Machine_Learning_Intro.ipynb

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      "name": "Machine Learning Intro",
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      "metadata": {
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      "source": [
        "# Machine Learning Intro #\n",
        "\n",
        "Let's go through an Regression problem. The main steps we are going to take is: \n",
        "\n",
        "1. Load the data\n",
        "2. Discover and visualize the data to gain insights\n",
        "3. Prepare data for machine learning algorithms\n",
        "4. Select an algorithm and train a model\n",
        "5. Validate our model\n",
        "\n",
        "The dataset we are going to use is the [California Housing Dataset](https://github.com/ageron/handson-ml/tree/master/datasets/housing) which contains data drawn from the 1990 U.S. Census. \n",
        "\n",
        "The first task you are asked to perform is to build a model of housing prices in California using the California census data. This data has metrics such as the **population**, **median income**, **median housing\n",
        "price**, and so on for each block group in California. Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). We will just call them “districts” for short.\n",
        "\n",
        "\n",
        "Your model should learn from this data and be able to **predict the median housing price in any district, given all the other metrics**."
      ]
    },
    {
      "metadata": {
        "id": "tQovSONHCHqX",
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      "cell_type": "markdown",
      "source": [
        "## Create the workspace ##\n",
        "\n",
        "If you are working *offline* you can create a conda environment now with `conda create -name linear` and install the libraries needed with `pip install matplotlib numpy pandas scipy scikit-learn`"
      ]
    },
    {
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        "id": "0iudJYF5B1Nd",
        "colab_type": "code",
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      },
      "cell_type": "code",
      "source": [
        "#Several imports that will be needed - Check them before starting\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "import sklearn\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3e9hmwdACTqS",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "## Data Loading ##\n",
        "\n",
        "We are loading the data using pandas. Write a small function for this, we are gonna need it later. We take a quick look into the data using pandas."
      ]
    },
    {
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        }
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      "cell_type": "code",
      "source": [
        "DATASET_PATH = \"housing.csv\"\n",
        "\n",
        "def load_data_csv(csv_path=DATASET_PATH):\n",
        "  return pd.read_csv(csv_path)\n",
        "\n",
        "housing = load_data_csv()\n",
        "housing.head() # Top 5 rows "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <th>median_house_value</th>\n",
              "      <th>ocean_proximity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.23     37.88                41.0        880.0           129.0   \n",
              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
              "\n",
              "   population  households  median_income  median_house_value ocean_proximity  \n",
              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "sXjmSegnCuW6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data discovery ##"
      ]
    },
    {
      "metadata": {
        "id": "Q9M7D25QCx6A",
        "colab_type": "code",
        "outputId": "06f06c39-b8d7-44b1-e37b-342b25417b30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "cell_type": "code",
      "source": [
        "housing.info() # Quick description of the data (types, columns, entries etc)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 10 columns):\n",
            "longitude             20640 non-null float64\n",
            "latitude              20640 non-null float64\n",
            "housing_median_age    20640 non-null float64\n",
            "total_rooms           20640 non-null float64\n",
            "total_bedrooms        20433 non-null float64\n",
            "population            20640 non-null float64\n",
            "households            20640 non-null float64\n",
            "median_income         20640 non-null float64\n",
            "median_house_value    20640 non-null float64\n",
            "ocean_proximity       20640 non-null object\n",
            "dtypes: float64(9), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
        "id": "7shvn9w7C1oV",
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      "cell_type": "markdown",
      "source": [
        "There are 20,640 instances in the dataset. Notice that the total_bed\n",
        "rooms attribute has only 20,433 non-null values, meaning that 207 districts are missing this feature. We will need to take care of this later. \n",
        "\n",
        "Also, all attributes are number except the oceant_proximity field. It is type object, since we loaded from csv, it is a text field. Take a look back at the top5 rows."
