPiIiIrPExBQRERER6eRyci7ySsphb2WBNl4ODXotTWKKfaaIiIjMExNTRERERKSTqMoyvi7+zpDLGqa/lEZ4YEWfqTO3slFYWt6g1yIiIiLDY2KKiIiIiHRyPK7h+0tp+LpYo6mTNcrVIo7Hsc8UERGRuWFiioiIiIjqTBRF7YypUP+GT0wJgoBwbTlfeoNfj4iIiAyLiSkiIiIiqrPotAJkFJTCykKG9j6OBrkmG6ATERGZLyamiIiIiKjONLOlOjVzgpWF3CDX1MyYOp+Yg9ziMoNck4iIiAyDiSkiIiIiqrN/+0u5GuyaXo7WCHCzhVoEomIyDXZdIiIianhMTBERERFRnRmyv9Sd/u0zxXI+IiIic8LEFBERERHVSUJWIRKzi2AhE9DZz8mg1w4PZAN0IiIic8TEFBERERHViaaMr11TR9hYWhj02t0rE1NXUvKQWVBq0GsTERFRw2FiioiIiIjqRFvGF2DYMj4AcLe3QssmdgCAYzEs5yMiIjIXTEwRERERUZ1EStRfSqNHkBsAIIJ9poiIiMwGE1NEREREdE9peSWISSuAIADdJEpM/dsAnX2miIiIzAUTU0RERER0Tycq+0u1amIPRxuFJDF0D3CFIADRaQW4nVssSQxERESkX0xMEREREdE9RUrYX0rD0UaBYG8HAEAE+0wRERGZBSamiIiIiOiepGx8fqfwytX5jt5gYoqIiMgcMDFFRERERHeVU1SGyym5AKRrfK6hbYDOGVNERERmgYkpIiIiIrqrUzezIIpAgJstPByUksbSLcAFcpmA+MxCJGQVShoLERER3T8mpoiIiIjorjT9pbr5O0scCWBnZYEOPo4AgIhozpoiIiIydUxMEREREdFdRcVWJIBCA1wljqRCj6CKOJiYIiIiMn2SJqYOHTqEkSNHwtvbG4IgYNu2bVX2C4JQ48/7779f6zkXL15c7fjWrVs38CMhIiIiMk9FpSqcT8wBAIRJ3PhcIzywos/U0egMiKIocTRERER0PyRNTBUUFCAkJARr166tcX9ycnKVn6+//hqCIGD8+PF3PW9wcHCV+x05cqQhwiciIiIye6dvZaFMJcLTQQkfZ2upwwEAdPFzhqVchpTcYsRlsM8UERGRKbOQ8uLDhg3DsGHDat3v6elZ5fZvv/2Gfv36ITAw8K7ntbCwqHZfIiIiItJdVGV/qdAAFwiCIHE0Fawt5ejYzAlRsZk4Gp2OADdbqUMiIiKiejKZHlOpqan4/fffMWPGjHsee/36dXh7eyMwMBAPP/ww4uPjDRAhERERkfm5MzFlTNhnioiIyDxIOmNKF9988w3s7e0xbty4ux4XFhaGDRs2oFWrVkhOTsaSJUvQq1cvXLhwAfb29jXep6SkBCUlJdrbubm5AICysjKUlZXp70EYmCZ2U34MpoTjbVgcb8PieBsOx9qwON53V1quxqn4LABAZ1+H+x4nfY53qJ8TgIrEVGlpqdHM5jImfH0bDsfasDjehsXxNixzGW9d4hdEI+kYKQgCtm7dijFjxtS4v3Xr1hg0aBA++ugjnc6bnZ0NPz8/rFy5stbZVosXL8aSJUuqbf/hhx9gY2Oj0/WIiIiIzEVcHrDqggVsLUS83VUFY8r9lKuB/zsuR5lawP+FlMOLb9mIiIiMRmFhIaZOnYqcnBw4ODjc9ViTmDF1+PBhXL16FT///LPO93VyckLLli1x48aNWo9ZsGAB5s6dq72dm5sLX19fDB48+J4DaMzKysqwd+9eDBo0CAqFQupwzB7H27A43obF8TYcjrVhcbzv7vPDscCF6+jRoglGjOh43+fT93j/mn4S/0RnQOHTDsO7N7vv85kbvr4Nh2NtWBxvw+J4G5a5jLemEq0uTCIx9dVXX6FLly4ICQnR+b75+fmIjo7Go48+WusxVlZWsLKyqrZdoVCY9AtBw1weh6ngeBsWx9uwON6Gw7E2LI53zU7G5wAAwgJd9To++hrvHs3d8E90BiJjszCjV5AeIjNPfH0bDsfasDjehsXxNixTH29dYpe0+Xl+fj7OnDmDM2fOAABiY2Nx5syZKs3Kc3Nz8csvv+DJJ5+s8RwDBgzAxx9/rL398ssv4++//0ZcXByOHj2KsWPHQi6XY8qUKQ36WIiIiIjMiUot4niccTY+19A0QI+MzYRKbRTdKYiIiEhHks6YOnHiBPr166e9rSmne+yxx7BhwwYAwE8//QRRFGtNLEVHRyM9PV17OyEhAVOmTEFGRgbc3d3Rs2dPHDt2DO7u7g33QIiIiIjMzNWUPOQVl8PWUo62XsbZ2qB9U0fYWVkgp6gMl5Nz0a6po9QhERERkY4kTUz17dsX9+q9/tRTT+Gpp56qdX9cXFyV2z/99JM+QiMiIiJq1KJiMwAAXfxdYCGXdJJ9rSzkMoQGuGD/lduIiM5gYoqIiMgEGee7DCIiIiKS1PG4LABAmJGW8WloyvmORqff40giIiIyRkxMEREREVEVoigiMraiv1Q3f+NOTHUPrEhMRcVmokylljgaIiIi0hUTU0RERERURWx6AdLzS2BpIUMHH+Muj2vr5QBHawUKSlU4n5gjdThERESkIyamiIiIiKiKqMrZUh19naBUyCWO5u5kMgHdAytmdUVEZ0gcDREREemKiSkiIiIiqiIqriIxZez9pTR6BLkBYGKKiIjIFDExRURERERVRJlIfykNTQP043GZKClXSRwNERER6YKJKSIiIiLSSsouQkJWEeQyAZ39nKUOp06ae9jBzc4KJeVqnInPljocIiIi0gETU0RERESkdbyyjK+dtwPsrCwkjqZuBEFAeOWsqaMs5yMiIjIpTEwRERERkVakiZXxaYQHViSm2GeKiIjItDAxRURERERamv5SoSbS+FxD02fq9K0sFJWyzxQREZGpYGKKiIiIiAAAGfkluHE7H4DpzZjyc7WBl6MSZSoRJ25mSh0OERER1RETU0REREQEADgelwUAaNXEHs62lhJHo5s7+0yxnI+IiMh0MDFFRERERAD+LePrFmAaq/H9V48gNwBsgE5ERGRKmJgiIiIiIgBAVFxFQic0wFXiSOpHM2PqfGIO8orLJI6GiIiI6oKJKSIiIiJCXnEZLiXlAgBCTay/lEZTJ2v4udpApRZxPI59poiIiEwBE1NEREREhJM3s6AWK5qIezoqpQ6n3sIDK2ZNHb3Bcj4iIiJTwMQUEREREf3bX8pEZ0tpaBugxzAxRUREZAqYmCIiIiIibWIqNMA8ElOXknORXVgqcTRERER0L0xMERERETVyxWUqnEvIAQCEmXhiysNeieYedhBF4FgM+0wREREZOyamiIiIiBq5M7eyUapSw8PeCs1cbKQO57710JTzRadLHAkRERHdCxNTRERERI3cnWV8giBIHM390zZAj2afKSIiImPHxBQRERFRI6dJTJl6GZ9G98rE1PXb+UjLK5E4GiIiIrobJqaIiIiIGrEylRqn4rMAAKEBrhJHox/OtpZo4+UAgKvzERERGTsmpoiIiIgasYtJuSgsVcHRWoEWHnZSh6M3//aZYmKKiIjImDExRURERNSIRcVWJG66+btAJjP9/lIabIBORERkGpiYIiIiImrEzK2/lEa3ABfIBCAuoxBJ2UVSh0NERES1YGKKiIiIqJFSq0Ucj6voL9XNzBJTDkoF2vs4AWA5HxERkTFjYoqIiIiokbp2Ow85RWWwsZQj2NtB6nD0Lrxydb6jTEwREREZrftOTOXm5mLbtm24fPmyPuIhIiIiIgPRlPF18XOGQm5+31dq+kwdi8mAKIoSR0NEREQ10fkdyKRJk/Dxxx8DAIqKitC1a1dMmjQJHTp0wJYtW/QeIBERERE1DE1iKtTfvMr4NLr6O0MhF5CYXYT4zEKpwyEiIqIa6JyYOnToEHr16gUA2Lp1K0RRRHZ2Nj788EMsXbpU7wESERERkf6JoqhNTJlbfykNG0sLdPR1AsA+U0RERMZK58RUTk4OXFwq3rzs3r0b48ePh42NDUaMGIHr16/rPUAiIiIi0r+bGYW4nVcCS7lMm7wxR+FBbgDYZ4qIiMhY6ZyY8vX1RUREBAoKCrB7924MHjwYAJCVlQWlUqn3AImIiIhI/zSzpUJ8HaFUyCWOpuHc2QCdfaaIiIiMj86JqZdeegkPP/wwfHx84O3tjb59+wKoKPFr3769vuMjIiIiogYQFVfZX8pMy/g0OjVzgpWFDOn5JYhOy5c6HCIiIvoPC13v8NxzzyE0NBS3bt3CoEGDIJNV5LYCAwPZY4qIiIjIRGj7S5lp43MNpUKOLn7OOBqdgaPRGWjuYS91SERERHQHnRNTANC1a1d07dq1yrYRI0boJSAiIiIialjJORWr1MkEoIufs9ThNLgeQa44Gp2BiOgMTAv3lzocIiIiuoPOiSmVSoUNGzZg3759uH37NtRqdZX9+/fv11twRERERKR/mtlSwd6OsFcqJI6m4VU0QL+GiJgMqNUiZDJB6pCIiIioks6JqRdffBEbNmzAiBEj0K5dOwgC/2MnIiIiMiXH4xpHGZ9GBx9H2FjKkV1YhispeWjr7SB1SERERFRJ58TUTz/9hE2bNmH48OENEQ8RERERNTDNjClzb3yuoZDLEBrggoNX03A0Op2JKSIiIiOi86p8lpaWaN68eUPEQkREREQNLKugFNdSK1an6+Zv/v2lNMIDXQEAEdEZEkdCREREd9I5MTVv3jysWbMGoig2RDxERERE1IA0ZXwtPOzgamclcTSG0yPIDUDFbLFylfoeRxMREZGh6FzKd+TIERw4cAB//PEHgoODoVBUbZj566+/6i04IiIiItIvTRlft0ZSxqfR1tsBDkoL5BaX40JSLjr6OkkdEhEREaEeiSknJyeMHTu2IWIhIiIiogYWVTljKqyRJabkMgFhga7YeykVEdEZTEwREREZCZ0TU+vXr2+IOIiIiIiogeWXlONCYg6AxrMi3516BFUkpo5Gp+PZvkFSh0NERESoR2JKIy0tDVevXgUAtGrVCu7u7noLioiIiIj079TNLKhFwNfFGt5O1lKHY3DhQRUN0E/EZaG0XA1LC53brRIREZGe6fy/cUFBAZ544gl4eXmhd+/e6N27N7y9vTFjxgwUFhbqdK5Dhw5h5MiR8Pb2hiAI2LZtW5X9jz/+OARBqPIzdOjQe5537dq18Pf3h1KpRFhYGKKionSKi4iIiMgcaftLNcLZUgDQ0sMerraWKCpT4WxCttThEBEREeqRmJo7dy7+/vtv7NixA9nZ2cjOzsZvv/2Gv//+G/PmzdPpXAUFBQgJCcHatWtrPWbo0KFITk7W/vz44493PefPP/+MuXPnYtGiRTh16hRCQkIwZMgQ3L59W6fYiIiIiMyNJjHV2PpLachkAroHVsyaOnojQ+JoiIiICKhHYmrLli346quvMGzYMDg4OMDBwQHDhw/HF198gc2bN+t0rmHDhmHp0qV3baZuZWUFT09P7Y+zs/Ndz7ly5UrMnDkT06dPR9u2bfHpp5/CxsYGX3/9tU6xEREREZmT4jIVzlTOEgoNcJU2GAlpyvkiYtIljoSIiIiAeiSmCgsL0aRJk2rbPTw8dC7lq4uDBw/Cw8MDrVq1wrPPPouMjNq/3SotLcXJkycxcOBA7TaZTIaBAwciIiJC77ERERERmYpzCTkoLVfDzc4K/q42UocjmR6VialTN7NRXKaSOBoiIiLSufl5eHg4Fi1ahG+//RZKpRIAUFRUhCVLliA8PFyvwQ0dOhTjxo1DQEAAoqOj8dprr2HYsGGIiIiAXC6vdnx6ejpUKlW1xFmTJk1w5cqVWq9TUlKCkpIS7e3c3FwAQFlZGcrKyvT0aAxPE7spPwZTwvE2LI63YXG8DYdjbViNabwjbqQBALr5OaG8vFySGIxhvH0cLdHE3gqpeSWIiklDeKD5zh4zhvFuLDjWhsXxNiyOt2GZy3jrEr8giqKoy8kvXLiAIUOGoKSkBCEhIQCAs2fPQqlU4s8//0RwcLBu0WoCEQRs3boVY8aMqfWYmJgYBAUF4a+//sKAAQOq7U9KSkLTpk1x9OjRKkmyV199FX///TciIyNrPO/ixYuxZMmSatt/+OEH2Ng03m8UiYiIyHysuyTDlRwZxvur0NtLp7d/Zue76zKcSJdhcFM1RjRTSx0OERGR2SksLMTUqVORk5MDBweHux6r84ypdu3a4fr169i4caN2FtKUKVPw8MMPw9q6YZcdDgwMhJubG27cuFFjYsrNzQ1yuRypqalVtqempsLT07PW8y5YsABz587V3s7NzYWvry8GDx58zwE0ZmVlZdi7dy8GDRoEhUIhdThmj+NtWBxvw+J4Gw7H2rAay3iXq9R47eQBACo8PqInWnvaSxKHsYx3wclEnNh2EWkyZwwfHiZZHA3NWMa7MeBYGxbH27A43oZlLuOtqUSrC50TUwBgY2ODmTNn1ueu9yUhIQEZGRnw8vKqcb+lpSW6dOmCffv2aWdeqdVq7Nu3D7Nnz671vFZWVrCysqq2XaFQmPQLQcNcHoep4HgbFsfbsDjehsOxNixzH+/LqdkoKFXBQWmB4KbOkMkESeORerx7tfQAcBHnE3NRohZgZ1Wvt8QmQ+rxbkw41obF8TYsjrdhmfp46xJ7nf4X3r59O4YNGwaFQoHt27ff9dhRo0bV+eL5+fm4ceOG9nZsbCzOnDkDFxcXuLi4YMmSJRg/fjw8PT0RHR2NV199Fc2bN8eQIUO09xkwYADGjh2rTTzNnTsXjz32GLp27YrQ0FCsXr0aBQUFmD59ep3jIiIiIjInUbGZAIBu/i6SJ6WMga+LDXxdrHErswjH4zLRr5WH1CERERE1WnVKTI0ZMwYpKSnw8PC4aw8oQRCgUtV9dZMTJ06gX79+2tuacrrHHnsM69atw7lz5/DNN98gOzsb3t7eGDx4MP73v/9Vmd0UHR2N9PR/l/udPHky0tLSsHDhQqSkpKBjx47YvXt3jSsJEhERETUGkZWJqdAAF4kjMR7hga64lZmAY9EZTEwRERFJqE6JKbVaXeO/71ffvn1xt97rf/755z3PERcXV23b7Nmz71q6R0RERNRYqNUiTsRVzphiYkqrR5AbNp1IwNHoDKlDISIiatRkut7h22+/RUlJSbXtpaWl+Pbbb/USFBERERHpx420fGQVlsFaIUc7b0epwzEa4UGuAIALSTnIKTTtJbmJiIhMmc6JqenTpyMnJ6fa9ry8PPZxIiIiIjIymjK+zn5OsLTQ+a2f2WrioESguy1EEYiM5awpIiIiqej87kQURQhC9aaZCQkJcHTkt3BERERExuS4pr+Uv6vEkRif8MCKMWE5HxERkXTqvDZup06dIAgCBEHAgAEDYGHx711VKhViY2MxdOjQBgmSiIiIiHQniuK/K/IFOEscjfHpEeSGjZHxOBbDxBQREZFU6pyY0qzGd+bMGQwZMgR2dnbafZaWlvD398f48eP1HiARERER1c+tzCKk5BZDIRfQyZeJqf/qHljRDP5KSh4y8kvgamd1j3sQERGRvtU5MbVo0SIAgL+/PyZPngylUtlgQRERERHR/dP0Turg4wRrS7nE0RgfVzsrtPa0x5WUPByLycSIDl5Sh0RERNTo6Nxj6rHHHmNSioiIiMgEHI+r7C8V4CJxJMZLszrf0eh0iSMhIiJqnHROTKlUKqxYsQKhoaHw9PSEi4tLlR8iIiIiMg5R2sbnfI9WG00D9Ag2QCciIpKEzompJUuWYOXKlZg8eTJycnIwd+5cjBs3DjKZDIsXL26AEImIiIhIV6m5xYjLKIQgAF382V+qNmGBrpAJQEx6AVJyiqUOh4iIqNHROTG1ceNGfPHFF5g3bx4sLCwwZcoUfPnll1i4cCGOHTvWEDESERERkY40s6XaejnAQamQOBrj5WitQLumjgCAiBiW8xERERmazomplJQUtG/fHgBgZ2eHnJwcAMCDDz6I33//Xb/REREREVG9aPpLdWMZ3z2xnI+IiEg6OiemfHx8kJycDAAICgrCnj17AADHjx+HlRWX2CUiIiIyBpoZU2FsfH5P/zZAZ2KKyNip1CIiYzNxMl1AZGwmVGpR6pCI6D5Z6HqHsWPHYt++fQgLC8Pzzz+PRx55BF999RXi4+MxZ86choiRiIiIiHSQXViKKyl5AIBuTEzdUzd/F1jIBCRkFeFWZiF8XWykDomIarD7QjKW7LiE5JxiAHJ8e/0EvByVWDSyLYa285I6PCKqJ50TU8uWLdP+e/LkyfDz88PRo0fRokULjBw5Uq/BEREREZHuTsRlAQCC3G3hZscZ7fdia2WBEF8nnLyZhYjoDCamiIzQ7gvJePb7U/jv/KiUnGI8+/0prHukM5NTRCZK51K+Q4cOoby8XHu7e/fumDt3LoYNG4ZDhw7pNTgiIiIi0l1UZX+pUM6WqjNNn6mj0WyATmRsVGoRS3ZcqpaUAqDdtmTHJZb1EZkonRNT/fr1Q2ZmZrXtOTk56Nevn16CIiIiIqL6i4xlYkpXPSr7TEXEZEAU+eGWyJhExWZWlu/VTASQnFOs7a1HRKZF58SUKIoQBKHa9oyMDNja2uolKCIiIiKqn4KSclxIrFg1OTTAVeJoTEdnP2dYWsiQmluCmPQCqcMhojvczqs9KVWf44jIuNS5x9S4ceMAAIIg4PHHH6+yAp9KpcK5c+fQo0cP/UdIRERERHV2Oj4bKrWIpk7WaOpkLXU4JkOpkKNzMycci8lERHQGgtztpA6JiCp52Cv1ehwRGZc6z5hydHSEo6MjRFGEvb299rajoyM8PT3x1FNP4fvvv2/IWImIiIjoHqJiMwCwjK8+egS5AQAiojMkjoSI7hQa4AIvRyWq1+1UEAB4OSr5d4/IRNV5xtT69esBAP7+/nj55