Housing_Data_.ipynb

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      "name": "Housing Data .ipynb",
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      "cell_type": "markdown",
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      "source": [
        "# Predicting Housing \n",
        "Build a frame-work to predict the housing prices is Califonia. During the course of this notebook, I will try to analysi the data and create a model to predict\n",
        "\n",
        "## Problem Statement\n",
        "\n",
        "Task is to predict median house values in Californian districts, given a number of features from these districts.\n",
        "\n",
        "## Dataset\n",
        "\n",
        "Dataset is based on data from the 1990 California census. It is located at `datasets/housing/housing.csv`"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "# Data Exploration\n",
        "## Getting the Data"
      ]
    },
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      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import io\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "# I could'nt find the path so for now lets just find the data using the following read_csv function : BUT please come-back \n",
        "# to this a bit later to figure things out thanks"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-66eee85b-ce5b-449e-9a9c-9e7a158d4db6\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-66eee85b-ce5b-449e-9a9c-9e7a158d4db6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving housing.csv to housing.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYRaziDT7c_V",
        "colab_type": "code",
        "outputId": "c3b569b7-82f9-4dca-fcbd-9ec5a613be98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "housing = pd.read_csv(io.BytesIO(uploaded['housing.csv']))\n",
        "housing.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-a42bb3761321>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muploaded\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'housing.csv'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mhousing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'housing.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1C7hT1R7c_m",
        "colab_type": "text"
      },
      "source": [
        "Each row represents one district. \n",
        "There are 10 attributes:\n",
        "1. longitude \n",
        "2. latitude \n",
        "3. housing_median_age\n",
        "4. total_room\n",
        "5. total_bedrooms \n",
        "6. population\n",
        "7. households \n",
        "8. median_income \n",
        "9. median_house_value\n",
        "10. ocean_proximity\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQ_Sx6uI7c_o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The info() method is useful to get a quick description of the data\n",
        "# in particular the total number of rows,\n",
        "# and each attribute’s type and number of non-null values\n",
        "\n",
        "housing.info()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gfEO6c47c_s",
        "colab_type": "text"
      },
      "source": [
        "There are 20,640 instances in the dataset.\n",
        "\n",
        "Notice that the total_bedrooms attribute has only 20,433 non null values, meaning that 207 districts are missing this feature.\n",
        "\n",
        "All attributes are numerical, except the ocean_proximity field. Its type is object, so it could hold any\n",
        "kind of Python object, but since you loaded this data from a CSV file you know that it must be a text\n",
        "attribute. \n",
        "\n",
        "When you looked at the top five rows, you probably noticed that the values in that column were\n",
        "repetitive, which means that it is probably a categorical attribute."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djfXBWRl7c_t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Find out what categories exist\n",
        "# and how many districts belong to each category by using the value_counts() method\n",
        "# Since this is a categorical data. We would like to see how many of each value is getting repeated\n",
        "housing[\"ocean_proximity\"].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "BR0m9iEs7c_w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's look at the other fields.\n",
        "# The describe() method shows a summary of the numerical attributes\n",
        "\n",
        "housing.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9hfPrlC7c_y",
        "colab_type": "text"
      },
      "source": [
        "The count, mean, min and max rows are self-explanatory.\n",
        "\n",
        "Note the count of total_bedrooms is 20,433, not 20,640. It means that null values are ignored\n",
        "\n",
        "**std** rows shows the standard deviation (which measures how dispersed the values are)\n",
        "\n",
        "** 25%, 50%, 75% ** shows the corresponding percentiles\n",
        "\n",
        "** Points to Note**\n",
        "\n",
        "1. ** 25th percentile is called 1st quartile ** - 25% of the districts have a housing_median_age lower than 18. \n",
        "2. ** 50th percentile is called median ** - 50% of the districts have a housing_median_age lower than 29. \n",
        "3. ** 75th percentile is called 3rd quartile ** - 75% of the districts have a housing_median_age lower than 37. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bloiLf287c_0",
        "colab_type": "text"
      },
      "source": [
        "## Go back to slide - Plot histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "ORrifsqY7c_1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's plot a histogram to get the feel of type of data we are dealing with\n",
        "# We can plot histogram only for numerical attributres\n",
        "# the %matplotlib - this tells jupyter to start using it's own backend\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "housing.hist(bins=50, figsize=(20,15))\n",
        "plt.show()\n",
        "# The histogram is showing u\n",
        "# -figsize is showing us the size of the image \n",
        "# household = normalised curve\n",
        "# Meian-income = is not expressed in USD\n",
        "# Housing Median Age and hourse_value -> are also capped \n",
        "#this might be a problem soon because median_house_calue is the predicted variable and therefore it might be harder to make prediction going forward "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjZel_hS7c_5",
        "colab_type": "text"
      },
      "source": [
        "Refer to slide [Things to Note in Histogram]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4V-yQXm_7c_6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# To make this notebook's output identical at every run\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "np.random.seed(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6g2HPExQ7c_8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# For illustration only. Sklearn has train_test_split()\n",
        "# test ratio is usalyy 0.2 representng how much of the data you \n",
        "# would like to keep as a testing point\n",
        "def split_train_test(data, test_ratio):\n",
        "  # create an array \n",
        "    shuffled_indices = np.random.permutation(len(data))\n",
        "    # split the data\n",
        "    test_set_size = int(len(data) * test_ratio)\n",
        "    test_indices = shuffled_indices[:test_set_size]\n",
        "    train_indices = shuffled_indices[test_set_size:]\n",
        "    #iloc method -> acces the rows of the data\n",
        "    #EXAMPLE is housing.iloc[0\n",
        "    #return tuple\n",
        "    return data.iloc[train_indices], data.iloc[test_indices]\n",
        "\n",
        "train_set, test_set = split_train_test(housing, 0.2)\n",
        "print(len(train_set), \"train +\", len(test_set), \"test\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qC9rPZ0fV3P1",
        "colab_type": "text"
      },
      "source": [
        "The problem with the previous function ;in order to split the data into test and train ; when new data gets added it creates a big problem . So we came up with another way to slit the data which is using hash.\n",
        "\n",
        "The line :\n",
        "\n",
        "**`hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio`**\n",
        "is taking an identifier in from the dataset that we know for sure. The converting into a hash and pickick all the values befoe it\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8B9BOC67c__",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# With unique and immutable identifier\n",
        "\n",
        "import hashlib\n",
        "\n",
        "def test_set_check(identifier, test_ratio, hash):\n",
        "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
        "\n",
        "def split_train_test_by_id(data, test_ratio, id_column, hash=hashlib.md5):\n",
        "  # pick all the values for a given colum \n",
        "    ids = data[id_column]\n",
        "    # Apply a function on the given data so a call back to a prev function and then get a binary return val\n",
        "    in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio, hash))\n",
        "    # split the data from the identifier and to make sure that the values less go into training and the \n",
        "    # values greater than go into testing \n",
        "    return data.loc[~in_test_set], data.loc[in_test_set]\n",
        "\n",
        "housing_with_id = housing.reset_index()   # adds an `index` column\n",
        "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"index\")\n",
        "\n",
        "print(len(train_set), \"train +\", len(test_set), \"test\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bxRQWRL7dAC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_set.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "MBKu-mFH7dAF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Combining latitude and longitude into an ID\n",
        "\n",
        "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
        "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, \"id\")\n",
        "\n",
        "print(len(train_set), \"train +\", len(test_set), \"test\")\n",
        "\n",
        "test_set.head()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "5s6tyUF07dAV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# With sklearn train_test_split\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
        "\n",
        "print(len(train_set), \"train +\", len(test_set), \"test\")\n",
        "\n",
        "test_set.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "v0gYHH057dAb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing[\"median_income\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "OnmzLETi7dAh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a histrogram of median income\n",
        "\n",
        "housing[\"median_income\"].hist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xb76DMh88eox",
        "colab_type": "text"
      },
      "source": [
        "Based on the histogram we can see that, its mostly right skwed\n",
        "1. Also we see that most of the people are between are 2-5\n",
        "2. There are some people beyond 6.\n",
        "\n",
        "We want there not to be any bias when we select people from each of the category. Since we noted condition 1 and 2 when we sample are data to **train** we should make sure that it reflects the distribution graph."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBpXq8Do7dAt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Divide by 1.5 to limit the number of income categories\n",
        "# Round up using ceil to have discrete categories\n",
        "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
