Basic Data Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a34b6c09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78bbc8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13580.000000</td>\n",
       "      <td>1.358000e+04</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13518.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>7130.000000</td>\n",
       "      <td>8205.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.937997</td>\n",
       "      <td>1.075684e+06</td>\n",
       "      <td>10.137776</td>\n",
       "      <td>3105.301915</td>\n",
       "      <td>2.914728</td>\n",
       "      <td>1.534242</td>\n",
       "      <td>1.610075</td>\n",
       "      <td>558.416127</td>\n",
       "      <td>151.967650</td>\n",
       "      <td>1964.684217</td>\n",
       "      <td>-37.809203</td>\n",
       "      <td>144.995216</td>\n",
       "      <td>7454.417378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.955748</td>\n",
       "      <td>6.393107e+05</td>\n",
       "      <td>5.868725</td>\n",
       "      <td>90.676964</td>\n",
       "      <td>0.965921</td>\n",
       "      <td>0.691712</td>\n",
       "      <td>0.962634</td>\n",
       "      <td>3990.669241</td>\n",
       "      <td>541.014538</td>\n",
       "      <td>37.273762</td>\n",
       "      <td>0.079260</td>\n",
       "      <td>0.103916</td>\n",
       "      <td>4378.581772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1196.000000</td>\n",
       "      <td>-38.182550</td>\n",
       "      <td>144.431810</td>\n",
       "      <td>249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.500000e+05</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>3044.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>-37.856822</td>\n",
       "      <td>144.929600</td>\n",
       "      <td>4380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.030000e+05</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>3084.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>-37.802355</td>\n",
       "      <td>145.000100</td>\n",
       "      <td>6555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.330000e+06</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>-37.756400</td>\n",
       "      <td>145.058305</td>\n",
       "      <td>10331.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000e+06</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>3977.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>433014.000000</td>\n",
       "      <td>44515.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>-37.408530</td>\n",
       "      <td>145.526350</td>\n",
       "      <td>21650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
       "count  13580.000000  1.358000e+04  13580.000000  13580.000000  13580.000000   \n",
       "mean       2.937997  1.075684e+06     10.137776   3105.301915      2.914728   \n",
       "std        0.955748  6.393107e+05      5.868725     90.676964      0.965921   \n",
       "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
       "25%        2.000000  6.500000e+05      6.100000   3044.000000      2.000000   \n",
       "50%        3.000000  9.030000e+05      9.200000   3084.000000      3.000000   \n",
       "75%        3.000000  1.330000e+06     13.000000   3148.000000      3.000000   \n",
       "max       10.000000  9.000000e+06     48.100000   3977.000000     20.000000   \n",
       "\n",
       "           Bathroom           Car       Landsize  BuildingArea    YearBuilt  \\\n",
       "count  13580.000000  13518.000000   13580.000000   7130.000000  8205.000000   \n",
       "mean       1.534242      1.610075     558.416127    151.967650  1964.684217   \n",
       "std        0.691712      0.962634    3990.669241    541.014538    37.273762   \n",
       "min        0.000000      0.000000       0.000000      0.000000  1196.000000   \n",
       "25%        1.000000      1.000000     177.000000     93.000000  1940.000000   \n",
       "50%        1.000000      2.000000     440.000000    126.000000  1970.000000   \n",
       "75%        2.000000      2.000000     651.000000    174.000000  1999.000000   \n",
       "max        8.000000     10.000000  433014.000000  44515.000000  2018.000000   \n",
       "\n",
       "          Lattitude    Longtitude  Propertycount  \n",
       "count  13580.000000  13580.000000   13580.000000  \n",
       "mean     -37.809203    144.995216    7454.417378  \n",
       "std        0.079260      0.103916    4378.581772  \n",
       "min      -38.182550    144.431810     249.000000  \n",
       "25%      -37.856822    144.929600    4380.000000  \n",
       "50%      -37.802355    145.000100    6555.000000  \n",
       "75%      -37.756400    145.058305   10331.000000  \n",
       "max      -37.408530    145.526350   21650.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melbourne_file_path = '.\\melb_data.csv'\n",
    "melbourne_data = pd.read_csv(melbourne_file_path)\n",
    "melbourne_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8934342",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melbourne_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4b941c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "melbourne_data = melbourne_data.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e49aeba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = melbourne_data.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59451b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "melbourne_features = [\"Rooms\", 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63b71b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = melbourne_data[melbourne_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fac57242",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adf6c7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2624448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "melbourne_model = DecisionTreeRegressor(random_state=1)\n",
    "melbourne_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "965b0760",
   "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",
      "Predictions are \n",
      "[1035000. 1465000. 1600000. 1876000. 1636000.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Making Predictions for the following 5 houses: \")\n",
    "print(X.head())\n",
    "print(\"Predictions are \")\n",
    "print(melbourne_model.predict(X.head()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b4feb1fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Libraries to hanle document\n",
    "import pandas as pd\n",
    "\n",
    "#load data Note: Is easier to define a path that calling the file over and over\n",
    "melbourne_file_path = '.\\melb_data.csv'\n",
    "melbourne_data = pd.read_csv(melbourne_file_path)\n",
    "#filter row with missing prices (removing nan values)\n",
    "filtered_melbourne_data = melbourne_data.dropna(axis=0)\n",
    "#choose target and feature\n",
    "y = filtered_melbourne_data.Price\n",
    "melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', \n",
    "                        'YearBuilt', 'Lattitude', 'Longtitude']\n",
    "X = filtered_melbourne_data[melbourne_features]\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "#Define Model\n",
    "melbourne_model = DecisionTreeRegressor()\n",
    "#fit model\n",
    "melbourne_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8031783f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "434.72\n"
     ]
    }
   ],
   "source": [