ZdZtkdERERkhNhfqv7Cg1yBvRV9ptRqETJZbR+DiciQ5DIBi0a2xTPfn6pxvwhg0ci2kPN3lsgk6dxjatGiRVWSUn///Td27dqFrKwsvQZGRERERLopKVfhzK1sAExM1UeIjxOsFXJkFpTi2u08qcMhojsMbeelXaSgJo7WlgaMhoj0qc6JqeXLl+PNN9/U3hZFEUOHDkW/fv3w4IMPok2bNrh48WKDBElERERE93Y+IQcl5Wq42Vki0I2z23VlaSFDt8qE3tEbLOcjMiaFpeU4W5l4f2N4K0xrocL3T3TF5G4+AICXfzmLvOIyCSMkovqqc2Lq559/Rrt27bS3N2/ejEOHDuHw4cNIT09H165dsWTJkgYJkoiIiIjuTVPG183fpcZVlOnewgMrZmRExDAxRWRM/ryYgoJSFZq52GBa92bo4iYiLMAFbz4YDF8XayRmF2HpzstSh0lE9VDnxFRsbCw6dOigvb1r1y5MmDABDzzwAFxcXPDGG28gIiKiQYIkIiIionuLYn+p+6YpFToWkwGVWpQ4GiLS2HwyAQAwvrNPlcS7nZUFVkwIgSAAP5+4hf1XUqUKkYjqqc6JqfLyclhZWWlvR0REoEePHtrb3t7eSE9P1290RERERFQnKrWIkzcren4yMVV/wd4OsLeyQF5xOS4m5UgdDhEBSMouwtHK1TLHdW5abX9YoCtmPBAAAJi/5TyyCkoNGh8R3Z86J6aCgoJw6NAhAEB8fDyuXbuG3r17a/cnJCTA1bX2ZnRERERE1HAuJ+civ6Qc9lYWaO3pIHU4JstCLkNYYEViLyKa5XxExmDr6USIItA90AW+LjY1HvPykFZo7mGHtLwSvPnbBQNHSET3o86JqVmzZmH27NmYMWMGhg0bhvDwcLRt21a7f//+/ejUqVODBElEREREd6fpL9XV35lLpt+n7pV9po4yMUUkOVEUq5Tx1UapkGPlpBDIZQJ2nkvGjrNJhgqRiO5TnRNTM2fOxIcffojMzEz07t0bW7ZsqbI/KSkJTzzxhN4DJCIiIqJ7i4qtSKKEBnAG+/3qEeQGADgel4kylVriaIgat1Px2YhNL4C1Qo5h7b3uemwHHyfM6tccAPDmbxdwO7fYECES0X2y0OXgJ554otbk0yeffKKXgIiIiIhIN6Io4nicpr+Us8TRmL7WnvZwtlEgq7AM5xKy0cWPPbuIpKKZLTWsvSfsrO798fX5/s2x/0oqLiTmYv6Wc/j68W5cpZTIyNV5xhQRERERGafotHxkFpTCykKG9k2dpA7H5Mlkgracj32miKRTXKbCznMVJXkT7lLGdyeFXIaVkzrC0kKGA1fT8PPxWw0ZIhHpARNTRERERCZO01+qczNnWFrw7Z0+9Ahinykiqe29lIq84nI0dbLWJovromUTe7w8uCUA4H87L+FWZmFDhUhEesB3LkREREQm7nhlYio0gCVn+hJemZg6cTMLxWUqiaMhapw0ZXzjOjeFTMdFHWb0DESovwsKSlV4+ZezUKvFhgiRiPSAiSkiIiIiEyaKonbGFBNT+hPkbgd3eyuUlqtxOj5b6nCIGp3U3GIcvp4GABhXxzK+O8llAlZMDIGNpRyRsZn4+p9YfYdIRHrCxBQRERGRCUvIKkJyTjEsZAI6NXOSOhyzIQiCtpwvIjpd4miIGp+tpxOhFoGufs4IcLOt1zmaudrg9RFtAADv/XkVN27n6TNEItITnVblA4CxY8fWuKqBIAhQKpVo3rw5pk6dilatWuklQCIiIiKqXVTlbKn2Po6wsdT5rR3dRXigK347k4SIGPaZIjIkURSxpbKMb3wX3WdL3WlqaDP8eTEVh66lYd6ms9jybA9YyDk/g8iY6Pwb6ejoiP379+PUqVMQBAGCIOD06dPYv38/ysvL8fPPPyMkJAT//PNPQ8RLRERERHc4HscyvobSI8gNAHDmVjYKS8sljoao8TifmIPrt/NhZSHDiA5e93UuQRDw3vgOcFBa4GxCDj45GK2nKIlIX3ROTHl6emLq1KmIiYnBli1bsGXLFkRHR+ORRx5BUFAQLl++jMceewzz589viHiJiIiI6A6aGVOh/kxM6ZuvizWaOlmjTCXiRFyW1OEQNRqapudDgj3hoFTc9/k8HZV4a3Q7AMCH+67jQmLOfZ+TiPRH58TUV199hZdeegky2b93lclkeP755/H5559DEATMnj0bFy5c0GugRERERFTV7bxixKQXQBCArn5MTOmbIAja1fmORrOcj8gQSspV2H42CcD9l/HdaXRHbwxr54lytYi5m85wtU0iI6JzYqq8vBxXrlyptv3KlStQqSp+uZVKZY19qIiIiIhIf47HVsziae3pAEeb+59VQNWFB7IBOpEh7b98G9mFZWjiYIWezd30dl5BELB0TDu42VniWmo+Vu29prdzE9H90Tkx9eijj2LGjBlYtWoVjhw5giNHjmDVqlWYMWMGpk2bBgD4+++/ERwcrPdgiYiIiOhf2v5S/s4SR2K+NDOmzifmILe4TOJoiMzfllMVZXxjO/lALtPvZAdXOyu8O64DAODzwzHav6FEJC2dl25ZtWoVmjRpgvfeew+pqakAgCZNmmDOnDnavlKDBw/G0KFD9RspEREREVURqekvFeAqcSTmy9vJGgFutohNL0BUTCYGtm0idUhEZistrwQHrqYBACZ0adog1xjUtgkmdPHB5pMJmLfpLP54sRdsrbiiKZGUdJ4xJZfL8frrryM5ORnZ2dnIzs5GcnIyXnvtNcjlcgBAs2bN4OOjv3pgIiIiIqoqp6gMV1JyAQDdAjhjqiF115TzxbDPFFFD+u1MIlRqESG+TmjuYd9g11k4si2aOlkjPrMQ7+y63GDXIaK60TkxdScHBwc4ODjoKxYiIiIiqqOTNzMhikCgmy087JVSh2PWerABOpFBbDmVCACY0LlhZktpOCgVeH9CRUnfxsh4/H0trUGvR0R3p3NiKjU1FY8++ii8vb1hYWEBuVxe5YeIiIiIGp6mjK+bP1fja2iaGVOXk3ORWVAqcTRE5uliUg4uJ+fCUi7DyBDvBr9ej+ZueLyHPwBg/uZzyClkDzkiqehcTPv4448jPj4eb775Jry8vLj6HhEREZEEorT9pZiYamju9lZo2cQO11LzERmTgWHtvaQOicjsbDlZMVtqYFsPONlYGuSa84e2xt/X0hCbXoDFOy5i1eSOBrkuEVWl84ypI0eOYOPGjXj22WcxZswYjB49usqPLg4dOoSRI0fC29sbgiBg27Zt2n1lZWWYP38+2rdvD1tbW3h7e2PatGlISkq66zkXL14MQRCq/LRu3VrXh0lERERktApLy3E+IQcAE1OG0iOoYtl6lvMR6V+ZSo3fzlQkpsZ3NlyvYmtLOT6YFAKZAGw9nYjdF5INdm0i+pfOiSlfX1+IoqiXixcUFCAkJARr166ttq+wsBCnTp3Cm2++iVOnTuHXX3/F1atXMWrUqHueNzg4GMnJydqfI0eO6CVeIiIiImNwJj4b5WoRXo5K+DhbSx1Oo8AG6EQN5+DVNGQUlMLNzgq9W7ob9NqdmznjmT5BAIDXtl5AWl6JQa9PRPUo5Vu9ejX+7//+D5999hn8/f3v6+LDhg3DsGHDatzn6OiIvXv3Vtn28ccfIzQ0FPHx8WjWrFmt57WwsICnp+d9xUZERERkrCLvKONjWwXD6B7oAkEAbtzOx+3cYng4sOE8kb5sOZkAABjT0RsK+X2tz1UvLw5sgf1XbuNKSh5e23oenz/ahX9biQxI59/6yZMn4+DBgwgKCoK9vT1cXFyq/DSknJwcCIIAJyenux53/fp1eHt7IzAwEA8//DDi4+MbNC4iIiIiQ2J/KcNzsrFEW6+K1ag5a4pIf7IKSrHvSioAYHwXw5Xx3cnKQo5VkztCIRew91KqdnVAIjKMes2YkkJxcTHmz5+PKVOmwMHBodbjwsLCsGHDBrRq1QrJyclYsmQJevXqhQsXLsDe3r7G+5SUlKCk5N8pm7m5uQAq+lyVlZnu6gya2E35MZgSjrdhcbwNi+NtOBxrwzLF8S4tV+NUfBYAoLOPg0nFborjfafuAc64mJSLf66nYXiwh9Th3JOpj7cp4VjX39ZTt1CmEtHWyx7N3azrNIYNMd7N3azxQr8gfPDXDSzefhHdmjnA24ml0gBf34ZmLuOtS/yCqK+GUfdJEARs3boVY8aMqbavrKwM48ePR0JCAg4ePHjXxNR/ZWdnw8/PDytXrsSMGTNqPGbx4sVYsmRJte0//PADbGxs6nwtIiIiooYWmwesvmABWwsRb3dVgdUmhnMxS8DnV+RwtRKxsLNK6nCIzMKKc3LcKhAw1l+Fvl7SfjRVicCHF+SIyxfQ0lGNZ9uoIePfWKJ6KSwsxNSpU5GTk3PPHE6dZkzl5uZqT6SZTVQbXZJGdVFWVoZJkybh5s2b2L9/v87nd3JyQsuWLXHjxo1aj1mwYAHmzp2rvZ2bmwtfX18MHjxY74/HkMrKyrB3714MGjQICoVC6nDMHsfbsDjehsXxNhyOtWGZ4nh/digWuHAdPVo0wYgRHaUORyemON536lVcjq/ePYCMEiCkRz80NfLZFKY+3qaEY10/11PzcSviKCxkAuY/NACutpZ1ul9Djnf77gUYuTYC13KAbLe2eCSs9t7GjQVf34ZlLuN9r9zRneqUmHJ2dkZycjI8PDzg5ORUYyM4URQhCAJUKv19e6RJSl2/fh0HDhyAq6urzufIz89HdHQ0Hn300VqPsbKygpWVVbXtCoXCpF8IGubyOEwFx9uwON6GxfE2HI61YZnSeJ+MzwYAdA9yM5mY/8uUxvtOLgoFOvg44nR8No7fzIG/u2l8gWmq422KONa6+e1cCgCgX2sPeDrZ6nz/hhjvFp5OWDCsDRZtv4jlf15D39aeCHDTPTZzxNe3YZn6eOsSe50SU/v379c2Nj9w4ED9oqpBfn5+lZlMsbGxOHPmDFxcXODl5YUJEybg1KlT2LlzJ1QqFVJSKv5wubi4wNKyIps+YMAAjB07FrNnzwYAvPzyyxg5ciT8/PyQlJSERYsWQS6XY8qUKXqLm4iIiEgKKrWIEzcr+kuFsfG5JMIDXXE6PhsRMRmY2NVX6nCITFa5So2tpyuajE+QqOl5bR7t7oc/L6bgaHQG5m06g1+e6QE5a/qIGkydElN9+vSp8d/368SJE+jXr5/2tqac7rHHHsPixYuxfft2AEDHjh2r3O/AgQPo27cvACA6Ohrp6enafQkJCZgyZQoyMjLg7u6Onj174tixY3B3d9db3ERERERSuJKSi7zicthZWaCNl2nM1jE3PYLc8MnBaEREZ2grBohId4dvpON2XgmcbRTo18q4FhOQyQS8PzEEQ1cdwqn4bHx2KBrP9W0udVhEZkvnVfnOnTtX43ZBEKBUKtGsWbMay+Jq0rdvX9yt93pd+rLHxcVVuf3TTz/V6dpEREREpiYqNhMA0MXPmd/eS6SLnzMUcgHJOcWIyyhkiQ9RPW05mQAAGN2xKSwtZBJHU11TJ2ssHNkWr2w+h1V7r6FfKw9+IUDUQHROTHXs2PGu3wwpFApMnjwZn332GZRK5X0FR2QqVGoRkbGZOJkuwDU2E+HNPfiBgYiI9E6TmAplGZ9krC3l6NTMGVGxmYiIzmBiiqgecgrLsOdSKgDjK+O704QuPvjzYir+upyKuZvO4rdZDxhlEo3I1On8W7V161a0aNECn3/+Oc6cOYMzZ87g888/R6tWrfDDDz/gq6++wv79+/HGG280RLxERmf3hWT0XL4fj3x9At9el+ORr0+g5/L92H0hWerQiIjIjIiiiONxTEwZgx5BFQvyHI1Ov8eRRFSTneeTUFquRqsm9gj2Nt5ZSIIg4N1x7eFia4nLyblYs++a1CERmSWdE1Nvv/021qxZgxkzZqB9+/Zo3749ZsyYgVWrVuGDDz7Aww8/jI8++ghbt25tiHiJjMruC8l49vtTSM4prrI9JacYz35/iskpIiLSm5j0AqTnl8LSQoYOPo5Sh9OohQdWJKaOxWTUqfUEEVWlKeOb0MXH6Pu0udtb4e0x7QAA6w5G41R8lsQREZkfnRNT58+fh5+fX7Xtfn5+OH/+PICKcr/kZH4gJ/OmUotYsuMSano7qtm2ZMclqNR8w0pERPdPU8bXydcJVhZyiaNp3Do2c4JSIUN6fimu386XOhwikxKdlo9T8dmQywSM7uQtdTh1Mqy9F8Z09IZaBF7edBZFpSqpQyIyKzonplq3bo1ly5ahtLRUu62srAzLli1D69atAQCJiYlo0qSJ/qIkMkJRsZnVZkrdSQSQnFOs/SBBRER0P45X/n8SxjI+yVlZyNHVr+J5iIjOkDgaItPy66mK2VK9W7jBw950ehIvGdUOng5KxKQXYPnuK1KHQ2RWdE5MrV27Fjt37oSPjw8GDhyIgQMHwsfHBzt37sS6desAADExMXjuuef0HiyRMbmdV3tS6k6vbT2P93ZfwcGrt5FXXNbAURERkbmKrExMdWNiyiiEs88Ukc5UahG/nkoEAEzo4itxNLpxtFFg+YQOAIANR+Nw9AZ/94n0RedV+Xr06IHY2Fhs3LgR165VNH+bOHEipk6dCnt7ewDAo48+qt8oiYxQXb/hiU0vwCcHo/HJwWjIBKBdU0eE+rsgLNAV3fyd4WRj2cCREhGRqUvIKkRidhHkMgGdmzlLHQ7h38TUsZhMqNUiZFyNl+ieIqIzkJxTDAelBQa08ZA6HJ31aemOqWHN8ENkPF7ZfA5/vNQLDkqF1GERmTydElNlZWVo3bo1du7ciWeeeaahYiIyCaEBLvB0UCIlt+aZUwIqmiXOHdwSJ+KyEBmbgVuZRTiXkINzCTn48kgsBAFo1cQeYQGaRJUL3O2tDPtAiIjI6GlW42vX1BG2Vjp/r0gNoENTR9hZWSCnqAyXknPRrikb0hPdy5bKMr5RHb2hVJhmr7zXh7fBkevpiM8sxP92XML7E0OkDonI5On0zkahUKC4uG7lS0TmTi4T0LulGzadSKi2T/Od6VujgzG0nRce6tYMAJCUXYSo2ExExmYiKjYD0WkFuJKShyspefgm4iYAIMjdFqEBrpXJKhd4OVob6iEREZGRioqtWAWK/aWMh4VchtAAF+y/chsR0RlMTBHdQ15xGf6oXLF6fGcfiaOpP1srC6yYGILJn0fgl5MJGBLsiYFt2V+Z6H7o/JXbrFmzsHz5cnz55ZewsOA3dtR4Rafl47czSQAAB6UFcovLtfs8HZVYNLIthrbzqnIfbydrjOnUFGM6NQUApOWV4HhcJiJjMhAZm4krKXmITitAdFoBfoyKBwD4ulgjLMAVoQEu6B7gCl8Xa6NfVpeIiPQrKraiwXY3fyamjEl4oGtFYiomAzN7B0odDpFR++N8CorL1Ah0t0VHXyepw7kvoQEumNkrEJ8fisH//Xoee/yc4WLL9hxE9aVzZun48ePYt28f9uzZg/bt28PW1rbK/l9//VVvwREZq3KVGnM3nUVJuRq9Wrhh/ePdcCw6DXsOR2JwrzCEN/eAvA69JtztrTC8vReGt69IYGUXluJ4XBYiYzIQFZeJC4k5uJVZhFuZCdh8smJmlqeDEmGBLggNcEFYgAuC3O2YqCIiMmPp+SWITisAAHTzZ38pY6LpMxUZk4EylRoKuc7rChE1Gpr3shO6+JjFe9e5g1ri4NXbuJaajze2ncfaqZ3N4nERSUHnxJSTkxPGjx/fELEQmYxP/47G2VvZsFda4L0JHWAhlyEswAUZl0WEBbjUKSlVEycbSwxq2wSDKqcD5xWX4eTNLG3537mEbKTkFuO3M0na2VqutpbaJFVogCtae9qzASsRkRk5XrkaX2tPey6YYWTaejnA0VqBnKIynE/MYWN6olrEZxQiKi4TggCMrawcMHVKhRwrJ3XEmLX/YNf5FGw/m4TRHc3jsREZms6JqfXr1zdEHEQm42JSDtbsuw4AWDIquEF7QNkrFejbygN9W1WsWlJUqsLp+CxExmYiMjYDp+OzkVFQij8upOCPCykAKsoKKxJVFeV/wd4OsOA3uEREJiuqsvE5y/iMj0wmoHugC/68mIqI6AwmpohqoWl63rO5m1n1T23X1BHP92+BVX9dw5vbLqB7oCuaONRt5W4i+hebRBHpoKRchbk/n0WZSsSQ4CYG/8bH2lKOHs3d0KO5mzaecwk52hlVJ+MykVtcjr8u38Zfl28DAGwt5ejiXzGjKizABe19HGFlYZqroBARNUZRlTOmQtn43Cj1CHLTJqZm9WsudThERketFrWJqQldTLfpeW2e6xeEfVdScS4hB69uPocN07uxpI9IR/VKTG3evBmbNm1CfHw8SktLq+w7deqUXgIjMkar9l7H1dQ8uNpa4p2x7SX/T8fKQo5u/i7o5u+CWf0qel9dTMpFZGwGomIzERVbkag6dC0Nh66lVd5Hhs7NnCtmVQW6oJOvM6wtmagiIjJGucVluJScC4CJKWOl6TN14mYmSspV/PKH6D+i4jKRkFUEOysLDG7rKXU4eqeQy/DBxBCM+OgI/r6Whh+jbmFqWDOpwyIyKTonpj788EO8/vrrePzxx/Hbb79h+vTpiI6OxvHjxzFr1qyGiJHIKJy8mYnPD0UDAN4Z1x6udlYSR1SdhVyGEF8nhPg64aneQVCpRVxNyauSqMooKEVETAYiYjKAfYBCLiDExwmhARUN1bv6u8DOipMpiYiMwcmbWRBFwN/VhuUhRqqFhx3c7CyRnl+KM/HZCAt0lTokIqOypbLp+YMdvMz2y9AWTezx6pBWWPr7ZSz9/RJ6NndDM1cbqcMiMhk6f/r85JNP8Pnnn2PKlCnYsGEDXn31VQQGBmLhwoXIzMxsiBiJJFdYWo65m85