California Housing Prices.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "California Housing Prices.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "9hSIqp-dKOtn",
        "nlyVy-t2xlUe"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EERS1GpUnle1"
      },
      "source": [
        "**Initialization**\n",
        "* I use these 3 lines of code on top of my each Notebooks because it will help to prevent any problems while reloading and reworking on a same Project or Problem. And the third line of code helps to make visualization within the Notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tGMVvlbk-IL"
      },
      "source": [
        "#@ Initialization:\n",
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "si11_bUdoCOs"
      },
      "source": [
        "**Downloading the Dependencies**\n",
        "* I have downloaded all the Libraries and Dependencies required for this Project in one particular cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vh75YvJKn90f"
      },
      "source": [
        "#@ Downloading the Libraries and Dependencies:\n",
        "import sys, os, tarfile, urllib.request\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "from pandas.plotting import scatter_matrix\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline, FeatureUnion\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "#@ Visualization Dependencies:\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc(\"axes\", labelsize=14)\n",
        "mpl.rc(\"xtick\", labelsize=12)\n",
        "mpl.rc(\"ytick\", labelsize=12)\n",
        "\n",
        "PROJECT_ROOT_DIR = \".\"\n",
        "ID = \"California Housing\"\n",
        "IMAGE_PATH = os.path.join(PROJECT_ROOT_DIR, \"Images\", ID)\n",
        "if not os.path.isdir(IMAGE_PATH):\n",
        "    os.makedirs(IMAGE_PATH)\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "  path = os.path.join(IMAGE_PATH, fig_id + \".\" + fig_extension)\n",
        "  print(\"Saving Figure\", fig_id)\n",
        "  if tight_layout:\n",
        "    plt.tight_layout()\n",
        "  plt.savefig(path, format=fig_extension, dpi=resolution)\n",
        "\n",
        "#@ Ignoring the Warnings:\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\", message=\"^ internal\")"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJBzxZ4qEnuU"
      },
      "source": [
        "#@ Downloading the Dependencies:\n",
        "# !pip install sweetviz\n",
        "# import sweetviz as sv\n",
        "import IPython"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQE1U4fqsbwO"
      },
      "source": [
        "**Getting the Data**\n",
        "* I have used Google Colab for this Project so the process of downloading and reading the Data might be different in other platforms. I will use **California Housing Prices** Dataset from the StatLib Repository for this Project. This Dataset was based on Data from the 1990 California Census. The Data has metrics such as Population, Median Income, Median House Price and so on for each block group in California. I will build a Model of Housing Prices in California using the California Census Dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "IXt0HTresFS3",
        "outputId": "e3a06483-d0d3-49a6-a2d9-99d8bb1bd773"
      },
      "source": [
        "#@ Getting the Data:\n",
        "import urllib.request\n",
        "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
        "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
        "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
        "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
        "  if not os.path.isdir(housing_path):\n",
        "    os.makedirs(housing_path)\n",
        "  tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
        "  urllib.request.urlretrieve(housing_url, tgz_path)\n",
        "  housing_tgz = tarfile.open(tgz_path)\n",
        "  housing_tgz.extractall(path=housing_path)\n",
        "  housing_tgz.close()\n",
        "\n",
        "fetch_housing_data()                                                           # Initializing the Function.\n",
        "\n",
        "#@ Getting the Data:\n",
        "def load_housing_data(housing_path=HOUSING_PATH):\n",
        "  csv_path = os.path.join(housing_path, \"housing.csv\")\n",
        "  return pd.read_csv(csv_path)\n",
        "\n",
        "#@ Inspecting the Data:\n",
        "housing = load_housing_data()\n",
        "housing.head(7)                                                                # Inspecting the Data."
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>919.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>413.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>4.0368</td>\n",
              "      <td>269700.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2535.0</td>\n",
              "      <td>489.0</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>514.0</td>\n",
              "      <td>3.6591</td>\n",
              "      <td>299200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  median_house_value  ocean_proximity\n",
              "0    -122.23     37.88  ...            452600.0         NEAR BAY\n",
              "1    -122.22     37.86  ...            358500.0         NEAR BAY\n",
              "2    -122.24     37.85  ...            352100.0         NEAR BAY\n",
              "3    -122.25     37.85  ...            341300.0         NEAR BAY\n",
              "4    -122.25     37.85  ...            342200.0         NEAR BAY\n",
              "5    -122.25     37.85  ...            269700.0         NEAR BAY\n",
              "6    -122.25     37.84  ...            299200.0         NEAR BAY\n",
              "\n",
              "[7 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuONIUkd0P0D"
      },
      "source": [
        "#@ Analyzing the DataFrame using Sweetviz:\n",
        "# report = sv.analyze(housing)\n",
        "# IPython.display.HTML(\"SWEETVIZ_REPORT.html\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hSIqp-dKOtn"
      },
      "source": [
        "### **Exploratory Data Analysis**\n",
        "* The analysis report presents that the DataFrame has 20640 rows and each row represents one district. The report also presents that the DataFrame has 10 features where one is categorical and 9 are numerical. The info method is standard and useful to get the quick description of the Data, in particular the attributes type and the number of non null values. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A42k99vfJouw",
        "outputId": "d44b039b-8be1-4094-b23e-53d2407dd732"
      },
      "source": [
        "#@ Exploratory Data Analysis:\n",
        "housing.info()                  # Inspecting the DataFrame."
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   longitude           20640 non-null  float64\n",
            " 1   latitude            20640 non-null  float64\n",
            " 2   housing_median_age  20640 non-null  float64\n",
            " 3   total_rooms         20640 non-null  float64\n",
            " 4   total_bedrooms      20433 non-null  float64\n",
            " 5   population          20640 non-null  float64\n",
            " 6   households          20640 non-null  float64\n",
            " 7   median_income       20640 non-null  float64\n",
            " 8   median_house_value  20640 non-null  float64\n",
            " 9   ocean_proximity     20640 non-null  object \n",
            "dtypes: float64(9), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKEs0l77Ms3v"
      },
      "source": [
        "**Exploratory Data Analysis**\n",
        "* The total bedrooms attribute has only 20433 non null values which means that 207 districts are missing here. The ocean proximity attribute is of object Data type and the values in this attribute is repetitive which means that it is probably categorical attribute. The value counts method can be used to find the categories and number of districts belonging to a particular category. The describe method shows the summary of the numerical attributes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "in0Zcm68MoZM",
        "outputId": "4743b39f-8973-4a43-da8a-9ad60106d288"
      },
      "source": [
        "#@ Exploratory Data Analysis:\n",
        "print(housing[\"ocean_proximity\"].value_counts())          # Inspecting the Categorical Attribute.\n",
        "print(\"\\n\")\n",
        "IPython.display.display(housing.describe())               # Inspecting the Numerical Attributes. "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<1H OCEAN     9136\n",
            "INLAND        6551\n",
            "NEAR OCEAN    2658\n",
            "NEAR BAY      2290\n",
            "ISLAND           5\n",
            "Name: ocean_proximity, dtype: int64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20433.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.569704</td>\n",
              "      <td>35.631861</td>\n",
              "      <td>28.639486</td>\n",
              "      <td>2635.763081</td>\n",
              "      <td>537.870553</td>\n",
              "      <td>1425.476744</td>\n",
              "      <td>499.539680</td>\n",
              "      <td>3.870671</td>\n",
              "      <td>206855.816909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.003532</td>\n",
              "      <td>2.135952</td>\n",
              "      <td>12.585558</td>\n",
              "      <td>2181.615252</td>\n",
              "      <td>421.385070</td>\n",
              "      <td>1132.462122</td>\n",
              "      <td>382.329753</td>\n",
              "      <td>1.899822</td>\n",
              "      <td>115395.615874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.800000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1447.750000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>787.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>2.563400</td>\n",
              "      <td>119600.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.534800</td>\n",
              "      <td>179700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.010000</td>\n",
              "      <td>37.710000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>647.000000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>605.000000</td>\n",
              "      <td>4.743250</td>\n",
              "      <td>264725.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>39320.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  ...  median_income  median_house_value\n",
              "count  20640.000000  20640.000000  ...   20640.000000        20640.000000\n",
              "mean    -119.569704     35.631861  ...       3.870671       206855.816909\n",
              "std        2.003532      2.135952  ...       1.899822       115395.615874\n",
              "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
              "25%     -121.800000     33.930000  ...       2.563400       119600.000000\n",
              "50%     -118.490000     34.260000  ...       3.534800       179700.000000\n",
              "75%     -118.010000     37.710000  ...       4.743250       264725.000000\n",
              "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Fjy_cb3GPTDR",
        "outputId": "e96a58d1-b010-4b1e-bad0-ea82290d5a6f"
      },
      "source": [
        "#@ Inspecting the Data using Histograms:\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "housing.hist(bins=50, figsize=(20, 15))  \n",
        "save_fig(\"Histogram Plots\")              \n",
        "plt.show()                                      # Plotting the Histograms of Numerical Attributes."