        "housing[\"income_cat\"].hist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mZKyGS9_OQ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing[\"income_cat\"].value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIxdYgv8-2vr",
        "colab_type": "text"
      },
      "source": [
        "There is not a lot of cluster after 5 because well they represent the rich. So lets just put a cap on them and just group them all together."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Cujv63B7dA0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Label those above 5 as 5\n",
        "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)\n",
        "housing[\"income_cat\"].hist()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1IxBi3Y7dAv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing[\"income_cat\"].value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8PRAGuO7dA_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Stratified Sampling using Scikit-learn's StratifiedShuffleSplit Class\n",
        "\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
        "  #Traiing Data\n",
        "    strat_train_set = housing.loc[train_index]\n",
        "    #Testin Data\n",
        "    strat_test_set = housing.loc[test_index]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRzyOjwo7dBE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Income category proportion in test set generated with stratified sampling\n",
        "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jH-cap337dBL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Income category proportion in full dataset\n",
        "housing[\"income_cat\"].value_counts() / len(housing)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MdU9tAC37dBQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let’s compare income category proportion in Stratified Sampling and Random Sampling\n",
        "\n",
        "def income_cat_proportions(data):\n",
        "    return data[\"income_cat\"].value_counts() / len(data)\n",
        "\n",
        "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
        "\n",
        "compare_props = pd.DataFrame({\n",
        "    \"Overall\": income_cat_proportions(housing),\n",
        "    \"Stratified\": income_cat_proportions(strat_test_set),\n",
        "    \"Random\": income_cat_proportions(test_set),\n",
        "}).sort_index()\n",
        "compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100\n",
        "compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "IKB9U90-7dBT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compare_props"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4X-ZqKAFE9u",
        "colab_type": "text"
      },
      "source": [
        "As we can see that Stratified Sampling gives better test set than Random Sampling. This can be seen by seeing the amount of Rand%Error and Strat%Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "me5ZMnjG7dBc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for set_ in (strat_train_set, strat_test_set):\n",
        "    set_.drop(\"income_cat\", axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejUw9u7a7dBf",
        "colab_type": "text"
      },
      "source": [
        "# Discover and visualize the data to gain insights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57kWptfW7dBg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing = strat_train_set.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S930GQX7dBj",
        "colab_type": "code",
        "outputId": "d5f5865a-ffaf-4483-a39b-ee73243c4bb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f90a069f160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BPdHqEjST94IhUGDpF/tx8Z8+Y2ElBiMAzDEYcOkFvQI+n2Tj8cqtRRiV08uf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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "ec6p9Pic7dBs",
        "colab_type": "code",
        "outputId": "061dbe6e-f6d1-4d8d-d6bf-f26728009e30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f90a069fe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SglwZ6r7Nfj8hSRUOFivNADOywb5b/AlJS5x5snkSoZ+yga2k5MnxTDiGJFV8516X3W5y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\n+NjZCc7O1mjYBhuDCD9M+eOXl3eMgsa+oyOuYT8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mpB/BWt9nRrg8e6lJnGUEqcKWGl/8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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPviJboA7dB5",
        "colab_type": "code",
        "outputId": "18021ae9-bbe4-4caf-a4e3-774453f52728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "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()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f90a3466320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qQGKiQWHhmd3g33gXvihTs/cevE8HWUNhCMhOh8x0KPeo7umRuOyyeNraQrjd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D6Ax0xuDAGMQyMJUHWYaOdjgioXeZiZd3yaZhqXYfHIHeYlOPJikdS1JKV1C6PDBE0LGlwPgi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\nGZJGwAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "qr-0LVSq7dB_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.image as mpimg\n",
        "\n",
        "california_img=mpimg.imread('images/end_to_end_project/california.png')\n",
        "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
        "                       s=housing['population']/100, label=\"Population\",\n",
        "                       c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
        "                       colorbar=False, alpha=0.4,\n",
        "                      )\n",
        "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5)\n",
        "plt.ylabel(\"Latitude\", fontsize=14)\n",
        "plt.xlabel(\"Longitude\", fontsize=14)\n",
        "\n",
        "prices = housing[\"median_house_value\"]\n",
        "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
        "cbar = plt.colorbar()\n",
        "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
        "cbar.set_label('Median House Value', fontsize=16)\n",
        "\n",
        "plt.legend(fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUN42nst7dCE",
        "colab_type": "code",
        "outputId": "9d33ad73-df88-4f4a-fc7b-ea36f3f47657",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "source": [
        "corr_matrix = housing.corr()\n",
        "corr_matrix"
      ],
      "execution_count": 0,
      "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>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.924478</td>\n",
              "      <td>-0.105848</td>\n",
              "      <td>0.048871</td>\n",
              "      <td>0.076598</td>\n",
              "      <td>0.108030</td>\n",
              "      <td>0.063070</td>\n",
              "      <td>-0.019583</td>\n",
              "      <td>-0.047432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>-0.924478</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005766</td>\n",
              "      <td>-0.039184</td>\n",
              "      <td>-0.072419</td>\n",
              "      <td>-0.115222</td>\n",
              "      <td>-0.077647</td>\n",
              "      <td>-0.075205</td>\n",
              "      <td>-0.142724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</th>\n",
              "      <td>-0.105848</td>\n",
              "      <td>0.005766</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.364509</td>\n",
              "      <td>-0.325047</td>\n",
              "      <td>-0.298710</td>\n",
              "      <td>-0.306428</td>\n",
              "      <td>-0.111360</td>\n",
              "      <td>0.114110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rooms</th>\n",
              "      <td>0.048871</td>\n",
              "      <td>-0.039184</td>\n",
              "      <td>-0.364509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.929379</td>\n",
              "      <td>0.855109</td>\n",
              "      <td>0.918392</td>\n",
              "      <td>0.200087</td>\n",
              "      <td>0.135097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bedrooms</th>\n",
              "      <td>0.076598</td>\n",
              "      <td>-0.072419</td>\n",
              "      <td>-0.325047</td>\n",
              "      <td>0.929379</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.876320</td>\n",
              "      <td>0.980170</td>\n",
              "      <td>-0.009740</td>\n",
              "      <td>0.047689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>0.108030</td>\n",
              "      <td>-0.115222</td>\n",
              "      <td>-0.298710</td>\n",
              "      <td>0.855109</td>\n",
              "      <td>0.876320</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904637</td>\n",
              "      <td>0.002380</td>\n",
              "      <td>-0.026920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>households</th>\n",
              "      <td>0.063070</td>\n",
              "      <td>-0.077647</td>\n",
              "      <td>-0.306428</td>\n",
              "      <td>0.918392</td>\n",
              "      <td>0.980170</td>\n",
              "      <td>0.904637</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010781</td>\n",
              "      <td>0.064506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_income</th>\n",
              "      <td>-0.019583</td>\n",
              "      <td>-0.075205</td>\n",
              "      <td>-0.111360</td>\n",
              "      <td>0.200087</td>\n",
              "      <td>-0.009740</td>\n",
              "      <td>0.002380</td>\n",
              "      <td>0.010781</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.687160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_house_value</th>\n",
              "      <td>-0.047432</td>\n",
              "      <td>-0.142724</td>\n",
              "      <td>0.114110</td>\n",
              "      <td>0.135097</td>\n",
              "      <td>0.047689</td>\n",
              "      <td>-0.026920</td>\n",
              "      <td>0.064506</td>\n",
              "      <td>0.687160</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    longitude  latitude  ...  median_income  median_house_value\n",
              "longitude            1.000000 -0.924478  ...      -0.019583           -0.047432\n",
              "latitude            -0.924478  1.000000  ...      -0.075205           -0.142724\n",
              "housing_median_age  -0.105848  0.005766  ...      -0.111360            0.114110\n",
              "total_rooms          0.048871 -0.039184  ...       0.200087            0.135097\n",
              "total_bedrooms       0.076598 -0.072419  ...      -0.009740            0.047689\n",
              "population           0.108030 -0.115222  ...       0.002380           -0.026920\n",
              "households           0.063070 -0.077647  ...       0.010781            0.064506\n",
              "median_income       -0.019583 -0.075205  ...       1.000000            0.687160\n",
              "median_house_value  -0.047432 -0.142724  ...       0.687160            1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FICapAIV7dCH",
        "colab_type": "code",
        "outputId": "19731fc3-1d7c-4457-edf3-fcf322de589e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "corr_matrix[\"median_house_value\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude            -0.047432\n",
              "latitude             -0.142724\n",
              "housing_median_age    0.114110\n",
              "total_rooms           0.135097\n",
              "total_bedrooms        0.047689\n",
              "population           -0.026920\n",
              "households            0.064506\n",
              "median_income         0.687160\n",
              "median_house_value    1.000000\n",
