ML_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Load Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression , LogisticRegression\n",
    "from mlxtend.plotting import plot_decision_regions , plot_confusion_matrix\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Load dataset \n",
    "\n",
    "df = pd.read_csv(\"Dataset .csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "\n",
       "                           Cuisines  ...          Currency Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...  Botswana Pula(P)               Yes   \n",
       "1                          Japanese  ...  Botswana Pula(P)               Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "\n",
       "   Aggregate rating  Rating color Rating text Votes  \n",
       "0               4.8    Dark Green   Excellent   314  \n",
       "1               4.5    Dark Green   Excellent   591  \n",
       "2               4.4         Green   Very Good   270  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9551 entries, 0 to 9550\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Restaurant ID         9551 non-null   int64  \n",
      " 1   Restaurant Name       9551 non-null   object \n",
      " 2   Country Code          9551 non-null   int64  \n",
      " 3   City                  9551 non-null   object \n",
      " 4   Address               9551 non-null   object \n",
      " 5   Locality              9551 non-null   object \n",
      " 6   Locality Verbose      9551 non-null   object \n",
      " 7   Longitude             9551 non-null   float64\n",
      " 8   Latitude              9551 non-null   float64\n",
      " 9   Cuisines              9542 non-null   object \n",
      " 10  Average Cost for two  9551 non-null   int64  \n",
      " 11  Currency              9551 non-null   object \n",
      " 12  Has Table booking     9551 non-null   object \n",
      " 13  Has Online delivery   9551 non-null   object \n",
      " 14  Is delivering now     9551 non-null   object \n",
      " 15  Switch to order menu  9551 non-null   object \n",
      " 16  Price range           9551 non-null   int64  \n",
      " 17  Aggregate rating      9551 non-null   float64\n",
      " 18  Rating color          9551 non-null   object \n",
      " 19  Rating text           9551 non-null   object \n",
      " 20  Votes                 9551 non-null   int64  \n",
      "dtypes: float64(3), int64(5), object(13)\n",
      "memory usage: 1.5+ MB\n"
     ]
    },
    {
     "data": {
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       "    }\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>Restaurant ID</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.551000e+03</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.051128e+06</td>\n",
       "      <td>18.365616</td>\n",
       "      <td>64.126574</td>\n",
       "      <td>25.854381</td>\n",
       "      <td>1199.210763</td>\n",
       "      <td>1.804837</td>\n",
       "      <td>2.666370</td>\n",
       "      <td>156.909748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.791521e+06</td>\n",
       "      <td>56.750546</td>\n",
       "      <td>41.467058</td>\n",
       "      <td>11.007935</td>\n",
       "      <td>16121.183073</td>\n",
       "      <td>0.905609</td>\n",
       "      <td>1.516378</td>\n",
       "      <td>430.169145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-157.948486</td>\n",
       "      <td>-41.330428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.019625e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.081343</td>\n",
       "      <td>28.478713</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.004089e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.191964</td>\n",
       "      <td>28.570469</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.835229e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.282006</td>\n",
       "      <td>28.642758</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>131.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.850065e+07</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>174.832089</td>\n",
       "      <td>55.976980</td>\n",
       "      <td>800000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>10934.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant ID  Country Code    Longitude     Latitude  \\\n",
       "count   9.551000e+03   9551.000000  9551.000000  9551.000000   \n",
       "mean    9.051128e+06     18.365616    64.126574    25.854381   \n",
       "std     8.791521e+06     56.750546    41.467058    11.007935   \n",
       "min     5.300000e+01      1.000000  -157.948486   -41.330428   \n",
       "25%     3.019625e+05      1.000000    77.081343    28.478713   \n",
       "50%     6.004089e+06      1.000000    77.191964    28.570469   \n",
       "75%     1.835229e+07      1.000000    77.282006    28.642758   \n",
       "max     1.850065e+07    216.000000   174.832089    55.976980   \n",
       "\n",
       "       Average Cost for two  Price range  Aggregate rating         Votes  \n",
       "count           9551.000000  9551.000000       9551.000000   9551.000000  \n",
       "mean            1199.210763     1.804837          2.666370    156.909748  \n",
       "std            16121.183073     0.905609          1.516378    430.169145  \n",
       "min                0.000000     1.000000          0.000000      0.000000  \n",
       "25%              250.000000     1.000000          2.500000      5.000000  \n",
       "50%              400.000000     2.000000          3.200000     31.000000  \n",
       "75%              700.000000     2.000000          3.700000    131.000000  \n",
       "max           800000.000000     4.000000          4.900000  10934.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.info()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID           0\n",