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Figure Histogram Plots\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPyjd7AhVi9t"
      },
      "source": [
        "**Processing the Data**\n",
        "* I will create a Test Dataset and set them aside. I will pick 20 percent of the instances randomly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FSOhTYcqSeFM",
        "outputId": "c51748dc-01d8-40b4-d4c9-b4437077f331"
      },
      "source": [
        "#@ Splitting the Dataset:\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=11)\n",
        "\n",
        "#@ Inspecting the Dataset:\n",
        "print(f\"Training set: {len(train_set)}\")                                             # Number of Training Dataset.\n",
        "print(f\"\\nTesting set: {len(test_set)}\")                                             # Number of Testing Dataset.\n",
        "IPython.display.display(train_set.head(3))    \n",
        "print(\"\\n\")                    \n",
        "IPython.display.display(test_set.head(3))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training set: 16512\n",
            "\n",
            "Testing set: 4128\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "  <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>20025</th>\n",
              "      <td>-119.05</td>\n",
              "      <td>36.07</td>\n",
              "      <td>21.0</td>\n",
              "      <td>2472.0</td>\n",
              "      <td>523.0</td>\n",
              "      <td>1238.0</td>\n",
              "      <td>504.0</td>\n",
              "      <td>1.7756</td>\n",
              "      <td>62900.0</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10772</th>\n",
              "      <td>-117.89</td>\n",
              "      <td>33.66</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2736.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>1279.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>5.5422</td>\n",
              "      <td>253100.0</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16691</th>\n",
              "      <td>-120.61</td>\n",
              "      <td>35.13</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3431.0</td>\n",
              "      <td>721.0</td>\n",
              "      <td>1777.0</td>\n",
              "      <td>701.0</td>\n",
              "      <td>2.7301</td>\n",
              "      <td>190400.0</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_house_value  ocean_proximity\n",
              "20025    -119.05     36.07  ...             62900.0           INLAND\n",
              "10772    -117.89     33.66  ...            253100.0        <1H OCEAN\n",
              "16691    -120.61     35.13  ...            190400.0        <1H OCEAN\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>2734</th>\n",
              "      <td>-115.56</td>\n",
              "      <td>32.78</td>\n",
              "      <td>34.0</td>\n",
              "      <td>2856.0</td>\n",
              "      <td>555.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>522.0</td>\n",
              "      <td>3.2083</td>\n",
              "      <td>76200.0</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9087</th>\n",
              "      <td>-118.17</td>\n",
              "      <td>34.66</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1561.0</td>\n",
              "      <td>253.0</td>\n",
              "      <td>731.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>5.7049</td>\n",
              "      <td>173200.0</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15789</th>\n",
              "      <td>-122.40</td>\n",
              "      <td>37.78</td>\n",
              "      <td>52.0</td>\n",
              "      <td>464.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>1.6125</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_house_value  ocean_proximity\n",
              "2734     -115.56     32.78  ...             76200.0           INLAND\n",
              "9087     -118.17     34.66  ...            173200.0           INLAND\n",
              "15789    -122.40     37.78  ...            112500.0         NEAR BAY\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tq2YghgKlG5O"
      },
      "source": [
        "**Exploratory Data Analysis**\n",
        "* I will inspect the median income attribute more closely using Histogram."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fWwKRCWhN3H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "682fc686-5560-4a89-f514-b104c8fbefaa"
      },
      "source": [
        "#@ Inspecting the median income attribute:\n",
        "housing[\"median_income\"].hist()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7aad495748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cX9T5ng8lvOS"
      },
      "source": [
        "* By observing the Histogram more closely, the values of median income are clustered around 2 and 6 but some median income values goes beyond 6. I will create an income category attribute with 5 categories which are labeled from 1 to 5. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "a0b2Y_y8lt3K",
        "outputId": "3c9003e9-3905-427f-ac47-146d53eafadc"
      },
      "source": [
        "#@ Creating the Income Categories:\n",
        "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
        "                               bins=[0., 1.5, 3, 4.5, 6, np.inf],\n",
        "                               labels=[1, 2, 3, 4, 5])\n",
        "\n",
        "#@ Inspecting the Income Categories:\n",
        "print(housing[\"income_cat\"].value_counts())\n",
        "print(\"\\n\")\n",
        "housing[\"income_cat\"].hist()                                      # Inspecting the Income Categories using Histogram."
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3    7236\n",
            "2    6581\n",
            "4    3639\n",
            "5    2362\n",
            "1     822\n",
            "Name: income_cat, dtype: int64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7aaa346eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPgz83wfp0vt"
      },
      "source": [
        "**Stratified Sampling**\n",
        "* Now, I will do Stratified Sampling based on the Income Category. I will use Scikit Learn's StratifiedShuffleSplit class for the same. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Kf5w9lLAoZ0k",
        "outputId": "e89ca4b8-f1cd-4370-d1df-2c28d340d55f"
      },
      "source": [
        "#@ Stratified Sampling based on Income Category:\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=11)\n",
        "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
        "  strat_train_set = housing.loc[train_index]\n",
        "  strat_test_set = housing.loc[test_index]\n",
        "\n",
        "#@ Inspecting the Sampling:\n",
        "print(strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set))            # Income Category Proportions in Test set.\n",
        "print(\"\\n\")\n",
        "print(housing[\"income_cat\"].value_counts() / len(housing))                          # Income Category Proportions in Full Dataset.\n",
        "\n",
        "#@ Comparing the Proportions:\n",
        "def income_cat_propotions(data):\n",
        "  return data[\"income_cat\"].value_counts() / len(data)\n",
        "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=11)     # Splitting the Dataset.\n",
        "compare_props = pd.DataFrame({\n",
        "    \"Overall\": income_cat_propotions(housing),                                      # Full Dataset.\n",
        "    \"Stratified\": income_cat_propotions(strat_test_set),                            # Stratified Sampling Dataset.\n",
        "    \"Random\": income_cat_propotions(test_set)                                       # Random Smapling Dataset.\n",
        "}).sort_index()\n",
        "compare_props[\"Random % Error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] -100\n",
        "compare_props[\"Stratified % Error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] -100\n",
        "\n",
        "#@ Inspecting the Proportions:\n",
        "print(\"\\n\")\n",
        "IPython.display.display(compare_props)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3    0.350533\n",
            "2    0.318798\n",
            "4    0.176357\n",
            "5    0.114583\n",
            "1    0.039729\n",
            "Name: income_cat, dtype: float64\n",
            "\n",
            "\n",
            "3    0.350581\n",
            "2    0.318847\n",
            "4    0.176308\n",
            "5    0.114438\n",
            "1    0.039826\n",
            "Name: income_cat, dtype: float64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Overall</th>\n",
              "      <th>Stratified</th>\n",
              "      <th>Random</th>\n",
              "      <th>Random % Error</th>\n",
              "      <th>Stratified % Error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.039826</td>\n",
              "      <td>0.039729</td>\n",
              "      <td>0.032946</td>\n",
              "      <td>-17.274939</td>\n",
              "      <td>-0.243309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.318847</td>\n",
              "      <td>0.318798</td>\n",
              "      <td>0.312984</td>\n",
              "      <td>-1.838626</td>\n",
              "      <td>-0.015195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.350581</td>\n",
              "      <td>0.350533</td>\n",
              "      <td>0.353440</td>\n",
              "      <td>0.815368</td>\n",
              "      <td>-0.013820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.176308</td>\n",
              "      <td>0.176357</td>\n",
              "      <td>0.184835</td>\n",
              "      <td>4.836494</td>\n",
              "      <td>0.027480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.114438</td>\n",
              "      <td>0.114583</td>\n",
              "      <td>0.115795</td>\n",
              "      <td>1.185436</td>\n",
              "      <td>0.127011</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Overall  Stratified    Random  Random % Error  Stratified % Error\n",
              "1  0.039826    0.039729  0.032946      -17.274939           -0.243309\n",
              "2  0.318847    0.318798  0.312984       -1.838626           -0.015195\n",
              "3  0.350581    0.350533  0.353440        0.815368           -0.013820\n",
              "4  0.176308    0.176357  0.184835        4.836494            0.027480\n",
              "5  0.114438    0.114583  0.115795        1.185436            0.127011"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MuNfQW-t72W"
      },
      "source": [
        "#@ Removing the Income Cat from the Data:\n",
        "for set_ in (strat_train_set, strat_test_set):\n",
        "  set_.drop(\"income_cat\", axis=1, inplace=True)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlyVy-t2xlUe"
      },
      "source": [
        "### **Exploratory Data Analysis**\n",
        "* So far, I have only taken the quick glance at the Data to get the general understanding of the Data. Now, the goal is to go little more in the depth. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQJUQIsDzGPZ"
      },
      "source": [
        "**Visualizing the Geographical Data**\n",
        "* Since, there is a Geographical Information latitude and longitude it will be a good idea to create the scatter plot of all districts to visualize the Data. I will use alpha option to 0.1 which makes it much easier to visualize the places where there is high density of data points."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pqKAQASxxXRq",
        "outputId": "158685ee-2ba4-4875-cbd7-2fe4ec83d3e9"
      },
      "source": [
        "#@ Creating the copy of the Training set:\n",
        "housing = strat_train_set.copy()\n",
        "\n",
        "#@ Visualizing the Geographical Data:\n",
        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)\n",
        "save_fig(\"Geographical Plot\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Figure Geographical Plot\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SxMjcd2n3Xyg"
      },
      "source": [
        "* Now, let's look at the Housing prices. The radius of each circle represents the district's population option s, the color represents the price option c and I will use predefined color map option cmap called jet which ranges from blue low values and red high values. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rXGkKDTq2yQf",
        "outputId": "4c9f31cb-1d21-452c-8075-d6bfc07905ed"
      },
      "source": [
        "#@ Visualizing the Housing Prices:\n",
        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
        "             s=housing[\"population\"]/100, label=\"Population\", figsize=(10, 7),\n",
        "             c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True, \n",
        "             sharex=False)\n",
        "plt.legend()\n",
        "save_fig(\"Geographical Plots\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Figure Geographical Plots\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJYZ9Y1C9XN1"
      },
      "source": [
        "* The Image tells that the Housing prices are very much related to the location and to the population density. It will probably be useful to use a clustering algorithm to detect the main clusters and add new features that measures the proximity to the cluster centres. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xv4ghkmwFtDb"
      },
      "source": [
        "**Correlations**\n",
        "* Now, I will compute the standard correlation coefficient betweeen every pair of the attributes using corr method. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWHEDO8u86JI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb4a0c04-984e-4593-bad6-2e4c7d925ff2"
      },
      "source": [
        "#@ Correlations:\n",
        "corr_matrix = housing.corr()\n",
        "print(corr_matrix[\"median_house_value\"].sort_values(ascending=False))            # Correlations of each attributes with median house value."