      ]
    },
    {
      "metadata": {
        "id": "cyIjFmCYC7Bk",
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        "colab": {
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      },
      "cell_type": "code",
      "source": [
        "# Let's find all about this field\n",
        "housing[\"ocean_proximity\"].value_counts()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<1H OCEAN     9136\n",
              "INLAND        6551\n",
              "NEAR OCEAN    2658\n",
              "NEAR BAY      2290\n",
              "ISLAND           5\n",
              "Name: ocean_proximity, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "fDRTV6bxC9Tn",
        "colab_type": "code",
        "outputId": "324b0e49-e64b-4a22-d925-f11cfca2d11a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "# Some further information about all the fields\n",
        "housing.describe()\n",
        "\n",
        "# STD = Standard Deviation, 25% = 25th percentile, 50% = median , 75% = 75th percentile"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <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  housing_median_age   total_rooms  \\\n",
              "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
              "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
              "std        2.003532      2.135952           12.585558   2181.615252   \n",
              "min     -124.350000     32.540000            1.000000      2.000000   \n",
              "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
              "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
              "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
              "max     -114.310000     41.950000           52.000000  39320.000000   \n",
              "\n",
              "       total_bedrooms    population    households  median_income  \\\n",
              "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
              "mean       537.870553   1425.476744    499.539680       3.870671   \n",
              "std        421.385070   1132.462122    382.329753       1.899822   \n",
              "min          1.000000      3.000000      1.000000       0.499900   \n",
              "25%        296.000000    787.000000    280.000000       2.563400   \n",
              "50%        435.000000   1166.000000    409.000000       3.534800   \n",
              "75%        647.000000   1725.000000    605.000000       4.743250   \n",
              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
              "\n",
              "       median_house_value  \n",
              "count        20640.000000  \n",
              "mean        206855.816909  \n",
              "std         115395.615874  \n",
              "min          14999.000000  \n",
              "25%         119600.000000  \n",
              "50%         179700.000000  \n",
              "75%         264725.000000  \n",
              "max         500001.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "QxDB7JdHDDK7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data visualization ##\n",
        "\n",
        "### General visualization ###\n",
        "\n",
        "Plotting histograms for each numerical value also helps us understand the data. A histogram shows the number of instances\n",
        "(on the vertical axis) that have a given value range (on the horizontal axis)."
      ]
    },
    {
      "metadata": {
        "id": "leBbHwyQDFxd",
        "colab_type": "code",
        "outputId": "bba9d689-6f4d-417c-d6cc-60a5dfd2caf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt # Necessary import\n",
        "housing.hist(bins=50, figsize=(20,15)) #Using like this plots for each \n",
        "#nummerical value, we choose the number of bins - the number of segments -.\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LdvOeW5p/RdxyxngfIuqviYkJRvF1bcX2jrZ+tdfpbJIkSZJqJTMfAL4CvBiYHxFTmY+F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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RYoaQPUZDN8p",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We notice:\n",
        " * Some have been capped (median income at 15, median house age, median house value)\n",
        " * Different scales\n",
        " * Tail heavy histograms (they extend much farther to the right of the median than to the left)"
      ]
    },
    {
      "metadata": {
        "id": "Crl_B1hoDQ3r",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualizing Geographical Data ##\n",
        "\n",
        "Since there is geographical information (latitude and longitude), it is a good idea to\n",
        "create a scatterplot of all districts to visualize the data. \n",
        "\n",
        "Here is California in the map to help us understand our data better.\n",
        "\n",
        "<img src=\"http://www.orangesmile.com/common/img_city_maps/california-state-map-3.jpg \" alt=\"california\" width=\"400\"/>"
      ]
    },
    {
      "metadata": {
        "id": "bOzOOdp_DUqj",
        "colab_type": "code",
        "outputId": "d250b0f0-efa8-4c2a-c513-19b0b48203c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "cell_type": "code",
      "source": [
        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\") # Scatter with longitute in the x axis and the latitude in th y axis. "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f94a5aa04a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8qMCVvDY4G8igEbsLLir0M+viUUmrdJLh97RNqWtzfH0vYJqS/U3BFtltidUQ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QJPl-ASBDXXX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This looks like California all right, but other than that it is hard to see any particular pattern. Setting the alpha option to 0.1 makes it much easier to visualize the places where there is a high density of data points."