              "Name: median_house_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "NVxGUmgy7dCS",
        "colab_type": "code",
        "outputId": "936cda93-50da-4bea-8c26-3201519849c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "median_house_value    1.000000\n",
              "median_income         0.687160\n",
              "total_rooms           0.135097\n",
              "housing_median_age    0.114110\n",
              "households            0.064506\n",
              "total_bedrooms        0.047689\n",
              "population           -0.026920\n",
              "longitude            -0.047432\n",
              "latitude             -0.142724\n",
              "Name: median_house_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17XPaCAEUWBG",
        "colab_type": "text"
      },
      "source": [
        "We can see that there is a corelation between **median_house_value** and **median_income** , a positive one at that, which means as the median house value tend to go up when the median income goes up.\n",
        "\n",
        "We can also see a negative corelation betweern **median_house_value** and **latitude**, implying as the prices will have the tendency to go down when you go north."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "3RtYqb1v7dCb",
        "colab_type": "code",
        "outputId": "a83bd8f1-ead4-4740-c68f-fc4e0d2c882b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        }
      },
      "source": [
        "# from pandas.tools.plotting import scatter_matrix # For older versions of Pandas\n",
        "\n",
        "from pandas.plotting import scatter_matrix\n",
        "\n",
        "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
        "              \"housing_median_age\"]\n",
        "scatter_matrix(housing[attributes], figsize=(12, 8))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f90947117f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f909471d860>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9094732dd8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f90946c9390>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f90946da908>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f90946ece80>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9094684438>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f90946979e8>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9094697a20>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f90946c04e0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9094658a58>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f909467afd0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9092de8588>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f90946155c0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9092d9db38>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9092d4d0f0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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41fv8L9+/yFKhjk8RGUkEyFULN1zeIwsokkgi4KFY15nNVLh/NM6pxQKm7YYR6JZDMqiy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7wwWUJHcm6lTtXxCcKIIJJESPrTBhnbwBACL4g7EpLJYrOjqQ5iyVShQcOLGMwk+Cd//grzZYdD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xnIhJuLaLxcb0P3GnBs64jywskZNE3jd969kd50CE1o9KVD2K7klaE5/vQHZyg37IXPdqRE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\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HgV2ac086kITd3olXIwjkRGO6PkOJCJqqko5qxAyNqKExEDd4pdBiqWyyUbdkgyjdRltV\n5fhanYvlDr/2ycP85TumeOFihXLLZ71ps3ckwfsPjXDX3CC/9fgirh+iqQozAzFObTSZHoyzWbe4\nq1vpBlnNenG5xp7hBJsNh2LLwfakXdct01nO5JrYXsDFcoevndgkqquMpqPsGU7ySw/t2xUyAfDA\ngRHunBvg88+sYHfTcV8P0wNxzuaaxAxNWqsFYU+WZHtXt23bRx8/6rhnzxDPLpW5dTrLmc0minKp\nlawGvSJSuEM/uFHdJs2Fpturbi+X23RcWbRa7vp6b8H1pZwkDCERVXG67xXV1N48Wbd3LAR2HMrO\nIXrnXPpqmNtFb9o76jjODgKw85gUdav0sV2tB6hYV+YcK5U2ddOj7fqkIhquL/AUweHJDKYnHcue\nXChxbKWGAJqmtIcMu++hayqhEJTaDhOZGOt1i5nBOKWWzYWizBi5dTrLDRMZ4obKf/neAl98YY1Y\nROULv3Q/f/X+OXRVvS5C++3TObm7udHg731wvrdL+DN3TdMwPUZSET599yzPLlXQVeVNDVE7ulLj\nifMlNFXhr943d83BRn0yfhVo2h7PLVYotRwSEZ1vndzkyHKNYlPKMoQQHF2tY7oBXiB6loNSVqHS\nsX2pqb0KXCtv2q6yvzauRMh2NtRd7lisrR/5jsds8QVNVXrV9J3YyQd0TcF81Ye3fEEYhl29GpiO\nharKQQzA3yHPCUJo9HQOIReKLSaycVTkAqHccTFdHycQ3DqVJRs3UBTBSqlDy5HNdYenMggE8YhG\n3FBlZT+QgTZ+EHT1ZE1atksQ6GTjBpWOS0STDhqj6SiTA7KPYKnU5vRGg1fyTUptByFgMhvnpp60\nSdCwXAwtyj/88AEKTYfDkxkimsrMYIJMTOc/fvd871xE34Ck4O49g0R1le+czrNRt/BDwa/96UvY\nvsCXgZi97+ZyC7G3A/wdx3W5azRgu08ibqgMJqLsHU1iOgEHxtJs1E3+5y+fkDs63clQQW4NB2GI\n6QrO5lqs10yeW6xycq1BueOQienUTZfBRIRzuSaVtqxSZWIGc8NJhKIwPRDveeXWOi5tx+epC2Xy\nDZvzhRam4xPVVfxQ0HZ8fuzmCT52eJzvnSvyjZM5GWSlK3Qc6el+pV7deETnM/fOku+6A7webp3J\nMjsU70pk5I/xJ26bJNewr5geerWwXJ/PfX8JNwj5u+/f/7qSmT76eLtiz0iCRGScVEzn2Eq1F2+/\nEzuXruaOO3cWt1S2x0/L3Z7v3EB0iysCFKjZ3X4ZoONs/9jLO1izfQWmfbXDs7jC3zunWMvdvsfc\ncRyXK9hd7r3zTUe6ZfmCzbop7xfw3TN5nl2somsKN4ynt89JsN13FNOlreNCscOPHR6n5Uir3Zun\ns/zmYxeodVwqHYdf+dCBHln/wvMruEGAZ4V853SOX/nQQfmZvIDfeGSBluPzTz52ENsPqXWkfeLl\n3NZA7uCvVU2mBuJoyvZj9o2k+Ld/5Zbe/7d2DzuOz5eOrOF4AZ+6fYpYRKNlS7OJa0W17VBo2kR0\nlZbt98n4DwJ/fmKTUsumafsyQbLUlil+imwM8EI5iTl+V/OFlGnIcJXrD3W5HC5XOb+ad3gzudhW\n1TvCpUT81e8lhMDcwbgUJBHWdhBugSTdChDTQdekleTlCKQbCMotGxSFZETD0FVQFAbjOrmGxS0z\nWZbLJqtVGfqSiBqczzfxghBVUWg5sjnVFyFN26NpS9LWdgIECrYfMprUuXvvEB3b4779QyQM6a1d\nbjs4fkilbWL7gWzUS0TITujUTLkoaFoen/v+ItWO24t3//KRNf7OQ/sZy0T58gtru8/VG5T3/sXp\nXDdMIZRNkztml7cp/75mBN2yUssJaTkW6zWLmKEihHQQ2to6jmgKEU1Wu2IRXdqFCTlhHl2u8fzF\nctfyC5q2z4nVOn/r944QMaSM7LbpLP/gA/MsVUze44fct38IEQr+7PgGL65UGU5GZXw2kIzo/P0P\nzvPFF1a5cSLD1ECcF5erHBhNcXAsxWgqQihCJjJxhpKyefQrx9f5+XtmL+urO5iMXFMz0atJ8oGx\n9OXdDa4R3zyZ5zuncwhgMG7wyx+Yf8Ov2UcfbwX8IOT0ZoM7ZgdIRHQMTfZuBUHA603LhgpbPDYT\n06l1WfSr9nHoWwAAIABJREFUubQidkxiO7CT5F9tIe7Nws5j9K9jEth5bpIRjUaX67ywVKbWLedr\naqv3GH2HtDbfcghFi4bt8dxSlTO5BrmGzZl8k7rpEgipUW/bDqfWG4TTggNdiaehKcwOxviVLxxF\n11TmR5L84fMrCCFo2y4tJ6Bpefz8vbP85Tum6TgB2cRuKctGzWSl2iHsym9bpk8iql2xj2a50qHc\nkoWYo6s1Tq1L17xP3jLJew+MXNN5U1VFBi4aKhH92nWhfTJ+FQiFrLdFNJXFooyWFkKga9KGzfUF\nbfeHQ33eTgTrahqZncuMBoJLBwkVpCQlHuGhA8McWa6zWdutQ4dud3l3dOs4AcmIQkTTUJADR75h\n4XgBpW6EsGN6WK7UGUc1lUxMxwlCik0H05UWUaqq9KLdY7rKKwWTtlti32iKfMPm+aUK5baLgpD6\nMkVq3xVFkvdUTCce0UlEdFYqHc7lW9LWsWqiKgqGplJq2vzce2Z36RUBLuTqV3m2t/H4K0UWim32\njyR5/mKVzbr1mv0FP2oIkRKmM7kmHXfbvSUMBVFDZSwdoWH5vYVioWnznx5dkLsiioK0EBY4fogd\nCHRfLuzKbYdTm03ed2AEQ1N4/JUSv//MMoWmTcfxmRyIs28kyWfvn2NuKEkoBP/s4zfSdny+8Nwq\nx1ZqRA2Vv/sBuT3q+YKVSoflSoeZwQTFprTxvBLpDkLBs4sVvDDkgfnhSyQtO9FxfF64WGU4FeG2\nq2xiurpzG9KwPIQQu3WoffTxDsO3T+VYq1mUmjZzwwlKbZdUVKfavvzEFVO23aQSEXC6SpXmlcrZ\nbBPXt+sv5Vplr69GtStnCQXUzG2WXm1tS15qOyr/jidYr1uEAp5bKvd2tc9sbpN3gP/6vQus1x3O\n5JocGIl3te2Cr5/I8cxSFQXYmEjRcaR09Wy+RbEpfdG//lKOlzebrFZNPnnrFD9113Za90bdRlel\nC9zXTmzyrVM5JrIx/tVPHiYM5U79TmI+N5QgGzdw/JBUROfYag0h4Ol45ZrJeL4hndmi3cr4taJP\nxl8Hbcfnx2+ZYKnU4avH1vjeuQrVjoMIBcmogeMHOG/bn+LbD1c6UyFSpmI6HqYTEATBJUT8cq8l\nF0E+dcsnoiu4QUgqqu/uQPcFCtJiMqqrmE5Ax90KrKGnd7tpIsHcUIqRVIQT6w2ev1ihZcuwJD+U\nldea6RHRlG5VX5CKarRtj0fPFlivmuSaNkEQEgQhqqZguQEd4UsP+NX6rk53gNWazbXAcgOOr9Y5\ns9ngfzy/QtPycPzwXXcFCiB4lf5zi6Q3TR/bDwlDiEVU2RgqZFjEaFLHDWW1yw1g/4gkycWWQ8uu\nUW65VFsOThjyndN5Ck0br9tYnYxqgODMZpOHzxR4bqlCreNxeCpDy/FZq3UIQhk8FI9oLFdM/CBk\n/2iShUKLD9wwyqn1OmfyTW6dHkBXFVaqJu/dP8zsUIKzuSZHuqm8CUO7rO/5Fp5c6AYlAWNpGfv9\nwsUqy5UO9+8b3tW8dC2YG0wylJRWk/tHktf1Gn308XbAZkO6lZTaLoenMiiKQkzX0FW43OQidty+\ns2ZyNV0Yb9fx940W5e0dVbNd1f4dH3hnE6zKdqGt9WobrR0oNi0qHenMUu/YeKG0wn15o0GnW+Wz\nPWk5iYBUVGcjsHD9kCAMefyVEi3bIxSCBw4Ms1hqc8N4mp+4bYKvHc9x7/5Bnlkss14zKbUc/vjI\nKl8+ukE2pvP//PwdvLzZRFcVHpgf4W89KLNkCk2Z0Nx2fPaNJPmtxxZ4eaPJZ+6bw/ZCnlwo8bHD\n4zx0cPSyn+n0Ro3T6w0MDSrXYczQJ+OvgcdfKXLkYpV4RKPWcXnkTFHa+XQvNtf03vDF3sdudFzB\nY6+UcK5R5BwiB4583cZ6FUnbap4JQ6nfq1uSnCnd7vmtdzqf7+B4gueXHDw/wBVKLxAHJGlv2h6O\nJ8mvpkC94/DImSLxiIbthcQiKvGIztxwRNphOQGaojCcjmL7Ieno7qroxW7a69UiqivYns/Ti6Ve\n084V5pYfeThX+NAN25f2nArYbrj9GxVQavsYuoLthQwlIxwYTfFKvtX1BYa6Wec/PmKzZziO6cjo\n+rF0jPmRJN99OceLF6scW6nLfgpX2p3lGja3TKVZrXSwXbkwG4hHsD2fjhPQsj32jaSkX/3LBblT\n0nJIRnQUReHJhTK/eN9ct99B6h5fvf26E8vlDmc2pfPKWCZGzFBpOz5PXygD8IRf4rPDe67rnJY7\nNi3bJxSCQst5/SdcJ/b+i2++oecv/4dPvklH0sePKgYTETqORSqqs1w2CYKAmuUSixi0Pe+Sx+8c\nT8yrLGy+XUn4DxM7Z1v/Cre/GptNWfEOA8HOb6LY2iaxpZbds3asdhxa3S/oQrFJ1Qzwg5Dz+Ra/\n/vB5NuoWh8ZSPH2hzPlCmyPLFe7eM8h6zSIZ1fjuy0U2ayY5ReG3H7/A2XwLtbs4u3ffEADpmM5Q\nIiozVTT40pE1bC+g0nEwvZCW7XMu17wiGf/jIxsESNOB33vyAg/MX1tlvU/Gr4AXl6t88YVVOk5A\nIqKyVDbpuP4uHXOfiP9gcK1EfCfaryMGlDHuMmI4VHZv4wXAUmWnu8lurbuKDF7YegdfQNMJUba0\n84rCQDzCQCKCAuQbDuOZGB87PE7HlZ7UpVcRnMY1FMbrpssXnl/h6EoNx9t2x1FV3pWzwuvtslzp\nPimdEmzUbf746PouL16Atu2Rr6s0bY+hpIEIBWdzDWn35YVsNkwG4hHiEQ1dNZgejKGrUnceIMl0\nsWUT1aXbyU2TGY6u1LE8n1RUx/IC1mrS03c8E2N6UDYLzQ4l+MX75mTKqRA8uVBi/2gKTYHzhTbF\nlk0QCr55chPHk30CH0pHsdyAwaTGcCpCpe0yPRCnaXsUmw57hxOX1ahfCZs12RshhAzy6KOPdypU\nBImIiq7CeDbKRsMiFdPxfUnqXkvCofPariZ9vDFc6bzvPOeVznZ40Zb1IkCu6WF0HdccL2C53KHc\ntjFUhXMF6Z1eMT2eulDE9KDjBhwa82maHrquslKWydoAd8wUeOyVIh3X51O3T9F0PNwgpNC0qbYc\n7CAkE9VpOAFNy8X1A5ZKbf78xCYPHBjmPfu2dy93Hvtj5yrXfE76ZPwyEEJwcr1OMqqz2A2C6bjB\nZfXPfbwzEQDW65STd9peCa7QjS4EmqrIFNKIys1Tad53YJT/76mLaKpC05bNha4fYjoyoXQnWpd5\nyZ04tlpjIS8bYh49W+RsronjB7ucR97kHuF3FRwvJGqoaCokDZVoRCMMBFXTwfZC2o5PsekQMTQZ\nlqVIv94DYynyDYd0UmWl1OGphQpxQ2MiE5P2hK70yr17zyCWKwd3Q1XYP5pkLC39yoMQPnnbJDdM\nbNtrLRTanFqvs1TqEISCYtuR5NryqHRcvCBE78qlRtNRViodPvf9ReaGEnzi1gmiukYyqvH5Z1aw\n3IAbJ9J84tZLI15dP2S9ZjKRjfUCswBWqh3p/AKslK/fdrOPPt5qjGXj2IFgJBVlLBMjGdEZiBuc\nz0li91qzeZ+Iv/V4Lbq1NefZXsCprrSl0tm927HTBvLYah1PgOeFHF+t081a4jsv53vhRZbr8+T5\nMqYboCJwgpAghHpXCuoFAtMN+MxvP0Ox7fG5JxY5+msfJRm7dBfzeq6fPhnvYkt+MJqO8sRCmc26\nzcn1Bpt1C9sL+1XwdyGuVitoBwI7CGjaAV89nuM7pwsYmvQ8H0pFeGKhBALWayZucPVXUrFp8wfP\nLLNesyi1HVqWuyvquI83jgC6QVgCVxHoXRck0wl6tqGCgFhEJ2FoZGIG6ZhO1NCw/YBiy8J0fJxA\n7lDMEMdQVUzHJ0S+VsfxsbyAshtgXazxwPwIqYhO3fb4k6PrpGM6t88OdhuXqwSh4Ey+iaGqNCyP\nsXS05xE8MxgnEzeYysbQVJXFUptqx+HEWp1TGw3pIGB7NG2f22cGrthI9I2Tm6xUTAYSBn/jgb0o\nXRuwdFTvbQ3HY/0kzz7euXjowAhPXShz28wAy+UOrh/Qtn2c/mT+I4PWDquamrmbjO/c+dh5V2WH\nyH2xZPYe89jZAqWOvO/xcwX87jjYdvyedn5nMJ3thTxxocCZjTaHxt+4X3mfjAOVtsMXX1jFCwQf\nOzxOw/JIRnWyMZ1lv0/E+7gyLqeoMb0QvK6NYxhSaDoYmorl+rsaYl4Pn39mmcdfKeIGIQlDp272\nifgPAl4oUJHx2Yqi0HG8XVWZqKqQjRtMDcRYKLR7/ruFpoXjy+du2XNu1GWTUaPbUHx0ucbe4YRM\nsQtlkuuJ9To3TKZZr1s8v1RFIDvxCw2b8XSUfNPhgfkRmqZLrmlT7cgGtDvmBhiIR7hrbpBiy5EL\nvfMlvnxklULTIVe3sTyfqKGRjOg4Xsj988OcyzfZM5TcFSjU6JL7li3jqPVuGNVaN4RKAXL1a2su\n7qOPtxO+e6bAhVKbUsvBDUIqHY+W0695v1twNXPlzsdsEXGA9g7yvnPOfvVr/us/PUWhE3LtRoaX\nok/GkVGtW416pZbD+w+OENFUlistjq6+xQfXxzsWAumNLXHtLZYvrdXRFTADgYXfXxT+ACGDOgJU\nZff2qIL0CTZUBdeT/SOW65NruNie1JrvamAKodhyMDSFtiNoOR4vLlcYTUcxVAUvFAwlI7Rsn6iu\nEQoZGPQXLxc4k2vy0RvH+UcfPkAgBCfXGzx6pkCp7WDoKrfNDDAzmODRswVOrjdIRDT2j6RQlK41\np6ZghCqaopBNGMSjGn/43IqUz2Rj/MJ75nrH+fGbJzi5XufAWHqXprzU3PaGKjX7ZLyPdxa+d67A\nK/k29+0f4kKpje0GrNetXrO+25ea9vEmotCRo/+bcVX1yTiwfyTFHXMD1Douh8bTDCQi3Lt3kF/9\n4tG3+tD6eBfjztkBzuQaBF3pRB8/WARAfYcdlwIMJQw0TcHvZg0UW670vhWXH4A1ZPqnGwjplVs1\n8QJQFJfZwRh7RlL4gcDzQ/7a/bOczTVpWi5+KCi1HPJNG11T0YE9wwkWi21e2qizdzjFr35YJtNt\nbceabsCZfIOEoTI3nGDvcJK54TjvOzDCY+eKCCEjmu+cG5QBSDswmo6ydyR5SeT0ZnO7Uap6HfZc\nffTxVsH1Q15aawBwbKWG78smdyUQjKUM7KaLptLTC/fRx9sJfTIOsnu2YfPkQolHXs5z2+wAC4XW\nVYXa9NHHDwqTg3HCIHzbRtn/qEIGQEE2ZjA/lsINBJmYzlDS4MWV1x4UNLVro9n9zoKgq10U0HFD\nym2X22ayJKI6j50vUek4dNwAXZF68F94z2y3STdEBV7ON7HcgM2GxR+9uMrHb5nk9pkspZbNTRMZ\nvnJsjVMbTbJxg5+7d4a790ibrtWqxWKxzXv2DXHrdJabp7O7jvORMwXO5VtEdJW/9b59PQlLYoeU\n5bVCh/ro4+2GiK5yaDzNQrHF4akMhi6Jt64qzA4nqJgumahBqXOprWEffbzVeNeTcSEETy2UOble\n53y+xXrd4pFzRdw+A+rjDUJTLq8pfz3YXsBXj23w3ZfzO2QuffwwoCpy8t4/kmIsE2UoGSEd04kZ\nGrYrQypEINB15RJ3JYXLf98CiBsq9+0fJh3TuW/fEHuGk3zzVA7TDfACwVA6yi8/tJ+OG/D1l9YZ\nTBjsHUkyP5rifKGFqsC5XIumLaU0theyWjPJJiKMpqOMpqO0bJ8zm00Wii1umxng7j2DjKQil5Dq\nasdlsdQmDAVeEOKFIXHkY7YaOQFU7c1QQvbRxw8Pn7xtEiEmUBSFgZiB6TjEIwqLRRPHh2rYJ+J9\nvD3xriXjx1drVNoubhBwdLnORt3G9UOcvnNKH9cBmc24rR+OGzJQoOn4hKEkald7XT27WOZrx9dp\n2W7/WvwBQUEulkJkFTsR0cjEpGNKCNw0lebwZAbTDbC9kGRUo9xyGU4auIFg73CCi+WObIQU0vZS\nIOOWI5qK6Ur7SVWBVEznEzeP0+pWxpNR6TfvByGqIh1MdE1FU1V+7+llcnWLA2Np7tk7yAcPjXLL\nVAa7WykXocDvcmRDU/nsfXN861Se8WyMW6azfOG5VUIhqLTdXrLcTlhuwBdfWKXt+Biayl+6ZQJV\nURBCoCgKP3n7JK989wIAH7lh/If0bfTRx5uHrQVl05HbUrYnU5rhyvkDl7wG2zK0Nxop30cfV4Mf\nCTKuKMqvA/cAx4QQ//j1Hp9v2Hz+mWVatkdMV6lbPqmoxsdvnuB3n75IX577o4e4Boqq4gay8zmm\ng+3LQXYwEaHcdncNuDrbyZ0gky+TUZ2W7fdCieKawsdvneCVfAvHC2k4HoqAiWyMmcEEhqZwfKVO\ny/W5aSJNvmHTcQNK7W0t7sxlUsv/3bfOsVBs/2BOxLsEKlIy4oXy70xcJxHRKLYcghAGEgaKojA/\nkiCTMLhrdpDDUxl+67FF6pbHYMLA0FQKzQ4bdYuZgTiBCFFVldlslL/z/n18/3yFlXKL9ZpF1fSJ\nGSpTA3FG01EW8q3e9xzVVAKhsHcoQa5h8z+eX2N6MM5A3ODO2UFQ4P59wyyW21Q78jrcMxznifNl\n6pbH7GCcZFTnXL7FRDbOJ26dYKVicstUlmzC4MZJKUERQjCUNCi3XUbSUcJQcHKjga4q3NKVqbhB\niBeERHWNvSMJzuZbfPNUnpsmM/z4LRPcOJFlMC491W+fG3hrvrw++rhOuH5IpS3D1mxPLohdP+Tw\nVIpX8h3iEY1URGOjIcPXVC5fJDGArVH63Zpw3McPF+94Mq4oyl1ASgjxkKIo/01RlHuFEEde6zm5\nhsXxlRpBN7BFVxUsL2QgbpCJ69T6FnJvCjRFEiI/AF0BRVXwA0HQvU9ht3NFRJPa2qsNsblhPMly\nuUMoZNOcwnaksQLS+aL7YjPDSW6dylI1XRZLHZJRjQ/eMIYfhJxYq+P4IR3HJ6qr3Ld/iAcPjvL1\nE5vkmzaGpnD//mHu3TvEbzyywGY3NjMR0/m/fvZ2Viomn3t8kUREww9CLlZMKh2XdExneihBKATz\nY2n+zadu4anFCv/2G2d6n0G5jJVzn4jvhgIMJQ0aHa8XphDVwAvkRGqoEI9oGKpGxZRTaAgMxnQC\nAcPJCHfMDjKWjWG7AR3XY6lkMp6JkooZfObeWe6cGwRkc+RSqcNypcN6zaLQdGhYHqtVk5rpoSsQ\nighPLFQYSUV59GyBuuWRjGj82iduYjgT5cjFGjFdY71qUrVcoprGatVk30gC2w+YzMYZiEe4b98Q\nP3v3LKPpKMmozp+/tMne4SR+GLJnOMm3TuWpmS5hKMg3ZfrmRt1iMhtnZvDSVZyiKPzcvbOU2y4T\nmRgn1ut8/5USIKvoN0ykycYNPnnrJBt1iztmB/j8M8sALHfTNr9ydJ16Nw3rj46s8fP3zl3yPn30\n8XaEEIJ/+bXTLJc73L9/uCcZE8D//TO38Y1TBW6ezPCfHj0PXTKu7Ch776qAa6B2x5e4sdvqbguv\nVTFPR2Ar2V3f4dC0M0iuj3c+Htw/yLG1BpmYQf5V6drXinc8GQfuBx7u/v0I8F6gR8YVRfll4JcB\n5ubkxJKM6IxnY7h+iNpNTwxaLtl4hPvnhzm+WqdhetIv+kcUr7f1tm8oxsWqJJ0qUvOqdSNoDVXB\n8sNdNlG6puzS2Q/GFBLRKB+4YYRbpgdoWB7DySi25zOQiHDH3ABfen6Vrxxbp2X7DCcjfOqOKR45\nW2CzbhMzVBw/JB3ReN/BMQ5OpPj+2RxPLTV67/H3PniArx5dJ6KpTA7EuXEizdMXypzJNbllOsun\n75nhD59bpeMG3D6VYXoowYVSp0fQP3Z4Aj8IiXQt5hwvZDIb41c+fJA75wY5MJri/2fvvaPkOs8z\nz993U+Xqqq7q3OiATBAASRAEkyiKwRYlirKtNJIcxvL4HNleyz7e2bXlCfaM7Tlrz9mzY9kery1r\nR5Yt27KClSMVKGaCAIic0eicqiunm7/941Y3uoGGCAYJpFzPP0BX3fDVvW/der/3e97n+ftnJ+hP\nRXhoZy93bcpyZr7Cx5+eQEhW+LhbexK857YNTOYb7BlO8Z+/cAIIJhaD6Qg1y+WR3f1s60uyrS+5\nJhl/6+7BV34zX4eIaIL/9o6dXFhs8C+HppmrrH2QqUBIV4gaKm/YmuU9e4fYM5QmV7Vo2C4ffXyM\n58cL2K6PLyEZ1vClpGzauC1akKIo/MSObmxHcu/2Lt5+08DK8csNh0dPLRDR1ZWqMcADN/QQC+VJ\nRXWWqoEtPIDr+TQ0D00IwrpKVA8enTXLDSQLPckDO3oJ6QqzRRNNEdy/vZt8zWb/eIF4SOPBHb28\ndVc/T55fYqgzyj1bu9Z85p/c0cNAKkJvR5j5cpNNXTHydZ1bh1Mcna4wXzbZNZBEVa7O5Q5pKgOp\nCBA4gK5cz1WTvi09Cbb0JAB4w+YsJ+cqQYWeYFKzfPjVzZxttPFaR8MOrNEBTs1VSIRUaq0Cy5bu\nJP/nm4MY/4OvnGS5HaIzppNrZdrpqEKhEfw2dEY0QoaO7fps7Yrx/QuBfboGPHhjN0emyrx1Vx+f\neHocVwbPmw/cOcTnj8yxIR1h12CSb55YJKKr3Leti3/cP4mC4H9/cCN/8/Qkvid5954BPvrUBAAh\n4FpSuZghqNtX/nJ3hARlK3j9bTu7+MaJHL6EmzckODRVXRm7L4Lf8K74pc+tCNawAVZPGELqpQLX\nhqTBVCWYYaRCUGoNWAeW5yp9MZW5erBDwhBU1xnrq4Hl/CUdUSm+mJU20BcVzDWCsXTHVBZbY1y9\nMqIA2/sS5KoWv/6mUf7o62dwPBhKh5guWvgEE6tM3GChahPRBA/t6iMVD5MM6/zj/lemgy2kfH3X\ngIUQ/4GAnvINIcSDwF1Syj9Yb9u9e/fKAwcOAPCdUwvMFJu8YUuWA+NFfCQxI2iu8n3J02N5DowX\nGMvVmCk2qTQtHD/gVeqKQiYeYmKpgUsQGLoCMUPlXXsHODpTZWKpjuO6aKpgOJOgOxliqWYBgmxc\n5yvHFgHYnAmTSUawncDx78b+BIW6w6MnF/GBqAY/s2cDEUPF8yWVhs181WI8V2OmYqMK+MBdQwgE\nnzo4TVxXeNO2Hnzp8ZmDcwDs7o/Tk4owlTfRNMGH7t+M7UkWyiaHp0rcMpzioe3d3Pv/fB/bg4d3\n9vCfHrmRf3x2kotLNW4b7SQe0pguNpHAQzt7ObtQ5cxcFYlkqDOKrij89ePnObfYoCdp8C+/ejdC\nCNIxg7C+/o+67/t86cgcni/5mVv6EULw3Fie2ZJJ3XZJxwwe2N5NpGXX7fs+N/7+N2k6PvtGUnz6\nV+6m1LCZK5uMZAJTE8fzmSuZdCdDK+etmg4T+QYj2RhjuRoff2qc/o4wv/XgVhRFcGq+gudLOiI6\nQ53RNU1sixWTiumyqSuGEALLdvngJw8xXzH57Tdv4/4bruTVfuvEHM+OFXjLrj529ndguR6p6CUJ\nuZ//6FM8MVYiqglO/tFbV8cnBw4c4L9+8Tgff2YCBXjPrQOcX2pQNy2KDZtiw8X2ggdrWIemC0Je\nehgChFocG11Zv6KzjAe3pdFUnTNzZWbLFkJAZyyE5frYrkM2ppNveHj+spaqgsCnEdCkiYcEmWgI\nV0riIR3H8dneH+fhnf187tA0M2WTkCrYMdBB0/I4Plvijo0ZhjMxepMRHr6pn3zN4slzS0wWGzx1\nLsdEocHNG1L87lu207B96rbLDX1JEutYDs+Xm3zpyCyeD2/b3cunnp/i8GSJE3MVwprCn733ZiYK\nTSzX5627+q6Q8ftBcD2f0/NVOmMGTcfj+fECpuORDOncszVLw/aYKjR56twi3zy1yJ0bM/y3n9l1\nxXF8X3JkuoSuKmuS/mvBRL5O1NBIRXWeHcujCsHtGzM/MBlfDSklJ+cq6GqgMnEtKNQtfv0fXsDx\nfP7He29eqcAvx+YyRj781Zf0WV5rGP/jh6/3ENp4lbA6Nj/+5EUOTBR5eHcvEU3lk/snePvufn7q\nlktFj88emOKPv3GabMzgf/3irfynL5ykI6Lz4bds530ffQ7b8/m7D9zG/skii2WL9+4b4Df/6Qhj\nS3U+9MAm3nXrEAsVk/5UhO+cWuBT+6d4884e3ri1m2cu5NmQDgpDn3thhv6OMA/t7OPETImwobKp\nK8FsqYnrSYYyUQ6OF7iYr/PIrl62/963kMC2rig/vWcDXz02wwfuHmUkG+fvnxnn4Z29bMjE+NNv\nn+OO0U4qpsNHvnOesKbw9IfvZ7FmEtE1BjtjnFuo4ng+N/Ql+fBnj3B2scZfvG8PT5zL8cJkiV++\nZyPlpsOXjkzzc3eO8O6/eppy00NX4JnffYCPPXmR20c6CekKv/2ZI/Qkw/yvX9zHpw9OE9UVHrqx\nh3d99BlM2+MTH9hH2XK5mKvxjls2cGa+ygvTBX7q5kE+/LnDfP/MEu/aO0AmFuaTz05w79Yufu1N\nm/njb5zizk1ZDozn+fLRBQA+/8Hb+ch3LxDWFf7rT+3gkT9/iobj85lfuZPf+ewRpopN/uK9N/PC\ndIVDE0U++KaNTC01+cqxOd63b5CZksnfPTPOG7d08eANPfznLx7npsEOPvK+PXzn1CJ9HWE6whof\n/ORBdFXw4bds5ec+dgBPwh8+sp333D6K6Xokwzqn5iqcmqtw/7Zu/uQbp/jemRyP7O7nl+8Z5UtH\n57hnc5aRbIxjM2V6EmF++s++R6E1QfnUL97EHduDmBNCHJRS7n2xOP5xSMb/NyAnpfy0EOIdwKCU\n8s/W23Z1Mt5GG681XJ7wtNHGawU/bsn4K0U7mX/toP3cbOO1jH9Nyfge4INSyg8KIf4S+Fsp5f71\nts1ms3JkZORHOr422rhWjI+P047PNl6LaMdmG69VtGOzjdcyDh48KKWU63SHrcXrnjMupTwkhDCF\nEE9pmVkwAAAgAElEQVQAh6+WiAOMjIxcMYN+8lyOfz4wxebuOL909yhfPz6P50uQkmcv5jkzV6XY\ncBAi0Oe1PYkCGJrA8yTO63su80OB4FKD3dXYXFqLp/ZqsPIFAUWoYXtrjqcJ2NqboOl4XFxqrNln\nmTO4zJfzW9J0uoBERCdiqFiuR9V0ieoqluuv9BBEdIVkRGOgI8KZhRrpqM5vPriF58YKPHexgO16\nhHWV7mSYPRtSPHpqgVzVQlMVEiGVyeIlm/HVFbblCs+3TszzpSOz9CTDqELymYPTFBtrHRR/HBHV\nlZVrrAqIhVQMVVA1PVRVkI2FmK+Y+FKiCFZMuaK6YPdAiumSScRQycQNTs2WqbTcNBUR3FtVBMYg\nQ51RfuvBrSzUbDQBz4zlWahY3DaSJhnReWGyBEA8pKEqgr3DaZqOx00bUuweDBRGvnNqga8fm2Ox\narFQNekI6wykIwx1xrAcj8fO5oiFNNJRnXMLNW4eSjGQCjNXNnnvviFuG+7k0VMLjOVqgXpKZ4w3\n39izxpre9+VK7PSnwswUm9zQl2TvSOc1X9ODEwVOzla4ZSjNzoEOpJQ8dibH0ekSCNg9mOK+bd0r\n23/phWl+85+PIIHf+ckt/Or9W4FLsXlytshb/+zpl3mHr0RMV7hvew97hlPEQjqO53P35gxHpspY\nrsdDN/bREb2SotRGG8toV8bbeC1DCHHoWrZ73SfjANciZ3g1/MuhGRYrFosVi6F0lJlik/mKSdPy\nODBRpNiwsVpNYsvwAdNtZ+FXgwTMF+mpeDUvnwRq69iluhJOz1dZb/Fnudf0cpMWRwaKGqWms3LP\nLXdtItx0fFzfJtdql7cqHh974iIN2wtUL7xApafcdJlYalA2HSzXR+BRNdeSuEc+/NUrlry/cHiG\nfM3m6HQJIcS/ikQcWNMw7UlWkunlFybt5jp7QcORvDBVWmlIni81qNprjwXBhMuxfcbzDT725EXu\n3JTl0ESRyWID1/PJVS1GsxFOz9cIaUED8UAqwthSjTs3Znny/BK7B1PULJdvn1xgqtjk2EwZXRFM\nuI0Vnrnr+cxXLFQhsD0PVQieOJujPxVIFH7p8CyD6SgnZyucX6zh+YG/wY7+JKPZ2Mq4F6omJ2cr\nAOy/mGc0G+fJ80vsGUqjXANvXErJE+eWkBKePL/EzoEOig2Hw1MlTswGjdC+DzcPpki3+PT/9Ssn\nV67jn3/vwkoyvox/9/FnX/S8LwV1x+epCzkQEs+HrT0Jvnp0Dqd1047OlLhnS9eLHKWNNtpo4/WN\nFy2d/7hjuamqOxnipg0d6KogEzPoTYXpjBlEdBVdFVdcqGvsoWrjR4Sr3Y+YoRLWXlqYa6ogpCno\nSlB1v3z34DWFZFhDEQJdVbh1OE1EV1as1EO6QiyksrErSlgTKzKP12IxvqMvCcBgKsLGzjDav9Jg\nU8WlFQxBsGqxHgSQihroqkJYV+mIGoirbKsIiOgqt40G1eWNXbEVTfGRTJT+VJRkWCeiq9iuz2Sh\nwXCriXE0EyTKEV1ltCuOqgg6YwaGppCM6EHVPR1lOBPDUAVRQ6UnHkZRBD0dYfpTYRQBOweSZGIG\nHRGddEwnHTOIhzS6EqE1Y01Hg20AbuwPnlPDmeg1JeIQSB2OtMa8/G8irJGNG6QiOqmIQTYRIh6+\nVJO5d5XCy67B5BXH/Df7Xn2pw+5EmK5EiMF0oAJz04YUIV1BVQRDnesI8bfRRhtt/JjhunLGhRAR\nYEhKeeZHcb6rNXAuVkxS0eBH1XS8lR9yx/UpNx2qTZv/+dgYx2dKVCyXm/qSvP2WQbb1xPjtzxxm\noWohhcJoJspbdvfhuR5PnVsiHNLwfcnEUgPTcdjYlWBDOsIXjsxhuz5hQ2FzV4zxXJVCEzoiCsmw\nwW88sIlCpc5nD87RlzLYMZhlutgkElKxHJ+9w0nOLTbYf34RTYGHb97Att4kU8UGO3riHJgq8e1T\nOTrCGqmYztt295GKhnjs9CLJiMLR2Qaeu0y/8EBCqelgeT7VukV3KkqhUseXClt7E8TDOsOdUaT0\n+NyhGaTvEw4bhHSVkAiS1qJp4kuNd+/dwA39aTQVNCFYrDUpVG1SMZ2wobFYsvDxePZiGXyPWETn\nYq7G4ekKQgj2bOjg5+8c5osvzDFTqRM3VH75ns2UmzZJQ+PQeIEnLiyxczDNbKsK/W/vHqI3EcO0\nbc7MV7B8n539aUoNh5uG0vi+z4GJAgulBhfyDeZLJumoxt6RLLsGklSbHk3PxXV9epMRFFUQM3Q8\n6ZOvWnQmwviuy+GpComIytbeBHXLZ6gzyrmFKpmEQToa4iPfPofpuqQiBu+8dQNRQ6UjojNXbgbX\nGUFUV3jH//sM8y0pv6986G52DqSW43NluXWhYpKJGpieT75qMleqk6+7DGfCfOf0Ao+fXqTc9Khb\nDrmahy7AlJAOQVcizIUlk/Xq6VczuYBgmWxZHSjW+rfaei8sggqzSyB9FVYgHYapRvB3XIeN2eDa\nbe9PsbkrRsN2uJBrMJIKIRSNZyeKdIR0PCnRVIWYoTCUieEDD93Qja4bnJorogjBUGcMTVW4kKvz\n/bM5hIA339hLJqZTNz26EiEWq01qpsem7jipWJi6aWNoKooi0ICnx/MMp8P4UlCs2wxlYjRtj0w8\nRE9HhLrlEtZVaqaD6Xp0Ro3A0ElKvnJkls8emkZKuH97N//27lFihrqitOP7knLTIaQJ5isW3bEQ\ntvRJhDQsT1JrOhi6gqEqLFYCmgkCapZHJmYghMD1/JYJlUBTg0nd5VjeJqKr1G1vzRiuBVLKK/bz\nfInpBKsOYV29Qp3l6XM5TMfj/h29K6+tjs3/8vkD/O1zC9c8hmX0hSAZV7lpsINMMs7e4Qyj3TF6\nOgIFI0MNTLmihtaSrJRXVWJqo41ltGkqbbyWca0NnNeNpiKEeAT4vwEDGBVC3Az8gZTy7T/qsXQn\nwyv/X/3wD2kq8bCO1xEhYihULQ+BYLpi8Zffv0BnVKdo+jRciIUUtvZ28Lbdg5xdqPDkhRLzFYdk\nREPVVcKqyi0jGXqSIT57eB5HQk80TDoR4dhsDQ9JyfQxNEmpKTm+aCN0ncWmYMSV7Ojv4ItHZgFo\nOi7TJYuZqkvT8bn42EVSER1VEeiaguv5xEI6qZjGe/eNcmymzKnjOR7Z1cdSw2auVEBTQMRVTFfQ\nFTe4eSTLmfkqi2GLTdkYx9zAEKFgSjriBjNVh6WqTV+mA9fzydccChWHe7d2cctQmotLdboSId6z\nbwhVETRsl0dPLqAqgoduGuTsQpXTc1VuHkqRr9nU7AKdUYM7N2WpWz6qUkMIwWBnjJmyTToRojcd\nYTQb545NXbiez3//5hlemCwykk2iqgrDmTgDqQhRPcSBiSJdiRBNT5CveZzPNXjghu4VSbwHbujD\n8yUfe2KMqYKFoRnUbJ/pss2RqSJzZZOBVISKJXlwR/dKBXsgHVQUyw0Hlwq2L+hJRle4vbta+sy+\nLxnOxpgvm9y0IcWGziieL/nu6UUqTYf7t3evUAGK9UuKsiOZ+Lox2dOKybimEA/FGc4G25WbDk17\nkbIFuYZLMmww0iWYKZlIx6doQcVePxE3VALqhCtXuPyCS8YXfuvvsCbIJsMBDcfxiBkKtwylObtQ\npdx06YwavO2mPibzdYoX8iBBaIKpiovtSapOhXftHWGq2GQom2bfSCdPns+xsRFYup9ZqOJLn00b\nO7l1NMNUvonpK2ztinN2sca3Ty4wmG7Q1xG4WWbjYcaWapxfrLMQ1snGDfZ1JdjUc2Xl9nunF+mI\n6Ny/vZuHdw1c8f5qxELB468jarAsOtiSD2fvSCdfPT6P6XhUTJcnzuZ44IYeDC1IXKuWy2NnF4no\nGg/e0L2G631gIs9UocGdmzJ0dgbGQssI65ceuYoQPH0+T7npcN/27nWlFzVVWTl2vDXemVKTp84v\nMZiKcNfmLBBwwy8s1tk7kmZj16WYEkKs7LcMVRErn3093PUitJDtA910RPKUmy+NPrVggakqjPRk\n+ZU3bV63wr/8WY2XuJrVRhttvDbwSpWW/rUqFV1Pzvh/AfYBjwFIKQ8LIUav43iuikLdRlMUNqQj\nVE2XYt2m6XhM5OsYraRtYzbGjQMdZGIGB8ZLKEJQqtuYjoehKewbTbFzIMUXD88Q1gS2C5brcWii\ntMJp9iUUGjZfOjJDw/EoNWyiusbpuQozRQ1fQq5qslgJjE/MFpfd9jyqVqAR6vkQ1hVCmsPO/iTP\nXSzw1PklPF/y/z01zkA6gi8lNdMjX3dWtLV/YkcvSzUbx5Mcny3j+ZKqGfCmZ8tNLMcnGdERAgZT\nUcbzDSzX48Rsmd2DKX7p7lFioUtVtmPTZcZygQFDX0eYJ8/l8aVkqWZhuf6KLnjFdOnpiPCGzVlC\nmkLVdHnq/BIbszFuG+nk7uVkY7LI904vUmrYTBUa/OSOXt6wpZP7tnXxl4+N4UvJt08tkI0bXMjV\n2TOUJhM31vBNLy7VadgeG7tiFOs2uarF/ot55suBlvipuSr3bpUMpiPctGGtFfjByQIT+aAJdDQb\nu0K3WVEE79m7gZrprjScjefrHJ8JuLnPjxf4yRuDSqO1igr9198/y79/845rjsWnzi/xwlSJ+YqJ\n9CWuL9ncGWOmGPCpJVfn47seSEWuaaqVcAWnvulKporNFa51w/YpN4PYUJHULIejUyXmKhbxkEbT\n8fB9KJhBo/NkvsGffOM0t49mAJgtNWnYHqmoTq4aNGD6EkayMY5OlWnYHnNlk+09CT59YIqa6bJ/\nvMBbdvaSq1nsGkjiS8kLk0VSUYNsPMSW7gRDmbUUhv0XC0wWgnu0uTvOyCr+9UvF5p4Ef/G+W3ji\n/BLnFmqcnq+yoTO6Qms7NFlkfOlSPGzrDeKh3HR4diwPBDzt9/0AWsdkocGxVnzsv5jnoZ191zS2\np84tMVNqMlNssr0vSdRQefzsEgCNs+6aZPyHgWfH8lRfYiIOwWSv2HD49MFp3rKrj9Ef8jjbaKON\nNl4vuJ7JuCOlLF+25Pqa7IpMRjTKTYeZkkk2bpCJGcxVTDRFRVMUapbDkakSDctFAMWGjef7eEhy\nVRPL9VmqWnz92Dw1y8VxPCSQq1r4/lragKYIkJLZYoO67RPRXBAwW5LMlU00VaEzquP6l6zel+H4\nAcdWElS/popNwoZG3XIxHY+OzihLVQtVEUR0jblKDcvxMHviHJkqcXymxOGpEk3bI6wrREMaVdOl\nOx5CUWCq0CBqaJiOR7Fu07A9LMfj+Yt5TNfj9tEMi1WTscUaT5zLcWahxs0bOnC9DDOlJqoCd23K\nUDU9njyXo2o69KYiTOYbRA2VVEQnV7NJRTRcX/LVY3M8eX6JX79/M93xEJ6UNGwPRcDR6RIbu+P4\nBDzgb59aIGqodCUMapbLZKFOek7Hcnzu295Nvm7x7Fie+UpgJ769N0mp6TDcoi5Yro/n+VxcqpGJ\nX+mKOZCKcnS6jK4qdMVDV7xP65ovJ+JN2wuaAwt1BtNRBlp82Mvxtl0brhp3VdPhe2dyRHWVN23r\n4vhshcfOLDJTbOJ6Pp4vcV2PXNVa4366GqudVn2Chr1rwerDSeDUXJUWgwPF9Tg8XcJzJVJAKqrj\n+RKl5dCqCEkqojO2VMNQFOYrTY7PVujrCPPg9m6+f3aJqKFiqArj+TrjS3USYY2PfPccpXow2bJc\nn5OzZe7d1s2FXJ3FqkW61cMRMVQ64wYHxoMJ0h2bMgykIgykIpyaq6y8/0rRETW4oS/JhcU6qnJp\ntQJgIBXhyFRg5rOa6x01VNJRnWLDWXHCvBoycYOIodK0PQZS186NHkhHmCk1SUV14iENTRF0JULk\nqhYD6R8ux9r1fE5Ml16RClLVdPin/RMsVm2y8RC/ft9mUi/BkKmNNtpo48cN1zMZPyGEeD+gCiG2\nAL8BvHqaWa8ifD9IsPs7wiTCOv/9XbvJ1Uyy8RB/8/hFvnh4mqrpcSFX54uHZ7h5Q4ruZAhVERyZ\nLuH6PgsVE69lmxs2FBQfbM9f86OWiqjcuTGLrqkcmwtUFEzXx/N9Fqs2vi/RNPj1+zfzzFies3MV\nyqZDwwoq4wCZmM6tw50oiqDSdJktNdnWk6BmufSnwhiayu7BJF89Oo/l+5QbNirwxPkck4UGVdNt\nyfz5COERNzTiEY24oeH7UDYddFVgux5CSJqOz+GpMrYnWWzxoI9Nlzg6U0ZXBUstO/CeZAjL9Xjz\njl5qtstXj87SsF32jxVIx3QSYY1iI6jmh3SFiKFxcKIIwDePz9PbEeHeLVm+cXweXVMoNmwWK4Ha\nRK5qoqsKUkIqYnDPliyThQZzZZO65bGhM8rJuTK5qkU2HuKR3f1s7IpRqNskI/pKNf7ETBlFERTq\n9orz4DK29Sbo7QhjqAqRa7AJPzpdYrrYpL8jwu2jmZUGvOA+a5RalcWyZQHruyMenChyYbEGBA3G\n3zg2x8WlOtL3CelBY6oQwX3WVQXf9a+YzQoCnviLGwZfgkKQgItVNsnuqkD1AatVfleBsKYwnI1R\ntzxs1+cNm7NEQxqlpk216XJ0poLteswUm5xZqNGdCBEzVJ69WMBx/ZXJ4nTRRFNF4DxrKJSb7sqq\nVDZu8At3jhA1VMK6iu35PHEuqAY7ns979w2xa7CDoc4oIV151bjGm7rifOANI2iKIGpcelxu7UnQ\nk7wyHnRV4f23D1Oz3Bd1/EyEdX7xrhEsx39J8n13b85yQ1