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "median_house_value    1.000000\n",
            "median_income         0.687819\n",
            "total_rooms           0.136257\n",
            "housing_median_age    0.100173\n",
            "households            0.066018\n",
            "total_bedrooms        0.050808\n",
            "population           -0.026287\n",
            "longitude            -0.042994\n",
            "latitude             -0.148049\n",
            "Name: median_house_value, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXZBVXMDHxA3"
      },
      "source": [
        "* Now, I will use Pandas scatter matrix function which plots every numerical attribute to other numerial attribute to check Correlations between attributes. I will focus only on those attributes who are strongly correlated to each other. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZ98XI-_Hssr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6ad91e9b-3941-4230-a1fd-652386426c53"
      },
      "source": [
        "#@ Correlations using Scatter matrix:\n",
        "from pandas.plotting import scatter_matrix\n",
        "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
        "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
        "save_fig(\"Correlations\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Figure Correlations\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtGnoi-CT0DG"
      },
      "source": [
        "* The plot obtained above presents that the most promising attribute to predict the median house value is the median income. I will inspect the scatter plot of these attributes in more detail. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9u7w6LcnTHTF",
        "outputId": "7eeddd4e-e5df-4de2-dece-fa0dda269204"
      },
      "source": [
        "#@ Correlations of Median House Value with Median Income. \n",
        "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\", alpha=0.1)\n",
        "save_fig(\"Median House value vs Median Income\")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving Figure Median House value vs Median Income\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F02Nbe5oWtnR"
      },
      "source": [
        "**Attribute Combinations**\n",
        "* I will create the number of rooms per household, the total number of bedrooms per rooms and the total number of populations per household."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iqGUAZNQVvd2",
        "outputId": "1563957a-9bea-4958-ed3c-302c960015e9"
      },
      "source": [
        "#@ Attribute Combinations:\n",
        "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]   # Rooms per Household.\n",
        "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"] # Bedrooms per Room.\n",
        "housing[\"population_per_household\"] = housing[\"population\"]/housing[\"households\"] # Populations per Household.\n",
        "\n",
        "#@ Inspecting the Correlations:\n",
        "corr_matrix = housing.corr()\n",
        "print(corr_matrix[\"median_house_value\"].sort_values(ascending=False))\n",
        "\n",
        "#@ Inspecting the Data:\n",
        "print(\"\\n\")\n",
        "housing.describe()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "median_house_value          1.000000\n",
            "median_income               0.687819\n",
            "rooms_per_household         0.172885\n",
            "total_rooms                 0.136257\n",
            "housing_median_age          0.100173\n",
            "households                  0.066018\n",
            "total_bedrooms              0.050808\n",
            "population_per_household   -0.022291\n",
            "population                 -0.026287\n",
            "longitude                  -0.042994\n",
            "latitude                   -0.148049\n",
            "bedrooms_per_room          -0.260035\n",
            "Name: median_house_value, dtype: float64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>rooms_per_household</th>\n",
              "      <th>bedrooms_per_room</th>\n",
              "      <th>population_per_household</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16343.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "      <td>16343.000000</td>\n",
              "      <td>16512.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.577933</td>\n",
              "      <td>35.643025</td>\n",
              "      <td>28.624939</td>\n",
              "      <td>2621.313166</td>\n",
              "      <td>534.398030</td>\n",
              "      <td>1419.400254</td>\n",
              "      <td>496.420119</td>\n",
              "      <td>3.869500</td>\n",
              "      <td>206507.410126</td>\n",
              "      <td>5.419345</td>\n",
              "      <td>0.212989</td>\n",
              "      <td>3.111436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.004669</td>\n",
              "      <td>2.141642</td>\n",
              "      <td>12.581716</td>\n",
              "      <td>2147.406404</td>\n",
              "      <td>413.639435</td>\n",
              "      <td>1126.143367</td>\n",
              "      <td>375.014618</td>\n",
              "      <td>1.902174</td>\n",
              "      <td>115368.678905</td>\n",
              "      <td>2.150106</td>\n",
              "      <td>0.057694</td>\n",
              "      <td>11.604933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.300000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.800000</td>\n",
              "      <td>33.940000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1445.000000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>792.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>2.561150</td>\n",
              "      <td>119375.000000</td>\n",
              "      <td>4.442941</td>\n",
              "      <td>0.175468</td>\n",
              "      <td>2.429003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.500000</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2125.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.531300</td>\n",
              "      <td>179200.000000</td>\n",
              "      <td>5.227321</td>\n",
              "      <td>0.203227</td>\n",
              "      <td>2.819632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.010000</td>\n",
              "      <td>37.720000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3136.000000</td>\n",
              "      <td>644.000000</td>\n",
              "      <td>1716.000000</td>\n",
              "      <td>601.000000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>264525.000000</td>\n",
              "      <td>6.056697</td>\n",
              "      <td>0.239648</td>\n",
              "      <td>3.286105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>37937.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "      <td>62.422222</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1243.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  ...  bedrooms_per_room  population_per_household\n",
              "count  16512.000000  16512.000000  ...       16343.000000              16512.000000\n",
              "mean    -119.577933     35.643025  ...           0.212989                  3.111436\n",
              "std        2.004669      2.141642  ...           0.057694                 11.604933\n",
              "min     -124.300000     32.540000  ...           0.100000                  0.750000\n",
              "25%     -121.800000     33.940000  ...           0.175468                  2.429003\n",
              "50%     -118.500000     34.260000  ...           0.203227                  2.819632\n",
              "75%     -118.010000     37.720000  ...           0.239648                  3.286105\n",
              "max     -114.310000     41.950000  ...           1.000000               1243.333333\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gICrWwm7jC0S"
      },
      "source": [
        "### **Preparing Data for Machine Learning**\n",
        "\n",
        "* Now, I will prepare the Data for Machine Learning Algorithms. Firstly, I will revert to a clean training set and separate the predictors and target. The drop method creates the copy of the Data without affecting the Training set. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tfxeo0TMi5H8"
      },
      "source": [
        "#@ Preparing the Data for Machine Learning:\n",
        "housing = strat_train_set.drop(\"median_house_value\", axis=1)                   # Drop the labels from Training set. \n",
        "housing_labels = strat_train_set[\"median_house_value\"].copy()"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RZxGJ9Ll92q"
      },
      "source": [
        "**Data Cleaning**\n",
        "* Most of the Machine Learning Algorithms cannot work with missing values. The total bedrooms attribute has some missing values as obtained above in the analysis. Getting rid of missing values can be done in three ways. \n",
        "  * Getting rid of the Corresponding districts. \n",
        "  * Getting rid of the whole attribute.\n",
        "  * Setting the values to some value such as median, mean or zero."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lYiXNCE0lynN",
        "outputId": "72207d31-39c5-461b-e0f5-353f88bda0ed"
      },
      "source": [
        "#@ Working on missing values:\n",
        "sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()\n",
        "IPython.display.display(sample_incomplete_rows)                                             # Inspecting the missing values.\n",
        "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])                                    # Getting rid of corresponding districts. \n",
        "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)                                       # Getting rid of whole attribute. \n",
        "median = housing[\"total_bedrooms\"].median()                                                 # Calculating the median.\n",
        "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True)                       # Imputing the median value.\n",
        "print(\"\\n\")\n",
        "IPython.display.display(sample_incomplete_rows)                                             # Inspecting the Data."