      ]
    },
    {
      "metadata": {
        "id": "MsoRQNp3DZt2",
        "colab_type": "code",
        "outputId": "ece4b165-5cda-44a8-9bab-2f404f342682",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "cell_type": "code",
      "source": [
        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1) # Scatter with longitute in the x axis and the latitude in th y axis. "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f94a2a810b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MIZ9vDIRC2E73Ocy/s8C1EBh2etFDDW6+Y7HWDFhpBQSOgxCSl5ZqfPnFDkYI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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uaqil19sDbuG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can now see the high density areas (around the main cities of california).\n",
        "\n",
        "More generally, our brains are very good at spotting patterns on pictures, but you may need to play around with visualization parameters to make the patterns stand out.\n",
        "\n",
        "Let's include the housing prices. The radius of each circle represents the district’s population (option s), and the color represents the price (option c). We will use a predefined color map (option cmap) called jet, which ranges from blue (low values) to red (high prices).\n"
      ]
    },
    {
      "metadata": {
        "id": "cXcQEgXVDeS4",
        "colab_type": "code",
        "outputId": "b59f997f-93fd-4f18-ed5d-f81e2b0ae59b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "cell_type": "code",
      "source": [
        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
        "s=housing[\"population\"]/100, label=\"population\",\n",
        "c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
        ")\n",
        "plt.legend()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f94a2b43e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PjqYJzjvviDSgQ/j9Bs3NQVJSnNhs6uezd28AXReHBOVBUlPtFBX5mDXrm10MTgdykqJY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\n6PfnVhcVywAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "1lmRaUCmDqLw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Prepare the data for Machine Learning Algorithms #\n",
        "\n",
        "Instead of just doing this manually, you should write functions to do that, for several good reasons:\n",
        " \n",
        " * Reproduce the transformations on any dataset\n",
        " * Build a library of transformations functions\n",
        " * Easily try various transformations\n",
        " \n",
        " Let's work with our training set and separate the features (input values) from the labels (output values - median house value)"
      ]
    },
    {
      "metadata": {
        "id": "X7B4eZyZDtEQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "x = housing.drop(\"median_house_value\", axis=1) # Creates a copy without the specified column\n",
        "y = housing[\"median_house_value\"].copy() # Copies the column to the specified variable"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MCKiUiweFj3A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning ##\n",
        "\n",
        "Most Machine Learning algorithms cannot work with missing features, so let’s create a few functions to take care of them. You noticed earlier that the total_bedrooms attribute has some missing values, so let’s fix this. You have three options:\n",
        "\n",
        "1. Get rid of the corresponding districts.\n",
        "2. Get rid of the whole attribute.\n",
        "3. Set the values to some value (zero, the mean, the median, etc.).\n",
        "\n",
        "In cases where you have more columns with missing values you can easily use the [Simple Imputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer) from the scikit learn library. \n"
      ]
    },
    {
      "metadata": {
        "id": "RmSTvA6DFnfv",
        "colab_type": "code",
        "outputId": "98aed207-5c7d-4a45-f2fd-332ed7418aec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1125
        }
      },
      "cell_type": "code",
      "source": [
        "#housing.dropna(subset=[\"total_bedrooms\"]) # option 1\n",
        "# housing.drop(\"total_bedrooms\", axis=1) # option 2\n",
        "median = x[\"total_bedrooms\"].median() # Get the median of this column\n",
        "x[\"total_bedrooms\"].fillna(median) # option 3"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         129.0\n",
              "1        1106.0\n",
              "2         190.0\n",
              "3         235.0\n",
              "4         280.0\n",
              "5         213.0\n",
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              "7         687.0\n",
              "8         665.0\n",
              "9         707.0\n",
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              "19        298.0\n",
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              "25        123.0\n",
              "26        244.0\n",
              "27        421.0\n",
              "28        492.0\n",
              "29        160.0\n",
              "          ...  \n",
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              "20614     460.0\n",
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              "20617     109.0\n",
              "20618     247.0\n",
              "20619     340.0\n",
              "20620      41.0\n",
              "20621     247.0\n",
              "20622     147.0\n",
              "20623     244.0\n",
              "20624     300.0\n",
              "20625      17.0\n",
              "20626     184.0\n",
              "20627      65.0\n",
              "20628     421.0\n",
              "20629    1856.0\n",
              "20630     505.0\n",
              "20631     493.0\n",
              "20632     416.0\n",
              "20633     412.0\n",
              "20634     395.0\n",
              "20635     374.0\n",
              "20636     150.0\n",
              "20637     485.0\n",
              "20638     409.0\n",
              "20639     616.0\n",
              "Name: total_bedrooms, Length: 20640, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "JI7nwYF_FrwX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Handling Text and Categorical Attributes ##\n",
        "\n",
        "The ocean_proximity fields is a text attribute. Most machine learning algorithms prefer to work with numbers, so is it better to convert these text labels to numbers."
