model_validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../../data/melb_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13580, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropped = df.dropna(axis=0) # Drop rows which is not available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6196, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dropped['Price'] # select prediction target, its 'Price'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dropped[features] # selecting feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
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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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_model = DecisionTreeRegressor(random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=1, splitter='best')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_model.fit(X, y) # fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making prediction for 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. ...  385000.  560000. 2450000.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Making prediction for following 5 houses\")\n",
    "print(X.head())\n",
    "print(\"Predictions are\")\n",
    "print(ml_model.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_houses_prices = ml_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1115.7467183128902"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y, predicted_houses_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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