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\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>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</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13932</th>\n",
              "      <td>-117.28</td>\n",
              "      <td>34.26</td>\n",
              "      <td>18.0</td>\n",
              "      <td>3895.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1086.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>3.3672</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2420</th>\n",
              "      <td>-119.44</td>\n",
              "      <td>36.58</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>879.0</td>\n",
              "      <td>257.0</td>\n",
              "      <td>2.5234</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19332</th>\n",
              "      <td>-122.91</td>\n",
              "      <td>38.46</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>912.0</td>\n",
              "      <td>329.0</td>\n",
              "      <td>4.5000</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>-121.98</td>\n",
              "      <td>37.96</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2987.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1420.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>3.6500</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15397</th>\n",
              "      <td>-117.20</td>\n",
              "      <td>33.24</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4992.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2106.0</td>\n",
              "      <td>801.0</td>\n",
              "      <td>6.2079</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_income  ocean_proximity\n",
              "13932    -117.28     34.26  ...         3.3672           INLAND\n",
              "2420     -119.44     36.58  ...         2.5234           INLAND\n",
              "19332    -122.91     38.46  ...         4.5000        <1H OCEAN\n",
              "1456     -121.98     37.96  ...         3.6500           INLAND\n",
              "15397    -117.20     33.24  ...         6.2079        <1H OCEAN\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\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>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</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13932</th>\n",
              "      <td>-117.28</td>\n",
              "      <td>34.26</td>\n",
              "      <td>18.0</td>\n",
              "      <td>3895.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>1086.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>3.3672</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2420</th>\n",
              "      <td>-119.44</td>\n",
              "      <td>36.58</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>879.0</td>\n",
              "      <td>257.0</td>\n",
              "      <td>2.5234</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19332</th>\n",
              "      <td>-122.91</td>\n",
              "      <td>38.46</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>912.0</td>\n",
              "      <td>329.0</td>\n",
              "      <td>4.5000</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>-121.98</td>\n",
              "      <td>37.96</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2987.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>1420.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>3.6500</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15397</th>\n",
              "      <td>-117.20</td>\n",
              "      <td>33.24</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4992.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>2106.0</td>\n",
              "      <td>801.0</td>\n",
              "      <td>6.2079</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_income  ocean_proximity\n",
              "13932    -117.28     34.26  ...         3.3672           INLAND\n",
              "2420     -119.44     36.58  ...         2.5234           INLAND\n",
              "19332    -122.91     38.46  ...         4.5000        <1H OCEAN\n",
              "1456     -121.98     37.96  ...         3.6500           INLAND\n",
              "15397    -117.20     33.24  ...         6.2079        <1H OCEAN\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zka_leNXq440"
      },
      "source": [
        "**Missing Data**\n",
        "* Scikit Learn provides a handy class to take care of missing values: Simple Imputer. I will create the Imputer instance and replace the missing values with median of the attribute. Since the median can be calculated only on the numerical attributes, I will create the copy of Data without the text attribute ocean proximity. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QbtCpA8Yp7PG",
        "outputId": "3cd79b96-c7cf-4ca7-9715-95904308fc94"
      },
      "source": [
        "#@ Missing Data:\n",
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(strategy=\"median\")                               # Instantiating the Imputer.\n",
        "housing_num = housing.drop(\"ocean_proximity\", axis=1)                    # Removing the Text Attribute.\n",
        "imputer.fit(housing_num)                                                 # Fitting the Imputer instance to the Training Data.\n",
        "print(imputer.statistics_)                                               # Inspecting the values.\n",
        "print(housing_num.median().values)                                       # Inspecting the median values.\n",
        "X = imputer.transform(housing_num)                                       # Transforming the Training set with trained imputer.\n",
        "housing_imputed = pd.DataFrame(X, columns=housing_num.columns)           # Creating the DataFrame with numpy arrays.\n",
        "print(\"\\n\")\n",
        "IPython.display.display(housing_imputed.head())                          # Inspecting the DataFrame. "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-118.5      34.26     29.     2125.      434.     1166.      409.\n",
            "    3.5313]\n",
            "[-118.5      34.26     29.     2125.      434.     1166.      409.\n",
            "    3.5313]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "<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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-117.80</td>\n",
              "      <td>33.69</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1161.0</td>\n",
              "      <td>289.0</td>\n",
              "      <td>630.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>3.3438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-121.91</td>\n",
              "      <td>36.85</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2442.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>1301.0</td>\n",
              "      <td>290.0</td>\n",
              "      <td>3.1563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-121.41</td>\n",
              "      <td>38.64</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>287.0</td>\n",
              "      <td>682.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>1.9167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.40</td>\n",
              "      <td>34.25</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1872.0</td>\n",
              "      <td>497.0</td>\n",
              "      <td>1927.0</td>\n",
              "      <td>432.0</td>\n",
              "      <td>2.2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-117.08</td>\n",
              "      <td>32.67</td>\n",
              "      <td>31.0</td>\n",
              "      <td>3008.0</td>\n",
              "      <td>764.0</td>\n",
              "      <td>2088.0</td>\n",
              "      <td>757.0</td>\n",
              "      <td>2.5662</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  households  median_income\n",
              "0    -117.80     33.69  ...       296.0         3.3438\n",
              "1    -121.91     36.85  ...       290.0         3.1563\n",
              "2    -121.41     38.64  ...       280.0         1.9167\n",
              "3    -118.40     34.25  ...       432.0         2.2019\n",
              "4    -117.08     32.67  ...       757.0         2.5662\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRsFYT6aw5R-"
      },
      "source": [
        "**Handling Text and Categorical Attribute**\n",
        "* Most of the Machine Learning Algorithms prefer to work with numpy arrays, so I will convert categories from text to numbers. I will use Scikit Learn's Ordinal Encoder Class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "N8ds4sE4u4Pk",
        "outputId": "660826aa-b24b-44a6-b718-4464f60fadee"
      },
      "source": [
        "#@ Handling Text and Categorical Attributes:\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "housing_cat = housing[[\"ocean_proximity\"]] \n",
        "print(housing_cat.head())                                                  # Inspecting the Categorical attribute.\n",
        "ordinal_encoder = OrdinalEncoder()                                         # Instantiating the Encoder.\n",
        "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)           # Encoding the Text.\n",
        "IPython.display.display(housing_cat_encoded[:10])                          # Inspecting the Encoded Text.\n",
        "print(\"\\n\")\n",
        "print(ordinal_encoder.categories_)                                         # Inspecting the list of categories."
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      ocean_proximity\n",
            "10641       <1H OCEAN\n",
            "18680      NEAR OCEAN\n",
            "12776          INLAND\n",
            "3442        <1H OCEAN\n",
            "14851      NEAR OCEAN\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[0.],\n",
              "       [4.],\n",
              "       [1.],\n",
              "       [0.],\n",
              "       [4.],\n",
              "       [0.],\n",
              "       [1.],\n",
              "       [1.],\n",
              "       [0.],\n",
              "       [1.]])"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
            "      dtype=object)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vc_FubFJ0uuK"
      },
      "source": [
        "* One issue with this representation is that ML Algorithms assume that the two nearby values are more similar than two distant values. So, I will apply the process of One Hot Encoding. Scikit Learn provides OneHotEncoder class to convert Categorical values into One Hot Vectors. The new attributes are called dummy attributes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjHm7lwN0T2M",
        "outputId": "ab0f6bba-fa1e-4213-a489-871bc754e8b5"
      },
      "source": [
        "#@ OneHot Encoding:\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "cat_encoder = OneHotEncoder(sparse=False)                                      # Instantiating the OneHot Encoder class.\n",
        "housing_cat_onehot = cat_encoder.fit_transform(housing_cat)                    # Encoding the Text.\n",
        "print(housing_cat_onehot[:10])                                                 # Converting into numpy arrays.\n",
        "print(\"\\n\")\n",
        "print(cat_encoder.categories_)                                                 # Inspecting the list of categories."
