Medium_Tutorial_Create_Testset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the required dependencies\n",
    "\n",
    "import pandas as pd # python's library for data manipulation and preprocessing\n",
    "import numpy as np  # python's library for number crunching\n",
    "\n",
    "import matplotlib            # python's library for visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "\n",
    "import seaborn as sns        # also python's library for visualisations\n",
    "color = sns.color_palette()\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "\n",
    "import sklearn                #python's machine learning library\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = pd.read_csv('housing.csv')   # reading the data into a pandas dataframe\n",
    "housing.head()                         # calling the first five rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()  # exploratory analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7d6c7f06a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d6c7f0080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = housing.corr()   #creating a heatmap of the attributes in the dataset\n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.heatmap(correlation_matrix, center=0, annot=True, linewidths=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = housing.corr()      # showing correlations by target variable\n",
    "corr['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d6cd53860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(housing.median_income) # showing the distribution of the median_income variable in the dataset\n",
    "plt.show()                                  # check the distplot params though. see how you can make it prettier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0      822\n",
       "2.0     6581\n",
       "3.0     7236\n",
       "4.0     3639\n",
       "5.0     1423\n",
       "6.0      532\n",
       "7.0      189\n",
       "8.0      105\n",
       "9.0       50\n",
       "10.0      14\n",
       "11.0      49\n",
       "Name: median_income_category, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Divide by 1.5 to limit the number of income categories\n",
    "housing[\"median_income_category\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "\n",
    "# showing the frequency of each category\n",
    "housing.median_income_category.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     822\n",
       "2.0    6581\n",
       "3.0    7236\n",
       "4.0    3639\n",
       "5.0    2362\n",
       "Name: median_income_category, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label those above 5 as 5\n",
    "housing[\"median_income_category\"].where(housing[\"median_income_category\"] < 5, 5.0, inplace=True)\n",
    "\n",
    "\n",
    "housing.median_income_category.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>median_income_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5241</th>\n",
       "      <td>-118.39</td>\n",
       "      <td>34.12</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6447.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>8.2816</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10970</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>33.77</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4159.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>4.6111</td>\n",
       "      <td>240300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20351</th>\n",
       "      <td>-119.05</td>\n",
       "      <td>34.21</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4357.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3.0119</td>\n",
       "      <td>218200.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6568</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>4.1518</td>\n",
       "      <td>182100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13285</th>\n",
       "      <td>-117.68</td>\n",
       "      <td>34.07</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>4.0375</td>\n",
       "      <td>121300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "5241     -118.39     34.12                29.0       6447.0          1012.0   \n",
       "10970    -117.86     33.77                39.0       4159.0           655.0   \n",
       "20351    -119.05     34.21                27.0       4357.0           926.0   \n",
       "6568     -118.15     34.20                52.0       1786.0           306.0   \n",
       "13285    -117.68     34.07                32.0       1775.0           314.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "5241       2184.0       960.0         8.2816            500001.0   \n",
       "10970      1669.0       651.0         4.6111            240300.0   \n",
       "20351      2110.0       876.0         3.0119            218200.0   \n",
       "6568       1018.0       322.0         4.1518            182100.0   \n",
       "13285      1067.0       302.0         4.0375            121300.0   \n",
       "\n",
       "      ocean_proximity  median_income_category  \n",
       "5241        <1H OCEAN                     5.0  \n",
       "10970       <1H OCEAN                     4.0  \n",
       "20351       <1H OCEAN                     3.0  \n",
       "6568           INLAND                     3.0  \n",
       "13285          INLAND                     3.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"median_income_category\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]\n",
    "strat_test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Overall</th>\n",
       "      <th>Random</th>\n",
       "      <th>Stratified</th>\n",
       "      <th>Rand. %error</th>\n",
       "      <th>Strat. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Overall    Random  Stratified  Rand. %error  Strat. %error\n",
       "1.0  0.039826  0.040213    0.039729      0.973236      -0.243309\n",
       "2.0  0.318847  0.324370    0.318798      1.732260      -0.015195\n",
       "3.0  0.350581  0.358527    0.350533      2.266446      -0.013820\n",
       "4.0  0.176308  0.167393    0.176357     -5.056334       0.027480\n",
       "5.0  0.114438  0.109496    0.114583     -4.318374       0.127011"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def income_cat_props(data):\n",
    "    return data['median_income_category'].value_counts() / len(data)\n",
    "\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"Overall\": income_cat_props(housing),\n",
    "    \"Stratified\": income_cat_props(strat_test_set),\n",
    "    \"Random\": income_cat_props(test_set),\n",
    "}).sort_index()\n",
    "\n",
    "\n",
    "compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100\n",
    "compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100\n",
    "\n",
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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