       "Restaurant Name         0\n",
       "Country Code            0\n",
       "City                    0\n",
       "Address                 0\n",
       "Locality                0\n",
       "Locality Verbose        0\n",
       "Longitude               0\n",
       "Latitude                0\n",
       "Cuisines                9\n",
       "Average Cost for two    0\n",
       "Currency                0\n",
       "Has Table booking       0\n",
       "Has Online delivery     0\n",
       "Is delivering now       0\n",
       "Switch to order menu    0\n",
       "Price range             0\n",
       "Aggregate rating        0\n",
       "Rating color            0\n",
       "Rating text             0\n",
       "Votes                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  checking the null values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  cusienes column has 9 missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_2248\\717271624.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Cuisines'].fillna(df['Cuisines'].mode()[0], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Handle missing values by filling with the mode\n",
    "df['Cuisines'].fillna(df['Cuisines'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns to drop (unique identifiers and highly verbose columns)\n",
    "columns_to_drop = ['Restaurant ID', 'Restaurant Name', 'Address', 'Locality Verbose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the columns\n",
    "df.drop(columns=columns_to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encode categorical variables\n",
    "categorical_columns = ['City', 'Locality', 'Cuisines', 'Currency', 'Has Table booking',\n",
    "                       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
    "                       'Rating color', 'Rating text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoders = {}\n",
    "for column in categorical_columns:\n",
    "    le = LabelEncoder()\n",
    "    df[column] = le.fit_transform(df[column])\n",
    "    label_encoders[column] = le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into features and target variable\n",
    "\n",
    "X = df.drop(columns=['Aggregate rating'])\n",
    "y = df['Aggregate rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Split the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7640, 16), (1911, 16), (7640,), (1911,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shapping the data\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Select and Train a Regression Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1st TRY - Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127.247503553242, 44.094283667874755)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a Linear Regression model\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_lr = lr_model.predict(X_test)\n",
    "\n",
    "# Evaluate the Linear Regression model\n",
    "mse_lr = mean_squared_error(y_test, y_pred_lr)\n",
    "r2_lr = r2_score(y_test, y_pred_lr)\n",
    "\n",
    "(mse_lr)*100, (r2_lr)*100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.623233908948193, 97.52945314442802)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a Decision Tree Regression model\n",
    "dt_model = DecisionTreeRegressor(random_state=42)\n",
    "dt_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_dt = dt_model.predict(X_test)\n",
    "\n",
    "# Evaluate the Decision Tree Regression model\n",
    "mse_dt = mean_squared_error(y_test, y_pred_dt)\n",
    "r2_dt = r2_score(y_test, y_pred_dt)\n",
    "\n",
    "(mse_dt)*100, (r2_dt)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 5.623233908948193\n",
      "R-squared: 97.52945314442802\n"
     ]
    }
   ],
   "source": [
    "# Train a Decision Tree Regression model\n",
    "\n",
    "dt_model = DecisionTreeRegressor(random_state=42)\n",
    "dt_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_dt = dt_model.predict(X_test)\n",
    "\n",
    "# Evaluate the Decision Tree Regression model\n",
    "mse_dt = mean_squared_error(y_test, y_pred_dt)\n",
    "r2_dt = r2_score(y_test, y_pred_dt)\n",
    "\n",
    "print(f'MSE: {(mse_dt)*100}')\n",
    "print(f'R-squared: {(r2_dt*100)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(X_train, y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.52945314442802"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(X_test, y_test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Steps to Validate Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores: [0.96817909 0.97232087 0.97730139 0.97500686 0.97710549]\n",
      "Mean CV Score: 0.973982739465286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv_scores = cross_val_score(dt_model, X, y, cv=5)\n",
    "print(f'Cross-Validation Scores: {cv_scores}')\n",
    "print(f'Mean CV Score: {cv_scores.mean()}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_model = RandomForestRegressor(random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.82060474174213"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.score(X_train, y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.71163947241686"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.score(X_test, y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest MSE: 0.029324489795918344\n",
      "Random Forest R-squared: 0.9871163947241687\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf = rf_model.predict(X_test)\n",
    "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
    "r2_rf = r2_score(y_test, y_pred_rf)\n",
    "\n",
    "print(f'Random Forest MSE: {mse_rf}')\n",
    "print(f'Random Forest R-squared: {r2_rf}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 10}\n",