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1.]\n",
            " [0. 1. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0.]]\n",
            "\n",
            "\n",
            "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
            "      dtype=object)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7B1kr5pI7kXJ"
      },
      "source": [
        "**Custom Transformers**\n",
        "* Now, I will create a custom Transformers to add extra attributes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xz87v68b3pQN",
        "outputId": "3975ab16-87c7-47cc-d0e4-8fffb8f81350"
      },
      "source": [
        "#@ Custom Transformers:\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
        "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
        "  def __init__(self, add_bedrooms_per_room=True):\n",
        "    self.add_bedrooms_per_room = add_bedrooms_per_room\n",
        "  def fit(self, X, y=None):\n",
        "    return self\n",
        "  def transform(self, X, y=None):\n",
        "    rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
        "    population_per_household = X[:, population_ix] / X[:, households_ix]\n",
        "    if self.add_bedrooms_per_room:\n",
        "      bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
        "      return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
        "    else:\n",
        "      return np.c_[X, rooms_per_household, population_per_household]\n",
        "attribute_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)                          # Instantiating the Transformer.\n",
        "housing_extra_attrs = attribute_adder.transform(housing.values)\n",
        "\n",
        "#@ Getting the columns indices:\n",
        "col_names = [\"total_rooms\", \"total_bedrooms\", \"population\", \"households\"]\n",
        "rooms_ix, bedrooms_ix, population_ix, households_ix = [\n",
        "  housing.columns.get_loc(c) for c in col_names\n",
        "]\n",
        "\n",
        "#@ Creating the DataFrame:\n",
        "housing_extra_attrs = pd.DataFrame(\n",
        "    housing_extra_attrs,\n",
        "    columns = list(housing.columns) + [\"rooms_per_household\", \"population_per_household\"],\n",
        "    index = housing.index\n",
        ")\n",
        "#@ Inspecting the DataFrame:\n",
        "housing_extra_attrs.head(7)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ocean_proximity</th>\n",
              "      <th>rooms_per_household</th>\n",
              "      <th>population_per_household</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10641</th>\n",
              "      <td>-117.8</td>\n",
              "      <td>33.69</td>\n",
              "      <td>13</td>\n",
              "      <td>1161</td>\n",
              "      <td>289</td>\n",
              "      <td>630</td>\n",
              "      <td>296</td>\n",
              "      <td>3.3438</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>3.9223</td>\n",
              "      <td>2.12838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18680</th>\n",
              "      <td>-121.91</td>\n",
              "      <td>36.85</td>\n",
              "      <td>22</td>\n",
              "      <td>2442</td>\n",
              "      <td>624</td>\n",
              "      <td>1301</td>\n",
              "      <td>290</td>\n",
              "      <td>3.1563</td>\n",
              "      <td>NEAR OCEAN</td>\n",
              "      <td>8.42069</td>\n",
              "      <td>4.48621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12776</th>\n",
              "      <td>-121.41</td>\n",
              "      <td>38.64</td>\n",
              "      <td>38</td>\n",
              "      <td>1384</td>\n",
              "      <td>287</td>\n",
              "      <td>682</td>\n",
              "      <td>280</td>\n",
              "      <td>1.9167</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>4.94286</td>\n",
              "      <td>2.43571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3442</th>\n",
              "      <td>-118.4</td>\n",
              "      <td>34.25</td>\n",
              "      <td>13</td>\n",
              "      <td>1872</td>\n",
              "      <td>497</td>\n",
              "      <td>1927</td>\n",
              "      <td>432</td>\n",
              "      <td>2.2019</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>4.33333</td>\n",
              "      <td>4.46065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14851</th>\n",
              "      <td>-117.08</td>\n",
              "      <td>32.67</td>\n",
              "      <td>31</td>\n",
              "      <td>3008</td>\n",
              "      <td>764</td>\n",
              "      <td>2088</td>\n",
              "      <td>757</td>\n",
              "      <td>2.5662</td>\n",
              "      <td>NEAR OCEAN</td>\n",
              "      <td>3.97358</td>\n",
              "      <td>2.75826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4897</th>\n",
              "      <td>-118.25</td>\n",
              "      <td>34.01</td>\n",
              "      <td>31</td>\n",
              "      <td>1301</td>\n",
              "      <td>403</td>\n",
              "      <td>1952</td>\n",
              "      <td>377</td>\n",
              "      <td>2.1466</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>3.45093</td>\n",
              "      <td>5.17772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12319</th>\n",
              "      <td>-116.71</td>\n",
              "      <td>33.94</td>\n",
              "      <td>12</td>\n",
              "      <td>549</td>\n",
              "      <td>109</td>\n",
              "      <td>209</td>\n",
              "      <td>90</td>\n",
              "      <td>3.0208</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.32222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      longitude latitude  ... rooms_per_household population_per_household\n",
              "10641    -117.8    33.69  ...              3.9223                  2.12838\n",
              "18680   -121.91    36.85  ...             8.42069                  4.48621\n",
              "12776   -121.41    38.64  ...             4.94286                  2.43571\n",
              "3442     -118.4    34.25  ...             4.33333                  4.46065\n",
              "14851   -117.08    32.67  ...             3.97358                  2.75826\n",
              "4897    -118.25    34.01  ...             3.45093                  5.17772\n",
              "12319   -116.71    33.94  ...                 6.1                  2.32222\n",
              "\n",
              "[7 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIOmQA-_B1Hf"
      },
      "source": [
        "**Feature Scaling**\n",
        "* One of the most important transformations is to apply Feature Scaling to the Data. Machine Learning Algorithms don't perform well with the input numerical attributes having very different scales. The two common ways to get all attributes to have the same scale is Min max Scaling and Standardization.\n",
        "* Scikit Learn provides Pipeline class to help with the sequence of Transformations. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veOj_XZPBjUC",
        "outputId": "3409c5be-e590-422d-ef4f-074859593ea6"
      },
      "source": [
        "#@ Transformation Pipelines:\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "#@ Pipeline for Numerical attributes:\n",
        "num_pipeline = Pipeline([\n",
        "                         (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
        "                         (\"attribs_adder\", CombinedAttributesAdder()),\n",
        "                         (\"std_scaler\", StandardScaler())\n",
        "])\n",
        "housing_num_tr = num_pipeline.fit_transform(housing_num)\n",
        "#@ Inspecting the Transformations:\n",
        "housing_num_tr[:10]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.88692303, -0.91195641, -1.24191428, -0.68005636, -0.59366726,\n",
              "        -0.70099792, -0.534449  , -0.27637633, -0.69628808, -0.08471288,\n",
              "         0.53663725],\n",
              "       [-1.16335311,  0.5635916 , -0.52656888, -0.08350473,  0.22017306,\n",
              "        -0.105141  , -0.55044886, -0.37495074,  1.39594812,  0.11846795,\n",
              "         0.63734546],\n",
              "       [-0.91392779,  1.39942417,  0.74515627, -0.57620702, -0.59852601,\n",
              "        -0.65482123, -0.5771153 , -1.02664592, -0.221618  , -0.05822887,\n",
              "        -0.09694691],\n",
              "       [ 0.58761264, -0.65046689, -1.24191428, -0.34894925, -0.08835745,\n",
              "         0.45075532, -0.17178549, -0.87670767, -0.50511212,  0.11626547,\n",
              "         0.789259  ],\n",
              "       [ 1.24609549, -1.38824089,  0.18877652,  0.18007702,  0.56028544,\n",
              "         0.59372546,  0.69487364, -0.68518415, -0.67243615, -0.0304345 ,\n",
              "         0.61388122],\n",
              "       [ 0.66244024, -0.76253383,  0.18877652, -0.61485946, -0.31671862,\n",
              "         0.47295566, -0.31845088, -0.90578055, -0.91552532,  0.17805751,\n",
              "         1.46426853],\n",
              "       [ 1.43067023, -0.79522002, -1.32139711, -0.96505995, -1.0309546 ,\n",
              "        -1.07485152, -1.08377755, -0.44618719,  0.31657786, -0.06800881,\n",
              "        -0.2315384 ],\n",
              "       [-0.95882435,  1.32938234,  0.26825934,  0.64809762,  0.46553985,\n",
              "         0.54399672,  0.47620887, -0.2631805 ,  0.24457232, -0.00870911,\n",
              "        -0.50416252],\n",
              "       [ 0.67740576, -0.70650036,  0.90412192,  0.05946275,  0.1132806 ,\n",
              "        -0.03942802,  0.03887935,  0.50233527, -0.0184634 , -0.0362472 ,\n",
              "        -0.04181403],\n",
              "       [-0.79919214,  1.45078818, -1.00346582,  0.0864729 , -0.18067366,\n",
              "        -0.19394233, -0.17978542,  0.48609021,  0.52267796, -0.0268775 ,\n",
              "        -0.765551  ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03nALvDkGmBQ"
      },
      "source": [
        "* Until now, The Categorical columns and The Numerical columns are handled separately. It would be more convenient to have a single Transformer able to handle all columns, applying the appropriate transformations to each columns. Scikit Learn has ColumnTransformer class for this purpose. I will apply all the Transformations to the Housing Data using ColumnTransformer. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K38_0eREGBdS",
        "outputId": "590970d9-3963-46ac-e5c9-5068f02f9462"
      },
      "source": [
        "#@ Implementation of ColumnTransformer:\n",
        "from sklearn.compose import ColumnTransformer\n",
        "num_attribs = list(housing_num)                          # List of numerical attributes.\n",
        "cat_attribs = [\"ocean_proximity\"]                        # List of categorical attributes. \n",
        "pipeline = ColumnTransformer([\n",
        "                              (\"num\", num_pipeline, num_attribs),\n",
        "                              (\"cat\", OneHotEncoder(), cat_attribs)\n",
        "])\n",
        "housing_prepared = pipeline.fit_transform(housing)\n",
        "\n",
        "#@ Inspecting the Transformers:\n",
        "print(housing_prepared[:10])\n",
        "print(housing_prepared.shape)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.88692303 -0.91195641 -1.24191428 -0.68005636 -0.59366726 -0.70099792\n",
            "  -0.534449   -0.27637633 -0.69628808 -0.08471288  0.53663725  1.\n",
            "   0.          0.          0.          0.        ]\n",
            " [-1.16335311  0.5635916  -0.52656888 -0.08350473  0.22017306 -0.105141\n",
            "  -0.55044886 -0.37495074  1.39594812  0.11846795  0.63734546  0.\n",
            "   0.          0.          0.          1.        ]\n",
            " [-0.91392779  1.39942417  0.74515627 -0.57620702 -0.59852601 -0.65482123\n",
            "  -0.5771153  -1.02664592 -0.221618   -0.05822887 -0.09694691  0.\n",
            "   1.          0.          0.          0.        ]\n",
            " [ 0.58761264 -0.65046689 -1.24191428 -0.34894925 -0.08835745  0.45075532\n",
            "  -0.17178549 -0.87670767 -0.50511212  0.11626547  0.789259    1.\n",
            "   0.          0.          0.          0.        ]\n",
            " [ 1.24609549 -1.38824089  0.18877652  0.18007702  0.56028544  0.59372546\n",
            "   0.69487364 -0.68518415 -0.67243615 -0.0304345   0.61388122  0.\n",
            "   0.          0.          0.          1.        ]\n",
            " [ 0.66244024 -0.76253383  0.18877652 -0.61485946 -0.31671862  0.47295566\n",
            "  -0.31845088 -0.90578055 -0.91552532  0.17805751  1.46426853  1.\n",
            "   0.          0.          0.          0.        ]\n",
            " [ 1.43067023 -0.79522002 -1.32139711 -0.96505995 -1.0309546  -1.07485152\n",
            "  -1.08377755 -0.44618719  0.31657786 -0.06800881 -0.2315384   0.\n",
            "   1.          0.          0.          0.        ]\n",
            " [-0.95882435  1.32938234  0.26825934  0.64809762  0.46553985  0.54399672\n",
            "   0.47620887 -0.2631805   0.24457232 -0.00870911 -0.50416252  0.\n",
            "   1.          0.          0.          0.        ]\n",
            " [ 0.67740576 -0.70650036  0.90412192  0.05946275  0.1132806  -0.03942802\n",
            "   0.03887935  0.50233527 -0.0184634  -0.0362472  -0.04181403  1.\n",
            "   0.          0.          0.          0.        ]\n",
            " [-0.79919214  1.45078818 -1.00346582  0.0864729  -0.18067366 -0.19394233\n",
            "  -0.17978542  0.48609021  0.52267796 -0.0268775  -0.765551    0.\n",
            "   1.          0.          0.          0.        ]]\n",
            "(16512, 16)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5FQb7lOKNV9"
      },
      "source": [
        "### **Training the Model**\n",
        "* Now, I will train the Machine Learning Model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77uVmw--J9nH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc6e4404-6514-46c0-a1c6-6d9d94651028"
      },
      "source": [
        "#@ Training the Model:\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()                                              # Instantiating the Model. \n",
        "lin_reg.fit(housing_prepared, housing_labels)                             # Training the Linear Model.\n",
        "\n",
        "#@ Inspecting the Model on few Instances:\n",
        "some_data = housing.iloc[:5]\n",
        "some_labels = housing_labels[:5]\n",
        "some_prepared = pipeline.transform(some_data)\n",
        "print(f\"Predictions: {lin_reg.predict(some_prepared)}\")                   # Inspecting the Predictions.\n",
        "print(f\"Labels: {list(some_labels)}\")\n",
        "\n",
        "#@ Inspecting the Errors:\n",
        "print(\"\\n\")\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "housing_predictions = lin_reg.predict(housing_prepared)\n",
        "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "lin_rmse = np.sqrt(lin_mse)\n",
        "print(lin_rmse)                                                            # Inspecting the root mean squared error.\n",
        "lin_mae = mean_absolute_error(housing_labels, housing_predictions) \n",
        "print(lin_mae)                                                             # Inspecting the mean absolute error."
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions: [188446.87674932 217822.72225147  83876.69336975 116932.35842273\n",
            " 195090.38923287]\n",
            "Labels: [333300.0, 300000.0, 64400.0, 134200.0, 118200.0]\n",
            "\n",
            "\n",
            "68376.58316280936\n",
            "49337.35060481577\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPp1bC8iSFdP"
      },
      "source": [
        "**Decision Trees**\n",
        "* The score obtained above is not the best score. It is an example of Model underfitting the Training Data which means that the features don't provide enough information to make good predictions or that the Model is not powerful enough. The main ways to fix underfitting is to select more powerful Model or to feed the Training Algorithms with better features. Firstly, I will train the Model with Decision Trees which is more powerful Model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVQ3cQH9Ry70",
        "outputId": "e64f280c-7bb0-41e2-f37c-cc9564708b00"
      },
      "source": [
        "#@ Implementation of Decision Trees:\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "tree_reg = DecisionTreeRegressor(random_state=11)                      # Instantiating the Model.\n",
        "tree_reg.fit(housing_prepared, housing_labels)                         # Training the Model. \n",
        "housing_predictions = tree_reg.predict(housing_prepared)               # Making predictions.\n",
        "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "tree_rmse = np.sqrt(tree_mse)\n",
        "print(tree_rmse)                                                       # Root mean squared error."
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ywc2mPC1U7Qp"
      },
      "source": [
        "* As the result obtained above, there is no error at all. It is more likely that the Model has badly overfit the Data. I will use part of Training set for Training and part for Model Validation. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pheNuPPEixlE"
      },
      "source": [
        "**Cross Validation**\n",
        "* I will use Scikit Learn's Cross Validation for Evaluating the Model. The result obtained is an array of a number of Evaluation scores. Scikit Learn's Cross Validation features expect a utility function greater is better rather than a cost function lower is better. So the scoring fucntion is actually the opposite of MSE which is a negative value. I will use minus sign before calculating the square root of scores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MuM-XiqU2Nx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "131776d3-3100-4240-9224-50d3d58532d5"
      },
      "source": [
        "#@ Implementation of Cross Validation with Decision Trees:\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
        "                         scoring=\"neg_mean_squared_error\", cv=10)\n",
        "tree_rmse_scores = np.sqrt(-scores)\n",
        "\n",
        "#@ Inspecting the Result:\n",
        "def display_scores(scores):\n",
        "  print(f\"Scores: {scores}\")\n",
        "  print(f\"Mean: {scores.mean()}\")\n",
        "  print(f\"Standard Deviation: {scores.std()}\")\n",
        "display_scores(tree_rmse_scores)                                                   # Inspecting the scores.\n",
        "\n",
        "#@ Implementation of Cross Validation with Linear Regression:\n",
        "print(\"\\n\")\n",
        "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
        "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
        "lin_rmse_scores = np.sqrt(-lin_scores)\n",
        "display_scores(lin_rmse_scores)                                                    # Inspecting the scores. "
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [68464.55752138 69690.9609981  68004.17300349 73051.18946449\n",
            " 70316.54492485 74434.78497541 66650.7160466  71101.89208675\n",
            " 67728.25960442 72953.62410336]\n",
            "Mean: 70239.67027288451\n",
            "Standard Deviation: 2474.991722588162\n",
            "\n",
            "\n",
            "Scores: [67946.34706551 72043.62662071 66935.91382681 69505.87156287\n",
            " 70581.05232874 70161.98711779 65314.14210918 70476.91199502\n",
            " 66917.30707745 67533.8165875 ]\n",
            "Mean: 68741.69762915772\n",
            "Standard Deviation: 2008.159996178294\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myqbQ-QcrQRM"
      },
      "source": [
        "* From the result obtained above, Decision Tree Model is overfitting so badly and it's performance is comparable to the Linear Regression Model. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpTRky1TzPH5"
      },
      "source": [
        "**RandomForest Regressor**\n",
        "* Random Forests work by training many Decision Trees on random subset of the features and then averaging out the predictions. Building a model on top of many other models is called Ensemble Learning and it is often a great way to push Machine Learning Algorithms even further. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRW3uqBxwpr1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a1d1c45-9beb-4321-806b-125b79c3a06c"
      },
      "source": [
        "#@ Random Forest Regressor:\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "forest_reg = RandomForestRegressor(n_estimators=160, min_samples_leaf=3,                            # Instantiating the Model.\n",
        "                                   n_jobs=-1, random_state=11)\n",
        "forest_reg.fit(housing_prepared, housing_labels)                                                    # Training the Model.\n",
        "housing_predictions = forest_reg.predict(housing_prepared)\n",
        "forest_mse = mean_squared_error(housing_labels, housing_predictions)                                # Calculating mean squared error. \n",
        "forest_rmse = np.sqrt(forest_mse)                                                                   # Calculating root mean squard error. \n",
        "print(forest_rmse)\n",
        "print(\"\\n\")\n",
        "#@ Random Forest Regressor with Cross Validation:\n",
        "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,                       # Random Forest with Cross Validation.\n",
        "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
        "forest_scores_rmse = np.sqrt(-forest_scores)\n",
        "display_scores(forest_scores_rmse)                                                                  # Inspecting the scores."