+SeEhboXO897YNlJvOS3IZfTl44lyO\nqZL54hteBQrB5Pmrx+YBQX8qzJePzvLzd468WkNso4022njd4Xom4x8C/iNgAf8EfBP4w+s4nqvC\n0BQ6IjoLFQtdFfzlY+eRvuSbJxeoWQ6OJ5ESbN9npthgIl+j0gxI17br4XiXqpMSaNo+6+UK5abH\nk+eXSEV1ZEv3WhI087l+8IciBJ87OE1IVwP5NqEgxCXpxabrM1du4vgQ0hTOLlQJ6yr9HWGOTgXa\nwl0Jg41dMU7PV2naHgcniyzVbJq2u1JJlQSqIpbnoykK+bpNzXZp2oE9uhDgeSBatIXDkwXOL1ZJ\nhHU6YwZRQ8OXkpFMjA3pCMdnKlRMh6MzJQp1h1zNwnF9kmENy/E5v1DF8SVNx6UnGWYyX2dsqUEy\novPE+Ry241MyHTxfYpkuqiJ4YbJITyJMfyrKybkqS/VLcoCdMQNFCC7kapxbqNKTCDO+1MBQFQ5P\nlViomty1KcunD0xybLrC7aNpwoaKIgTpqM53Ti1guz47B5J86cgsTdvn3XsHGeqM8tT5JfJ1mzs3\ndvK1Y/NM5Ou8c88ANw5corb0JMMoQhDSVQY7I/z5d89Rajj88j2jK81zABuzV69k9naE8XyfqUKD\nk7MhluoW04UG+bqFL8FyFKK6AlLieFcm4svx9lKrmMvbX0tvty+hbLpcWKxSMT2EEDRPztOfjnDL\nhjTJsE4ipFH0fKIhjYFUlItLdeqWS6nhtJRnNGqWgyoU0lGdpWpAZcrGDTZ3xRnPN8jGQ2TixgqX\nX1UEHa3qe9PxOL9YY3N3/Iqk9shUifF8nb0jnVdUqstNh8fP5uiI6NyzJbtuY2S54fD4uRzpqMHd\nmzNrtllWOrkck4UGx2fK9CZDLFQtBlKRq2qDh3X1ZU0cLk+6NVUhc5UVm5eChu3yl9+7gON5/Oqb\nNpOKrj3P5q44uiowX6bapgSqpksiHEhZ2q5HMvxjobDbRhtttPGycd2eglLKBkEy/h+v1xiuFaoi\nuHW4k46wzrMXC9jTZU7PVbA9iSSo9ER1lXhYo2K61Cx3TSVxPShAKmbQEdGYKDRXjlW3PBqOh9qq\nSgoC85yQptCTDKgaM6Umri/JxEM4rr+S6dueRBUBPWUwHWWu1EQS8LRdP0hmhYTHzy7xrlsHOTpV\nZqEqmCk1MR2PsKaSiRnYnociFIbSMYazEc4tBDSCXMUiqitIBMl4MGHQVQXT9alZPvlGgy3dcXYN\ndvA7D21nqWZxQ38SXVFYqtqEdZWvHJ2nWLfoiOiEdYU7NmY4OVvhmQt5FCEZbyVqE/kGnu/jeiqH\nJ8sIEfCyY+GAwqILqDRdzueqvHFrNxFD4YsvzDJRaNAZM9g9mGK0K4ahKpxbrPHTtwzwc3cM88S5\nHBP5BpOFBvGQxucOzgBQMR3+4O07ES06ztHpgMt7ZCqg7khA1wTv3DPI/osFAKaLDZ48F/DxG7bH\nH7/zUjI+ko3xi3eNIBTYP1bgyVYV95+fn1qjUf+tEwu8/471WyW29yaZbXG3j0yXg8qzFwSW54Mi\nfHwpSEU05lua51fEmbhSS30ZqyksLxcKYNoeluPjtmaE8xULieCX7k7xhi1dvG/fBo5Oldg9mOLJ\n80ssVk2OTpeoWS6JsM6GzhA3b+jA82H3YAfHZgIe+Vt29nLvtm4WqybJsL5GFlJXFX72jiH+x7fO\nYmgKj55cYHP3Wg5yw3b57ulFIEgAf+6O4TXvPzeW53xr5WGoM7oux/yZy7a5nKe+Hr51ch7L8fnG\n8Tm29SYZy9XZ1BVfaeB9LePrx+Z4fjyI7385NMMvvWFtbG7IxHj4pj4+tX/6JR9bAMu3UFdVdE+S\nCGmcmK3yU7e80pG30UYbbbx+cT3VVL7MlblAGTgA/LWU8uWvhb7KmCrU2T+W5/nxAnXbw3Y94iGN\nQiMwcFl2+KtbLrGQ1jKFuXo2vmyKY9VsSk0Hueoy+ICOwGklNsvvmK5Pvm4RD+k0TBtPChp24PRo\n2g4NJ3Al1BQN2/W4kKvh+35AJxDgeAqO66OrCnXL4Y++cpK67ZGJB5KOVdPF91wIa9RsD0VIig2L\n6rRDrmYjpcR2g4q4qggkGsmI1nJC9PECqju+hImlBv/uE8/Tkwzze4/cSH8qzNnFKhdzdXo7QlRb\ndAbT8Ti3UGOxatK0PRRFsKHTIBsPM1syV7iwpuNTMx1iIZW64yOlJBkLEQ8Hzphn5its6YmDgIrp\nEjVUYiGVXNVirtxkc3ecVCTQcx5IRZjIN1ZWC1JRnVKL3xsxVB47s8BzY0VyNYvhTJSRbJQzC1Uc\nz2cgFaUjohPSFSwnaEJ9YbJEpekw2BlUXX1f8vSFPDXL5Z4tWWIhjaFMFF1VcDyf4c7omiT41tH0\nD4y90a44R2fKCKBmOoQ0lYoM4s71oOx5OP7VvypXS8RXx9YrgQeIVheoABDB9yEeVpnI1yk0bEKa\nyoGJAn/1+BjSl5SbbjCpkFBsWByfCcyQ7t7SRTysMVtq4gNThSZ1y6U7EV733CFNZSQbY65sXqHR\nDWCowYpWuenQ3Xq/Ybs83uKsZ1uvGZpCah2aiO9LZkvNq1bdr4aueCigKLUq8fGQdk3UptcCNnTG\nKDcdfCmvynnf1Z/iM0y/JOoTtFZpfDB0gaYGSi9hQyUR1viXQ9OUmw4jmRj3bMmuUadpo4022vhx\nx/VcHxwDuggoKgD/hkDWeCvwN8DPX6dxXYF/fn6KU/NVSs2AxlG3XH73rTfwzRNzeB40HJfJQhMB\n3D7aiaEqPHpyntKL2FBKuKLxLqwFy98Ll1U6BWA6PhFd0puKMZGvg+/juB4IEdBaJEQMgetrlOo2\nkiAZMwRUTY/RbJSooTFfsVisBvzuTMxgz1CaQxMlyqaD6fj4PiiKz1LdRkpwvYAGYWhKkAhHDAZS\nYd64pYuK6VCzXI5Ol9k50EFIU/juqUXmKyaThQZ/9f3zPLC9h4l8nbrtslCBW4c7uZCrka9ZnF2o\nBsvWEY2IrvErb9zIba1GUNP1GOiI8t3TOc4uVDg9V8HQfAxV4f7tXbxr7xD/8NwEjic5PVdjNBvF\n9nzihobZam7NJkK8YXN2pSp5+8YMI9kY8ZBGLKTxJ+/czUS+wY39SY7OlHn05CKThQZ9HWH2DKW5\nb3s3d23qwvE9RjKB0csv3DlC3QroNLeNpMlVLba01FXGluorlcWQrnDftm629iT4k3fuoma5bOtN\n8vtfPEbF8lEF1Jo/eAllNBvjF+4c4QsvTJMI62TjIc4uVMnXLRYqwT1u2P6rUuV+JQhrASXn3q1d\ndCfCxMIq00WTUycXSccMnruYp2YG1K2QqjCajRILaZyer1I2HaaKTfYOp3nmQp5ERGcq3yBXs9g/\nXlhj13453nnrILmqtZJsr4amKrz/9iGKDZueVkL//HiRU61+jId39/Gztw8RMdQVCczVGFuqUW46\n9HWEuaEvcVVayuX46VsGWv0JBvm6TSpivG70siO6yh0bO/F8ue4EBeC+7d0MZyKM5dd3RP1BiIVU\ntvUkAmMkXWFzVxzRknicLja4bbSTbDzErsGOFz9YG2200caPCa5nMn6XlPK2VX9/WQjxvJTyNiHE\niR/lQKSUPDtWoG653LU5s6ZRC6A/HV1xAlzmU3/+hRlKDYewrpKvNik3HaSEk3MVCnX7RRPx1ViT\nSPlgXYXj4ksQrYr1Mq1FBcK6wCZQtvBb+3vy0jFtH3TpUzVdQppKzbRXKqYV0yGsKZiui+36K1x1\nzwMpvaDpVAg8GdjAh1RBIqxTaDgcmCiiq4Jy0yFXsXistkg8pLJYNXE8iS8lZ+YqnJytMFtqkgjr\ndCfDGJrCjv4kj55YwHZ8NEXQ8AMe7Pa+DgbSUS7k6nz24BTdiRCLFZuy6ZCKGhyfLWM6Prc2Uxyb\nKeN4ksl8je5kiOFMnNmSRSqq050I8f2zOUpNJ3DT1BRu7O/g/GLAId812IGhtfjjFYupQqNlxhI0\n63YnQ2zsCmgLl1MT4iFtRbu5KxGma1XlNhXV0RQR0IhW0RLWqFwsG8dIGEhfWfV1PZ+nLuTx/WAl\nw3R8UlGdZ8fynF+sY7kBLWR1H8L1hCRo6NRVn/0XCyiKIBXRMd1AdceTwQQ2oNZIbCQNx2PfaCdT\nhQblZlDpPzFb5uBkkaiuEjNULuRqqIrAtD12DXaQiYV46vwSQoDrS9JRg32jnSsV6PUQ1lX6OjQy\n4ToAACAASURBVC69v3xPVEW04mT9qjtAR8RY0eR+KWonuqowW2pybKbMXZsyr5uqOASxfW6hhufL\ndZ1rPc/jp//iCRZrzss6vuMFfS2dMYOIoXHzUJqpQoOIoWJoCtPFBmcWKmzvS6xrgnQtMB2Pp84v\ntSYWmWt2LG2jjTbauF64nsl4XAgxJKWcBBBCDAHLpM/1CbA/JIwt1Ve0gTVV8KbLKnHvuXWQb5+c\nx3J9io3A5e/YTBlNUXA8L2jedANpt/OL9R+YHK1XwUwYAssH6UukImjaa7ujOqMaqqIEnPRG4JS5\nDFWBkWwUzxcU6oFii+2unQgIQCgCy5WYtreGs9y0Xc4t1hCIlUR8eR/PJ5Cr8y9t70vJrcNpjkwX\nOTNfQVEUHNej0nSxfZ/FSlChFwQyi+dzNbzWxCEdM9g7nOYdewaZKzVZqlrk6zaaAopQeHBHD9v7\nkhTrNh99/MJKo18mHiIZ1tnaE281kEq+emyBiKExV2oSaSlsPLC9mwe2d5OKGhyaLGK7PvPlJk+f\n97Fdn8FUhK8dm8PzA979nuE0L0wWeX68yIZ0lE3dgX64rirEQtrLaojLxkP8/J3DmI5Pb8f6iV6j\npXwjgTPzVXo71iZ6x2crHJooUqzbmK5HX0cEVYHFikWuaiIE2C/WlPAjhiCo0DccC8+Hxaq1Minx\nPLmmKTSiq8QNjXfsGWTPcJqvHp1DUQRfPzaPJyUxQ+XWkTRLNZunLyxRrNvMlk02dcU4NlNmLFcj\nFTXojBn0dYTZ8BIs03cOdJCNhwhpyotyuLsSIX7hRe7lepgpNVeUXqSU16wf/lrAkekSTSd4ph2a\nKnHT0Foa1R9+7eTLTsQheCYIAqO1h3f30dcRYfdAB3uG0pyaq/D8eIGpQpOj0yVuHV6/6fXFcGC8\nuNLzkU2ErvAEaKONNtp4reF6JuP/HnhSCHGB4Pk8CvyaECIGfOJHOZBEWENVRGtp1qDcdHj+YoFs\nPETddvGlpCcZ5tRsBUUJbKwdV6LosmVUE+iGe9eQH62XqLsySNFdH4Qvr1C/qJpuYJhSsTBdb01z\nqO3DibkacUMFIalbV6pqLBu72K5HvuHjraLGNBzJuYUqjr92bMsqHFIGCfnyoDRVUKhbNG2PquUS\nUlUajktz1aAUgn0UJaC1uD7oamB3faxFZxlMR8jEQ3i+ZL5qEtXhn56b4P/62im29gQyeY4XeEI2\nbS9wjmypkDiexHI8FioWCxWTiukwkolTatocmizRn4qQiuikonrLRtzl+EyZ47NlCnWbYsPmrk2Z\nFu1AoAjBQtWkPx1GSEHdchlIR9AEfOLpcS7kauwZSnH35iwXcnVMx+POTZnAsbRisW9jJ8lVNIfL\nFSiWMZlvcHq+ssaUZ7jzyqpuqmWuZGjKSlVPFQTcesfDUBUIpk/XvSq+DA8uLRsR3CPnMgqWQjB5\n9HxJ0/F4dqzA8dkyluvTmwwHMWW6ZOMhLDeQA61bgSGQ5XrkaxbFho3t+hydKmHogeRhdyLEQDrK\nbKmJ6fjEw0FFdHmF69Rchelik1uH03TGjJeUWF9cqlOo28TD2hVOlgBz5SbHpsts6Ukw2moAjRsa\nuipwPHnVWFiN5edNTzJ83ekZfR3hFh2NdTnjO3pfWWLbdIJ+lqcv5NAUwbbeJLePdq441p6cqyBl\nsCoBQSFg/3hh5Tu3uon3alim1yhCrPlettFGG228VnE91VS+1tIX39566cyqps0//VGOpTsR5mdv\nH6LpeAymo3z5yCznF2vMV0ySYY2ooaEKQTKi4/oB/SIVDST8hjsjfOd07hWdXxFipdK5XnLl+FBt\nOtje1WXqavbVaTGGAoYmWhOOYIl+dWeftc6uCkHirQqJJ0EToKqCbd1xCvWAkpOOGjRsj6a9NhFP\nRQPli0RYYaFqk4lpJMIa/ekIhYbN5w5O8xsPbOH9tw/x5SOzRA2V75/NMZ5v4PmSharJvuE0g+kI\nQ50Rzi7UiYVUGo5PVyJEqelgqAozpUAXvdhwiBomH39qnKrpYqgKH7x3I7/14FZOzlX4+2fGsT3J\nx564yEgmSiqi09cRZlNXnPffPoShCRYrFvMli1RUoyOis70nydNjBb58ZJa5cpMLi1XG840Vub+G\n7XFmvgqA6Xq8bXf/1W8wQYX0y0dnr6hof/nIHB96cK2t+0g2xvv3DQXykoqg1HT468cuYLk+mhLc\nP0MRxEIqVdMJFE1eajfdjxgCSEU1EmEdy/UxHZ/PvzBDoW6RjRm8bXcfd27KMltsUmzaHJkqU2o4\nbOyKMV1oYrseE/kGm7qDRt2q5WA3fD59YIpbhtIs1WbpSYQ5NV9h73Anvg8P7uihajp888Q8UkKp\nYfPuvVc3Wbocs6Umj50JvtuOJ3loZ+8V23z92DzlpsOZ+Sq/dt/mFeOnn7tjmKrpMngVs6fV+P7Z\nHBcWaxybKdOXCpN9FSQKXy5u35jh9x7ZgeP63LThyubiXYOdGIrA9l/eNNCTYNo+By4WyVVt7tyY\nJRXVuaEvuSbulw2Wzi3WeOZCsGppaAp3bcq+6Dl2DnSQjhmENOW6Xss22mijjWvF9RZ43QJsA8LA\nTUIIpJR/dz0GspqSkGjp3sZDGlqL/7lQMVGExHF9LM/HdnzKTYdq0w60h1tqEi/nJ6rpeCu58bLh\nyuV4JclWoAnu43oSV0oEL86h9AkSEKEJFAFKqyA1UWhiaBa+lJeOJy5pUvuA5/tEQzp1y8N0fDTh\nEQtreJ7k4lKdqUKDv3l8jLs2ZxhfqnNosojn+yhIXGjRZYJxHrhYCnjcdZvpYpNMNETNDDSqz85V\ncJE0rOCz+b4kX7epWy7fOjHP//HQdgY7I0jAcT0cL9BLH+6MUTVd/uTrp/AlVJo2J2YD58auRIhU\nxKA7GcKXEtPxMJ2AhjNZaFCs28RCKsWGzdhSnWxUZ6bYIBnWuXtThgMTRTxfko7qPHF+icF0hAdu\n6GlNTjTytbUMrO0961u2d69ye+xoKcEYmorluEGTrZTEwyqeD/ZrpTz+AyCAeFinYrr4vqTSdKg0\nLQxNRQj4/KFp/vn5KYYzURJhnbFcjbrloas2VdOmUA9iznQ9pJQoQmCoAT3JUBVSER1dFYQ0FUUE\n3+Fj02XmK00UIWg6QTJ/fKa8Ymnv+XJFpnLfaGcwSV2FqKGuUG0u18IuNWxemCzRaFHKYqFAWejS\nvhrHZyo8fWGJ7kSIfaMZYutU1uHS88bQrjQrqpgOB8eL9HaEuaEvud7urzp29F29Oh80PqvYzZcp\nNE7LCVZKclWLx8/luHU4tfLZVsc9BM+uY9NlPF9y58bMNZ/jxdxP22ijjTZeS7ie0oa/D7wJ2AF8\nDXgL8CRwXZLx1Xjjli6GOqN0xgzOL9b4xNPjNB0PUBAKWGbgnOl5kvmqTSaq4yOpmS6299IT8tWr\n+T8UJrAQSASeFzR2qkKiK0HF/QdheViZuAESFms2VctFsQURQ8V1fUSrSmuu+hCm6xMLaRTqNooI\nluG7Egan56tYjkfZ8Xj01AKn5quUGzam65OO6rxlVx+HWxbjuqpxPlehYXu4no+qBHSSjohONKSS\nr9sUzZbyS+scIU2sOFt+/cQ8t450kqta3NjfwXi+zsZsDF/Cxu4YT1/I8/jZHI7nBw2qvo+uKEQN\nje6EwnAmSt3y2L2hA10VaKrCxVwNT0KlKTg9VyVqqMyXm3RGDT61f5Ka6a7III7lahQbDomwRkfE\n4O7NWd596wZmSk0+8p1zK9dqpvTi7RGaqvChB7awb7STP/32WU7NVfAlVJveDydefgjoShq4rRip\nWm7gCioVRrqixHSFg5MlmrbH0ekS6ahBKqqjCIWa5VK3PRqWGxjGWAGNZUtPgp+9fYjBdBTXl/Qm\nw8xXTAxN4LcMrz71/BQAw5kopYZDqWHz6MkFepJhuhIhjs2UV3pFoobKTRtSa8acihq8//YhKqbL\nyGVNvI+eXGC62MSTPj+5o5fN3fE1hkAHJgo8dmaRYzNltvTEMR2ft+xanzt+75YuhlvPm8upMN87\nvchYro6YDigk10J7+WFiQ2eUn7q5n799ZvJlH0MAW3oSXFyqU2rYfPK5iZZXwJUUlKPTJYrNwH34\n9HyFOzZde0LeRhtttPF6wfWsjL8LuAl4QUr5ASFED/DJ6zieFSiKYGNX0Esa0VVOz1VYrFpIggbI\n1QmQL6HYCBQ73JeRiP8oYLkSBYnWKrt7rQbLq0Hlkn265QY29xFdxfcvmRyZthfQXjwfx1+7KqAK\ngen4OF5QPReKoGH7hDQFywPPDvSeTcdFSqhZDrbjMZAK05sMM1loMFOqY6iCJrQS8aCSWbc8DFW0\ntM5bVfnWh1EVsVLJNFQFBcFMsYnj+WzvTaAqCg3b5YXJEgtlE0URqFJgaAqWA54MnFSziRC6qjBZ\nqLBQNik1HDRFCazapSSTilCzXYQINLani02atsdkIeAXd0QCOkax4aCrCoW6xaf2T5CKBkn5agxn\nruQvF+o2x2bKDKTCLFYsTMfn0GSR84sVpPRpSXS/JmPtavA8iYPXWkESeDIwbtrWk8ByPRyvhJTB\nPahZDoYWOMtKEcSdpiq4nkRBoKsKW3sT3L35kmvmbKnJxVwN0/XZ1BUnFdVxPJ/ZUpOBVJjRrhiH\nJx00RRDSFQp1m5OzZcpNh46ITiykYjqBA+5sqckdGzNs7UmQiYfWbeRdrnJHDY2tPQkMVeHgRAHX\nk+wd6SRmaCsTSF1ViF6lKg5rnzeXY5n3HkxQr11dZKlmcWK2wsZs7CU1uF4L6ubLb+CEIG7nSs1g\nZU2VFOo2nzs0xc/cMnjF6kEyHPR9SAnJa5SWbKONNtp4veF6JuNNKaUvhHCFEElgEbh2QuePCB99\nYozFmkXZvGQVv0wlWU6GXAnui5WZrzMkBPJhMkik3csyueXkXFVAVwSqqlBrkckdHxwrcAWNGgq+\nL1HVgA7guD4QGA75Egw1kI9zfR/f81CEIBFSScd09o1meOZCngmvHkjTRTTKuCzVoW67HJwokYxo\nVE0XicaN/UkalsdgOsLFXI2Fmo3r+QykY9w4kGRTV5xzizWOz5RRhGCwM0J3IsxMocn77hjC9Xyi\nhkrFlLz/9mE8X/I/v3eei0t1XM/nF+4cYkM6ykA6yteOzbFQbhLWNe7amKFiOnzn1CLjrQa+WEil\nM26wqy/Fxmyc2zd28uUjs5yYLlOoB1XwE7MVuhIhHtrZy4Z0jGOzJaK6yqMnFzk0WSQV0a+QrZwr\nX1kZ/8bxeRYqJl86HOid779Y4PR8Fdfzca+lS/g1Bk0JKtpJQ0U3VHwkvg839CX5Dw/fwN89PU5f\nRwjb8YMVCk1tyTkaOI7khv4kNdOlryMEKLx77yA3bUitJOJSSr5weIYTMxUqpsOeoTQ/e/sQmbhB\n1XSZLZm8dVcfg6kI6ZhBMqzzD89NsFgJFF/eflM/m7rjPHZmkc8dnKZQt7m4VOc37t9yVcWVn9jR\nw+buON2JEGFd5cRsmcfPBgoqmho49iYjOo/c5BLSFDZdJdl+Mdy3rYvhTJRsPHRVmst6+NqxOfI1\nm2P/P3tvHiTXdZ15/u7bcl9q36uwEyBAgCDBVaREUdS+2pKlkGRZHsvdbns8tjvCM/ZEOMbj7piw\n29Exbtttu9s90T0Ob5JGtrVYiyVT1EJJFBcQJEECRGGrvSorK/d8+fY7f9ysRBUKIAESJEg6v4iM\nrMqX+fLmfS/znXvOd75vvsK/ecvOa2aiU2q6fOWZ5Ze9n+W6x46+JE4QETN0vvLUEnFD5yMXcfof\n2DdEb8oilJJbJ1/YIKuLLrro4vWK6xmMPy6EyKMMfp4AGsCPruN4NmF6pc5qwyVuCKKL3DRff+GQ\nCsbrzgVJvUttBwgipWAQF5s/pda+RZHE8SVmFNCXjtNCuSkKIZChJJJguz7C15DtrCBCsFxzWSjb\n5JMmM2uqMVEKDT9SkpAyFHihujDrWoAfyvZF2MOLIg5O9vDMfJXza01Waw57hzP8qzfv5I8enGax\nougJQQiVposQgunlOnFLp+kGOH5Ise7y6Pm1dtOnD0ienqsSMwzScZPDEz38fysNFsoOJ5ZqLFUc\nqo6qeAihpBctXSeMJGM9cQ5P9jBbspkt2SRjhnIxdQPils5SxSETN7ln1wC2F/DdU0W0dgPsxWoQ\ng9mtWde4qQKnhKUjhMA0lISm66ujdL0Nfl4MhsYmxZ9IggwlThChtXXDgwiWqjY/Or3aofIUfKfd\nqxARtSsmuhBUbI+aE5KwdG6Z7GF7f4qn56vsG8nSm7IQQnSe1/QCnpqrcMNwhoFMnForwDKVzvzu\nDRJ3XhAxW7IZzyeYalNQ4qbiiPthxGrdpdR0LxuMm7q2STJvI9c7ZqjzpNR00TWN/W0Ky1zJ5sm5\nMnFD59apniuSzjQueh+AufZ5t380u4W2Umy4PL9cx20nByxDQxMvVAfbjCiK+MLRBfww4qduncC6\nSGvc0rVNUqcvB6t1h13tqoIbRBxfqJJNmtyxvQ8viDi1UmfXYJoj216axGEXXXTRxesF11NN5Zfa\nf/4XIcQ3gKyU8un17UKI/VLKV9X8Zx3FhstXn1lCStgxkGG8t0apnR13XuMZ8BfCxpHr4vJW6RFK\n8nAj+tMmXiipO0G7AUuwezDNUDbG2WKTs6sNak5AEEHVDTE0FVin4zo1JyTwfJ6YqZBPGh1ubMLU\niBkaKcvAEIpnPtoOjiIJS1UH2wupNH3+l7fu5rnJKn/4rWlqTsCj50r8w5MLCGDXQJqlWouWG3B2\nVQnyzJSa3Lmjj4YbMNGT5He/foKK7VN3fMZ6EixUHH58rsQPTq/x/ptH8fyImZJNxfb428dm2dGf\npi8d432HRnlypgJtqcGYoXfoAu86MMzOgRRnV5tUWz4nluosVFp8+2SBE8t1PnPPdlIxg0/dNcXt\n23vJxAz2jW5uwjtdqPHA/s1c4vfcNMKZ1QajuTjFhsdQNsaJpRqO7yN47S8GNXHh/DLaFRME+JGk\n6Ucd9Z75sssff/sMn757ioSld4JwNIkQAtsLiRkas6UWjh/iegF7hjJ88ZhSpTm72uBTd20DYDSX\nYKI3waPnFFXks4/O8r+/Zx87B1KM5BJbFkEtPyRhKurRetb4ju29ZOMGf/XILMmYzrdOFNg5eGVS\nfjsH0vzkLWMEkWTnQJonZsqdTHnM0Njen+Ifjs7zyLkSSUtnqerw6bu3XfXcukHIF59cIIgksyWb\nj98+uWn7V55apGL76Jrg7TcOMtGbuirTm28cX+HvnpgHlHjmJ+7YvP9i3VX7uwYBed2L6MtYfPy2\nKf7x6UVOLqvvT7np0XBDai2fZxaq/MKbd2zi43fRRRddvNFwvdVUAJBSnr/Ew38J3PIqDwVQWVsB\nnCk2mF6uU