Machinelearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#melbourne_data=pd.read_csv(\"Machine Learning Data/melb_data.csv\")\n",
    "melbourne_file_path = 'Machine Learning Data/melb_data.csv'\n",
    "melbourne_data=pd.read_csv(melbourne_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#melbourne_data.describe()#only show the integer columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#melbourne_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
       "       'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n",
       "       'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n",
       "       'Longtitude', 'Regionname', 'Propertycount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melbourne_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>124 Yarra St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1876000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>7/05/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8024</td>\n",
       "      <td>144.9993</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>98 Charles St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1636000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8060</td>\n",
       "      <td>144.9954</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb          Address  Rooms Type      Price Method SellerG  \\\n",
       "1  Abbotsford  25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "2  Abbotsford     5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "4  Abbotsford      55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "6  Abbotsford     124 Yarra St      3    h  1876000.0      S  Nelson   \n",
       "7  Abbotsford    98 Charles St      2    h  1636000.0      S  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode      ...       Bathroom  Car  Landsize  \\\n",
       "1  4/02/2016       2.5    3067.0      ...            1.0  0.0     156.0   \n",
       "2  4/03/2017       2.5    3067.0      ...            2.0  0.0     134.0   \n",
       "4  4/06/2016       2.5    3067.0      ...            1.0  2.0     120.0   \n",
       "6  7/05/2016       2.5    3067.0      ...            2.0  0.0     245.0   \n",
       "7  8/10/2016       2.5    3067.0      ...            1.0  2.0     256.0   \n",
       "\n",
       "   BuildingArea  YearBuilt  CouncilArea Lattitude  Longtitude  \\\n",
       "1          79.0     1900.0        Yarra  -37.8079    144.9934   \n",
       "2         150.0     1900.0        Yarra  -37.8093    144.9944   \n",
       "4         142.0     2014.0        Yarra  -37.8072    144.9941   \n",
       "6         210.0     1910.0        Yarra  -37.8024    144.9993   \n",
       "7         107.0     1890.0        Yarra  -37.8060    144.9954   \n",
       "\n",
       "              Regionname Propertycount  \n",
       "1  Northern Metropolitan        4019.0  \n",
       "2  Northern Metropolitan        4019.0  \n",
       "4  Northern Metropolitan        4019.0  \n",
       "6  Northern Metropolitan        4019.0  \n",
       "7  Northern Metropolitan        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melbourne_data = melbourne_data.dropna(axis=0)\n",
    "melbourne_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=melbourne_data.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1035000.0\n",
       "2    1465000.0\n",
       "4    1600000.0\n",
       "6    1876000.0\n",
       "7    1636000.0\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = melbourne_data[melbourne_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6196.000000</td>\n",
       "      <td>6196.000000</td>\n",
       "      <td>6196.000000</td>\n",
       "      <td>6196.000000</td>\n",
       "      <td>6196.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.931407</td>\n",
       "      <td>1.576340</td>\n",
       "      <td>471.006940</td>\n",
       "      <td>-37.807904</td>\n",
       "      <td>144.990201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.971079</td>\n",
       "      <td>0.711362</td>\n",
       "      <td>897.449881</td>\n",
       "      <td>0.075850</td>\n",
       "      <td>0.099165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-38.164920</td>\n",
       "      <td>144.542370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>-37.855438</td>\n",
       "      <td>144.926198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>-37.802250</td>\n",
       "      <td>144.995800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>-37.758200</td>\n",
       "      <td>145.052700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>37000.000000</td>\n",
       "      <td>-37.457090</td>\n",
       "      <td>145.526350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rooms     Bathroom      Landsize    Lattitude   Longtitude\n",
       "count  6196.000000  6196.000000   6196.000000  6196.000000  6196.000000\n",
       "mean      2.931407     1.576340    471.006940   -37.807904   144.990201\n",
       "std       0.971079     0.711362    897.449881     0.075850     0.099165\n",
       "min       1.000000     1.000000      0.000000   -38.164920   144.542370\n",
       "25%       2.000000     1.000000    152.000000   -37.855438   144.926198\n",
       "50%       3.000000     1.000000    373.000000   -37.802250   144.995800\n",
       "75%       4.000000     2.000000    628.000000   -37.758200   145.052700\n",
       "max       8.000000     8.000000  37000.000000   -37.457090   145.526350"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>-37.8024</td>\n",
       "      <td>144.9993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>-37.8060</td>\n",
       "      <td>144.9954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n",
       "1      2       1.0     156.0   -37.8079    144.9934\n",
       "2      3       2.0     134.0   -37.8093    144.9944\n",
       "4      4       1.0     120.0   -37.8072    144.9941\n",
       "6      3       2.0     245.0   -37.8024    144.9993\n",
       "7      2       1.0     256.0   -37.8060    144.9954"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=1, splitter='best')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######################################################################################################\n",
    "#starting building the ML model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Define model. Specify a number for random_state to ensure same results each run\n",
    "melbourne_model = DecisionTreeRegressor(random_state=1)\n",
    "\n",
    "# Fit model\n",
    "melbourne_model.fit(X, y)# X versus houseprice y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making predictions for the following 5 houses:\n",
      "   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n",
      "1      2       1.0     156.0   -37.8079    144.9934\n",
      "2      3       2.0     134.0   -37.8093    144.9944\n",
      "4      4       1.0     120.0   -37.8072    144.9941\n",
      "6      3       2.0     245.0   -37.8024    144.9993\n",
      "7      2       1.0     256.0   -37.8060    144.9954\n",
      "The predictions are\n",
      "[1035000. 1465000. 1600000. 1876000. 1636000.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Making predictions for the following 5 houses:\")\n",
    "print(X.head())\n",
    "print(\"The predictions are\")\n",
    "print(melbourne_model.predict(X.head()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1115.7467183128902"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "predicted_home_prices = melbourne_model.predict(X)\n",
    "mean_absolute_error(y, predicted_home_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "274310.70819883794\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Since models' practical value come from making predictions on new data, we measure performance on \n",
    "data that wasn't used to build the model. The most straightforward way to do this is to exclude \n",
    "some data from the model-building process, and then use those to test the model's accuracy on data it \n",
    "hasn't seen before. This data is called validation data.\n",
    "1.separate the date set into two sets\n",
    "2.one set for model-building(training)\n",
    "3.the rest for testing(validation)\n",
    "\"\"\"\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)\n",
    "# Define model\n",
    "melbourne_model = DecisionTreeRegressor()\n",
    "# Fit model\n",
    "melbourne_model.fit(train_X, train_y)\n",
    "\n",
    "# get predicted prices on validation data\n",
    "val_predictions = melbourne_model.predict(val_X)\n",
    "print(mean_absolute_error(val_y, val_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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