      ]
    },
    {
      "metadata": {
        "id": "cj2r01QdFuuf",
        "colab_type": "code",
        "outputId": "247abcd7-c4cd-48da-b49b-6369b165192b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "housing_cat = x[\"ocean_proximity\"]\n",
        "housing_cat_encoded = encoder.fit_transform(housing_cat)\n",
        "print(housing_cat_encoded)\n",
        "print(encoder.classes_)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3 3 3 ... 1 1 1]\n",
            "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HqhXpS80FxNd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Each category is mapped to a value ex. <1H OCEAN is mapped  to 0, INLDAND is mapped to 1 etc.\n",
        "\n",
        "One issue with this representation is that ML algorithms will assume that two nearby values are more similar than two distant values. Obviously this is not the case (for example, categories 0 and 4 are more similar than categories 0 and 1).\n",
        "\n",
        "To fix this we create one binary category for each attribute. One attribute is equal to 1 when the category matched the ocean proximity of the district and 0 to all other attributes.\n",
        "\n",
        "\n",
        "Scikit-Learn provides a [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) encoder to convert integer categorical values into one-hot vectors. Let’s encode the categories as one-hot vectors. Note that fit_transform() expects a 2D array, but housing_cat_encoded is a 1D array, so we need to reshape it."
      ]
    },
    {
      "metadata": {
        "id": "AEVnYzMWF0b0",
        "colab_type": "code",
        "outputId": "a69a2ef7-ce9e-4503-b393-729f5d5a77f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "encoder = OneHotEncoder()\n",
        "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
        "housing_cat_1hot"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:371: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<20640x5 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 20640 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "gumEUtX8F3hO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Notice that the output is a SciPy sparse matrix, instead of a NumPy array. This is very useful when you have categorical attributes with thousands of categories. After one-hot encoding we get a matrix with thousands of columns, and the matrix is full of zeros except for one 1 per row. Using up tons of memory mostly to store zeros would be very wasteful, so instead a sparse matrix only stores the location of the nonzero elements. You can use it mostly like a normal 2D array,19 but if you really want to convert it to a (dense) NumPy array, just call the toarray() method."
      ]
    },
    {
      "metadata": {
        "id": "vMCZYSqWF2dd",
        "colab_type": "code",
        "outputId": "7cd9cc57-d0a7-4a23-b47b-2795fc90270d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "cell_type": "code",
      "source": [
        "housing_cat_1hot.toarray()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 1., 0.],\n",
              "       ...,\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "-u_WTb9iGXxO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Pipelines ##\n",
        "\n",
        "You can combine all the transformations into one [Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). The Pipeline constructor takes a list of name/estimator pairs defining a sequence of steps. All but the last estimator must be transformers (i.e., they must have a fit_transform() method). The names can be anything you like."