2n5ih7wWk5HtnGlWVNTAxld/pp68X6ySYtS8wKlQgjQNcl0oUHCNEhZOlVHNU+um/5I\nKWm6AbItpyil0khRf6umy7LtoQExUydmaqzWXTJxgzu293FiuYYXRHhS8sRMmbrrd4yWGo7PbLHB\nozNlNaAIVpseSKXT7vsRa3UHXddYa7rtBlzFz93en6baCqjYnuKNC4GuK5dI2wtxvbCjyHFkWx9u\nILG9gNW6S9MNODpT5uxqg/2jOe7ZrezfH3q+QDpukDB1tc/2bWatyXLV4ZbJnks2qPVcoiEvburs\nH1WKFj2pGGtNj2zcpN5Smvev9fMwap9XAnUfobj1QSiRhJ1zS1VrfM4Wm0TRBdfYtGlg+yGRlBia\noBlGHT75mUKDg+M5CjUXL4xUpSYIOblco2IHpCwD09DQNUE6ZmziSzdcpTc/3pMgaeoMZOJkEybl\npsfJ5To7B1LcOJpj70iGtYZ31Q6QU30XlHGsDa81dZWd1vX2uaaJl+wuqQm1eAii8JL7WH8saekc\nGMtv2f5iiG84RxPm1vM1bupcy7jY0nTetKufZxdrrNTcztxYulpyGproBuJddNHFGx6viWD8Mrjs\nL7AQ4t8CH5ZS3iOE+APgCHBUSvmr1+KN80mLmyfy/M2js5QaHkF0gRP9YjAEW/jYrybiBkgEXpsy\nsnFcUl5ozAwljORi+IEkjCKqraATDI3nLFp+RNlWWXABrNZcEpZOJq7jBZJ03ODkssqG60KQiatM\ndxjBRE+CWiug6viEgSRp6Yy0pdkWKi0Wyy0arpK480OJZWhkE6pRr1BzKNkaN47m+P0PH+SvfjzL\nXNnmrx+dVYFXGBI3DUxd53vTa8yVbNUMqIFlGOSTBv2ZOMWGS90LiemwUm9yeDxHzNI5NJbjwHge\nLwj51nMr7B/NMpCJ88/PrSgJRyEYzSe5b+8gt0z2MN6TpC9tYbsBX3lqkafmqxydLSOlZNtAilRM\n57HzFRxfNZb+ytt2E0aS4VwcL4z44pOLRFJSqLu8/9BWLfKG++IScXfu6OPff+gAf/LtaebLLRYr\nzmsyO77+hQ0lHbnLjvMm6vujo5Gx1HPScZNUzOCHp9douopb3ZcyScdMVuouIInpOmZamUe5oaTm\nBJxfs1VQCBybr3ButcETs2WabsA9e/rZN6z6CS5uXPyn48vMlmwMTfCx2yZYrjls60/xxScXWGt4\nHJur8Atv3sFPHB7jXLG5Kbi+WhwYyxIzNYwNzZkfu22SmyfyWIbOjS9RptDUNT56ZJyFSovdl8ja\nf+jwGGdXG0z1vrSx3793EF2AG0Q8sG9wy/aYpZNPWLR89yXtHyBraUQCEqbBaD6O60d84o5Jbpns\nIWZq3DSWww+jl30Muuiiiy5eL3gtB+OXDGmFEDHg5vbftwBpKeW9Qog/E0LcJqV87Fq8+VA2rlRF\n2hHtRi3ty0EHLF0QXMdoPIoAbfNY16kNmgZadCFAst0AU9fwApWVNHWhNMlRPGXRTmFKVFk/FTPo\nS8WoOj4Cge0GeEFE3NTbNAMTL4xIWgYNN8Rouy2GkWSyN4muKf54NmGyWHWIpOKaC6HcOTVTULFV\nFcJ2fT7/xBzpmM5A2uIU6hgkTIN0DMpNn5rjEUQqaxoJgROEVFuQiYckTEV7CCOIaYJWENGbVlno\npWqLI1O9/PL9Waq2z6lCHSlUcOj4EZoGuwczpGMGj50v0fICzqw2qbsetZZPJBWNQmu7dwpgpebQ\nn45x4wYaSt3xqbU8ZtZsYm3u7cWmPxfrSl8OmhDomtZWGeE1mR032nKZAi45vghARoRSI2ZppONK\nszoMlYlSGKkFZMLS2rx5QT5pIiVk4jrLVVWZmFlrMtGbJJKSh08VqbRcGk7AupfVu28aYaXm8Pj5\nEgfGVBZ9teGoqgLquPzjM4vcsa2PbNzscKo1oc6xTNzEMjRm1pocGM1tonkEYcQzC1UycZNdg5dv\nyhRCbOF696dj9O8a2PSY395fPmFeVlHlYlxO4QWUDvjB8avPiG9E0w1xg7Zs6EXJd12AJl7eyZeM\nGwxmlBTlmUKTbxxfIp+yuO+GgQ6dKG7ql/wcy1WH+bLN3pHsJR1Ru+iiiy5ej3g9/pp9BvgL4N8B\ndwLfaj/+z8BdwDUJxveOZEnGTSxbmfrkYjqFhv+CGclsUrl10vRo+Vtt6S+H4YzFWtN7Ud3vK4Gr\nCN2bHpO0s5RSXUzTpobtRZRbIYKwM84wlGQSBlVbBZwb9xKEEj8M2dGfoukF2F6g9L+FoDdp8onb\nJ/jyU8uUbVeVm3XlbFq2PTRNo1B3Waq63DKZZ7Hq4EcR5aZqkExaBv3pGDFDw3ZDdF3w2PkyPzhT\nYjgX46NHJrh39yCFusNgJsYXHp/jO8+v0vJDDF0j1S6tl20f2w3xg4ihbIxU3CRh6WTjBlEU8f3p\nIt8+WWC8J0nLC3nbviG+/NQCxYaHqQt+4vAoPzizxkg+Qbnp8cjZNZaqLb757AqDmRhzZVsZAmVi\n/MThMW4YyXJ4soey7XO22CCIVDZv3RZdE4LVhstSzeHJuTLTK3WeXaxd9TGt2h6//aXjFBsuQSRJ\nWRo197WXGw8j1eT7QnBDFdC2goiEaTDZm2c0F+PLTy1Ra4VUnYB80mJbXxpdgw/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Qid46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\nyoSdjFssFovFYrFYLGXi/wdenu+NiEMx/AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DE00Rcy87dCo",
        "colab_type": "code",
        "outputId": "74211170-c110-4bd9-dac5-2e5735930ecd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
        "             alpha=0.1)\n",
        "plt.axis([0, 16, 0, 550000])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 16, 0, 550000]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qe2c5k0zTSyOujTIOlyW/eX3MrDRUjeVhbfn2rSlppDYpshdK8sKytK1rLlYh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\nD4y3k8oRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTxSIG55V8op",
        "colab_type": "text"
      },
      "source": [
        "By the graph we see that this is postively correlated and there is just one line because its all capped at 500,000 so all the poits after that are getting capped there. Not only that we see that there are caps at 450,000 and 350,000  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLr-Qj7_7dCy",
        "colab_type": "code",
        "outputId": "fd6939b7-bd80-478c-cfbe-b16430649557",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Experimenting with Attribute Combinations\n",
        "\n",
        "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
        "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
        "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]\n",
        "\n",
        "housing.head(5)"
      ],
      "execution_count": 0,
      "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>median_house_value</th>\n",
              "      <th>ocean_proximity</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>17606</th>\n",
              "      <td>-121.89</td>\n",
              "      <td>37.29</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1568.0</td>\n",
              "      <td>351.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>339.0</td>\n",
              "      <td>2.7042</td>\n",
              "      <td>286600.0</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>4.625369</td>\n",
              "      <td>0.223852</td>\n",
              "      <td>2.094395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18632</th>\n",
              "      <td>-121.93</td>\n",
              "      <td>37.05</td>\n",
              "      <td>14.0</td>\n",
              "      <td>679.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>306.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>6.4214</td>\n",
              "      <td>340600.0</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>6.008850</td>\n",
              "      <td>0.159057</td>\n",
              "      <td>2.707965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14650</th>\n",
              "      <td>-117.20</td>\n",
              "      <td>32.77</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1952.0</td>\n",
              "      <td>471.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>2.8621</td>\n",
              "      <td>196900.0</td>\n",
              "      <td>NEAR OCEAN</td>\n",
              "      <td>4.225108</td>\n",
              "      <td>0.241291</td>\n",
              "      <td>2.025974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3230</th>\n",
              "      <td>-119.61</td>\n",
              "      <td>36.31</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1847.0</td>\n",
              "      <td>371.0</td>\n",
              "      <td>1460.0</td>\n",
              "      <td>353.0</td>\n",
              "      <td>1.8839</td>\n",
              "      <td>46300.0</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>5.232295</td>\n",
              "      <td>0.200866</td>\n",
              "      <td>4.135977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3555</th>\n",
              "      <td>-118.59</td>\n",
              "      <td>34.23</td>\n",
              "      <td>17.0</td>\n",
              "      <td>6592.0</td>\n",
              "      <td>1525.0</td>\n",
              "      <td>4459.0</td>\n",
              "      <td>1463.0</td>\n",
              "      <td>3.0347</td>\n",
              "      <td>254500.0</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>4.505810</td>\n",
              "      <td>0.231341</td>\n",
              "      <td>3.047847</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  bedrooms_per_room  population_per_household\n",
              "17606    -121.89     37.29  ...           0.223852                  2.094395\n",
              "18632    -121.93     37.05  ...           0.159057                  2.707965\n",
              "14650    -117.20     32.77  ...           0.241291                  2.025974\n",
              "3230     -119.61     36.31  ...           0.200866                  4.135977\n",
              "3555     -118.59     34.23  ...           0.231341                  3.047847\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSfrbAh4K__L",
        "colab_type": "text"
      },
      "source": [
        "Since having total number of rooms, doesn't really make much sence as it won't really tell us anything about the median house value. We would rather have rooms per household and bedroom_per_room ans poplation_per_household"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwiccE3Y7dC8",
        "colab_type": "code",
        "outputId": "0e0b6809-112f-458a-c56d-6469d24dddba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "corr_matrix = housing.corr()\n",
        "corr_matrix"
      ],
      "execution_count": 0,
      "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>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>longitude</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.924478</td>\n",
              "      <td>-0.105848</td>\n",
              "      <td>0.048871</td>\n",
              "      <td>0.076598</td>\n",
              "      <td>0.108030</td>\n",
              "      <td>0.063070</td>\n",
              "      <td>-0.019583</td>\n",
              "      <td>-0.047432</td>\n",
              "      <td>-0.028345</td>\n",
              "      <td>0.095603</td>\n",
              "      <td>-0.000410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>-0.924478</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005766</td>\n",
              "      <td>-0.039184</td>\n",
              "      <td>-0.072419</td>\n",
              "      <td>-0.115222</td>\n",
              "      <td>-0.077647</td>\n",
              "      <td>-0.075205</td>\n",
              "      <td>-0.142724</td>\n",
              "      <td>0.107621</td>\n",
              "      <td>-0.116884</td>\n",
              "      <td>0.005420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</th>\n",
              "      <td>-0.105848</td>\n",
              "      <td>0.005766</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.364509</td>\n",
              "      <td>-0.325047</td>\n",
              "      <td>-0.298710</td>\n",
              "      <td>-0.306428</td>\n",
              "      <td>-0.111360</td>\n",
              "      <td>0.114110</td>\n",
              "      <td>-0.147186</td>\n",
              "      <td>0.136788</td>\n",
              "      <td>0.015031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rooms</th>\n",
              "      <td>0.048871</td>\n",
              "      <td>-0.039184</td>\n",
              "      <td>-0.364509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.929379</td>\n",
              "      <td>0.855109</td>\n",
              "      <td>0.918392</td>\n",
              "      <td>0.200087</td>\n",
              "      <td>0.135097</td>\n",
              "      <td>0.127519</td>\n",
              "      <td>-0.192606</td>\n",
              "      <td>-0.024810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bedrooms</th>\n",
              "      <td>0.076598</td>\n",
              "      <td>-0.072419</td>\n",
              "      <td>-0.325047</td>\n",
              "      <td>0.929379</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.876320</td>\n",
              "      <td>0.980170</td>\n",
              "      <td>-0.009740</td>\n",
              "      <td>0.047689</td>\n",
              "      <td>0.000377</td>\n",
              "      <td>0.086960</td>\n",
              "      <td>-0.028370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>0.108030</td>\n",
              "      <td>-0.115222</td>\n",
              "      <td>-0.298710</td>\n",
              "      <td>0.855109</td>\n",
              "      <td>0.876320</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904637</td>\n",
              "      <td>0.002380</td>\n",
              "      <td>-0.026920</td>\n",
              "      <td>-0.074692</td>\n",
              "      <td>0.037778</td>\n",
              "      <td>0.076225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>households</th>\n",
              "      <td>0.063070</td>\n",
              "      <td>-0.077647</td>\n",
              "      <td>-0.306428</td>\n",