CLQLjOjfFkGDT+LZHLhPQ1tsBbb0dMP2BAIiiiOi0/IoeVTEVfapSc0tw4mYWTtzMwicHoyGXCQj2dtA2Uw/1d4GjjULqh0JE1ChpyvjYX8p4CYKA7oGu2HkuGRExGUxMEd2hsLQcu84nAwDGm2EZ352eeCAAey6lIio2Ey//chY/PtW93gsiETU2Oiem4uPj0aNHDwCAtbU18vLyAACPPvoounfvjo8//li/ERIZgWV/XMHNjEJ4OSqxaGSw1OHUmyAIaO5hj+Ye9ng4zA+iKCI+s1CbqIqKy8CtzCKcS8jBuYQcfHE4FoIAtGpij+6BrtpZVW5GOFuMiMgcsb+UaegR5Iad55JxNDoDLw2UOhoi47H7QgoKSlXwc7VBVz/zXhxAJhPwwcQQDF19CFFxmfj6SCxm9g6UOiwik6BzYsrT0xOZmZnw8/NDs2bNcOzYMYSEhCA2NhaiKDZEjESSOnI9Hd9G3AQAvDehAxytzWf2kCAI8HO1hZ+rLSZ19QUAJGUXVTZTz0BkbCZi0gpwJSUPV1LysOFoHAAgyN0WoQGu6B5Ykagyp9VViIiMRXGZCucSsgEAYQGchWPMNH2mTsdnoahUZbblSkS62lxZxje+s4/kvVkNwdfFBm882BYLfj2P9/dcRZ9W7mjZxF7qsIiMns6Jqf79+2P79u3o1KkTpk+fjjlz5mDz5s04ceIExo0b1xAxEkkmp6gMr2w+CwB4tLsferVwlziihuftZI0xnZpiTOWKg2l5JZX9qSoSVVdS8hCdVoDotAL8GBUPAGjmYlPRTD3ABWEBrvB1sW4Ubz6IiBrS6fhslKlENHGwgq8LvwAwZv6uNvByVCI5pxgnb2ahZws3qUMiklxidlFFT1PA4CtZS+mhbr7482IKDl5Nw9xNZ7D1uQegkMukDovIqOmcmPr888+hVqsBALNmzYKrqyuOHj2KUaNG4emnn9Z7gERSWrLjIpJziuHvaoMFw1tLHY4k3O2tMKKDF0Z08AIAZBeWahupR8Zm4mJSDuIzCxGfWaj9VszLUVmZqKoo/wtyt2WiiohIR/+W8bnyb6iREwQB4UGu+PVUIo5GpzMxRQRg66kEiCIQHugKX5fG0whcEAQsH98Bg1cdwoXEXHy8/wbmDGopdVhERk3nxJRMJoNM9m/G96GHHsJDDz2k16CIjMGfF1Pw66lEyATgg0khsLHkSnUA4GRjicHBnhhc2QA+r7gMJ25maZNV5xKykZxTjN/OJOG3M0kAADc7y4r+VP4uCAt0Rasm9pCxGSQR0V1FxVXMNGB/KdMQHliRmNLMECFqzERRxJZTiQDMv+l5TZo4KPG/Me3wwo+n8fGBGxjYpgna+zhKHRaR0arXJ+3s7GxERUXh9u3b2tlTGtOmTdNLYERSSs8vwWu/ngcAPNU7CF38+KGgNvZKBfq18kC/Vh4AgKJSFU7HZ+FYZfnf6fhspOeXYtf5FOw6nwIAcLRWoJt/ZelfoAvaejnAQocpziq1iMjYTJxMF+Aam4nw5h5c9YSIzEqZSo1TN7MBAGFMTJkETZ+pcwk5yCsug73SfHpSEunqVHwWYtMLYGMpx7B2prGatb6NCvHGnxdT8Pu5ZMzddAY7nu8JpYL954hqonNiaseOHXj44YeRn58PBweHKlPLBUFgYopMniiKeH3reWQUlKK1pz3mDGohdUgmxdpSjh7N3dCjeUUZQ0m5CucSchAZU9Gj6uTNLOQUleGvy6n463IqAMDWUo4umkRVgAs6+DjB0qLmRNXuC8lYsuMSknOKAcjx7fUTlasltsXQdl6GephERA3qQmIOispUcLJRoLm7ndThUB34ONugmYsN4jMLcTwuE/1bN5E6JCLJaNo7DGvnBVurxlt18L/R7RAZk4nrt/PxwZ6reH1EW6lDIjJKOv+VmDdvHp544gm88847sLFpPLXC1HhsPZ2IPy+mQiEX8MGkEFhZ8JuN+2FlIUc3fxd083fBbFTMAriYlIvImIyK8r+4TOQVl+PQtTQcupYGAFAqZOjk64ywylX/OjdzhlIhx+4LyXj2+1P47/qfKTnFePb7U1j3SGcmp4jILGj6S3Xzd2HpswnpEeSK+MxCRERnMDFFjVZxmQo7zyYDAMZ3aTxNz2viYmuJ5ePbY8Y3J/DlkVgMbNMEYYFcZZXov3ROTCUmJuKFF15gUorMUnJOERZtvwgAeHFACwR7sxZc3xRyGTr6OqGjrxOe7hMElVrElZTcimbqMRWJqsyCUkTEZGj7dCjkAjo0dcSV1LxqSSkAEAEIAJbsuIRBbT1Z1kdEJk+TmGIZn2kJD3LFT8dv4Wg0+0xR47XnUirySsrR1Mka3QOYhBnQpgkmdfXBphMJeHnzWfzxYm/YNeJZZEQ10fk3YsiQIThx4gQCAwMbIh4iyYiiiFc3n0NecTlCfJ3wTJ8gqUNqFOQyAcHejgj2dsT0BwIgiiJu3M5HZOWqf5ExGbidV4KT8dl3PY8IIDmnGFGxmdo+H0REpkitFnE8TrMiHxNTpiS8cibEpeRcZBeWwsnGUuKIiAxPU8Y3vnNTzvis9OaDbfHPjQzcyizC279fxrvj2ksdEpFRqVNiavv27dp/jxgxAq+88gouXbqE9u3bQ6Go2thx1KhR+o2QyEC+j4zH4evpsLKQ4YOJITo14yb9EQQBLZrYo0UTezzS3Q+iKOJmRiE+OxSNH6Nu3fP+t/OKDRAlEVHDuZqah9zicthaytHWy0HqcEgHHg5KNPeww43b+TgWk4mhjbTpMzVeKTnFOHK9ojXDuM6NbzW+2tgrFXh/YgdM/SISP0bFY3BwE+3CQURUx8TUmDFjqm176623qm0TBAEqleq+gyIytLj0Arzz+2UAwPyhrdHcg41mjYUgCPB3s8WokKZ1Skx52CsNEBURUcPRlPF19nPmlyQmKDzQtTIxlcHEFDU6W08nQi0C3fyd4e9mK3U4RqVHkBumP+CP9f/EYf7mc9gzpzdnVRJVqtO7HbVaXacfJqXIFKnUIl7+5SyKylQID3TF4z38pQ6JahAa4AIvRyXuNiHcxlKOEF/2BSMi08b+UqatR2U5+dHodIkjITIsURSx5ZSmjI+zpWoyf2hrBLrb4nZeibavLRHVMTFFZM6+OByDEzezYGdlgfcndmAtvJGSywQsGlmxxG5tz1BhqQoTP41ATFq+4QIjItIjURQRFffvinxkejQrbl1LzUdaXonE0RAZzrmEHNy4nQ+lQobhHbhKck2UCjlWTuoIuUzAb2eSsOt8stQhERmFOiem9u/fj7Zt2yI3N7favpycHAQHB+PQoUN6DY6ooV1JycXKPdcAAAsfbAsfZ642acyGtvPCukc6w9Oxarmel6MSs/oFwcXWEheTcvHgR0ewpbLxJhGRKYnLKERaXgks5TKE+DpJHQ7Vg4utJdpU9gY7FsPV+ajx0DQ9HxLsCQel4h5HN14dfZ3wXN+KRZZe33qe/VGJoENiavXq1Zg5cyYcHKo34XR0dMTTTz+NVatW6TU4ooZUWq7G3J/PolSlxoDWHpjYlVOOTcHQdl44Mr8/vn+iK6a1UOH7J7riyPz+eGVIa/zxYi+EB7qisFSFeb+cxZyfzyC/pFzqkImI6iwqtiKR0dHXCUqFXOJoqL7+LedjYooah5JyFbafTQLAMr66eL5/C7T1ckBWYRle+/U8RFGUOiQiSdU5MXX27FkMHTq01v2DBw/GyZMn9RIUkSF8tP86LiXnwtlGgXfHt4cgsITPVMhlAsICXNDFTURYgAvkleWXTRyU+P7JMLw8uCXkMgFbTyfiwQ8P43xCjsQRExHVTWRlf6lQ9pcyaeGV5XycMUWNxb7Lt5FTVAZPByUeaO4mdThGz9JChpWTQ2Apl+Gvy7fxC2f6UyNX58RUamoqFIrap2RaWFggLS1NL0ERNbQzt7LxycFoAMDSMe25kpsZkcsEzO7fAj8/1R3ejkrEZRRi3Lp/8OXhGH4bRURG77imvxQTUyYtNNAFMgGITS9Ack6R1OEQNThNC4WxnZtqvzCku2vt6YA5g1oCAN7acQkJWYUSR0QknTonppo2bYoLFy7Uuv/cuXPw8mKTOzJ+xWUqzNt0Biq1iJEh3hjB5oxmqau/C3a92AtDgpugTCVi6e+X8cSG48jIZyNaIjJOSdlFuJVZBJkAdPFzljocug8OSgXaN61YJTaC5Xxk5tLySnDwWsUEBZbx6eap3oHo4ueM/JJyvPLLOajV/BKVGqc6J6aGDx+ON998E8XF1ZuzFRUVYdGiRXjwwQf1GhxRQ3hv91VEpxXAw94K/xsdLHU41ICcbCzx6SNd8L8x7WBpIcOBq2kYtuYwl/AmIqOkmS3Vrqkj7KwsJI6G7ld4UEU5E/tMkbn77UwiVGoRHX2d0NzDTupwTIpcJuCDiSGwVsgREZOBbyLipA6JSBJ1Tky98cYbyMzMRMuWLfHee+/ht99+w2+//Ybly5ejVatWyMzMxOuvv96QsRLdt4joDHz9TywAYPn4DnCysZQ4ImpogiDg0e5++G3WA2juYYfbeSV4+MtIrPjzKspVaqnDIyLSitL0l/JnGZ850DRAj4jOYCk5mTXNanzju3C2VH34u9niteGtAQDL/riC6LR8iSMiMrw6J6aaNGmCo0ePol27dliwYAHGjh2LsWPH4rXXXkO7du1w5MgRNGnSpCFjJbovecVlePmXswCAKaG+6NfaQ+KIyJDaeDlg++wH8FA3X4gi8PGBG5j8+THW8xOR0dAkpthfyjx09XeGQi4gsbJEk8gcXUzKwZWUPFjKZRjVwVvqcEzWI9390KuFG0rK1Zi36Sy/PKVGp86JKQDw8/PDrl27kJ6ejsjISBw7dgzp6enYtWsXAgICGipGIr1YuvMyErOL4OtijddHtJU6HJKAjaUFlo3vgI+mdIK9lQVO3szC8DWHsftCstShEVEjl5Ffguu3K74l78YZU2bBxtICHX2dAIAl5GS2NLOlBrVtAkeb2hfKorsTBAHvTegAe6UFztzKxmeHYqQOicigdEpMaTg7O6Nbt24IDQ2FszObc5Lx238lFT+fuAVBAFZMCGHvjkZuZIg3fn+hF0J8nZBbXI5nvj+FN7adR3GZSurQiKiROh6XBQBo2cQOLrYsMzcX4YGV5Xwx7DNF5qe0XI3fziQBACawjO++eTlaY8moiv63q/+6hktJuRJHRGQ49UpMEZmSrIJSzN9yHgAw44EAhFW+SaTGrZmrDTY/E46n+wQCAL4/Fo8xa//B9dQ8iSMjosZI0/ics6XMy50N0NlniszNwau3kVlQCnd7K/Rq4SZ1OGZhbKemGNy2YkXpuZvOoKScX5pS48DEFJm9N367gLS8EjT3sMPLQ1pJHQ4ZEYVchgXD2uDbJ0LhZmeJKyl5GPnxEfwUFc8PEERkUNrG5+wvZVY6NXOCpYUMaXklbGhMZmfLqYoyvjEdvWEh58dKfRAEAe+Maw9X24r3pav/ui51SEQGwb8gZNZ2nE3C7+eSIZcJWDkpBEqFXOqQyAj1bumOXS/2Qq8WbiguU+P/fj2P2T+eRm5xmdShEVEjkFdchotJOQCYmDI3SoUcXf0q2l5ERLOcj8xHZkEp9l+5DYCr8embm50V3h7bHgDw2d/ROHkzU+KIiBpenRJTnTt3RlZWRe+Dt956C4WFXMWKjN/t3GK8+dsFAMCsfs3RwcdJ2oDIqHnYK/HN9FD837DWsJAJ+P1cMkZ8eBin47OkDo2IzNyp+GyoRaCZiw28HK2lDof0rEdQRQuBo0xMkRnZfiYRZSoR7Zo6oLWng9ThmJ2h7TwxrlNTqEVg3qazKCwtlzokogZVp8TU5cuXUVBQAABYsmQJ8vP1MxX50KFDGDlyJLy9vSEIArZt21ZlvyiKWLhwIby8vGBtbY2BAwfi+vV7T2dcu3Yt/P39oVQqERYWhqioKL3ES6ZDFEXM33IO2YVlaNfUAc/3by51SGQCZDIBz/QJwi/PhMPH2Rq3Mosw8dMIrDsYDbWapX1E1DCiYisSFuwvZZ7CKxNTx2Iy+H8JmY0tpxIBAOM7c7ZUQ1k0KhhejkrEZRRi2R9XpA6HqEHVaWmyjh07Yvr06ejZsydEUcSKFStgZ2dX47ELFy6s88ULCgoQEhKCJ554AuPGjau2/7333sOHH36Ib775BgEBAXjzzTcxZMgQXLp0CUqlssZz/vzzz5g7dy4+/fRThIWFYfXq1RgyZAiuXr0KDw+POsdGpu3n47dw4GoaLC1kWDmpIxSseycddGrmjF0v9sJrv57HznPJWL77Co5Gp+ODSSHwsK/5bw8RUX1p+kuFsYzPLHXwcYKNpRxZhWW4kpKHtt6cXUKm7WpKHs4n5kAhFzC6Y1OpwzFbjtYKvDehAx79KgrfRtzE4Lae6Mkm82Sm6vRpfcOGDXB1dcXOnTshCAL++OMPbN26tdrPf2c83cuwYcOwdOlSjB07tto+URSxevVqvPHGGxg9ejQ6dOiAb7/9FklJSXe9zsqVKzFz5kxMnz4dbdu2xaeffgobGxt8/fXXOsVGputWZiH+t/MSAODlwS3Rsom9xBGRKXJQKvDRlE5YPr49lAoZDl9Px/A1h3HoWprUoRGRGSkuU+HsLfaXMmcKuUw7Gy4ihuV8ZPo0Tc/7tfKAi62lxNGYt14t3PFodz8AwCubz7L/KZmtOiWmWrVqhZ9++gnHjx+HKIrYt28fTp8+Xe3n1KlTegssNjYWKSkpGDhwoHabo6MjwsLCEBERUeN9SktLcfLkySr3kclkGDhwYK33IfOiVot4+ZezKChVIdTfBTN6BkodEpkwQRAwuVsz7JjdE62a2CM9vxTTvo7Cu39cRplKLXV4RGQGzt7KRqlKDXd7K/i52kgdDjUQTZ+piOh0iSMhuj/lKjW2nq4o45vApucGsWB4a/i72iA5pxhLtl+SOhyiBlGnUr47qdWG+TCWkpICAGjSpEmV7U2aNNHu+6/09HSoVKoa73PlSu11uSUlJSgpKdHezs3NBQCUlZWhrMx0s9Ka2E35Mehq/dGbiIzNhI2lHO+ObQu1qhxqlWGu3RjHW0qGHG9/FyU2Px2Kd3dfxQ9RCfjs7xhERKdj1cQOaObSOD5I8vVtOBxrw5J6vDWJim5+TigvN//mtlKPt1RC/ZwAAMdiMlFUXAILA7UYaKzjLYXGMtYHr6UhLa8EzjYKPBDoLNnjbSzjDQAKAVg+rh2mfBmFLacSMLC1Gwa2MWyLmsY03sbAXMZbl/h1TkwBQHR0NFavXo3Lly8DANq2bYsXX3wRQUFB9Tmd5N59910sWbKk2vY9e/bAxsb0P3Tu3btX6hAMIqUQeP+cHICAB31KceHYQVyQII7GMt7GwpDjHSYHlC0F/Bgtw7mEXIz48DAmB6rR2a3xNLPl69twONaGJdV4/3FJBkAGm4Ik7NqVKEkMUmhsr2+1CFjL5cgvKccXW3bDr+ZWrQ2msY23lMx9rDdcq/ib1d6hBH/t2S11OGY/3nfq5yXDviQZXvnlNBaEqGCnMHwMjWm8jYGpj3dhYWGdj9U5MfXnn39i1KhR6NixIx544AEAwD///IPg4GDs2LEDgwYN0vWUNfL09AQApKamwsvLS7s9NTUVHTt2rPE+bm5ukMvlSE1NrbI9NTVVe76aLFiwAHPnztXezs3Nha+vLwYPHgwHB9NtUFlWVoa9e/di0KBBUCgk+MtlQOUqNSZ9EYVyMRe9W7hi6aOdIQiCQWNoTONtDKQa7+EApmUXYd4v53EyPhvfXJej0KEp3hjeCjaW9cr1mwS+vg2HY21YUo53uUqNBScPAFDh8eE90crT/HsiNubX9+/Zp/HXlTTIPNtgeO8Ag1yzMY+3oTWGsc4pKsPLUQcBiJg7tgeCJWzk3xjG+78GlKsxbt0xXLudj78LvfHxQyEG+7zTGMdbSuYy3ppKtLrQ+VPU//3f/2HOnDlYtmxZte3z58/XW2IqICAAnp6e2LdvnzYRlZubi8jISDz77LM13sfS0hJdunTBvn37MGbMGAAVpYf79u3D7Nmza72WlZUVrKysqm1XKBQm/ULQMJfHcTfrDl3H+cRcOCgt8N6EjrC0lK4RY2MYb2MixXj7uyvw89PhWLPvOj4+cAO/nEzE6Vs5+HhqJ7T2NN1kdl3w9W04HGvDkmK8L6Vko7BUBUdrBdo2dYZMZtgvVKTUGF/fPZq7468raYi6mY3ZBn7sjXG8pWLOY737ZBLKVCJae9ojpJmLwb8Erok5j/d/KRTAyskdMWbtP9hz6TZ+v3gbYzsZts9XYxpvY2Dq461L7DoXuF++fBkzZsyotv2JJ57ApUu6NWPLz8/HmTNncObMGQAVDc/PnDmD+Ph4CIKAl156CUuXLsX27dtx/vx5TJs2Dd7e3tqkEwAMGDAAH3/8sfb23Llz8cUXX+Cbb77B5cuX8eyzz6KgoADTp0/X9aGSibiQmIMP910HALw1uh08HZUSR0SNgYVchnmDW2HjjDB42Fvhxu18jPr4H3x37CZEsfGU9hFR/UXFZgIAuvk3rqRUY9WjeUUD9OOxmSgt5wIaZHo0q/FN6OJjFEmpxqhdU0e8OKAFAGDhbxeRnFMkcURE+qFzYsrd3V2bSLrTmTNn4OGhWxO2EydOoFOnTujUqROAiqRSp06dsHDhQgDAq6++iueffx5PPfUUunXrhvz8fOzevRtK5b+Jh+joaKSn/7vCyeTJk7FixQosXLgQHTt2xJkzZ7B79+5qDdHJPBSXqTB30xmUq0UMa+eJ0R29pQ6JGpkezd3wx4u90K+VO0rL1Xhz2wU88/1JZBeWSh0aERm5yMrEVGiAi8SRkCG09LCHi60lispUOJuQLXU4RDqJTsvH6fhsyGUCRndsKnU4jdqzfYMQ4uuEvOJyvLr5