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "27669.360463712765\n",
            "\n",
            "\n",
            "Scores: [48846.19835981 51112.85436327 49499.53377395 51424.72209762\n",
            " 50774.76151902 51919.31575877 47324.20338154 50141.00300135\n",
            " 48617.28070001 49747.04559718]\n",
            "Mean: 49940.691855252546\n",
            "Standard Deviation: 1351.0930527812056\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlGntkJZ4fS5"
      },
      "source": [
        "* Random Forest looks much more promising Model !!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Du4UZVFv31xQ",
        "outputId": "a720a5a2-3e67-4a55-fe91-f6c1836e48bc"
      },
      "source": [
        "#@ Saving the Random Forest Model:\n",
        "from sklearn.externals import joblib\n",
        "joblib.dump(forest_reg, \"ForestModel.pkl\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ForestModel.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIEPlklO6Ix8"
      },
      "source": [
        "**Grid Search and Tuning the Model**\n",
        "* I will use Scikit Learn's Grid Search to find the great combinations of hyperparameters values. It will evaluate all possible combinations of hyperparameters values using Cross Validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GsF0xt4F5TJz",
        "outputId": "080dae75-6db5-4730-c517-cf4d03beb5b4"
      },
      "source": [
        "#@ Grid Search and Tuning the Model:\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = [\n",
        "              {\"n_estimators\":[30, 60, 90, 120], \"max_features\":[2, 3, 4]},\n",
        "              {\"bootstrap\":[False], \"n_estimators\":[90, 120], \"max_features\":[2, 3, 4]}]\n",
        "forest_reg = RandomForestRegressor(random_state=11)\n",
        "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
        "                           scoring=\"neg_mean_squared_error\", \n",
        "                           return_train_score=True)\n",
        "grid_search.fit(housing_prepared, housing_labels)\n",
        "\n",
        "#@ Inspecting the Result:\n",
        "print(grid_search.best_params_)\n",
        "print(\"\\n\")\n",
        "print(grid_search.best_estimator_)\n",
        "print(\"\\n\")\n",
        "\n",
        "#@ Inspecting the Scores:\n",
        "cvresults = grid_search.cv_results_\n",
        "for mean_score, params in zip(cvresults[\"mean_test_score\"], cvresults[\"params\"]):\n",
        "  print(np.sqrt(-mean_score), params)\n",
        "print(\"\\n\")\n",
        "IPython.display.display(pd.DataFrame(grid_search.cv_results_))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': False, 'max_features': 4, 'n_estimators': 120}\n",
            "\n",
            "\n",
            "RandomForestRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
            "                      max_depth=None, max_features=4, max_leaf_nodes=None,\n",
            "                      max_samples=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",
            "                      n_estimators=120, n_jobs=None, oob_score=False,\n",
            "                      random_state=11, verbose=0, warm_start=False)\n",
            "\n",
            "\n",
            "52647.18598653378 {'max_features': 2, 'n_estimators': 30}\n",
            "51983.719956840825 {'max_features': 2, 'n_estimators': 60}\n",
            "51649.46445435719 {'max_features': 2, 'n_estimators': 90}\n",
            "51498.522509685485 {'max_features': 2, 'n_estimators': 120}\n",
            "50917.81072178314 {'max_features': 3, 'n_estimators': 30}\n",
            "50521.4565970279 {'max_features': 3, 'n_estimators': 60}\n",
            "50236.35697654476 {'max_features': 3, 'n_estimators': 90}\n",
            "50067.17174221468 {'max_features': 3, 'n_estimators': 120}\n",
            "50459.00067863546 {'max_features': 4, 'n_estimators': 30}\n",
            "50013.11213637414 {'max_features': 4, 'n_estimators': 60}\n",
            "49793.48119879792 {'max_features': 4, 'n_estimators': 90}\n",
            "49589.09304833969 {'max_features': 4, 'n_estimators': 120}\n",
            "50666.159806629956 {'bootstrap': False, 'max_features': 2, 'n_estimators': 90}\n",
            "50568.46502949306 {'bootstrap': False, 'max_features': 2, 'n_estimators': 120}\n",
            "49207.25720313552 {'bootstrap': False, 'max_features': 3, 'n_estimators': 90}\n",
            "49127.591620501444 {'bootstrap': False, 'max_features': 3, 'n_estimators': 120}\n",
            "48894.55811295254 {'bootstrap': False, 'max_features': 4, 'n_estimators': 90}\n",
            "48752.73781424451 {'bootstrap': False, 'max_features': 4, 'n_estimators': 120}\n",
            "\n",
            "\n",
            "    mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
            "0        0.614831      0.003176  ...     -4.323881e+08     4.923380e+06\n",
            "1        1.249847      0.015382  ...     -3.956520e+08     3.191042e+06\n",
            "2        1.890188      0.016035  ...     -3.841224e+08     4.049487e+06\n",
            "3        2.530878      0.014069  ...     -3.777513e+08     3.884776e+06\n",
            "4        0.842994      0.013178  ...     -4.043212e+08     6.943337e+06\n",
            "5        1.676018      0.014039  ...     -3.707432e+08     6.494112e+06\n",
            "6        2.484520      0.016356  ...     -3.606812e+08     5.537118e+06\n",
            "7        3.324909      0.039557  ...     -3.542157e+08     4.315228e+06\n",
            "8        1.059994      0.025090  ...     -3.906628e+08     4.705573e+06\n",
            "9        2.097759      0.013283  ...     -3.624607e+08     4.258807e+06\n",
            "10       3.151366      0.019295  ...     -3.536888e+08     4.082598e+06\n",
            "11       4.183862      0.020720  ...     -3.464593e+08     3.307393e+06\n",
            "12       3.056857      0.047171  ...     -9.504821e+00     8.297356e+00\n",
            "13       4.096714      0.032896  ...     -1.011305e+01     4.718867e+00\n",
            "14       4.072557      0.022251  ...     -1.232564e+01     1.814728e+01\n",
            "15       5.407652      0.024668  ...     -8.750579e+00     9.593292e+00\n",
            "16       5.260065      0.070319  ...     -3.904177e+00     6.194799e+00\n",
            "17       7.201758      0.091396  ...     -2.199674e+00     3.482847e+00\n",
            "\n",
            "[18 rows x 23 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjND98efBcPh"
      },
      "source": [
        "**Randomized Search**\n",
        "* The Grid Search Approach is fine when there is relatively few combinations. When the hyperparamters search space is large it is preferable to use Randomized Search. It evaluates the given number of random combinations by selecting a random value for each hyperparameter at every iteration. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_Z_BieRk_iKT",
        "outputId": "13748f50-f9ac-44eb-a325-057f73b0ada7"
      },
      "source": [
        "#@ Implementation of Randomized Search CV:\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import randint\n",
        "param_distribs = {\n",
        "    \"n_estimators\": randint(low=1, high=200),\n",
        "    \"max_features\": randint(low=1, high=10),\n",
        "    \"min_samples_leaf\": randint(low=1, high=8)\n",
        "}\n",
        "forest_reg = RandomForestRegressor(random_state=11)\n",
        "random_search = RandomizedSearchCV(forest_reg, param_distribs, cv=5,\n",
        "                                   n_iter=10, scoring=\"neg_mean_squared_error\", random_state=11)\n",
        "random_search.fit(housing_prepared, housing_labels)\n",
        "\n",
        "#@ Inspecting the Result:\n",
        "print(random_search.best_params_)\n",
        "print(\"\\n\")\n",
        "print(random_search.best_estimator_)\n",
        "print(\"\\n\")\n",
        "\n",
        "#@ Inspecting the Scores:\n",
        "cvresults = random_search.cv_results_\n",
        "for mean_score, params in zip(cvresults[\"mean_test_score\"], cvresults[\"params\"]):\n",
        "  print(np.sqrt(-mean_score), params)\n",
        "print(\"\\n\")\n",
        "IPython.display.display(pd.DataFrame(random_search.cv_results_))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_features': 9, 'min_samples_leaf': 1, 'n_estimators': 146}\n",
            "\n",
            "\n",
            "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                      max_depth=None, max_features=9, max_leaf_nodes=None,\n",
            "                      max_samples=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",
            "                      n_estimators=146, n_jobs=None, oob_score=False,\n",
            "                      random_state=11, verbose=0, warm_start=False)\n",
            "\n",
            "\n",
            "62113.601621035166 {'max_features': 1, 'min_samples_leaf': 4, 'n_estimators': 82}\n",
            "50302.40108738755 {'max_features': 8, 'min_samples_leaf': 6, 'n_estimators': 77}\n",
            "53899.903789261196 {'max_features': 2, 'min_samples_leaf': 3, 'n_estimators': 25}\n",
            "64256.52599128238 {'max_features': 1, 'min_samples_leaf': 6, 'n_estimators': 161}\n",
            "49543.66435147354 {'max_features': 5, 'min_samples_leaf': 3, 'n_estimators': 141}\n",
            "53381.86725394772 {'max_features': 2, 'min_samples_leaf': 3, 'n_estimators': 38}\n",
            "49978.851542364464 {'max_features': 6, 'min_samples_leaf': 5, 'n_estimators': 82}\n",
            "49481.73881888453 {'max_features': 9, 'min_samples_leaf': 1, 'n_estimators': 146}\n",
            "51269.54410478726 {'max_features': 4, 'min_samples_leaf': 7, 'n_estimators': 172}\n",
            "50720.45280234374 {'max_features': 3, 'min_samples_leaf': 3, 'n_estimators': 193}\n",
            "\n",
            "\n"
          ],
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              "      <td>0.028033</td>\n",
              "      <td>0.039028</td>\n",
              "      <td>0.003577</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>77</td>\n",
              "      <td>{'max_features': 8, 'min_samples_leaf': 6, 'n_...</td>\n",
              "      <td>-2.475141e+09</td>\n",