      "Best Model MSE: 0.0306703139923635\n",
      "Best Model R-squared: 0.9865251118804319\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "grid_search = GridSearchCV(estimator=dt_model, param_grid=param_grid, cv=5, n_jobs=-1, scoring='r2')\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(f'Best Parameters: {grid_search.best_params_}')\n",
    "best_model = grid_search.best_estimator_\n",
    "y_pred_best = best_model.predict(X_test)\n",
    "mse_best = mean_squared_error(y_test, y_pred_best)\n",
    "r2_best = r2_score(y_test, y_pred_best)\n",
    "\n",
    "print(f'Best Model MSE: {mse_best}')\n",
    "print(f'Best Model R-squared: {r2_best}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.88546816861812"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.score(X_train, y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.65251118804319"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.score(X_test, y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country Code            0\n",
       "City                    0\n",
       "Locality                0\n",
       "Longitude               0\n",
       "Latitude                0\n",
       "Cuisines                0\n",
       "Average Cost for two    0\n",
       "Currency                0\n",
       "Has Table booking       0\n",
       "Has Online delivery     0\n",
       "Is delivering now       0\n",
       "Switch to order menu    0\n",
       "Price range             0\n",
       "Aggregate rating        0\n",
       "Rating color            0\n",
       "Rating text             0\n",
       "Votes                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"Cuisines\" ,\"Aggregate rating\" ,\"Votes\"]\n",
    "x_new = df.iloc[:,:-1]\n",
    "y_new = df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_new_train , x_new_test, y_new_train , y_new_test = train_test_split(x_new,y_new, test_size=0.2 , random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.314181154044064"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_train, y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.094283667874755"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test, y_test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation R-squared Scores: [0.97884117 0.98275032 0.9866871  0.98647772 0.98590774]\n",
      "Mean Cross-Validation R-squared: 0.9841328072804361\n"
     ]
    }
   ],
   "source": [
    "# \n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv_scores = cross_val_score(best_model, X, y, cv=5, scoring='r2')\n",
    "print(f'Cross-Validation R-squared Scores: {cv_scores}')\n",
    "print(f'Mean Cross-Validation R-squared: {cv_scores.mean()}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Feature  Importance\n",
      "15                 Votes    0.907882\n",
      "13          Rating color    0.082911\n",
      "14           Rating text    0.007485\n",
      "4               Latitude    0.000483\n",
      "3              Longitude    0.000389\n",
      "6   Average Cost for two    0.000306\n",
      "5               Cuisines    0.000274\n",
      "2               Locality    0.000107\n",
      "8      Has Table booking    0.000048\n",
      "9    Has Online delivery    0.000036\n",
      "12           Price range    0.000035\n",
      "1                   City    0.000029\n",
      "7               Currency    0.000014\n",
      "0           Country Code    0.000000\n",
      "10     Is delivering now    0.000000\n",
      "11  Switch to order menu    0.000000\n"
     ]
    }
   ],
   "source": [
    "# Feature Importance:\n",
    "\n",
    "feature_importances = best_model.feature_importances_\n",
    "features = X.columns\n",
    "\n",
    "importances_df = pd.DataFrame({'Feature': features, 'Importance': feature_importances})\n",
    "importances_df = importances_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print(importances_df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "residuals = y_test - y_pred_best\n",
    "plt.scatter(y_test, residuals)\n",
    "plt.xlabel('Actual Ratings')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residuals vs Actual Ratings')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['restaurant_rating_model.pkl']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Assuming `best_model` is your trained model\n",
    "joblib.dump(best_model, 'restaurant_rating_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Evaluate the Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Interpret the Model's Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Feature  Importance\n",
      "15                 Votes    0.899631\n",
      "13          Rating color    0.081987\n",
      "14           Rating text    0.007401\n",
      "4               Latitude    0.002868\n",
      "3              Longitude    0.002496\n",
      "5               Cuisines    0.001737\n",
      "6   Average Cost for two    0.001645\n",
      "2               Locality    0.001361\n",
      "9    Has Online delivery    0.000348\n",
      "1                   City    0.000197\n",
      "12           Price range    0.000164\n",
      "8      Has Table booking    0.000104\n",
      "7               Currency    0.000027\n",
      "0           Country Code    0.000021\n",
      "10     Is delivering now    0.000013\n",
      "11  Switch to order menu    0.000000\n"
     ]
    }
   ],
   "source": [
    "feature_importances = dt_model.feature_importances_\n",
    "features = X.columns\n",
    "\n",
    "importances_df = pd.DataFrame({'Feature': features, 'Importance': feature_importances})\n",
    "importances_df = importances_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print(importances_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "noman",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}