HL8QJbOgcynfzJkz8dRTTyEmJgY9evQAUNFjavny5VX6NNVF37597/qLJAgC3nrrLbz11lu1HhMXF1dt2+zZs+9aukfmY9Xea7iWmg83O0ssHdOO396QJFztrPD1493w1ZFYLN99BX9eTMX5hMNYM6UTuvnzAycRVadWizhxU5OYcpU4GjIEmUxAeKArfj+fjIjoDP7/QCZly8mK2VJ9WrrD3b56CxQyHAu5DB9MDMGIDw/j8PV0bIyMxyPd/aQOi+i+6Dxj6s0338TChQvx0UcfoU+fPujTpw8+/vhjLF68GG+88UZDxEhUo+Nxmfj8cAwA4N1xHeBqx/8kSTqCIODJXoH49dkH4O9qg6ScYkz+LAIf7bsOlZrfZBFRVddv5yO7sAzWCrmkDYTJsMKDKpKQR6PT73EkkfFQqUX8eqpi1dAJXQzb04hq1tzDDvOHtgYAvLPrMm5mFEgcEdH90TkxJQgC5syZg4SEBOTk5CAnJwcJCQl48cUXOVuFDKagpBzzNp2FKFb8BzmoLUs1yTi093HEzhd6YWynplCLwAd7r+HhL48hNbdY6tCIyIhExWYAALr4OUMh1/ntGJkoTWLqVHw2istUEkdDVDdHo9ORklsMR2sFBrTRrXULNZzHe/gjPNAVhaUqzNt0ll+Ekkm7r3dC9vb2sLc3/6WNyfi8s+sy4jML0dTJGgtHtpU6HKIq7KwssGpyR3wwMQQ2lnIci8nEsDWHsf9KqtShEZGRYH+pxinQzRZNHKxQWq7GqZtZUodDVCeaMr5RId6wspBLHA1pyGQC3pvQAXZWFjhxMwtfVlaSEJkifkVHJufva2nYGBkPAHh/Qgc4KE13CU0yb+O7+GDn8z0R7O2AzIJSPLHhBN7acQkl5fyWnKgxE0URx+M0K/IxMdWYCEJFnykAiIjJkDgaonvLKy7D7ospACre15Bx8XWxwZsPtgEAfLDnGq6m5EkcEVH9MDFFJiWnsAzzN58DUDF9tUdzN4kjIrq7QHc7/PpcD0x/wB8A8PU/sRi/7ihi09kLgKixis8sRGpuCRRyAZ2aOUkdDhlYj6CK9y5Ho5mYIuO363wyisvUCHK3RYiPo9ThUA0mdfVF/9YeKFWpMXfTGZSWq6UOiUhnTEyRSVm84yJScosR4GarbfhHZOysLORYNDIYX07rCmcbBS4k5uLBDw9j6+kEqUMjIgloyvhCfJygVLAsprHR9Jk6eysbBSXlEkdDdHdbTmqanvuyn7CREgQBy8a1h5ONAheTcvHx/utSh0SkM50SU2VlZRgwYACuX+eLnQxv94VkbD2dCJkAfDApBNaWfDNPpmVg2yb448XeCAtwQUGpCnN+Pou5m87wgwlRIxPF/lKNmq+LDXycrVGu/rekk8gY3cwoQFRcJmQCMLZTU6nDobvwcFBi6Zh2AIC1B6Nx9la2tAER6UinxJRCocC5c+caKhaiWqXlleC1rRcAAM/0CULnZs4SR0RUP56OSvwwszvmDGwJmQD8eioRD350BBcSc6QOjYgMRNtfiompRqtH5aypCJbzkRHbcqpitlTPFu7wdFRKHA3dy4MdvDEyxBsqtYi5m85w5U8yKTqX8j3yyCP46quvGiIWohqJoojXtp5HZkEpWnva48WBLaQOiei+yGUCXhzYAj89FQ4vRyVi0wsw7pOj+PpILESRS/0SmbOUnGLczCiETAC6+PFLlsZKU87HBuhkrNRqUbsa3/jOnC1lKv43Ohge9laITivA+39elTocojqz0PUO5eXl+Prrr/HXX3+hS5cusLW1rbJ/5cqVeguOCKj4tmbvpVQo5AJWTe7IZWrJbIQGuOCPF3vh1c3nsOdSKt7aeQn/3EjH+xND4GJrKXV4RNQAoipnS7X1duCqso1YeGBFA/QLiTnIKSyDow1fC2RcImMzkZhdBHsrCwwJ9pQ6HKojJxtLLB/fAdM3HMfX/8RiUNsm6F65EiiRMdN5xtSFCxfQuXNn2Nvb49q1azh9+rT258yZMw0QIjVmidlFWLL9IgDgpYEt0cbLQeKIiPTLycYSnz3aBW+NDoalhQz7rtzGsDWHWN5BZKaOV/aX6ubPMr7GzNNRiUA3W6hFIDKWf+/J+Gw5VTFb6sEQLy7SYGL6tfbAlFBfiCLw8i9nkc9epmQCdJ4xdeDAgYaIg6gatVrEq5vPIq+kHJ2aOeHp3oFSh0TUIARBwLRwf3T1c8HsH08hJq0AU788huf7NccLA1rAQs4FVInMhabxeRj7SzV64UGuiEkvQERMBgZzRgoZkYKScuw6nwwAGN/ZR+JoqD5eH9EWh6+nIyGrCEt3XsKy8R2kDonorur9aefGjRv4888/UVRUBADsi0J6992xm/jnRgaUChlWTurID+dk9tp6O2Dn8z0xqasPRBH4cP8NTPniGJKyi6QOjYj0IKugFFdT8wBwxhQBPYIqyvk4Q5aMze4LKSgsVcHf1Ya98EyUnZUFVkwMgSAAPx2/hf1XUqUOieiudP6kn5GRgQEDBqBly5YYPnw4kpMrsukzZszAvHnz9B4gNU6x6QV494/LAIAFw9ogwM32HvcgMg82lhZ4b0II1jzUEXZWFjgel4Vhaw7jz4spUodGRPdJsxpfcw87uNpZSRwNSa17YEVy8kpKHjLySySOhuhfm7VNz30gCILE0VB9dQ90xRMPBAAA5m85j6yCUokjIqqdzompOXPmQKFQID4+HjY2NtrtkydPxu7du/UaHDVOKrWIeZvOoLhMjQeau+LR7n5Sh0RkcKM7NsXvL/REiI8jcorK8PR3J7Hwtwtc+pfIhGkSU5wtRQDgameF1p72AIBjMZkSR0NUISGrULta5FiuxmfyXhnSCs097JCWV4I3f7sgdThEtdI5MbVnzx4sX74cPj5V641btGiBmzdv6i0warw+OxSNU/HZsLeqmDkik/GbGmqc/Fxt8cszPbT91b6NuIkxa//Bjdt5EkdGRPXB/lL0X5rVsiJi0iWOhKjC1lOJAIAeQa7wcba5x9Fk7JQKOVZOCoFcJmDnuWTsOJskdUhENdI5MVVQUFBlppRGZmYmrKw4LZ3uz+XkXKzaew0AsHBkWzR1spY4IiJpWVrIsGB4G2yY3g2utpa4kpKHkR/9g03Hb7G3H5EJ+f/27juuqvr/A/jr3HvZspGhIKI4QLaKiuIqd2XmSlMrzW+21LQcfb8Nm45sm99safXVhportcxtojgRxMVwArL3utz7+f2B8MtcoHDOHa/n4+GjgAu+eHk8Xt73fD6npKIKCWmFAIBIDqbomqjW1YOp/dxnigyAEKL2bnzc9Nx0hHg74bk+/gCAV9cnILOwXOFERDeq92AqOjoa3333Xe3bkiRBr9dj4cKF6NOnT4OGI/NSWaXHjJ/joNUJ3B/ggREd+Q8iUY3e7dyxZVo0evi7oUyrw6w1JzD1x+MoLNcqHY2I6uDoxTzo9ALezjZoxhdd6JourVyhkoCUrBJc5Q+LpLAjF/JwPqcUdpZqDArmnSJNyQt9/RHU3AH5pVrMWRvPFzfJ4NR7MLVw4UIsW7YMgwYNQmVlJWbNmoWgoCDs2bMHCxYsaIyMZCY+3n4Wp9IL4WJnifceCeZmi0T/4O5gje8mRmLWwHZQqyRsjEvDA5/sQ9ylfKWjEdEd1Czji+T+UvQ3jjYW6NDMEQDvzkfKq9n0fFCwF2wtNQqnoYZkoa6+y7mlRoUdpzPx8+FLSkciuk69B1NBQUE4e/YsevTogaFDh6KkpASPPPIIjh07htatWzdGRjIDRy/mYemuZADAOw8Hoak9l4US3YxKJeHZ3v74+eluaO5kg4u5pRi+dD+W7UmGXs9Xv4gM1cGawRSX8dE//P9yPu4zRcop1+rw24nqu61zGZ9pauthj5f6twUAvLkxEZdySxVORPT/7moU7ujoiH//+98NnYXMVFmlDi/9HAe9AB4Oa4ZBwV5KRyIyeB19nbF5WjReWRuP3+LT8e7m09iXlIPFI0M52CUyMBVVOhy/dmUjB1P0T11bu+KLPSm1d0IjUsLvJzNQVFEFb2cb3qDBhE3q0QrbEq/i0Pk8vPRLHFZN7sobTZFBqPcVUwCQl5eH999/H5MmTcKkSZOwePFi5ObyNrd0dxZsPY2U7BJ4Olhj3kNBSschMhqONhb4bGw43h0WDCuNCnvOZmHQx3ux91yW0tGI6G9OXC5AZZUebk0s4edmp3QcMjCdW7pAo5JwKbeMVzCQYmqW8T0S4c1BhQlTqyS8PzIUtpZqHEzNxbf7zysdiQjAXQym9uzZg5YtW+KTTz5BXl4e8vLy8Mknn8DPzw979uxpjIxkwvYnZWP5tRPighEhcLS1UDYQkZGRJAlju7TAxhd6oK1HE2QXV2DCN7FYsPU0tDq90vGICH/bX8rPhfsn0g2aWGkQ4s19pkg5GQXl+Cupeinp8IjmCqehxubraodXBgcAABZuPY2kzGKFExHdxWDqueeew+jRo5Gamoq1a9di7dq1SElJwaOPPornnnuuMTKSiSos1+Ll1ScAAI91aYFebZsqnIjIeLX1sMeG53vgsS4tIASwdFcyRn0Rw1ffiQzAQW58TncQ1doNALicjxSx9thl6EX1OcrXlVd1moPHurRAz7ZNUVGlx8yfj6OKL2aSwuo9mEpKSsLMmTOhVqtr36dWqzFjxgwkJSU1aDgybW9tTMSV/DK0cLGtndoT0d2ztlDjnWHBWPpYBOytNTh2MR+DP9lbu5kpEcmvSqfHkfM1V0y5KpyGDNXfN0DnbdxJTkIIrLm2jG94R14tZS4kScLC4SFwsNYg7nIBPr92EyoipdR7MBUREYFTp07d8P5Tp04hNDS0QUKR6fsz8Sp+OXIZkgS8PzIUdla8JS1RQxkU7IXNU6MR0cIJReVVeG7lUcxdewJllTqloxGZnVPpRSip1MHeWoN2nvZKxyEDFeHrDEu1ClcLK5CaXaJ0HDIjcZcLkJxVAmsLFQbzBkRmxdPRGm8Ord7f95Pt55BwpUDhRGTO6jQNOHHiRO3/T506FdOmTUNSUhK6du0KADhw4ACWLFmC+fPnN05KMim5JZWYszYeADA5uhXvUETUCHxcbPHT093w0Z9n8fmuZKyKvYTD5/Pw2dgI/nBMJKODqdVLszq3dIGaGwrTLVhbqBHh64QDKbnYn5yDVk2bKB2JzMTqI5cAAAM7eMLemnu9mpuhYc3w+8kMbEnIwMyf47Dhhe6w0qjv/IlEDaxOg6mwsDBIknTdpcWzZs264XFjx47F6NGjGy4dmRwhBP6zLh7ZxRVo69EEM/q1VToSkcmyUKvw8oD2iGrthuk/Hce5zGI89Nk+vPZgIMZGtuAmzEQy+PvG50S3062VGw6k5CImJQfjuvoqHYfMQEWVDhvjqpf7D+/orXAaUoIkSXj74SAcOp+LM1eL8MG2s5g7iFuskPzqNJhKTU1t7BxkJjbEpWFzfAY0KgkfjAqDtQUn8kSNrbu/G7ZMi8ZLv8Rh15ks/PvXBOw7l435j/BOmESNSQiBQ+c5mKK6ifJ3xYd/AgeSc6DXC6h4hR01su2nMlFQpoWXo3XtBvxkflybWOG9R0Iw+bvDWLYnBf0CPNCJN+sgmdVpMOXry1dt6N5lFJTj1XUJAIAX+rZBUHNHhRMRmQ+3Jlb45vHO+HpfKhb+fhpbEjJw4nIBPhkTho6+fPJB1BiSMouRV6qFtYUKQc34bx7dXqi3E2ws1MgpqcTZzCK093RQOhKZuNXXNj0fFt6cS43NXL9ADwyP8Maao5cx85c4bJ4azT2ASVZ3dbSlpaVh3759yMzMhF5//a0lp06d2iDByLQIITB7zQkUllchxNsRz/ZprXQkIrOjUkmY3LMVurRywQurjuFCTilGfXEAM/q1xZRerfmklKiBHby2jC+ihTMsNfW+3wyZGUuNCp1aOmPvuWzEJOdwMEWNKrOoHLvPZgHgMj6q9vpDgYhJzsaFnFK8t+UU3n44WOlIZEbqPZhavnw5nn76aVhaWsLV1fW6PUokSeJgim5qVewl7D6bBUuNCh+MCoWFmk/QiZQS4u2ETS/0wH/WJWD98TQs+v0M/krKxoejw+DhYK10PCKTwf2lqL6iWrth77ls7E/OwZPd/ZSOQyZs/bE06PQC4S2c0Jqb7RMAB2sLLBoZise+OogfDlxE/0BP9GzbVOlYZCbqPR149dVX8dprr6GgoADnz59Hampq7a+UlJTGyEhG7mJOKd7+LREAMGtAO/i7845gREqzt7bAR6PDsGhECGws1NifnINBH+/FzjOZSkcjMglCiP8fTHGvDqqjbq1dAQAHU3Kg04s7PJro7gghsOZo9TK+4RG8Wor+X3d/NzzerXobn1mrT6CgVKtwIjIX9R5MlZaW4tFHH4VKxSte6M50eoGXfolDaaUOkX4umMhX/4gMhiRJGNnJB5um9kCAlwNySyrx5LeH8PamRFRW6e/8BYjoli7nlSGjsBwalYTwFs5KxyEjEdTMAfZWGhSWVyExrVDpOGSiTqYV4nRGESw1KjwY0kzpOGRg5gwKgJ+bHTIKy/HGxpNKxyEzUe/p0qRJk/DLL780RhYyQd/sS0Xs+VzYWaqxeGQo7zBDZIBaN22CX5+NwhNRLQEAX+1LxYj/7sf57BIA1QPmg6m5OJIt4WBqLl/FJ6qDmv2lQrwdYWPJO9BS3WjUqtqln/uTsxVOQ6aqZtPzfoEevDsv3cDGUo3Fo0KhkoBfj13B5hNpfB5Ija7ee0y99957eOCBB7B161YEBwfDwuL6k9kHH3zQYOHIuJ29WoRFf5wBAPzngUD4uNgqnIiIbsXaQo03HuqAqNaumLXmBE5cLsCQT/ZiVGcfbE3IQHpBOQA1vjt3GF6O1nj9wUAMDPJSOjaRwYpNzQEARPq5KpyEjE231q7YfjoTMSk5eLoXbxZDDauySo8NcWkAgBHc9JxuIaKFM6b0ao3PdyXj+VXHUD2L4vNAajx3NZj6/fff0a5dOwC4YfNzIgDQ6vSY8fNxVFbp0btdUzza2UfpSERUB/07eCLY2xHTfjyO2NRcfPvX+Rsek1FQjmd+OIql4yL4pIToFg6dzwMARPpxGR/VT1RrNwDVm+drdXreMIYa1M4zmcgtqURTeytE+7spHYcMWIBX9b7A/7xAis8DqTHUezC1ePFifPPNN3jiiScaIQ6Zis92JCHhSiEcbSywYHgIh5ZERsTL0QY/TOqCiLe2obii6oaPCwASgHkbE9Ev0BNqLtEluk5mYTlSs0sgSUBHX258TvXT3tMezrYWyCvV4sTlAnT05XCTGs6aa8v4hoU3h4ZDT7oFnV7g3c2nb/oxPg+kxlDvs5GVlRW6d+/eGFnIRJy4nI/PdiYBAN56OIi3nycyQkcu5N10KFVDAEgvKK+96xgR/b/Y89V/LwI8HeBow/1bqH5UKgldW1UvAY3hPlPUgHKKK7DjdPXdd3k3Prqd2NTca9s43ByfB1JDq/dgatq0afj0008bIwuZgHKtDjN+joNOLzAkxAsPhfJOH0TGKLPo1k9GrntcYd0eR2RODl17ol6ziTVRfXVrfW0wlZKjcBIyJRvi0lClFwhu7oh2nvZKxyEDVufngXV8HNGd1HspX2xsLHbs2IFNmzahQ4cON2x+vnbt2gYLR8Zn8R9nkJRZjKb2Vnh7aJDScYjoLrnb1+1Kx092nEMTaw36tnfnkl2iaw5yMEX3KOraYOrw+TyUa3WwtuCdHenerTlavYyPm57TndT1eWBdH0d0J/UeTDk5OeGRRx5pjCxk5A6m5OCrfakAgPmPBMPZzlLhRER0tyL9XODlaI2MgnLc7qbAyVklmLTiMAK9HPBCX38M6OAJFfcaIDOWX1qJM1eLAACdW3IwRXenddMmaGpvhayiChy7mF97BRXR3TqdUYiEK4WwUEtc0UB3VJfngV6O1nwBhhpMvQdT3377bWPkICNXXFGFl1bHQQhgVCdv3BfgoXQkIroHapWE1x8MxDM/HIUEXPekpGbsNH94CFKyi/F9zAUkphfimf8dRVuPJniujz8eCGnGzTDJLB0+nwchgFZN7dDU3krpOGSkJElCt1au2BCXhpiUHA6m6J7VbHret707XzymO7rd88Aarw4J5HM9ajC8FQM1iHd+O4VLuWVo7mSDVx8IVDoOETWAgUFeWDouAp6O11+m7elojaXjIjC6sw/mDgrAX7P74oW+/rC30uDs1WJM+/E4+n2wG6uPXIZWp1coPZEyDl3b+DySV0vRPapZzscN0OleVen0+PVYGgBgREcfhdOQsbjV88CaUdSF3FL5Q5HJqvcVU35+frfdRyQlJeWeApHx2XkmE6tiLwIAFo0Mgb0170BEZCoGBnmhX6AnYpIy8cfeg+gf3QXd/N2ve4XM2c4SM/u3w1PRrbBi/3l881cqUrJL8NIvcfh4+1k829sfwyO8YanhayFk+ri/FDWUmqukjl/KR2llFWwt6/20nQgAsOdcFrKLK+BqZ4ne7ZoqHYeMyM2eB14pqMDsNfH4cNtZ9GnfFO09HZSOSSag3j8lTJ8+HdOmTav99eyzz6Jbt24oKCjAv/71rwYP2LJlS0iSdMOv55577qaPX758+Q2PtbbmpmyNJb+0ErNXnwAAPNm9JaJauymciIgamloloYufCzq6CXTxc7nlZduONhaYel8b7JvdF7MHtoernSUu5ZZh7tp49Fq0E9/FnEe5VidzeiL5lFRUIeFKAQAOpujetXCxRXMnG2h1AofP5ykdh4zYmiNXAABDw5rDQs0Xiah+/vk8cFQnH9wf4IFKnR4v/hSHyipeHU/3rt4vvUybNu2m71+yZAkOHz58z4H+6dChQ9Dp/v8HmYSEBPTr1w8jR4685ec4ODjgzJkztW/zTlGN57X1J5FZVIFWTe0we