week5_p67_selectkbest.ipynb

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   "source": [
    "# https://www.kaggle.com/code/pasqualebalsebre/a-ml-analysis-of-california-housing-data/notebook\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -122.23     37.88                41.0        880.0           129.0   \n1        -122.22     37.86                21.0       7099.0          1106.0   \n2        -122.24     37.85                52.0       1467.0           190.0   \n3        -122.25     37.85                52.0       1274.0           235.0   \n4        -122.25     37.85                52.0       1627.0           280.0   \n...          ...       ...                 ...          ...             ...   \n20635    -121.09     39.48                25.0       1665.0           374.0   \n20636    -121.21     39.49                18.0        697.0           150.0   \n20637    -121.22     39.43                17.0       2254.0           485.0   \n20638    -121.32     39.43                18.0       1860.0           409.0   \n20639    -121.24     39.37                16.0       2785.0           616.0   \n\n       population  households  median_income  ocean_proximity_<1H OCEAN  \\\n0           322.0       126.0         8.3252                          0   \n1          2401.0      1138.0         8.3014                          0   \n2           496.0       177.0         7.2574                          0   \n3           558.0       219.0         5.6431                          0   \n4           565.0       259.0         3.8462                          0   \n...           ...         ...            ...                        ...   \n20635       845.0       330.0         1.5603                          0   \n20636       356.0       114.0         2.5568                          0   \n20637      1007.0       433.0         1.7000                          0   \n20638       741.0       349.0         1.8672                          0   \n20639      1387.0       530.0         2.3886                          0   \n\n       ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n0                           0                       0   \n1                           0                       0   \n2                           0                       0   \n3                           0                       0   \n4                           0                       0   \n...                       ...                     ...   \n20635                       1                       0   \n20636                       1                       0   \n20637                       1                       0   \n20638                       1                       0   \n20639                       1                       0   \n\n       ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \n0                             1                           0  \n1                             1                           0  \n2                             1                           0  \n3                             1                           0  \n4                             1                           0  \n...                         ...                         ...  \n20635                         0                           0  \n20636                         0                           0  \n20637                         0                           0  \n20638                         0                           0  \n20639                         0                           0  \n\n[20640 rows x 13 columns]",
      "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>ocean_proximity_&lt;1H OCEAN</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>20635</th>\n      <td>-121.09</td>\n      <td>39.48</td>\n      <td>25.0</td>\n      <td>1665.0</td>\n      <td>374.0</td>\n      <td>845.0</td>\n      <td>330.0</td>\n      <td>1.5603</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20636</th>\n      <td>-121.21</td>\n      <td>39.49</td>\n      <td>18.0</td>\n      <td>697.0</td>\n      <td>150.0</td>\n      <td>356.0</td>\n      <td>114.0</td>\n      <td>2.5568</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20637</th>\n      <td>-121.22</td>\n      <td>39.43</td>\n      <td>17.0</td>\n      <td>2254.0</td>\n      <td>485.0</td>\n      <td>1007.0</td>\n      <td>433.0</td>\n      <td>1.7000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20638</th>\n      <td>-121.32</td>\n      <td>39.43</td>\n      <td>18.0</td>\n      <td>1860.0</td>\n      <td>409.0</td>\n      <td>741.0</td>\n      <td>349.0</td>\n      <td>1.8672</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20639</th>\n      <td>-121.24</td>\n      <td>39.37</td>\n      <td>16.0</td>\n      <td>2785.0</td>\n      <td>616.0</td>\n      <td>1387.0</td>\n      <td>530.0</td>\n      <td>2.3886</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>20640 rows × 13 columns</p>\n</div>"
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   ],
   "source": [
    "# load dataFrame\n",
    "data = pd.read_csv(\"data/housing.csv\")\n",
    "# Convert Categorical Data Values to Features\n",
    "data = pd.get_dummies(data=data, columns=[\"ocean_proximity\"], prefix=\"ocean_proximity\")\n",
    "# Fill the NAN value with the intermediate value\n",
    "data.fillna(data.mean(), inplace=True)\n",
    "\n",
    "# Independent columns\n",
    "X = data.iloc[:, data.columns != \"median_house_value\"]\n",
    "# target column (median_house_value)\n",
    "Y = data.iloc[:, 8]\n",
    "\n",
    "X"
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     "text": [
      "                         Specs         Score\n",
      "3                  total_rooms  9.772920e+06\n",
      "5                   population  3.988363e+06\n",
      "4               total_bedrooms  1.402919e+06\n",
      "6                   households  1.303777e+06\n",
      "2           housing_median_age  2.319302e+04\n",
      "7                median_income  1.171746e+04\n",
      "9       ocean_proximity_INLAND  6.565265e+03\n",
      "11    ocean_proximity_NEAR BAY  4.079131e+03\n",
      "12  ocean_proximity_NEAR OCEAN  3.993245e+03\n",
      "8    ocean_proximity_<1H OCEAN  3.746993e+03\n"
     ]
    }
   ],
   "source": [
    "# Selection with SelectKBest class to extract top 10 best features\n",
    "bestFeatures = SelectKBest(score_func=chi2, k=10)\n",
    "# Negative longitude data is converted to positive data using absolute values.\n",
    "fit = bestFeatures.fit(X.abs(), Y)\n",
    "\n",
    "dfColumns = pd.DataFrame(X.columns)\n",
    "dfScores = pd.DataFrame(fit.scores_)\n",
    "\n",
    "featureScores = pd.concat([dfColumns, dfScores], axis=1)\n",
    "featureScores.columns = [\"Specs\", \"Score\"]\n",
    "\n",
    "print(featureScores.nlargest(10,\"Score\"))"
   ],
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