      ]
    },
    {
      "metadata": {
        "id": "wqI-trPJGZjz",
        "colab_type": "code",
        "outputId": "2a4ac0aa-8c63-4142-b9ed-7e186d0e0b18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "\n",
        "housing_num = x.drop(\"ocean_proximity\", axis=1) # Get only the numerical values\n",
        "\n",
        "\n",
        "# Set up the pipeline\n",
        "num_pipeline = Pipeline([\n",
        "('imputer', SimpleImputer(strategy=\"median\")),\n",
        "('normalization', MinMaxScaler()),\n",
        "])\n",
        "\n",
        "housing_num_tr = num_pipeline.fit_transform(housing_num)\n",
        "\n",
        "pd.DataFrame(housing_num_tr).head()\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          0         1         2         3         4         5         6  \\\n",
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              "\n",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "qwKo-AwAGcx2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's join the numerical values with the categorical. We do that with the [ColumnTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html)"
      ]
    },
    {
      "metadata": {
        "id": "npbtu8IDGgl4",
        "colab_type": "code",
        "outputId": "dfff6529-2637-42c9-c705-1e7cb65bd0a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.pipeline import FeatureUnion\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "# Specify the transformer and the columns to affect. You also use pipelines as transformer.\n",
        "full_pipeline = ColumnTransformer([\n",
        "    (\"num_pipeline\", num_pipeline, x.columns[:-1]),\n",
        "    ('label_binarizer', OneHotEncoder(),[x.columns[-1]])\n",
        "])\n",
        "housing_prepared = full_pipeline.fit_transform(x)\n",
        "pd.DataFrame(housing_prepared).head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </tbody>\n",
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              "</div>"
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            "text/plain": [
              "         0         1         2         3         4         5         6   \\\n",
              "0  0.211155  0.567481  0.784314  0.022331  0.019863  0.008941  0.020556   \n",
              "1  0.212151  0.565356  0.392157  0.180503  0.171477  0.067210  0.186976   \n",
              "2  0.210159  0.564293  1.000000  0.037260  0.029330  0.013818  0.028943   \n",
              "3  0.209163  0.564293  1.000000  0.032352  0.036313  0.015555  0.035849   \n",
              "4  0.209163  0.564293  1.000000  0.041330  0.043296  0.015752  0.042427   \n",
              "\n",
              "         7    8    9    10   11   12  \n",
              "0  0.539668  0.0  0.0  0.0  1.0  0.0  \n",
              "1  0.538027  0.0  0.0  0.0  1.0  0.0  \n",
              "2  0.466028  0.0  0.0  0.0  1.0  0.0  \n",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "TdLAkdzSGf7y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Select and Train a Model  ##\n",
        "\n",
        "Following all these steps made the process of training a model and evaluating it much simpler. In this sections will train a regression using the sklearn method of LinearRegression."
      ]
    },
    {
      "metadata": {
        "id": "v-W0Mj8bG96t",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Linear Regression (sklearn) ###\n",
        "\n",
        "We train a linear regression model using the scikit learn library."
      ]
    },
    {
      "metadata": {
        "id": "UQg9agyYGnH8",
        "colab_type": "code",
        "outputId": "478f74b6-15d8-4799-f29a-1805d349e8d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(housing_prepared, y)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "sBODOc85G6NE",
        "colab_type": "code",
        "outputId": "2a1cb92f-6af9-4e63-bfc6-01265b3f3af9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "# Let's try out on few instances from the training set\n",
        "some_data = x.iloc[:5] # Choosing some data\n",
        "some_labels = y.iloc[:5] # Don't forget to also get the labels\n",
        "some_data_prepared = full_pipeline.transform(some_data) # Transform thoses data with the pipeline\n",
        "print(\"Predictions:\\t\", lin_reg.predict(some_data_prepared))\n",
        "print(\"Labels:\\t\\t\", list(some_labels))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions:\t [407808. 423424. 378112. 320512. 255232.]\n",
            "Labels:\t\t [452600.0, 358500.0, 352100.0, 341300.0, 342200.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UIEiiIv2FFCo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Evaluation ##"
      ]
    },
    {
      "metadata": {
        "id": "l86SrB0IHDp1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Predictions doesn't seem very accurate. Let's measure this regression model’s Root Mean Square Error (RMSE)"
      ]
    },
    {
      "metadata": {
        "id": "_iZofMnMHGlc",
        "colab_type": "code",
        "outputId": "2d994294-2de6-422f-8f15-b594a3a7ed36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "housing_predictions = lin_reg.predict(housing_prepared) # Taking the predictions on the training set\n",
        "lin_mse = mean_squared_error(y, housing_predictions) # Computing mean squared error\n",
        "lin_rmse = np.sqrt(lin_mse) # Taking the root\n",
        "lin_rmse"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68711.65348248677"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    }
  ]
}