              "      <td>0.918392</td>\n",
              "      <td>0.980170</td>\n",
              "      <td>0.904637</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010781</td>\n",
              "      <td>0.064506</td>\n",
              "      <td>-0.083009</td>\n",
              "      <td>0.067162</td>\n",
              "      <td>-0.027416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_income</th>\n",
              "      <td>-0.019583</td>\n",
              "      <td>-0.075205</td>\n",
              "      <td>-0.111360</td>\n",
              "      <td>0.200087</td>\n",
              "      <td>-0.009740</td>\n",
              "      <td>0.002380</td>\n",
              "      <td>0.010781</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.687160</td>\n",
              "      <td>0.311563</td>\n",
              "      <td>-0.624674</td>\n",
              "      <td>0.021822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_house_value</th>\n",
              "      <td>-0.047432</td>\n",
              "      <td>-0.142724</td>\n",
              "      <td>0.114110</td>\n",
              "      <td>0.135097</td>\n",
              "      <td>0.047689</td>\n",
              "      <td>-0.026920</td>\n",
              "      <td>0.064506</td>\n",
              "      <td>0.687160</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.146285</td>\n",
              "      <td>-0.259984</td>\n",
              "      <td>-0.021985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rooms_per_household</th>\n",
              "      <td>-0.028345</td>\n",
              "      <td>0.107621</td>\n",
              "      <td>-0.147186</td>\n",
              "      <td>0.127519</td>\n",
              "      <td>0.000377</td>\n",
              "      <td>-0.074692</td>\n",
              "      <td>-0.083009</td>\n",
              "      <td>0.311563</td>\n",
              "      <td>0.146285</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.398546</td>\n",
              "      <td>-0.005314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms_per_room</th>\n",
              "      <td>0.095603</td>\n",
              "      <td>-0.116884</td>\n",
              "      <td>0.136788</td>\n",
              "      <td>-0.192606</td>\n",
              "      <td>0.086960</td>\n",
              "      <td>0.037778</td>\n",
              "      <td>0.067162</td>\n",
              "      <td>-0.624674</td>\n",
              "      <td>-0.259984</td>\n",
              "      <td>-0.398546</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.004279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population_per_household</th>\n",
              "      <td>-0.000410</td>\n",
              "      <td>0.005420</td>\n",
              "      <td>0.015031</td>\n",
              "      <td>-0.024810</td>\n",
              "      <td>-0.028370</td>\n",
              "      <td>0.076225</td>\n",
              "      <td>-0.027416</td>\n",
              "      <td>0.021822</td>\n",
              "      <td>-0.021985</td>\n",
              "      <td>-0.005314</td>\n",
              "      <td>0.004279</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          longitude  ...  population_per_household\n",
              "longitude                  1.000000  ...                 -0.000410\n",
              "latitude                  -0.924478  ...                  0.005420\n",
              "housing_median_age        -0.105848  ...                  0.015031\n",
              "total_rooms                0.048871  ...                 -0.024810\n",
              "total_bedrooms             0.076598  ...                 -0.028370\n",
              "population                 0.108030  ...                  0.076225\n",
              "households                 0.063070  ...                 -0.027416\n",
              "median_income             -0.019583  ...                  0.021822\n",
              "median_house_value        -0.047432  ...                 -0.021985\n",
              "rooms_per_household       -0.028345  ...                 -0.005314\n",
              "bedrooms_per_room          0.095603  ...                  0.004279\n",
              "population_per_household  -0.000410  ...                  1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLQTdwNg7dDA",
        "colab_type": "code",
        "outputId": "ef406ec7-2789-4ecf-e3d3-708749d7540c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "corr_matrix = housing.corr()\n",
        "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "median_house_value          1.000000\n",
              "median_income               0.687160\n",
              "rooms_per_household         0.146285\n",
              "total_rooms                 0.135097\n",
              "housing_median_age          0.114110\n",
              "households                  0.064506\n",
              "total_bedrooms              0.047689\n",
              "population_per_household   -0.021985\n",
              "population                 -0.026920\n",
              "longitude                  -0.047432\n",
              "latitude                   -0.142724\n",
              "bedrooms_per_room          -0.259984\n",
              "Name: median_house_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "MX87Lqmb7dDD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing.plot(kind=\"scatter\", x=\"rooms_per_household\", y=\"median_house_value\",\n",
        "             alpha=0.2)\n",
        "plt.axis([0, 5, 0, 520000])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Re2K55j-7dDG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxLykpPC7dDM",
        "colab_type": "text"
      },
      "source": [
        "# Prepare the data for Machine Learning algorithms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBL4uaeE7dDO",
        "colab_type": "code",
        "outputId": "6b2d5aca-85e5-4666-a338-19e9e71d664f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "# Let’s revert to a clean training set\n",
        "\n",
        "housing = strat_train_set.drop(\"median_house_value\", axis=1) # drop labels for training set\n",
        "housing_labels = strat_train_set[\"median_house_value\"].copy()\n",
        "\n",
        "# Note drop() creates a copy of the data and does not affect strat_train_set"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-7f3847341edb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhousing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstrat_train_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"median_house_value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# drop labels for training set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mhousing_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstrat_train_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"median_house_value\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Note drop() creates a copy of the data and does not affect strat_train_set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'strat_train_set' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHEguJ_Y7dDQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "isn = housing.isnull()\n",
        "isn.any(axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCTOi84u7dDf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let’s experiment with sample dataset for data cleaning\n",
        "\n",
        "sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head(100)\n",
        "sample_incomplete_rows"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0igKBGU7dDo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Option one\n",
        "# dropna() - drops the missing values\n",
        "\n",
        "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XhGqcHn7dEG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Option two\n",
        "# drop() - drops the attribute\n",
        "\n",
        "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KegjZ_tk7dEM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Option three\n",
        "# fillna() - sets the missing values\n",
        "# Let’s fill the missing values with the median\n",
        "\n",
        "median = housing[\"total_bedrooms\"].median()\n",
        "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True)\n",
        "sample_incomplete_rows"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88SUIbMc7dEU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's use Scikit-Learn Imputer class to fill missing values\n",
        "\n",
        "from sklearn.preprocessing import Imputer\n",
        "imputer = Imputer(strategy=\"median\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKVy_ju77dEb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Remove the text attribute because median can only be calculated on numerical attributes\n",
        "\n",
        "housing_num = housing.drop('ocean_proximity', axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLU-I2Lt7dEg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fit the imputer instance to the training data\n",
        "\n",
        "imputer.fit(housing_num)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYl71LP67dEo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "imputer.statistics_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "im1O1Msm7dE2",
        "colab_type": "text"
      },
      "source": [
        "Transform the training set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpXNk6qg7dE3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = imputer.transform(housing_num)\n",
        "X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWSNNxv87dE-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing_tr = pd.DataFrame(X, columns=housing_num.columns)\n",
        "housing_tr.info()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bA3Vx-07dFF",
        "colab_type": "text"
      },
      "source": [
        "Now let's preprocess the categorical input feature, `ocean_proximity`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dfLTcRT7dFH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert ocean_proximity to numbers\n",
        "\n",
        "housing_cat = housing['ocean_proximity']\n",
        "housing_cat.head(10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHUyX4DU7dFS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Pandas factorize() example\n",
        "\n",
        "df = pd.DataFrame({\n",
        "        'A':['type1','type3','type3', 'type2', 'type0']\n",
        "    })\n",
        "df['A'].factorize()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2lI2rK47dFX",
        "colab_type": "code",
        "outputId": "2be9eef3-0b21-4f2f-d484-fbe2243a3966",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Convert ocean_proximity to numbers\n",
        "# Use Pandas factorize()\n",
        "\n",
        "\n",
        "housing_cat_encoded, housing_categories = housing_cat.factorize()\n",
        "housing_cat_encoded[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 2, 0, 2, 0, 2, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdIeFkEP7dFb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Check encoding classes\n",
        "\n",
        "housing_categories"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mv1R9YOw7dFf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We can convert each categorical value to a one-hot vector using a `OneHotEncoder`\n",
        "# Note that fit_transform() expects a 2D array\n",
        "# but housing_cat_encoded is a 1D array, so we need to reshape it\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "encoder = OneHotEncoder()\n",
        "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
        "housing_cat_1hot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sg6cLYFc7dFk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The OneHotEncoder returns a sparse array by default, but we can convert it to a dense array if needed\n",