              "      <td>-2.599661e+09</td>\n",
              "      <td>-2.720803e+09</td>\n",
              "      <td>-2.441684e+09</td>\n",
              "      <td>-2.414369e+09</td>\n",
              "      <td>-2.530332e+09</td>\n",
              "      <td>1.144009e+08</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.390685</td>\n",
              "      <td>0.004131</td>\n",
              "      <td>0.014740</td>\n",
              "      <td>0.000319</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>{'max_features': 2, 'min_samples_leaf': 3, 'n_...</td>\n",
              "      <td>-2.842029e+09</td>\n",
              "      <td>-3.009596e+09</td>\n",
              "      <td>-2.970992e+09</td>\n",
              "      <td>-2.902153e+09</td>\n",
              "      <td>-2.801228e+09</td>\n",
              "      <td>-2.905200e+09</td>\n",
              "      <td>7.750776e+07</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.274546</td>\n",
              "      <td>0.019919</td>\n",
              "      <td>0.062898</td>\n",
              "      <td>0.001500</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>161</td>\n",
              "      <td>{'max_features': 1, 'min_samples_leaf': 6, 'n_...</td>\n",
              "      <td>-4.005973e+09</td>\n",
              "      <td>-4.225264e+09</td>\n",
              "      <td>-4.227979e+09</td>\n",
              "      <td>-4.138762e+09</td>\n",
              "      <td>-4.046528e+09</td>\n",
              "      <td>-4.128901e+09</td>\n",
              "      <td>9.065993e+07</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.636031</td>\n",
              "      <td>0.051125</td>\n",
              "      <td>0.084686</td>\n",
              "      <td>0.004948</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>141</td>\n",
              "      <td>{'max_features': 5, 'min_samples_leaf': 3, 'n_...</td>\n",
              "      <td>-2.387478e+09</td>\n",
              "      <td>-2.546320e+09</td>\n",
              "      <td>-2.599984e+09</td>\n",
              "      <td>-2.380766e+09</td>\n",
              "      <td>-2.358325e+09</td>\n",
              "      <td>-2.454575e+09</td>\n",
              "      <td>9.876685e+07</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.595039</td>\n",
              "      <td>0.007981</td>\n",
              "      <td>0.022767</td>\n",
              "      <td>0.001249</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>38</td>\n",
              "      <td>{'max_features': 2, 'min_samples_leaf': 3, 'n_...</td>\n",
              "      <td>-2.766424e+09</td>\n",
              "      <td>-2.934014e+09</td>\n",
              "      <td>-2.942147e+09</td>\n",
              "      <td>-2.823892e+09</td>\n",
              "      <td>-2.781643e+09</td>\n",
              "      <td>-2.849624e+09</td>\n",
              "      <td>7.468328e+07</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.926774</td>\n",
              "      <td>0.021707</td>\n",
              "      <td>0.041309</td>\n",
              "      <td>0.000581</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>82</td>\n",
              "      <td>{'max_features': 6, 'min_samples_leaf': 5, 'n_...</td>\n",
              "      <td>-2.463404e+09</td>\n",
              "      <td>-2.560023e+09</td>\n",
              "      <td>-2.647097e+09</td>\n",
              "      <td>-2.442657e+09</td>\n",
              "      <td>-2.376247e+09</td>\n",
              "      <td>-2.497886e+09</td>\n",
              "      <td>9.503709e+07</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9.913892</td>\n",
              "      <td>0.084997</td>\n",
              "      <td>0.137402</td>\n",
              "      <td>0.003016</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>146</td>\n",
              "      <td>{'max_features': 9, 'min_samples_leaf': 1, 'n_...</td>\n",
              "      <td>-2.387666e+09</td>\n",
              "      <td>-2.547765e+09</td>\n",
              "      <td>-2.595743e+09</td>\n",
              "      <td>-2.352564e+09</td>\n",
              "      <td>-2.358474e+09</td>\n",
              "      <td>-2.448442e+09</td>\n",
              "      <td>1.025116e+08</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.084183</td>\n",
              "      <td>0.030570</td>\n",
              "      <td>0.080154</td>\n",
              "      <td>0.001068</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>172</td>\n",
              "      <td>{'max_features': 4, 'min_samples_leaf': 7, 'n_...</td>\n",
              "      <td>-2.578386e+09</td>\n",
              "      <td>-2.705356e+09</td>\n",
              "      <td>-2.762649e+09</td>\n",
              "      <td>-2.580860e+09</td>\n",
              "      <td>-2.515580e+09</td>\n",
              "      <td>-2.628566e+09</td>\n",
              "      <td>9.103274e+07</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.146505</td>\n",
              "      <td>0.023121</td>\n",
              "      <td>0.112007</td>\n",
              "      <td>0.001657</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>193</td>\n",
              "      <td>{'max_features': 3, 'min_samples_leaf': 3, 'n_...</td>\n",
              "      <td>-2.507427e+09</td>\n",
              "      <td>-2.668101e+09</td>\n",
              "      <td>-2.678865e+09</td>\n",
              "      <td>-2.535825e+09</td>\n",
              "      <td>-2.472604e+09</td>\n",
              "      <td>-2.572564e+09</td>\n",
              "      <td>8.486672e+07</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
              "0       0.723207      0.018746  ...    9.080495e+07                9\n",
              "1       3.435737      0.028033  ...    1.144009e+08                4\n",
              "2       0.390685      0.004131  ...    7.750776e+07                8\n",
              "3       1.274546      0.019919  ...    9.065993e+07               10\n",
              "4       4.636031      0.051125  ...    9.876685e+07                2\n",
              "5       0.595039      0.007981  ...    7.468328e+07                7\n",
              "6       2.926774      0.021707  ...    9.503709e+07                3\n",
              "7       9.913892      0.084997  ...    1.025116e+08                1\n",
              "8       4.084183      0.030570  ...    9.103274e+07                6\n",
              "9       4.146505      0.023121  ...    8.486672e+07                5\n",
              "\n",
              "[10 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wn2oyBNWGsvS"
      },
      "source": [
        "**Analyzing the Best Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opPR0K1mE-BG",
        "outputId": "2042e763-ceb6-4378-eb81-f070a99f614d"
      },
      "source": [
        "#@ Analyzing the Best Models and Errors:\n",
        "feature_importances = grid_search.best_estimator_.feature_importances_\n",
        "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
        "cat_encoder = pipeline.named_transformers_[\"cat\"]\n",
        "cat_onehot_attribs = list(cat_encoder.categories_[0])\n",
        "attributes = num_attribs + extra_attribs + cat_onehot_attribs\n",
        "sorted(zip(feature_importances, attributes), reverse=True)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.27097713732926215, 'median_income'),\n",
              " (0.13124673076809507, 'INLAND'),\n",
              " (0.10371089641458281, 'population_per_household'),\n",
              " (0.0904329189429626, 'longitude'),\n",
              " (0.0893627545645655, 'bedrooms_per_room'),\n",
              " (0.0817695500573566, 'latitude'),\n",
              " (0.0716429098276723, 'rooms_per_household'),\n",
              " (0.04098485612211728, 'housing_median_age'),\n",
              " (0.02431254956610928, 'total_rooms'),\n",
              " (0.02240959196114921, 'population'),\n",
              " (0.0194121513284739, 'households'),\n",
              " (0.019242304716008664, 'total_bedrooms'),\n",
              " (0.01910206228086364, '<1H OCEAN'),\n",
              " (0.008334530522205928, 'NEAR OCEAN'),\n",
              " (0.006769557370065242, 'NEAR BAY'),\n",
              " (0.00028949822850994134, 'ISLAND')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtMHDACXIs9C"
      },
      "source": [
        "* From the result obtained above, I will drop some less useful attributes i.e ocean proximity. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKq9Nv7oJQMO"
      },
      "source": [
        "**Evaluating the System on Testset**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UTuk3jLDIlH4",
        "outputId": "31740dc8-9e40-48c9-8978-bd4e3dad8721"
      },
      "source": [
        "#@ Evaluating the System on Testset:\n",
        "final_model = grid_search.best_estimator_\n",
        "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
        "y_test = strat_test_set[\"median_house_value\"].copy()\n",
        "#@ Preparing the Data:\n",
        "X_test_prepared = pipeline.transform(X_test)\n",
        "final_predictions = final_model.predict(X_test_prepared)\n",
        "#@ Evaluating the Model:\n",
        "final_mse = mean_squared_error(y_test, final_predictions)              # Calculating the mean squared error. \n",
        "final_rmse = np.sqrt(final_mse)                                        # Calculating the root mean squared error.\n",
        "print(final_rmse)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "47466.442514815506\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_s56zZSiKywz",
        "outputId": "abbf8b29-9cc3-43ba-c9b8-d15b1860a16d"
      },
      "source": [
        "#@ Computing a 95% confidence interval:\n",
        "from scipy import stats\n",
        "confidence = 0.95\n",
        "squared_error = (final_predictions - y_test) ** 2\n",
        "np.sqrt(stats.t.interval(confidence, len(squared_error)-1, loc=squared_error.mean(),\n",
        "                         scale=stats.sem(squared_error)))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([45188.48103843, 49639.97896317])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1crslpR7M99h"
      },
      "source": [
        "**Launch, monitor and maintain the System !!**"
      ]
    }
  ]
}