    "#Mean Absolute Error is error = actual - predicted\n",
    "#Calculating MAE (Mean Absolute Error)\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "predicted_home_prices = melbourne_model.predict(X)\n",
    "result = mean_absolute_error(y, predicted_home_prices)\n",
    "print(\"%.2f\" % result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af7eefcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "261898.77\n"
     ]
    }
   ],
   "source": [
    "#Trainning a model\n",
    "from sklearn.model_selection import train_test_split\n",
    "# split data into training and validation data, for both features and target\n",
    "# The split is based on a random number generator. Supplying a numeric value to\n",
    "# the random_state argument guarantees we get the same split every time we\n",
    "# run this script.\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",
    "#Get predicted prices on validation data\n",
    "val_predictions = melbourne_model.predict(val_X)\n",
    "print(\"%.2f\" % mean_absolute_error(val_y, val_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c9f5d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn. tree import DecisionTreeRegressor\n",
    "\n",
    "def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):\n",
    "    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state = 0)\n",
    "    model.fit(train_X, train_y)\n",
    "    preds_val = model.predict(val_X)\n",
    "    mae = mean_absolute_error(val_y, preds_val)\n",
    "    return(mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc34ef0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max leaf nodes: 5 \t \t Mean Absolute Error: 347380\n",
      "Max leaf nodes: 50 \t \t Mean Absolute Error: 258171\n",
      "Max leaf nodes: 500 \t \t Mean Absolute Error: 243495\n",
      "Max leaf nodes: 5000 \t \t Mean Absolute Error: 254983\n"
     ]
    }
   ],
   "source": [
    "#Data Loading Code runs at this Point\n",
    "import pandas as pd\n",
    "melbourne_file_path = '.\\melb_data.csv'\n",
    "melbourne_data = pd.read_csv(melbourne_file_path)\n",
    "filtered_melbourne_data = melbourne_data.dropna(axis=0)\n",
    "#Choosing target and features\n",
    "y = filtered_melbourne_data.Price\n",
    "melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', \n",
    "                        'YearBuilt', 'Lattitude', 'Longtitude']\n",
    "X = filtered_melbourne_data[melbourne_features]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#split data into trainning and validation data for both features and target\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)\n",
    "\n",
    "#we can use a for-loop to compare accurancy of models built with different values for max_leaf_node\n",
    "#compare MAE with different values of max_leaf_node\n",
    "for max_leaf_nodes in [5, 50, 500, 5000]:\n",
    "    my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)\n",
    "    print(\"Max leaf nodes: %d \\t \\t Mean Absolute Error: %d\" %(max_leaf_nodes, my_mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e4e9c6e",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'SalePrice'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_40700/2359966417.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0miowa_file_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'.\\melb_data.csv'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mhome_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miowa_file_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhome_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSalePrice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'LotArea'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'YearBuilt'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'1stFlrSF'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'2ndFlrSF'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'FullBath'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'BedroomAbvGr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TotRmsAbvGrd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhome_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\NLP_Heat\\Py\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5485\u001b[0m         ):\n\u001b[0;32m   5486\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5487\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5489\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'SalePrice'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "iowa_file_path = '.\\melb_data.csv'\n",
    "home_data = pd.read_csv(iowa_file_path)\n",
    "y = home_data.SalePrice\n",
    "features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']\n",
    "X = home_data[features]\n",
    "train_X, val_x, train_y, val_y = train_test_split(X, y, random_state = 1)\n",
    "iowa_model = DecisionTreeRegressor(random_state = 1)\n",
    "iowa_model.fit(train_X, train_y)\n",
    "val_predictions = iowa_model.predict(val_X)\n",
    "val_mae = mean_absolute_error(val_predictions, val_y)\n",
    "print(\"Validation MAE: {:,.0f}\".format(val_mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9add818d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):\n",
    "    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)\n",
    "    model.fit(train_X, train_y)\n",
    "    preds_val = model.predict(val_X)\n",
    "    mae = mean_absolute_error(val_y, preds_val)\n",
    "    return(mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "965606ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "candidate_max_leaf_nodes = [5, 25, 50, 100, 250, 500]\n",
    "# Write loop to find the ideal tree size from candidate_max_leaf_nodes\n",
    "scores = {leaf_size: get_mae(leaf_size, train_X, val_X, train_y, val_y) for leaf_size in candidate_max_leaf_nodes}\n",
    "\n",
    "# Store the best value of max_leaf_nodes (it will be either 5, 25, 50, 100, 250 or 500)\n",
    "best_tree_size = min(scores, key=scores.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b066cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_leaf_nodes=500, random_state=1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill in argument to make optimal size and uncomment\n",
    "final_model = DecisionTreeRegressor(max_leaf_nodes = best_tree_size, random_state = 1)\n",
    "\n",
    "# fit the final model and uncomment the next two lines\n",
    "final_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4c02708c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "melbourne_file_path = '.\\melb_data.csv'\n",
    "melbourne_data = pd.read_csv(melbourne_file_path)\n",
    "melbourne_data = melbourne_data.dropna(axis=0)\n",
    "y=melbourne_data.Price\n",
    "melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', \n",
    "                        'YearBuilt', 'Lattitude', 'Longtitude']\n",
    "X = melbourne_data[melbourne_features]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_X, val_x, train_y, val_y = train_test_split(X,y, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "399f140d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191669.7536453626\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "forest_model = RandomForestRegressor(random_state=1)\n",
    "forest_model.fit(train_X, train_y)\n",
    "melb_preds = forest_model.predict(val_X)\n",
    "print(mean_absolute_error(val_y, melb_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9795b69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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