2B7peMQkQFoYqXBM71b4/EoX6w8eBHL9qQgvaAcr60/iU93JOHpnq0wtksLvvpPJufYxXxU6QWaO9nA29lW6Thk5CRJQtdWrlhz9DJiUnLQsy2vdKH6yy+txLbEqwCA4R2bK5yGTIEkSXjvkWAc+TAXp9IL8cn2c3hpQDulY5GRa7CR+aBBg7BmzZqG+nK1mjZtCk9Pz9pfmzZtQuvWrdGrV69bfo4kSdd9jocHN+JuDL+dSMeGuDSoVRI+GBXGWxkT0XVsLTV4KroV9szqg7eGdkAzR2tkFVXg7d9OIXrBTny+KwnFFVVKxyRqMLHX9pfq3NJZ4SRkKmr2mdqfnKNwEjJWG0+ko1KnR3tPe3Ro5qh0HDIRTe2t8M6wYADA57uScOwir+qke9Ngg6nVq1fDxaVxL1uvrKzEDz/8gIkTJ972Kqji4mL4+vrCx8cHQ4cOxcmTJxs1lznKLCrHf9bFAwCe7d0aYT5OygYiIoNlbaHG+G4tsevlPpj/SDBauNgip6QSC7eeQff5O/Dxn+dQUKZVOibRPYtNrR4eRPrxDmrUMGr2mYq/nI/Ccp4nqf5q7sY3oqO3wknI1AwO9sLQsGbQC2Dmz3Eoq+R2DXT36r2OIjw8/LqhkBACGRkZyMrKwueff96g4f5p3bp1yM/PxxNPPHHLx7Rr1w7ffPMNQkJCUFBQgPfffx9RUVE4efIkvL1vfkKuqKhARUVF7duFhYUAAK1WC63WeJ8E1GRv6O9BCIE5q08gr1SLAE97TIluadQ9NZTG6ptujn3LqyH6lgAMD/fC0BAPbDyRgaW7U5CaU4oP/zyLL/emYHxXHzzRzRcuZn4bax7b8mqoviuq9Dh2MR8AEOHjwD+/W+DxXT9N7TTwdbHFhdxSxCRloW89N65m3/IxxK6Ts0pw/FI+1CoJQ4LcDSrbvTLEvk3Zrfp+dXA7HEjOQUp2Cd7bnIhXh3Brl4ZgKsd3ffJLQghRny8+b968695WqVRo2rQpevfujfbtG/dAHDBgACwtLbFx48Y6f45Wq0VAQADGjBmDt95666aPeeONN274vgBg5cqVsLXlHhH/dCBTwqpkNdSSwEvBOjSzUzoRERkjvQCO50j4/bIKGWXVL3hYqgR6eAr08dLDwbznU2RkUouAjxI0aKIReLuTDtzekhrKT8kq7M9UobeXHsNacpNhqruNF1T4M02FIGc9JrfnsUON41SehP+ert7S5blAHdo61mu8QCastLQUY8eORUFBARwcbn/3xnoPppRy4cIFtGrVCmvXrsXQoUPr9bkjR46ERqPBqlWrbvrxm10x5ePjg+zs7DsWaMi0Wi22bduGfv36wcLCokG+5pX8Mgz5bD9KKnR4qV8bPN3Tr0G+rilojL7p1ti3vBqzb71eYNupTHy+OwWJ6UUAACuNCqM7eWNydEt4OpjXnVV5bMurofr+Yk8q3t92Dv0D3bFkTFjDBTQxPL7rb9OJdLz4SzwCPO2x4blu9fpc9i0fQ+tapxfotXgPrhZW4NNHQzGwg2ntuWtofZu6O/X96oZE/HjoMpo5WmPT891gb80/k3thKsd3YWEh3Nzc6jSYMppbIn377bdwd3fHkCFD6vV5Op0O8fHxGDx48C0fY2VlBSsrqxveb2FhYdQHQo2G+j70eoG5vyaipEKHjr7OeKZPm1veNt6cmcpxYyzYt7waq+8HwrwxJLQ5dp7JxCfbk3D8Uj6+O3ARPx66jJGdvPFM79Zmd5czHtvyute+D19bxte1lRv/3OqAx3fd9WjrASAepzKKUFwp4HwXy53Zt3wMpeuYs1m4WlgBRxsL9A/ygoXGNG9SZCh9m4tb9f3qAx2wPzkXF3NLMf/3c1g4IlSBdKbH2I/v+mSv8+bnKpUKarX6tr80msaZc+n1enz77bd4/PHHb/g9JkyYgLlz59a+/eabb+KPP/5ASkoKjh49inHjxuHChQt46qmnGiWbOVkRcx4xKTmwsVBj8chQDqWIqEFJkoS+7T3w67NR+H5SJCJbuqBSp8f/Dl5E70W7MGt1HM5nlygdk+gGOr3AkfPVdySK9GvcG8GQ+Wlqb4W2Hk0AAAdSeHc+qps1R6s3PR8a1gxWJjqUIsNhZ6XB+yNDIUnAz4cv48/Eq0pHIiNT50nSr7/+esuPxcTE4JNPPoFe3zhrl//8809cvHgREydOvOFjFy9ehEr1//O1vLw8TJ48GRkZGXB2dkbHjh2xf/9+BAYGNko2c5GcVYz5W04DAF4Z3B4t3bixFBE1DkmSEN2mKaLbNMWBlBx8uuMc/krKwc+HL2P1kcsYGtYcz/Xxh797E6WjEgEATqUXoqiiCvZWGgR4Ge8WAGS4urVyxdmrxYhJycGgYC+l45CBKyzXYmtCBgBgeATvxkfyiPRzweToVli2JwVz1sbjD19ns7+hDdVdnQdTN9vX6cyZM5gzZw42btyIxx57DG+++WaDhqvRv39/3GorrF27dl339ocffogPP/ywUXKYqyqdHjN+jkNFlR7Rbdwwrquv0pGIyEx0beWKrq1cceRCHj7dcQ67zmTh12NXsO74FQwO9sILff3R3pODAFJWbGouAKBjS2deTUyNoltrN6yIuYD9ybxiiu5s84l0VFTp4e/eBCHejkrHITMyo19b7DydiXOZxXh1XQI+GxsOiXcDoTqo81K+v0tLS8PkyZMRHByMqqoqHD9+HCtWrICvLwcWpuiLPSmIu5QPe2sNFo4I4cmFiGTX0dcZy5+MxIbnu6N/oAeEAH47kY6BH+3Fv747jPjLBUpHJDNWM5jiMj5qLF1buUCSgKTMYmQWlSsdhwxczTK+ER29+bydZGVtocYHo8KgUUn4LT4dG+LSlI5ERqJeg6mCggLMnj0b/v7+OHnyJLZv346NGzciKCiosfKRwhLTCvHRn2cBAPMe6gAvRxuFExGROQvxdsKyCZ2wZVo0hoR4QZKAPxKv4sHP9uGJb2Nx5EKe0hHJzAghcOh89WCqCwdT1EicbC0ReG2ZaAyvmqLbOJ9dgkPn86CSgGHhzZWOQ2Yo2NsRz/f1BwC8tv4krhZymE53VufB1MKFC9GqVSts2rQJq1atwv79+xEdHd2Y2UhhFVU6zPj5OLQ6gQEdPPiPGxEZjAAvBywZG4FtL/bEsPDmUEnArjNZGL50Px776gA3CCbZJGeVIKekElYaFYKbOykdh0xYt1auALgBOt3e2mtXS0W3aQoPB2uF05C5eq6PP4KbO6KgTItZq0/cclseohp13mNqzpw5sLGxgb+/P1asWIEVK1bc9HFr165tsHCkrI/+PIfTGUVwtbPEO8OCeSkwERkcf3d7fDg6DNPua4PPdyVh7dEr+CspB38l5SCypQteuM8fPfzdeP6iRlOzjC+8hRMsNXe1QwJRnUT5u+KrfancZ4puSa8XWHP0CgBgeEduek7KsVCr8MGoUAz5dB92n83CqthLGNulhdKxyIDVeTA1YcIEPrE3I0cu5OKL3ckAgHeGBcOtiZXCiYiIbq2lmx0WjgjFC33b4L+7k/HL4cuIPZ+L8V/HIszHCVPv80efdu78d4waXGxq9ZAg0s9V4SRk6jq3dIFaJeFCTimu5JehuRO3V6DrHUjNwZX8Mthba9A/0EPpOGTm2njYY9aAdnj7t1N4+7dE9PB3QwtXW6VjkYGq82Bq+fLljRiDDElpZRVm/hwHvQAeCW+OgUGeSkciIqoTHxdbvDMsuHZAtSr2Io5fysfE5YcR1NwBz/dpg/6BHlDxzmnUQA6dr97XLLIl95eixmVvbYHg5o44fikfMck5GMErYugf1hypvlrqgZBmsLZQK5yGCJjY3Q9/JF5FbGouXvolDj/+qyufg9FN8ZpzusH8LadxPqcUXo7WeP2hDkrHISKqN09Ha7zxUAfsnd0HT/dsBVtLNRKuFGLKD0cw6OO92BiXBp2e+x3QvbmcV33likYlIcLXSek4ZAaiWldfmbc/OVvhJGRoSiqqsCUhHQAwoiP3hSXDoFJJWDwyFHaWasSez8U3f6UqHYkMFAdTdJ1957LxXcwFAMDCESFwtLFQOBER0d1zt7fG3MEB2De7L57v4w97Kw3OXC3CC6uOod+Hu7HmyGVU6fRKxyQjVbO/VFBzR9ha1vkidKK71u3aYOpAcg43E6brbEnIQGmlDn5udoho4ax0HKJaPi62+M8DgQCAhb+fwbmrRQonIkPEwRTVKijT4uXVcQCA8V19Ed2mqcKJiIgahoudJV4a0A775vTFi/e3haONBVKySjDzlzj0XbwbP8ZeRGUVB1RUPzWDqS5+XMZH8ujk6wILtYS0gnJcyClVOg4ZkNVHLgEAhkc0536KZHAe7eyD3u2aorJKjxk/x0HLFwXpHziYolpvbkxEekE5WrraYu7g9krHISJqcI42Fph2fxvsm90Hswa2g4udJS7mlmLO2nj0eX8Xvo85j3KtTumYZCRiz1cPpjpzfymSiY2lGuHXrobh3fmoxqXcUhxIyYUkAcMiuPcYGR5JkrBgePVqnPgrBViyM0npSGRgOJgiAMAfJzOw5uhlqCRg8ahQLkkgIpNmb22BZ3v7Y9/sPvjPkAA0tbfClfwyvLr+JHot2omv96WirJIDKrq1rKIKpGSVQJI4mCJ5dWtVvZwvJoWDKar267HqTc+jWrvybo1ksDwcrPHm0Or9iz/bkYT4ywUKJyJDwsEUIae4Aq/8Gg8A+FfP1ujoyyfYRGQebC01eCq6FfbO6oM3h3ZAM0drXC2swFubEhG9cAf+uzsZxRVVSsckA3To2tVS7Tzs4WjL/RhJPjUboMdwnykCIITAmqOXAQDDebUUGbiHQpthSLAXqvQCM34+zqvUqRYHU2ZOCIF//5qA7OJKtPOwx4v92igdiYhIdtYWakzo1hK7Xu6D9x4Jho+LDbKLKzF/y2n0WLADn2w/h4IyrdIxyYDU7C8Vyf2lSGZhLZxgpVEhu7gCSZnFSschhR2+kIcLOaWws1RjYJCn0nGIbkuSJLz1cBDcmljhXGYxFv9xRulIZCA4mDJz645fwdaTGdCoJCweFQorjVrpSEREirHUqDAmsgV2zOyN90eGopWbHfJLtfhg21n0WLADi/84g7ySSqVjkgHgYIqUYqVR1y4f5T5TtPpw9dVSg4O9uBUHGQUXO0ssGB4MAPhqXyoOclkygYMps5ZeUIbX1p8EAEy7rw2CmjsqnIiIyDBYqFUY0dEb22b0wsePhqGtRxMUlVfh0x1J6LFgB97bcgrZxRVKxySFFJRpcSqjEAAQyf2lSAHd/racj8xXWaUOv8WnAwCGd+QyPjIe9wV4YFQnbwgBvLQ6DiXcNsHscTBlpoQQmLX6BIrKqxDq44RnerdWOhIRkcFRqyQMDWuOrdN6YuljEQj0ckBJpQ5f7E5BjwU78ObGRFwtLFc6JsnsyIVcCAH4udnB3cFa6ThkhmoHUyk50Ou5z5S5+iMxA8UVVfBxseGQnIzOqw8EormTDS7lluGdzaeUjkMK42DKTP1w8CL2nsuGlUaFxSNDoVHzUCAiuhWVSsKgYC/8NrUHvn68E0J9nFCu1eObv1IRvXAnXl2XgCv5ZUrHJJnEpuYBADq3dFY4CZmrkOaOaGKlQUGZFonphUrHIYWsPlK9jO+RcG+oVJLCaYjqx97aAotGhgAAVh68iF1nMhVOREriNMIMXcgpwbu/VU+lZw9sD3/3JgonIiIyDpIk4b4AD6x7NgrfTYxE55bOqKzS4/sDF9B70U7MWXMCF3JKlI5JjSw2tXr5VKSfq8JJyFxp1KrawegB7s9iltILyrAvKRsA78ZHxiuqtRueiGoJAJi95gQKSnmjGXPFwZSZ0ekFZv4chzKtDt1audaeCIiIqO4kSULPtk3x89PdsGpyV0S1doVWJ/DjoUvou3g3Zvx0HMlZvFuWKSqr1OHE5QIAQBdufE4KimrtBoAboJurtUevQIjqGzC0cLVVOg7RXZs9sD1audnhamEFXtuQoHQcUggHU2bmq70pOHwhD02sNFg0MoSX/RIR3QNJktCttStWTu6KNc90Q6+2TaHTC6w9dgX3f7Abz688ijMZRUrHpAZ07GIeqvQCXo7W8Ha2UToOmbGafaZiU3NRpdMrnIbkJITAmqPVy/hG8GopMnI2lmosHhUKlQSsP56Gzdc29CfzwsGUGTmTUYTFf5wFALz2QCC8nfnqChFRQ+no64IVEyOx/rnu6BfoASGATSfSMeCjPXj6+8NIuFKgdERqALHncwEAnVu6QJL44g4pJ8DLAY42FiiuqEI8zy9m5filfKRklcDGQo3BIV5KxyG6Z+EtnPFsb38AwL9/jUdmEW8sY244mDITlVV6zPj5OCp1evRt746RnfjqChFRYwj1ccKXEzph89RoDAn2giQBv5+8igc+3YeJyw/h2MU8pSPSPYhNrR5MRXIZHylMrZLQtVX1ccjlfOalZtPzgUGeaGKlUTgNUcOYel8bBHo5IK9Ui1fWxkMI3nHUnHAwZaJ0eoGDqbk4ki3hYGouPtl+FifTCuFka4H5jwTzVV4iokYW2MwBSx6LwB/Te+LhsGZQScCO05kY9vl+jP/6YO2A4+/+ee7W8TbwBqWySo+j1waL3F+KDEG3VtXL+bgBuvko1+qwMS4NADCiI19oJtNhqVHhg9GhsFSr8OepTPxybQBL5oEjdhO0NSEd8zYmIr2gHIAa3507XPuxtx8OgruDtXLhiIjMTBsPe3z0aDim3d8Wn+9Mwq/HrmDvuWzsPZeNSD8XTO3bBt39XfH7yYwbzt1ejtZ4/cFADAziUg1DkJBWgHKtHs62FryjLRmEKP/qDdAPnc9FRZUOVhq1womosW0/lYnC8io0c7SuHUwSmYr2ng54sV9bLNh6Gm9uTERUa1duP2MmeMWUidmakI5nfjh67QebG2m42TkRkSL83OywaGQodr7UG2O7tIClWoXY1FyM+/og+i7ehSk3OXdnFJTjmR+OYmsCNwI1BDVXuXF/KTIUbdybwK2JJcq1esRd4j5T5mD1kUsAgGERzXkTIzJJ/+rZChEtnFBcUYVZq09Az6vHzQIHUyZEpxeYtzERt/qrKwGYtzGRS0OIiBTk42KLd4cFY/es3ngiqiUs1RJSs0tv+tiaszXP3YaB+0uRoZEkCV2vXTWzPzlb4TTU2DILy7HnXPWf83DejY9MlFolYfGoMNhYqLE/OQffxZxXOhLJgIMpExKbmnvLK6WA6h9w0gvKb7qvCRERycvL0QZvPNQBH48Jv+3jeO42DDq9wKFrd+Tr4sflM2Q4urWuPh5juAG6yVt3/Ap0eoGIFk5o1ZTLicl0+bnZYe7g9gCA+VtPIyWrWOFE1Ng4mDIhdb2tJm+/SURkOCqr9HV6HM/dyjqTUYSi8irYWaoR4GWvdByiWlGtq/eZOnYxH2WVOoXTUGMRQmDNkSsAgBEdfRROQ9T4xnXxRQ9/N5Rr9ZjxcxyqdHV7vkTGiYMpE+JuX7dNzev6OCIianx1PScnphXWeYhFDS82tfpqlI4tXaBR8+kTGY6WrrbwcrRGpU6PIxfylI5DjeRkWiHOXC2CpUaFISG8IQaZPpVKwsIRIbC31uD4pXx8sSdF6UjUiPjMyoRE+rnAy9Eat9oGUQLg5WjNvTGIiAzInc7dNb7Yk4I+7+/C9wcuoKKKV0XILbZ2GR//DSXDIklS7d3ZYlK4z5SpWn3kMgCgf6AHHG0sFE5DJI9mTjZ448EOAICP/jyLxLRChRNRY+FgyoSoVRJefzAQAG74Aafm7dcfDISad/AgIjIYdzp3SwCGRzSHWxMrXMkvw6vrEtBz4U58sy+Vy3ZkIoTgxudk0Gr2mdrPfaZMUmWVHuuP1yzj46bnZF4eiWiO/oEe0OoEZvx8nC/OmSgOpkzMwCAvLB0XAU/H65eGeDpaY+m4CAwM4qW/RESG5k7n7sWjwrBvdh/Me6gDvBytcbWwAm9uSkT0wh34YncySiqqFEpuHlKzS5BdXAlLjQoh3o5KxyG6Qc1g6sTlAhTzfGBydpzORF6pFu72Vohu01TpOESykiQJ7z4SDFc7S5zOKMJHf55TOhI1Ao3SAajhDQzyQr9AT8QkZeKPvQfRP7oLuvm780opIiIDdqdzt7WFGo9HtcSjkT5Yc+QKPt+VhMt5ZXhvy2n8d3cyJvXww4SolnCw5hKPhlZztVSYjxOsNGqF0xDdyNvZFi1cbHExtxSHUnPRp7270pGoAa05Wr2Mb1h4cz6fJ7Pk1sQK7wwLxpQfjuCL3cm4P8AdHX15BbMp4RVTJkqtktDFzwUd3QS6+LnwHzEiIiNQl3O3lUaNsV1aYOdLvbFoRAj83OyQV6rF+3+cRff5O/DBtrPIL61UIL3pqhlMcX8pMmRRtcv5uM+UKckprsDO05kAgOFcxkdmbGCQJx4Jbw69AGb+HIfSSl4dako4mCIiIjJCFmoVRnbywbYXe+LjR8PQxr0Jisqr8Mn2c+g+fwcWbD2N7OIKpWOahJqNzzu35GCKDFfNcr6YFO4zZUrWH09DlV4gxNsRbT3slY5DpKjXH+oATwdrnM8pxYItp5WOQw2IgykiIiIjplGrMDSsOX6f3hOfPxaBAC8HlFTqsHRXMnos2IG3NiUis7Bc6ZhG60p+GS7nlUGtkhDh66x0HKJbqrkz38m0Ql41aUJqlvFx03MiwNHGAgtHhAAAVsRcwF9JvELUVHAwRUREZAJUKgmDg72weWoPfDmhE0K8HVGu1ePrfanosXAnXlufgLT8MqVjGp1D15bxBTVzQBMrbs1JhsvdwRqtm9pBCODgteOWjNup9EKcTCuEhVrCgyHNlI5DZBB6tm2KcV1bAABe/iUOheVahRNRQ+BgioiIyIRIkoR+gR5Y/1x3LH+yMzr6OqOySo/vYi6g16KdmLv2BC7mlCod02jU/IAfyf2lyAhEtXYDAMQkczmfKVhzpPpqqfvae8DZzlLhNESG45XBAfB1tUVaQTnmbUhUOg41AA6miIiITJAkSejdzh2rp3TDysld0K2VK7Q6gVWxl9Bn8S7M/DkOKVnFSsc0eIe4vxQZkdp9pjiYMnpanR7rjqcB4DI+on+ytdRg8chQSFL1ctc/TmYoHYnuEQdTREREJkySJES1dsOqf3XF6ind0LNtU+j0AmuOXsb9H+zG1FXHcPZqkdIxDVJ2cQWSMquHdxxMkTHoem2fqTNXi5BVxJsfGLM9Z7OQXVwBVztL9GrXVOk4RAanU0sX/KtnKwDAK7/GI4c3fDFqHEwRERGZiU4tXfDdxEise6477g9wh14AG+LS0P/DPZjy/REkXClQOqJBOXztaql2HvZcRkNGwcXOEgFeDgCAA7w7n1Gr2fT84fDmsFDzRzaim5nRry3aedgju7gS//41AUIIpSPRXeJZjoiIyMyE+Tjhq8c747epPTAoyBMAsPVkBh74dB8mLT+E45fylQ1oIGJT8wAAnf14Nz4yHjV354vhYMpo5ZdW4s/ETADA8Agu4yO6FSuNGotHhUKjkrD1ZAbWHb+idCS6SxxMERERmakOzRyxdFxH/PFiTzwU2gwqCdh+OhMPL/kL478+WLu/krmKPV/9g32kn6vCSYjqLor7TBm9jXFpqNTpEeDlgMBmDkrHITJoQc0dMe2+NgCA19afRHoB70BsjDiYIiIiMnNtPezxyZhw/DmjF0Z09IZaJWHvuWyM/G8MHl0Wg/1J2WZ3eXxhuRaJaYUAgEjuL0VGJLKVC1QSkJpdgvSCcqXj0F1YfbT6qg9uek5UN8/0bo1Qb0cUlVdh9pp4s3vOYgo4mCIiIiIAQKumTfD+yFDseqk3xkS2gIVawoGUXIz96iBG/DcGu85kms2TvSMX8qAXgK+rLTwdrZWOQ1RnDtYWCG7uCAA4mGreVz0ao6TMIsRdyodGJWFoWDOl4xAZBY1ahcWjwmClUWHP2Sz87+BFpSNRPRn0YOqNN96AJEnX/Wrfvv1tP+eXX35B+/btYW1tjeDgYGzevFmmtERERKbBx8UW7z0SjN0v98Hj3XxhqVHhyIU8PPHtIQxd8he2JV41+QHVoWs/0PNufGSMurV2AwBsiEvHkWwJB1NzodOb9t9ZU7H6SPXVUr3bucOtiZXCaYiMh797E8waWD0reHfzKVzIKVE4EdWHQQ+mAKBDhw5IT0+v/bVv375bPnb//v0YM2YMJk2ahGPHjuHhhx/Gww8/jISEBBkTExERmYZmTjaYNzQI+2b1wVM9/GBjocaJywWY/N1hDPp4L347kQ69if6wG3ttMBXpx8EUGR8LtQQA2JuUg+/OqTHum8PosWAHtiakK5yMbkenF/j1WPXd+EZ0bK5wGiLj82RUS3Rt5YLSSh1m/hzHgbwRMfjBlEajgaenZ+0vNze3Wz72448/xsCBA/Hyyy8jICAAb731FiIiIvDZZ5/JmJiIiMi0uDtY4z8PBGLf7D54pndr2FmqcTqjCM+tPIr+H+3BumNXUKXTKx2zwZRrdYi7nA8A6MLBFBmZrQnp+GxH0g3vzygoxzM/HOVwyoDtS8rG1cIKONlaoE97d6XjEBkdlUrCohGhaGKlweELefhqb4rSkaiODH4wde7cOTRr1gytWrXCY489hosXb71eNCYmBvfff/917xswYABiYmIaOyYREZHJc21ihdkD2+OvOX0x9b42sLfWICmzGNN/Oo77P9iNnw9fgtYEBlTHLuZDqxPwcLBCCxdbpeMQ1ZlOLzBvYyJudo1AzfvmbUzkVQQGas2R6qulhoY2g5VGrXAaIuPk42KLVx8IAAAs/uMszmQUKZyI6kKjdIDb6dKlC5YvX4527dohPT0d8+bNQ3R0NBISEmBvb3/D4zMyMuDh4XHd+zw8PJCRkXHb36eiogIVFRW1bxcWVt+FR6vVQqvVNsB3ooya7Mb8PRgT9i0v9i0v9i0fY+jazkLCC7398ERXb/xw8BK+3X8B53NKMWv1CXzy51n8q6cfHglvDiuNwb/+ddO+DyRnAQA6tXBGVVWVIrlMlTEc38bsYGrube/EJwCkF5QjJimTVwM2sHs9tovKtfj9ZPXPLENDPfl35A54LpGXsfU9LNQTW+LTsetsNl786Rh++VcXWBrBc5Iaxtb3rdQnvySMaPfS/Px8+Pr64oMPPsCkSZNu+LilpSVWrFiBMWPG1L7v888/x7x583D16tVbft033ngD8+bNu+H9K1euhK0tXyklIiK6nQod8NdVCTvSVCjSVu9t42gpcH8zPbq6C1ga2Qv/SxJVOFugwgg/HaI9jeZpEhGOZEv47tyd/8JNaKNDRzce24Zk/1UJP6Wo4WkjMCdUB0lSOhGRcSuoBObHqVFaJWFAcz0GtzD+K7qNTWlpKcaOHYuCggI4ODjc9rEGfcXUPzk5OaFt27ZISrpx3TwAeHp63jCAunr1Kjw9PW/7defOnYsZM2bUvl1YWAgfHx/079//jgUaMq1Wi23btqFfv36wsLBQOo7JY9/yYt/yYt/yMdauh6F6b6afDl/Gl/vO42phBdacV2N3tiUmdW+JMZ29YWdleE87/tm3VqfHnMM7AOjx5JAeaOtx4xXadPeM9fg2Fq6pufju3OE7Pu48PDChS3v4uvIF2IZyr8f2d1/GAsjHhJ5tMaSHX8MHNDE8l8jLWPt2aJ2B6T+fwJ/pavzrgW4I8XZUOlKdGGvf/1SzEq0uDO8Z4m0UFxcjOTkZ48ePv+nHu3Xrhu3bt2P69Om179u2bRu6det2269rZWUFK6sbb8dqYWFh1AdCDVP5PowF+5YX+5YX+5aPMXZtYWGBp3r6Y3yUH345fBlLdyXjSn4ZFvx+Fsv2puKp6FaY0M0X9taG933V9H0yIx9lWj0cbSwQ0MwZKhUvW2gMxnh8G4Nu/u7wcrRGRkH5TfeZqrHnXDb6f7wPD4U2w/N9/eHvzgFsQ7mbYzs1uwRHLuZDJQEjOrbg34164LlEXsbW98MRPvjzdBY2nUjHrLUJ+G1qNKwtjOcybmPr+5/qk92gF1q+9NJL2L17N86fP4/9+/dj2LBhUKvVtUv1JkyYgLlz59Y+ftq0adi6dSsWL16M06dP44033sDhw4fx/PPPK/UtEBERmR0rjRrjuvpi18u9sXB4CHxdbZFXqsWi38+g+/wd+HDbWRSUGua+CbGpOQCAzi1dOJQio6NWSXj9wUAAwD+PXunar1kD2qFPu6bQC2Dd8TT0+3APnvvfUZxKr/sr29Sw1h6t3vS8Z9umcHewVjgNkWl5a2gQmtpbITmrBIt+P6N0HLoFgx5MXb58GWPGjEG7du0watQouLq64sCBA2jatCkA4OLFi0hP//9b3kZFRWHlypVYtmwZQkNDsXr1aqxbtw5BQUFKfQtERERmy0KtwqjOPtg+oxc+HB2K1k3tUFhehY+3n0P3BTuwcOtp5BRX3PkLySg2NRcAuDE0Ga2BQV5YOi4Cno7XDzg8Ha2xdFwEnu3jj2+fjMTG53tgQAcPCAH8Fp+OQR/vxeTvDiP+coFCyc2TXi+w9ugVAMDwCG+F0xCZHmc7SywcHgIA+OavVBxIyVE4Ed2MQS/l+/HHH2/78V27dt3wvpEjR2LkyJGNlIiIiIjqS6NWYVi4Nx4KbY6tCRn4dMc5nM4owue7kvHtX+cxrmsLTO7ZCu72yl4poNcLHDqfBwCI5GCKjNjAIC/0C/RETFIm/th7EP2ju6CbvzvUf7sKMNjbEV+M74TTGYX4bEcSfotPx7bEq9iWeBW92zXFC33boKOvs4LfhXk4kJKDK/llsLfWoF+gx50/gYjqrU97dzza2Qc/HrqEl36Jw9bpPdHEAPe9NGcGfcUUERERmQ61SsKQEC9snhqNZeM7Iri5I8q0Ony5NxXRC3bijQ0nkV5Qpli+s5lFKCjTwtZSjQ7NjPfmJ0RA9d+3Ln4u6Ogm0MXP5bqh1N+193TAZ2MjsO3FXngkvDnUKgm7zmRh+NL9GPvlAV5d0MhWX1vG92BoM6Pa+4bI2PzngUB4O9vgcl4Z3t6UqHQc+gcOpoiIiEhWKpWE/h08seH57vj2yc6IaOGEiio9lu8/j54Ld2Lu2nhcyi2VPVfNMr6Ovs7QqPkUicyLv3sTfDA6DDtm9sLoTj7QqCTsT87Bo8sOYNR/Y7DnbBaEuN2W6lRfxRVV2BKfAYDL+IgaWxMrDd4fGQpJAn48dAk7Tl9VOhL9DZ91ERERkSIkSUKfdu5Y80wU/vdUF3Txc4FWJ7Aq9iJ6v78LL/0Sh9TsEtnyHLw2mIpsyWV8ZL58Xe2wYEQIdr3cG+O6toClWoXY87mY8E0shn2+H9tPXeWAqoFsiU9HmVaHVm52iGjhpHQcIpPXtZUrJnb3AwDMXhOPvJJKhRNRDQ6miIiISFGSJKG7vxt+erobfn66G6LbuEGnF1h95DLuW7wL0348hnNXixo1gxCi9oqpztxfigjezrZ4++Fg7JnVB092bwkrjQrHL+Vj0orDeODTfdiakA69ngOqe7H6SPUyvuEdvSFJvAsokRxeHtAOrZvaIauoAq+uT1A6Dl3DwRQREREZjEg/F3w/qQt+fTYK97V3h14A64+nof9He/Ds/44gMa1xbml/MbcMWUUVsFSrEObj1Ci/B5Ex8nS0xusPdsC+2X3xdK9WsLVU42RaIab8cBSDPt6LDXFp0HFAVW+XcktxMDUXkgQMC2+udBwis2FtocYHo8KgVknYdCIdG+PSlI5E4GCKiIiIDFB4C2d8/URnbHqhBwZ28IQQwOb4DAz+ZC+eWnEYJy7nN+jvF3vtbnyhPo7cgJjoJpraW2HuoAD8NbsvXujrD3srDc5cLcLUVcfQ74PdWH3kMqp0eqVjGo21R68AALq3dkMzJxuF0xCZl1AfJzzXuzUA4NX1CcgsLFc4EXEwRURERAYrqLkj/ju+I36f3hMPhjaDJAF/nrqKhz77C49/E4sjF3Ib5Pc5dKF6MBXJZXxEt+VsZ4mZ/dth35y+mNGvLRxtLJCSXYKXfolDn8W7sCr2IiqrOKC6HSEE1hytWcbHq6WIlPB83zbo0MwB+aVazFkbz73zFMbBFBERERm8dp72+HRMOP6c0QuPRFTf0n732SwMXxqDMcsOYH9y9j09qTx87Yqpztz4nKhOHG0sMPW+NvhrTl/MHtgernaWuJRbhrlr49F70U58F3Me5Vqd0jEN0qHzebiYW4omVhoM6OCpdBwis2SpUeGDUWGwVKuw43Qmfj58SelIZo2DKSIiIjIarZs2wQejwrBzZm882tkHFmoJMSk5GPvlQYz8bwx238Ut7fMrgEt5ZVBJQEdf50ZKTmSamlhp8Ezv1tg7uw/+MyQA7vZWSCsox2vrT6Lnwp34am8Kyio5oPq71UeqfwAeHOwJW0uNwmmIzFc7T3vM7N8WAPDmxkRcyi1VOJH54mCKiIiIjE4LV1vMHx6CXS/3wfiuvrDUqHD4Qh4e/yYWD3++H38m1v2W9slF1XfD6tDMEfbWFo0Zm8hk2Vpq8FR0K+yZ1QdvDu2AZo7WyCyqwNu/nUKPBTuwdFcyiiuqlI6puLJKHTbHZwAAhkd4K5yGiJ6KboXOLZ1RUqnDS7/E8W6jCuFgioiIiIxWcycbvPVwEPbO6oNJPfxgbaFC3KV8PPXdYQz5ZB+2xN/5lvbJhdWDKe4vRXTvrC3UmNCtJXa93AfzHwlGCxdb5JRUYsHW0+ixYAc+2X4OBWVapWMq5veTGSiuqEILF1suHSYyAGqVhPdHhsLWUo2Dqbn4dv95pSOZJQ6miIiIyOh5OFjj1QcCsW92X0zp1Rp2lmokphfimf8dxcCP92D98Ss33NJepxc4mJqLhNzqwVTHFlzGR9RQLDUqPBrZAjtm9sLikaFo5WaH/FItPth2Fj3m78DiP84gr6RS6ZiyW32ketPzRyKaQ6WSFE5DRADg62qHVwYHAAAWbj2NpMxihROZHw6miIiIyGS4NbHCnEHtsW92X0zt6w97aw3OXi3GtB+P4/4PduOXw5eg1emxNSEdPRbswLhvDqNAW/3D4RsbT2JrQrrC3wGRadGoVRje0RvbZvTCJ2PC0dajCYoqqvDpjiT0WLAD7205hayiCqVjyiItvwx/JWcD4DI+IkPzWJcWiG7jhooqPWb+fBxVOt5dVE4cTBEREZHJcbazxIz+7bBvdl/M7NcWTrYWSM0uwcurT6Dre9sx5YejSC8ov+5zsooq8MwPRzmcImoEapWEh0KbYeu0nvjvuAh0aOaAkkodvtidguiFOzBv40lk/OPvpKn59dgVCAF08XOBj4ut0nGI6G8kScLCESGwt9Yg7nIBPt+VrHQks8LBFBEREZksRxsLvHBfG+yb3RdzBrWHq50FcopvvnyoZqHfvI2JNyz7I6KGoVJJGBjkhU0v9MDXj3dCqI8TyrV6fPvXefRcuBP/WRePy3mmd2csIQTWXFvGN7wjr5YiMkRejjZ4c2gHAMAn288h4UqBwonMBwdTREREZPKaWGkwpVdrfDAq7LaPEwDSC8oRm5orSy4icyVJEu4L8MC6Z6Pw3cRIdG7pjEqdHj8cuIjei3Zh9uoTuJBTonTMBnPsUj5SsktgY6HG4GAvpeMQ0S08HNYcAzt4okovMPPnOFRU6ZSOZBY4mCIiIiKzkV/Hu4FlFpn2kiIiQyFJEnq2bYpfpkThx391RXd/V1TpBX46fAl9F+/GjJ+Om8RGxDWbng8K8kQTK43CaYjoViRJwjvDguDWxBJnrhbhg21nlY5kFjiYIiIiIrPhbm/doI8joobTtZUr/vdUV6x5Jgq92zWFTi+w9tgV9PtwN55beRSnMwqVjnhXyrU6bIpLAwCM4DI+IoPn2sQK7w4LBgAs25OCw+d5FXVj42CKiIiIzEaknwu8HK1xq5u0SwC8HK0R6eciZywi+puOvs5Y/mQkNjzfHf0CPSAE8NuJdAz8aC/+9d1ho9v35c9TV1FYXoXmTjbo2spV6ThEVAf9O3hieIQ3hABm/hKHkooqpSOZNA6miIiIyGyoVRJefzAQAG4YTtW8/fqDgVCrbjW6IiK5hHg74csJnbBlWjSGBHtBkoA/Eq/igU/34clvY3H0Yp7SEeukZhnfsPDmUPHcQmQ0Xn8oEM0crXEhpxTvbTmldByTxsEUERERmZWBQV5YOi4Cno7XL9fzdLTG0nERGBjEjYmJDEmAlwOWPBaBbS/2xMNhzaCSgJ1nsvDI5/sx7quDOJiSo3TEW8osLMees1kAeDc+ImPjYG2BhSNCAQA/HLhY+3eZGh4HU0RERGR2BgZ5Yd/svvhhYidMaKPDDxM7Yd/svhxKERkwf3d7fPRoOLbP7I1RnbyhUUnYl5SN0csOYNQXMdh3LhtCCKVjXufXY1egF9XLE/3c7JSOQ0T11KONGyZ08wUAzFp9AgWldbuJCtUPB1NERERkltQqCV38XNDRTaCLnwuX7xEZCT83OywcEYqdL/XGY11awFKtQmxqLsZ9fRCPLN2PHaevGsSASgiBNUerl/Fx03Mi4zVnUHu0dLVFRmE53th4Uuk4JomDKSIiIiIiMjo+LrZ4Z1gwds/qjSeiWsJKo8Kxi/mYuPwwHvxsH7YmZECvV25AlXClEGevFsNKo8KQEF6NSWSsbC01WDwqDCqp+irIrQnpSkcyORxMERERERGR0fJytMEbD3XA3tl98K+erWBrqUbClUJM+eEIBn+yFxvj0qBTYEC1+sglANV393KwtpD99yeihtPR1xlP92oNAPj3rwnILq5QOJFp4WCKiIiIiIiMnru9NV4ZHIB9s/viuT6t0cRKg9MZRXhh1TH0/3A31h69jCqdXpYsFVU6rI9LA8BlfESmYvr9bdDe0x45JZV4ZW28QSwZNhUcTBERERERkclwsbPEywPa46/ZfTH9/jZwsNYgOasEM36Ow30f7MZPhy6isqpxB1Q7T2civ1QLDwcr9PB3a9Tfi4jkYaVR44NRYbBQS/gj8SrWHr2idCSTwcEUERERERGZHEdbC0y/vy3+mtMXswa2g4udJS7klGL2mnj0eX8Xvj9wAeVaXaP83quPVP/AOizcmzdWIDIhgc0cMP3+tgCANzacRFp+mcKJTAMHU0REREREZLLsrS3wbG9/7JvdB/8ZEoCm9la4kl+GV9cloNeinfhmXyrKKhtuQJVdXIFdZzIBACM6Nm+wr0tEhuHpnq0Q3sIJRRVVmLX6hKI3WTAVHEwREREREZHJs7XU4KnoVtg7qw/mPdQBXo7WuFpYgTc3JSJ64Q58sTsZJRVV9/z7rD+ehiq9QKi3I/zd7RsgOREZEo1ahcUjQ2FtocK+pGz8cPCC0pGMHgdTRERERERkNqwt1Hg8qiV2vdwb7w4LhrezDbKLK/HeltPovmAHPt1+DoXl2rv++muOXAbATc+JTFmrpk0we2B7AMC7m08hNbtE4UTGjYMpIiIiIiIyO1YaNcZ2aYGdL/XGohEh8HOzQ36pFou3nUX3+TvwwR9nkF9aWa+veSq9CInphbBUq/BgaLNGSk5EhuDxbi3RrZUryrV6zPz5OHRc0nfXOJgiIiIiIiKzZaFWYWQnH/w5oxc+fjQMbdyboKi8Cp/sSEL3+Tswf8tpZBdX1Olr/Xo8DQBwf6A7nGwtGzM2ESlMpZKwaGQImlhpcPRiPpbtSVE6ktHiYIqIiIiIiMyeWiVhaFhz/D69J5Y+FoEALweUVOrw393J6LFgB97alIjMwvIbPk+nFziYmotDmRLWXLt9/PAILuMjMgfezrZ47cFAAMCH287idEahwomMEwdTRERERERE16hUEgYFe2Hz1B74akInhHo7olyrx9f7UtFj4U68ui4BV67dIn5rQjp6LNiBcd8cxg/JahSWV0ElAWXahrvLHxEZtpEdvXF/gDsqdXq8+FMcKqv0SkcyOhqlAxARERERERkaSZJwf6AH7gtwx55z2fh0+zkcvpCH7w9cwI+HLiKypQv+Ss654fP0Anhh5TFoVBIGBnkpkJyI5CRJEt59JBhHPtyDU+mF+GT7Obw0oJ3SsYwKr5giIiIiIiK6BUmS0KttU/wypRtWTu6Cbq1codWJmw6l/m7exkRuhkxkJtztrfHOsGAAwOe7knDsYp7CiYwLB1NERERERER3IEkSolq7YdW/uuKNa3vK3IoAkF5QjtjUXHnCEZHiBgd7YWhYM+gFMPPnOJRVcklvXXEwRUREREREVA/OdnW7415m0Y2bpROR6XrzoSB4OFghJbsEC7aeVjqO0eBgioiIiIiIqB7c7a0b9HFEZBocbS0wf3gIAGD5/vPYn5StcCLjwMEUERERERFRPUT6ucDL0RrSLT4uAfBytEakn4ucsYjIAPRp544xkS0AAC+vPoHCcq3CiQwfB1NERERERET1oFZJeP3aPlP/HE7VvP36g4FQq241uiIiU/bvIQHwcbHBlfwyvLUxUek4Bo+DKSIiIiIionoaGOSFpeMi4Ol4/XI9T0drLB0XgYFBXgolIyKlNbHSYPHIMEgS8MuRy/gz8arSkQyaRukARERERERExmhgkBf6BXoiJikTf+w9iP7RXdDN351XShERIv1c8FQPP3y5NxVz1sbjD19nuNTxxgnmhldMERERERER3SW1SkIXPxd0dBPo4ufCoRQR1ZrZvx3auDdBdnEFXl2XACGE0pEMkkEPpt577z107twZ9vb2cHd3x8MPP4wzZ87c9nOWL18OSZKu+2VtzbthEBEREREREZF8rC3U+GBUGDQqCb/Fp2NDXJrSkQySQQ+mdu/ejeeeew4HDhzAtm3boNVq0b9/f5SUlNz28xwcHJCenl7768KFCzIlJiIiIiIiIiKqFuztiOf7+gMAXlt/ElcLyxVOZHgMeo+prVu3Xvf28uXL4e7ujiNHjqBnz563/DxJkuDp6dnY8YiIiIiIiIiIbuu5Pv7YfioT8VcKMGv1CSx/sjMkict+axj0FVP/VFBQAABwcXG57eOKi4vh6+sLHx8fDB06FCdPnpQjHhERERERERHRdSzUKnwwKhSWGhV2n83CqthLSkcyKAZ9xdTf6fV6TJ8+Hd27d0dQUNAtH9euXTt88803CAkJQUFBAd5//31ERUXh5MmT8Pb2vunnVFRUoKKiovbtwsJCAIBWq4VWq23Yb0RGNdmN+XswJuxbXuxbXuxbPuxaXuxbXuxbXuxbPuxaXuxbXuy7YbR0scaM+/0xf+tZvP1bIrq0dEQLF9sbHmcqfdcnvySMZFv4Z555Blu2bMG+fftuOWC6Ga1Wi4CAAIwZMwZvvfXWTR/zxhtvYN68eTe8f+XKlbC1vfFAISIiIiIiIiKqD70APjupRnKRhNb2As930MFUb+RZWlqKsWPHoqCgAA4ODrd9rFEMpp5//nmsX78ee/bsgZ+fX70/f+TIkdBoNFi1atVNP36zK6Z8fHyQnZ19xwINmVarxbZt29CvXz9YWFgoHcfksW95sW95sW/5sGt5sW95sW95sW/5sGt5sW95se+GdTG3FA8uiUFppQ5zB7bFxO4tr/u4qfRdWFgINze3Og2mDHopnxACL7zwAn799Vfs2rXrroZSOp0O8fHxGDx48C0fY2VlBSsrqxveb2FhYdQHQg1T+T6MBfuWF/uWF/uWD7uWF/uWF/uWF/uWD7uWF/uWF/tuGK09HPGfIYF45dd4LP4zCX0DPNHGw/6Gxxl73/XJbtCbnz/33HP44YcfsHLlStjb2yMjIwMZGRkoKyurfcyECRMwd+7c2rfffPNN/PHHH0hJScHRo0cxbtw4XLhwAU899ZQS3wIRERERERERUa0xkT7o3a4pKqv0mPFzHLQ6vdKRFGXQg6mlS5eioKAAvXv3hpeXV+2vn376qfYxFy9eRHp6eu3beXl5mDx5MgICAjB48GAUFhZi//79CAwMVOJbICIiIiIiIiKqJUkSFgwPgaONBeKvFGDJziSlIynK4Jfy3cmuXbuue/vDDz/Ehx9+2EiJiIiIiIiIiIjujYeDNd4c2gHTfjyOz3Yk4b72Hgj2dlQ6liIM+oopIiIiIiIiIiJT9FBoMwwO9kSVXmDGz8dRUlGFg6m5OJIt4WBqLnR6g79XXYMw6CumiIiIiIiIiIhMkSRJePvhYMSm5uFcZjEi3/kTJZU6AGp8d+4wvByt8fqDgRgY5KV01EbFK6aIiIiIiIiIiBTgYmeJkR29AeDaUOr/ZRSU45kfjmJrQvrNPtVkcDBFRERERERERKQAnV5g3fErN/1YzUK+eRsTTXpZHwdTREREREREREQKiE3NRXpB+S0/LgCkF5QjNjVXvlAy42CKiIiIiIiIiEgBmUW3HkrdzeOMEQdTREREREREREQKcLe3btDHGSMOpoiIiIiIiIiIFBDp5wIvR2tIt/i4BMDL0RqRfi5yxpIVB1NERERERERERApQqyS8/mAgANwwnKp5+/UHA6FW3Wp0Zfw4mCIiIiIiIiIiUsjAIC8sHRcBT8frl+t5Olpj6bgIDAzyUiiZPDRKByAiIiIiIiIiMmcDg7zQL9ATMUmZ+GPvQfSP7oJu/u4mfaVUDQ6miIiIiIiIiIgUplZJ6OLngpxTAl38XMxiKAVwKR8RERERERERESmEgykiIiIiIiIiIlIEB1NERERERERERKQIDqaIiIiIiIiIiEgRHEwREREREREREZEiOJgiIiIiIiIiIiJFcDBFRERERERERESK4GCKiIiIiIiIiIgUwcEUEREREREREREpgoMpIiIiIiIiIiJSBAdTRERERERERESkCA6miIiIiIiIiIhIERxMERERERERERGRIjRKBzBEQggAQGFhocJJ7o1Wq0VpaSkKCwthYWGhdByTx77lxb7lxb7lw67lxb7lxb7lxb7lw67lxb7lxb7lZSp918xTauYrt8PB1E0UFRUBAHx8fBROQkRERERERERknIqKiuDo6Hjbx0iiLuMrM6PX65GWlgZ7e3tIkqR0nLtWWFgIHx8fXLp0CQ4ODkrHMXnsW17sW17sWz7sWl7sW17sW17sWz7sWl7sW17sW16m0rcQAkVFRWjWrBlUqtvvIsUrpm5CpVLB29tb6RgNxsHBwagPaGPDvuXFvuXFvuXDruXFvuXFvuXFvuXDruXFvuXFvuVlCn3f6UqpGtz8nIiIiIiIiIiIFMHBFBERERERERERKYKDKRNmZWWF119/HVZWVkpHMQvsW17sW17sWz7sWl7sW17sW17sWz7sWl7sW17sW17m2Dc3PyciIiIiIiIiIkXwiikiIiIiIiIiIlIEB1NERERERERERKQIDqaIiIiIiIiIiEgRHEwREREREREREZEiOJgiIiIiIiIiogan1+uVjkBGgIMpIjI7vBkpmTI+ASSie1VRUaF0BLNy9epVpKWlKR3DbFy8eBEnTpxQOoZZOH36ND7++GOlY5gNnU4HrVardIy7olE6ABme8+fPY9u2bSgrK0ObNm0waNAgpSOZtOTkZKxevRqFhYUIDQ3FkCFDYGdnp3Qsk5SbmwsXFxdIkgQhBCRJUjqSSbt06RJ27NiBvLw8hISEoG/fvkpHMmkFBQVwdHSESqWCXq+HSsXXnhpTWloaDh06hPLycrRp0wYRERFKRzJZqampWLduHbKystCtWzc8+OCDSkcyaYmJiZg8eTIWLFiAHj16KB3H5B07dgwPP/wwvv32WzRr1kzpOCbvxIkTGDp0KB544AHMmzcPLi4uSkcyWfHx8ejcuTMqKysRFRWFLl26KB3JpJ05cwYfffQRkpOT0b17d7zwwgtGdXzzWStdJz4+Hl26dMGqVavw66+/4oEHHsCECRMQGxurdDSTlJCQgE6dOmHz5s3Ys2cPxo4diyeffBLbtm1TOprJSUxMhIeHB6ZPnw4AtcMpahzx8fGIjo7GsmXLsGzZMgwcOBArV65UOpbJSkxMhK+vL959910AqB1OUeOIj49Hz5498fbbb2PWrFkYNmwYfvvtN6VjmaQTJ06gZ8+e2LRpE3bv3o2hQ4diw4YNSscyae+//z5iYmLwxBNPICYmRuk4Ji0uLg7R0dEYNmwYX7yRQVJSEu6//36MGDEC77//vlH90G5s4uLiEBkZidGjR6NXr17YtGkTAF7V3VgSEhLQo0cP5Obmok2bNnjnnXewbNkypWPVCwdTVCsnJwfjx4/H5MmTsWPHDuzcuRObNm3C//73P7z99tvYuXOn0hFNSllZGWbPno1x48Zh9+7d2Lt3L/bv34+UlBQsWrQI69evVzqiyUhLS8OTTz6JkJAQfPXVV3jxxRcBcDjVWFJTU/Hggw/i0Ucfxfbt27F7927MnTsXixcvxtWrV9l5A7t8+TLGjRsHDw8PLF68GPPnzwfA4VRjSU5OxqBBgzBixAhs27YNmzZtwuDBg/Htt9+ipKSEx3cDOnv2LIYMGYLx48djy5Yt2LRpEwYNGoTLly8rHc2kRUdHY9asWejduzcefPBB7N27V+lIJunkyZOIjo7GCy+8gI8++gg6nQ7Hjx/H/v37cfLkSaXjmaRNmzahX79+WLRoEdRqNf773/9izpw5WLp0Kc6ePat0PJNx7NgxREdHY+bMmVixYgU6d+6ML774AgUFBVCpVPx3soHl5+dj8uTJmDx5Mn766ScsWbIEc+fORVZWFqqqqpSOV2ccTFGt/Px8aDQajB07FkIIVFZWIiwsDAEBATh06BA+++wz5OXlKR3TZNjY2CAvLw/u7u4Aql9BiIyMxIoVK1BRUYEvvviC698bgBACO3fuhK+vLz799FN8+eWXWLp0KWbMmAGAw6mGVlVVhW+++Qbh4eF4/fXXYW1tjaZNmyIqKgrp6ekAwCWUDUiv12PNmjXw8/PDf//7X8yaNQvvvfceh1ONpLKyEkuWLEFUVBTeeustODk5ITg4GJ07d0ZMTAz0ej2P7wZSWVmJefPm4b777sNbb70FS0tLODs7w8bGBgcOHMCkSZP4vKSR2NraYu/evfj000/RrVs3jBgxAqdOncKrr76Kn376Sel4JqGiogLjx49HkyZNMG3aNADAiBEjMHHiRDz44IPo0qULFi1apHBK03PixAlYWVkBAPr27YtvvvkGhw8fxiuvvILp06djy5YtCic0fpmZmejevTuefvppvP322wBQu6SsZq8p/jvZsMrKylBWVoaePXvWvu/SpUuIjY1Fly5d8MwzzxjFsc09pqhWUVERjh49ioyMDAQGBsLS0hKlpaXw8fHBK6+8gnHjxmHgwIGYPHmy0lGNnhACJSUlsLS0RGZmJoDqHzCFEOjQoQM+++wzDBw4ECtWrMDixYsVTmvcJElCz549YW9vj6ioKERFRUEIgYkTJ0IIgQ8//JB7TjUgjUaDkJAQ2NjYwMbGpvb9Xbp0gUajQXZ2Njw8PBRMaFpUKhUGDx4Md3d39OnTB2FhYRBC4L333gMAzJkzh3tONSCVSgV/f3/4+fnBwsKi9rzRt29fvPnmmygoKIC9vb3SMU2CpaUlXnnlFaSlpUGtVgMA3n33Xfz6668YM2YMrK2tMXXqVJw5cwaffvqpwmlNS0REBCwtLWFjY4ONGzfi0UcfRXh4OJo0acKlfQ3EysoKH3zwAaZMmYIXX3wRZ8+ehZubGz755BNYW1sjJiYG06ZNg729PaZMmaJ0XKNXc6728fFBRkYG1q1bB2tra6xZswZNmzbFuXPnMHHiRHzzzTfcW/ceWVhYYOvWrdcNSTw8PBAeHo4//vgDr732GgDweXcDqqysxLlz5/DXX3+hWbNm2LBhA3788UfMmTMHzs7O+P7773Hp0iWEh4fD09NT6bi3Joiu0Wq1Yvz48cLf31989tlnYtWqVcLZ2Vk8++yzQgghpk+fLh599FGh1WqFXq9XOK1p+PHHH4UkSWL9+vVCCCF0Op2orKwUQgjx/fffC2dnZ3Hx4kUlI5qkqqoqsXLlSmFlZSVefPFFIUT18f/DDz+I+Ph4hdMZv7Kystr/rzlXFBUVCR8fH3Hs2LHaj8XGxsodzWT9/ZyclZUl5s+fLxwcHMR7770nhKg+5jds2CCysrKUimgy0tLSav+/pvcrV64IX19fcf78+dr3nTp1SpF8purEiRPi/vvvF5s3b67tePXq1UKj0YjTp08rnM70hIaGijNnzgghhBg7dqyws7MTzs7O4tChQwonM35/P1/v3LlTeHp6il69el13bhFCiJkzZ4rg4GCRk5PD590NZOvWrUKSJBEdHS2eeuqp6z528OBBIUmSOHLkiELpTJNOpxNCCJGQkCCsrKzE119/rXAi07R8+XJha2srBg8eLOzt7cXq1atrPxYfHy8kSRIbNmxQMOGd8YopM5aWloYrV64gJycH/fr1g0ajwezZs7FkyRK8/vrr8PT0xLPPPlt7GWZBQQHy8vKg0fCwuRtarRYWFhYAULt0bMSIEdi3bx9Gjx6NX3/9FQMHDqy9qsHZ2RleXl68Q99d+HvXN6NWqzFy5EgAwJNPPgmg+vaqS5cuRVJSkiwZTck/+7a2tq79f0mSUFVVheLiYlRVVcHW1hYAMHfuXCxYsACZmZlwc3OTPbMx+/u5+/7774dKpYJKpUJVVRU0Gg3c3NwwceJEANVXmAghkJOTg48//hgXL15UOL3xqek7OzsbAwYMqL3ir6ZvvV6PwsJClJaWwtLSEpIk1R7feXl5cHBw4KvCdXSrYxsAgoOD8d1338HLy6v28SqVCoGBgTyH3KV/Pg+UJAkqlQplZWVwdnZGUVERpk6dil27dmHHjh1YvHgxunbtiv379yMyMlLp+Ebn733fd999AIDevXtj06ZNSExMRNOmTa97vLW1NWxtbeHs7MxzyF345/kEAAYMGIDZs2dj4cKFcHJyQklJSe3zbGdnZ4SHh8PR0VHJ2EbpVufumqu1hRDw8/PDAw88gC1btmDs2LGwsrLicX2X/nkukSQJjz/+eO15ZdiwYQgLC6tdjePk5ITw8HDDv6Jb2bkYKSUuLk74+PiIwMBAodFoRFhYmFi2bJkoLS0VQghx+fLlG14VnjBhgpg9e7bQ6/V85aaeEhISxEMPPSROnjx5w8dSU1PFpEmThKWlpfjqq69ERkaGKC8vF7NnzxahoaEiNzdXgcTG63Zd/1NVVZX4/vvvhSRJfCX4LtWlb71eL7Kzs0WzZs3E+fPnxbx580STJk14xdRd+Oe5Ozw8XCxdulQUFRUJIaqP6RpZWVnivffe4/F9D27W9+eff17bd80rwcnJycLLy0vk5eWJN954Q9jb24uDBw8qGd3o3OnYFkLc8Nzj5ZdfFoMHDxaFhYVyxzV6t+q7oKBACFF9tY6NjY1o1qxZ7RUkFRUVYty4cbVXUlHd3azvJUuW1PZdc7X8302ZMkVMnDhRVFRU8Hl3Pd3q3F1SUiKysrLE008/LdRqtXj99ddFcnKyKC4uFq+99poICAgQV69eVTq+UbnTubvm30khhPjf//4nrKys+PzvHtzqXFLz72BKSopwc3MTf/75Z+3nvP7668Lf319cuXJFqdh1wsGUGcrKyhIBAQFi9uzZIjU1VWRmZooxY8aIzp07i+nTp4v8/PzrHp+cnCxeeeUV4eTkJBITExVKbbxSU1NFq1athCRJIiws7KZP6NLT08Wbb74pLCwsROvWrUVoaKhwc3MTR48eVSCx8apL13+n0+nEpEmThIODA4/tu1CfvktLS0VQUJDo37+/sLS0FIcPH5YxqWm41bm7S5cuYvr06bVPSv7+JHD8+PHCwcGhToNaul5d+xZCiKtXr4qQkBAxcuRIHt93oT5dC1G9nPI///mPcHJy4vLru3C7vqdNmyZKS0vF+vXrxZAhQ65bfk13526O71dffVU4Ozvz3H0XbvdzzowZM0RJSYkoLi4Wb731lrCyshK+vr4iNDRUeHl58Xl3PdX12P77i2bh4eFi/PjxQqfTceBaT3fqu+Zn+ClTpgiNRiMGDx4sBg0aJDw8PIziXM7BlBmKj48XLVu2FHFxcbXvq6ioEK+99pqIjIwU//73v2v3iMnKyhJTpkwR7dq148n6LpSXl4s33nhDDBs2TBw6dEhERkaKgICAW/4Af/ToUbFq1SqxcuVKkZqaKm9YI1ffroUQYvPmzcLPz49XktyF+vSt1+vFhQsXhCRJwsrK6rpzD9Vdfc7der1efP/998LDw4P7Zdyl+vSdkJAgJEkSNjY24vjx40pFNlr16frw4cNi3Lhxws/PzyieaBui2/XdqVMnMW/ePCGEuO5qNbp79Tm+Y2NjxciRI4W3tzeP77t0p+P71VdfFeXl5UIIIY4fPy7WrFkj1q5dKy5cuKBUZKNVn2O7xscffyzOnTsnd1STUJe+KysrRW5urliyZIkYOXKkeOWVV4zmKlcOpszQmTNnhJ+fn9i4caMQonrT55r/vvzyyyIsLEzs2bOn9vHJycni8uXLimQ1djqdTqxZs0b88ssvQggh8vLybvkDPF81uDf16brGlStXRHp6upwxTcbd9L1o0SK++nsP6nvuTklJEefPn1ckqymoT995eXnipZde4pWXd6k+XV++fFls2LBBpKSkKJbX2N2p75CQELF3714hBJ+bNIT6HN+XLl0Sv/zyi0hKSlIsr7G7U9+hoaFi9+7dSkY0GfU5tms+RnevLsf2vn37ah9vbOdvSYhruzCT2aioqECPHj3g6emJdevWQa1W127iKoRAaGgowsPDsWLFCqWjmgSdTld7q2sAyMnJweDBg1FUVIT169ejTZs2qKqqQmxsLDp27AgrKysF0xq3+nQdERFx3SbdVH/16btz587QaDTc6PIe1OfcLXgb5ntW338rKyoqeP6+S3XpOiwsDN99953SUU0CnwfKi8e3vHh8y4ddy8vUzyUqpQOQvPR6PaysrPDtt99iz549eOaZZwCg9oCWJAkPPfQQMjMzFU5qOmp+cK+ZAbu6uuK3336Dvb09hg4dipMnT+KFF17Aiy++iOLiYiWjGr36dF1SUqJkVJNQ176nTZuGoqIiDkruQX3P3ez63tSn75rjn0Opu1PXrrOyshROahr4PFBePL7lxeNbPuxaXuZwLuFgysyoVCrodDoEBQVhxYoVWLVqFSZMmICrV6/WPiY1NRXOzs7Q6XQKJjUdNT+01PygKISAm5sbNm/eDCcnJ4SEhGDFihVYsmQJXF1dlYxq9Ni1vOra99KlS+Hi4qJkVKPHc7e86tO3Xq9XMKnx47EtL/YtL/YtL/YtH3YtL3Pom0v5TNw/l3PUXO5XXFyMiooKHD9+HGPHjoWvry9cXFzg6uqK9evXIyYmBsHBwQomN07/7LtmqVNhYSH0ej2cnJyue/zEiROxYcMG7NmzB4GBgTKnNW7sWl7sW156vR4q1f+/dsRzd+Ni3/Jh1/Ji3/Ji3/Ji3/Jh1/Iyx755xZSJqpmU1swdhRC1B/T58+fRtm1bHDp0CPfddx9OnjyJwYMHo3nz5nB3d0dsbKzRHtBKuVXfarUa58+fR0BAAGJiYmofL4TAp59+iuXLl2Pbtm38wb0e2LW82Le8srOzAfz/K2NA9Z8Bz92Ng33Lh13Li33Li33Li33Lh13Ly6z7bvj91ElpZ86cEdOnTxePPPKImDdv3nV3rrl48aJwc3MTkyZNEnq9XlRVVQkh/n/Xfp1Op0hmY1aXvp966qnr7oyg1+vFzp07ebvUemLX8mLf8jpz5oywt7cXkydPrn1fzTma5+6Gx77lw67lxb7lxb7lxb7lw67lZe5984opExMfH4+oqCjk5eVBr9djy5YtWLVqFYQQ0Gq1WL9+PcaNG4cvv/wSkiRdd0ctgBvm1ldd+162bNl13UqShN69e8Pf31/B9MaFXcuLfcsvMTERNjY2iI+Px9NPPw2geoP5yspKbNiwAePHj8cXX3zBc3cDYd/yYdfyYt/yYt/yYt/yYdfyMvu+lZ2LUUNKTk4Wvr6+4t///nft+yZNmiSmTp163eNqJqx0b9i3fNi1vNi3MjZv3izatm0r5s+fL4KDg8XTTz9d+7FLly4pmMw0sW/5sGt5sW95sW95sW/5sGt5mXvfGqUHY9QwdDodtm3bhvvuuw8zZ86s3ajYxsYGCQkJ6NWrF3x9fTFlyhRERUXdsJEx1Q/7lg+7lhf7Vk5wcDA6duyIp556CpaWlli+fDlmzJiBgoICREZGYuLEibCwsFA6pslg3/Jh1/Ji3/Ji3/Ji3/Jh1/Iy+76Vm4lRQ0tJSREJCQm1b8+bN09YW1uLd999V7z22mti9OjRolWrVtftE0N3j33Lh13Li30ro6SkRISEhIhjx46JkpISsWzZMuHq6iokSRInTpwQQvAqtYbEvuXDruXFvuXFvuXFvuXDruVl7n1zjykT4ufnV3sHrIqKChw8eBCrV6/G3LlzMW/ePDz//PMoLi5GUlKSwklNA/uWD7uWF/uWn1arhZWVFTw9PVFcXAxbW1ts374dWq0W/v7++OqrrwDghj0F6O6wb/mwa3mxb3mxb3mxb/mwa3mxb4BL+YxYWloajh49isrKSvj6+qJjx46QJAk6nQ5WVlbYuHEjVCoV9Ho9VCoVXFxc4OHhARcXF6WjGyX2LR92LS/2La+/992yZUtERETUXprdsWNHJCUlYdmyZdizZw82btyI+Ph4zJ8/HxqNBosXL1Y4vfFh3/Jh1/Ji3/Ji3/Ji3/Jh1/Ji3zfHwZSRio+Px8MPPww3NzekpKSgZcuWmD17NkaMGFE7Sa3Z90Wlqr4w7vvvv4e1tTV8fX0Vy22s2Ld82LW82Le8btc3AFhZWWHixIlo2bIlNm3ahIiICISEhEClUmHAgAEKpzc+7Fs+7Fpe7Fte7Fte7Fs+7Fpe7Ps2lF5LSPWXlJQkvL29xaxZs0R+fr44fPiwePzxx8XEiRNFVVWV0Ov11z3+woUL4uWXXxbOzs4iLi5OodTGi33Lh13Li33L63Z9a7VaIYQQWq1WPPvssyI2NlYIIWr/DHQ6nWK5jRX7lg+7lhf7lhf7lhf7lg+7lhf7vj0OpoxMRUWFmDFjhhg1apSoqKioff/XX38tXF1dRXZ29nWPP3TokHj22WdFaGioOH78uNxxjR77lg+7lhf7lld9+6Z7w77lw67lxb7lxb7lxb7lw67lxb7vjEv5jIxer4e3tzcCAgJgaWlZe6v2qKgoNGnSBFqt9rrHd+rUCWVlZfjPf/4DLy8vhVIbL/YtH3YtL/Ytr/r2XfM5NcsnqX7Yt3zYtbzYt7zYt7zYt3zYtbzY951xMGVkrK2t8fDDD8PPz++69zs5OcHCwuK6g/rIkSPo2LEjoqOj5Y5pMti3fNi1vNi3vOrT97FjxxAeHm5WT0YaGvuWD7uWF/uWF/uWF/uWD7uWF/u+M/P6bo1Ueno6YmNjsXXrVuj1+toDWqfT1W5KXFBQgLy8vNrPee2119CvXz/k5ORACKFIbmPFvuXDruXFvuV1t33fd9997PsusG/5sGt5sW95sW95sW/5sGt5se96km/VIN2NuLg44evrK9q2bSscHR1F+/btxcqVK0VOTo4Q4v83RDtz5oxo2rSpyM3NFW+99ZawsbERhw8fVjK6UWLf8mHX8mLf8mLf8mLf8mHX8mLf8mLf8mLf8mHX8mLf9cfBlAHLzMwU7du3F6+88opITk4WV65cEaNHjxYBAQHi9ddfF5mZmbWPvXr1qggPDxejR48WlpaWZntA3wv2LR92LS/2LS/2LS/2LR92LS/2LS/2LS/2LR92LS/2fXc4mDJgJ0+eFC1btrzhAJ09e7YIDg4WCxcuFCUlJUIIIRITE4UkScLGxkYcO3ZMgbTGj33Lh13Li33Li33Li33Lh13Li33Li33Li33Lh13Li33fHe4xZcC0Wi2qqqpQWloKACgrKwMAzJ8/H3369MHSpUuRlJQEAHB2dsazzz6Lo0ePIiwsTKnIRo19y4ddy4t9y4t9y4t9y4ddy4t9y4t9y4t9y4ddy4t93x1JCHPbVcu4REZGokmTJtixYwcAoKKiAlZWVgCAzp07w9/fH6tWrQIAlJeXw9raWrGspoB9y4ddy4t9y4t9y4t9y4ddy4t9y4t9y4t9y4ddy4t91x+vmDIgJSUlKCoqQmFhYe37vvjiC5w8eRJjx44FAFhZWaGqqgoA0LNnT5SUlNQ+lgd0/bBv+bBrebFvebFvebFv+bBrebFvebFvebFv+bBrebHvhsHBlIFITEzEI488gl69eiEgIAD/+9//AAABAQH4+OOPsW3bNowcORJarRYqVfUfW2ZmJuzs7FBVVWV+t5O8R+xbPuxaXuxbXuxbXuxbPuxaXuxbXuxbXuxbPuxaXuy74WiUDkDVB3TPnj0xYcIEdOrUCUeOHMGTTz6JwMBAhIeH46GHHoKdnR2effZZhISEoH379rC0tMRvv/2GAwcOQKPhH2N9sG/5sGt5sW95sW95sW/5sGt5sW95sW95sW/5sGt5se+GxT2mFJabm4sxY8agffv2+Pjjj2vf36dPHwQHB+OTTz6pfV9RURHefvtt5ObmwtraGs888wwCAwOViG202Ld82LW82Le82Le82Ld82LW82Le82Le82Ld82LW82HfD45hOYVqtFvn5+RgxYgQAQK/XQ6VSwc/PD7m5uQAAIQSEELC3t8eCBQuuexzVD/uWD7uWF/uWF/uWF/uWD7uWF/uWF/uWF/uWD7uWF/tueGxFYR4eHvjhhx8QHR0NANDpdACA5s2b1x60kiRBpVJdt6GaJEnyhzUB7Fs+7Fpe7Fte7Fte7Fs+7Fpe7Fte7Fte7Fs+7Fpe7LvhcTBlANq0aQOgeoJqYWEBoHrCmpmZWfuY9957D1999VXtbv48qO8e+5YPu5YX+5YX+5YX+5YPu5YX+5YX+5YX+5YPu5YX+25YXMpnQFQqFYQQtQdszbT1tddew9tvv41jx45xk7QGxL7lw67lxb7lxb7lxb7lw67lxb7lxb7lxb7lw67lxb4bBq+YMjA1e9FrNBr4+Pjg/fffx8KFC3H48GGEhoYqnM70sG/5sGt5sW95sW95sW/5sGt5sW95sW95sW/5sGt5se97x9GdgamZsFpYWODLL7+Eg4MD9u3bh4iICIWTmSb2LR92LS/2LS/2LS/2LR92LS/2LS/2LS/2LR92LS/2fe94xZSBGjBgAABg//796NSpk8JpTB/7lg+7lhf7lhf7lhf7lg+7lhf7lhf7lhf7lg+7lhf7vnuSqLnujAxOSUkJ7OzslI5hNti3fNi1vNi3vNi3vNi3fNi1vNi3vNi3vNi3fNi1vNj33eFgioiIiIiIiIiIFMGlfEREREREREREpAgOpoiIiIiIiIiISBEcTBERERERERERkSI4mCIiIiIiIiIiIkVwMEVERERERERERIrgYIqIiIiIiIiIiBTBwRQRERGRGZEkCevWrVM6BhEREREADqaIiIiIZPHEE09AkiRMmTLlho8999xzkCQJTzzxRIP9fm+88QbCwsIa7OsRERERNQYOpoiIiIhk4uPjgx9//BFlZWW17ysvL8fKlSvRokULBZMRERERKYODKSIiIiKZREREwMfHB2vXrq1939q1a9GiRQuEh4fXvq+iogJTp06Fu7s7rK2t0aNHDxw6dKj247t27YIkSdi+fTs6deoEW1tbREVF4cyZMwCA5cuXY968eYiLi4MkSZAkCcuXL6/9/OzsbAwbNgy2trZo06YNNmzY0PjfPBEREdFNcDBFREREJKOJEyfi22+/rX37m2++wZNPPnndY2bNmoU1a9ZgxYoVOHr0KPz9/TFgwADk5uZe97h///vfWLx4MQ4fPgyNRoOJEycCAEaPHo2ZM2eiQ4cOSE9PR3p6OkaPHl37efPmzcOoUaNw4sQJDB48GI899tgNX5uIiIhIDhxMEREREclo3Lhx2LdvHy5cuIALFy7gr7/+wrhx42o/XlJSgqVLl2LRokUYNGgQAgMD8eWXX8LGxgZff/31dV/rnXfeQa9evRAYGIg5c+Zg//79KC8vh42NDZo0aQKNRgNPT094enrCxsam9vOeeOIJjBkzBv7+/nj33XdRXFyM2NhY2TogIiIiqqFROgARERGROWnatCmGDBmC5cuXQwiBIUOGwM3NrfbjycnJ0Gq16N69e+37LCwsEBkZiVOnTl33tUJCQmr/38vLCwCQmZl5x/2q/v55dnZ2cHBwQGZm5j19X0RERER3g4MpIiIiIplNnDgRzz//PABgyZIld/11LCwsav9fkiQAgF6vr9fn1XxuXT6PiIiIqKFxKR8RERGRzAYOHIjKykpotVoMGDDguo+1bt0alpaW+Ouvv2rfp9VqcejQIQQGBtb597C0tIROp2uwzERERESNgVdMEREREclMrVbXLstTq9XXfczOzg7PPPMMXn75Zbi4uKBFixZYuHAhSktLMWnSpDr/Hi1btkRqaiqOHz8Ob29v2Nvbw8rKqkG/DyIiIqJ7xcEUERERkQIcHBxu+bH58+dDr9dj/PjxKCoqQqdOnfD777/D2dm5zl9/+PDhWLt2Lfr06YP8/Hx8++23eOKJJxogOREREVHDkYQQQukQRERERERERERkfrjHFBERERERERERKYKDKSIiIiIiIiIiUgQHU0REREREREREpAgOpoiIiIiIiIiISBEcTBERERERERERkSI4mCIiIiIiIiIiIkVwMEVERERERERERIrgYIqIiIiIiIiIiBTBwRQRERERERERESmCgykiIiIiIiIiIlIEB1NERERERERERKQIDqaIiIiIiIiIiEgR/wf4Vv/abl5H8gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Function to fetch data from Open Charge Map\n",
+    "def fetch_data(url, headers):\n",
+    "    response = requests.get(url, headers=headers)\n",
+    "    if response.status_code == 200:\n",
+    "        return response.json()\n",
+    "    else:\n",
+    "        print(f\"Failed to retrieve data. Status code: {response.status_code}\")\n",
+    "        return []\n",
+    "\n",
+    "# Example URL and headers (update 'YOUR_API_KEY' with your actual API key)\n",
+    "url = \"https://api.openchargemap.io/v3/poi/?output=json&countrycode=AU\"\n",
+    "headers = {\n",
+    "    \"X-API-Key\": \"2f5df05b-4b9d-46a3-b38e-c138e919abea\"\n",
+    "}\n",
+    "\n",
+    "# Fetch data\n",
+    "data = fetch_data(url, headers)\n",
+    "\n",
+    "# Convert to DataFrame for analysis\n",
+    "df = pd.json_normalize(data)\n",
+    "\n",
+    "# Convert 'DateCreated' to datetime\n",
+    "df['DateCreated'] = pd.to_datetime(df['DateCreated'])\n",
+    "# Convert 'DateCreated' to naive datetime\n",
+    "df['DateCreated'] = df['DateCreated'].dt.tz_localize(None)\n",
+    "\n",
+    "# Extract year and month\n",
+    "df['YearMonth'] = df['DateCreated'].dt.to_period('M')\n",
+    "\n",
+    "\n",
+    "# Filter data for 2023 and 2024\n",
+    "df = df[(df['DateCreated'].dt.year == 2023) | (df['DateCreated'].dt.year == 2024)]\n",
+    "\n",
+    "# Extract year and month\n",
+    "df['YearMonth'] = df['DateCreated'].dt.to_period('M')\n",
+    "\n",
+    "# Count the number of charging stations per month\n",
+    "stations_per_month = df.groupby('YearMonth').size()\n",
+    "\n",
+    "# Convert to DataFrame for plotting\n",
+    "stations_per_month_df = stations_per_month.reset_index(name='Number of Charging Stations')\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(stations_per_month_df['YearMonth'].astype(str), stations_per_month_df['Number of Charging Stations'], marker='o', linestyle='-')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Number of Charging Stations')\n",
+    "plt.title('Number of Charging Stations by Month (2023 & 2024)')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c68c945-afb2-4ac4-91a2-bbc9def86b26",
+   "metadata": {},
+   "source": [
+    "The above line plot indicates that the growth of charging stations was seen the most in March 2024. Hence, it indicates that the people showed more interest in using EVs during that time. However, in August 2024 (till now), the growth eas the least indicating that there has been a decrease in poularity of the EVs currently. This can be mainly attributed to the high maintenance and charging costs of EVs. This shows that the EV infrastructure growth will take some time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f304fb93-4a8c-4d04-b65f-d83509bf1ba5",
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