        "\n",
        "housing_cat_1hot.toarray()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WRe5dxf7dFo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Just run this cell, or copy it to your code, do not try to understand it (yet).\n",
        "# Definition of the CategoricalEncoder class, copied from PR #9151.\n",
        "\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn.utils import check_array\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from scipy import sparse\n",
        "\n",
        "class CategoricalEncoder(BaseEstimator, TransformerMixin):\n",
        "    \"\"\"Encode categorical features as a numeric array.\n",
        "    The input to this transformer should be a matrix of integers or strings,\n",
        "    denoting the values taken on by categorical (discrete) features.\n",
        "    The features can be encoded using a one-hot aka one-of-K scheme\n",
        "    (``encoding='onehot'``, the default) or converted to ordinal integers\n",
        "    (``encoding='ordinal'``).\n",
        "    This encoding is needed for feeding categorical data to many scikit-learn\n",
        "    estimators, notably linear models and SVMs with the standard kernels.\n",
        "    Read more in the :ref:`User Guide <preprocessing_categorical_features>`.\n",
        "    Parameters\n",
        "    ----------\n",
        "    encoding : str, 'onehot', 'onehot-dense' or 'ordinal'\n",
        "        The type of encoding to use (default is 'onehot'):\n",
        "        - 'onehot': encode the features using a one-hot aka one-of-K scheme\n",
        "          (or also called 'dummy' encoding). This creates a binary column for\n",
        "          each category and returns a sparse matrix.\n",
        "        - 'onehot-dense': the same as 'onehot' but returns a dense array\n",
        "          instead of a sparse matrix.\n",
        "        - 'ordinal': encode the features as ordinal integers. This results in\n",
        "          a single column of integers (0 to n_categories - 1) per feature.\n",
        "    categories : 'auto' or a list of lists/arrays of values.\n",
        "        Categories (unique values) per feature:\n",
        "        - 'auto' : Determine categories automatically from the training data.\n",
        "        - list : ``categories[i]`` holds the categories expected in the ith\n",
        "          column. The passed categories are sorted before encoding the data\n",
        "          (used categories can be found in the ``categories_`` attribute).\n",
        "    dtype : number type, default np.float64\n",
        "        Desired dtype of output.\n",
        "    handle_unknown : 'error' (default) or 'ignore'\n",
        "        Whether to raise an error or ignore if a unknown categorical feature is\n",
        "        present during transform (default is to raise). When this is parameter\n",
        "        is set to 'ignore' and an unknown category is encountered during\n",
        "        transform, the resulting one-hot encoded columns for this feature\n",
        "        will be all zeros.\n",
        "        Ignoring unknown categories is not supported for\n",
        "        ``encoding='ordinal'``.\n",
        "    Attributes\n",
        "    ----------\n",
        "    categories_ : list of arrays\n",
        "        The categories of each feature determined during fitting. When\n",
        "        categories were specified manually, this holds the sorted categories\n",
        "        (in order corresponding with output of `transform`).\n",
        "    Examples\n",
        "    --------\n",
        "    Given a dataset with three features and two samples, we let the encoder\n",
        "    find the maximum value per feature and transform the data to a binary\n",
        "    one-hot encoding.\n",
        "    >>> from sklearn.preprocessing import CategoricalEncoder\n",
        "    >>> enc = CategoricalEncoder(handle_unknown='ignore')\n",
        "    >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n",
        "    ... # doctest: +ELLIPSIS\n",
        "    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,\n",
        "              encoding='onehot', handle_unknown='ignore')\n",
        "    >>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()\n",
        "    array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.],\n",
        "           [ 0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
        "    See also\n",
        "    --------\n",
        "    sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of\n",
        "      integer ordinal features. The ``OneHotEncoder assumes`` that input\n",
        "      features take on values in the range ``[0, max(feature)]`` instead of\n",
        "      using the unique values.\n",
        "    sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of\n",
        "      dictionary items (also handles string-valued features).\n",
        "    sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot\n",
        "      encoding of dictionary items or strings.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,\n",
        "                 handle_unknown='error'):\n",
        "        self.encoding = encoding\n",
        "        self.categories = categories\n",
        "        self.dtype = dtype\n",
        "        self.handle_unknown = handle_unknown\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        \"\"\"Fit the CategoricalEncoder to X.\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : array-like, shape [n_samples, n_feature]\n",
        "            The data to determine the categories of each feature.\n",
        "        Returns\n",
        "        -------\n",
        "        self\n",
        "        \"\"\"\n",
        "\n",
        "        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:\n",
        "            template = (\"encoding should be either 'onehot', 'onehot-dense' \"\n",
        "                        \"or 'ordinal', got %s\")\n",
        "            raise ValueError(template % self.handle_unknown)\n",
        "\n",
        "        if self.handle_unknown not in ['error', 'ignore']:\n",
        "            template = (\"handle_unknown should be either 'error' or \"\n",
        "                        \"'ignore', got %s\")\n",
        "            raise ValueError(template % self.handle_unknown)\n",
        "\n",
        "        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':\n",
        "            raise ValueError(\"handle_unknown='ignore' is not supported for\"\n",
        "                             \" encoding='ordinal'\")\n",
        "\n",
        "        X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)\n",
        "        n_samples, n_features = X.shape\n",
        "\n",
        "        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]\n",
        "\n",
        "        for i in range(n_features):\n",
        "            le = self._label_encoders_[i]\n",
        "            Xi = X[:, i]\n",
        "            if self.categories == 'auto':\n",
        "                le.fit(Xi)\n",
        "            else:\n",
        "                valid_mask = np.in1d(Xi, self.categories[i])\n",
        "                if not np.all(valid_mask):\n",
        "                    if self.handle_unknown == 'error':\n",
        "                        diff = np.unique(Xi[~valid_mask])\n",
        "                        msg = (\"Found unknown categories {0} in column {1}\"\n",
        "                               \" during fit\".format(diff, i))\n",
        "                        raise ValueError(msg)\n",
        "                le.classes_ = np.array(np.sort(self.categories[i]))\n",
        "\n",
        "        self.categories_ = [le.classes_ for le in self._label_encoders_]\n",
        "\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        \"\"\"Transform X using one-hot encoding.\n",
        "        Parameters\n",
        "        ----------\n",
        "        X : array-like, shape [n_samples, n_features]\n",
        "            The data to encode.\n",
        "        Returns\n",
        "        -------\n",
        "        X_out : sparse matrix or a 2-d array\n",
        "            Transformed input.\n",
        "        \"\"\"\n",
        "        X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)\n",
        "        n_samples, n_features = X.shape\n",
        "        X_int = np.zeros_like(X, dtype=np.int)\n",
        "        X_mask = np.ones_like(X, dtype=np.bool)\n",
        "\n",
        "        for i in range(n_features):\n",
        "            valid_mask = np.in1d(X[:, i], self.categories_[i])\n",
        "\n",
        "            if not np.all(valid_mask):\n",
        "                if self.handle_unknown == 'error':\n",
        "                    diff = np.unique(X[~valid_mask, i])\n",
        "                    msg = (\"Found unknown categories {0} in column {1}\"\n",
        "                           \" during transform\".format(diff, i))\n",
        "                    raise ValueError(msg)\n",
        "                else:\n",
        "                    # Set the problematic rows to an acceptable value and\n",
        "                    # continue `The rows are marked `X_mask` and will be\n",
        "                    # removed later.\n",
        "                    X_mask[:, i] = valid_mask\n",
        "                    X[:, i][~valid_mask] = self.categories_[i][0]\n",
        "            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])\n",
        "\n",
        "        if self.encoding == 'ordinal':\n",
        "            return X_int.astype(self.dtype, copy=False)\n",
        "\n",
        "        mask = X_mask.ravel()\n",
        "        n_values = [cats.shape[0] for cats in self.categories_]\n",
        "        n_values = np.array([0] + n_values)\n",
        "        indices = np.cumsum(n_values)\n",
        "\n",
        "        column_indices = (X_int + indices[:-1]).ravel()[mask]\n",
        "        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),\n",
        "                                n_features)[mask]\n",
        "        data = np.ones(n_samples * n_features)[mask]\n",
        "\n",
        "        out = sparse.csc_matrix((data, (row_indices, column_indices)),\n",
        "                                shape=(n_samples, indices[-1]),\n",
        "                                dtype=self.dtype).tocsr()\n",
        "        if self.encoding == 'onehot-dense':\n",
        "            return out.toarray()\n",
        "        else:\n",
        "            return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbrnfZ3X7dF2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The CategoricalEncoder expects a 2D array containing one or more categorical input features. \n",
        "# We need to reshape `housing_cat` to a 2D array:\n",
        "\n",
        "cat_encoder = CategoricalEncoder(encoding=\"onehot-dense\")\n",
        "housing_cat_reshaped = housing_cat.values.reshape(-1, 1)\n",
        "housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n",
        "housing_cat_1hot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKfwM5rt7dGA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat_encoder.categories_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-8H4vuL7dGJ",
        "colab_type": "text"
      },
      "source": [
        "Let's create a custom transformer to add extra attributes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "wKdbZJ5t7dGP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "# column index\n",
        "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
        "\n",
        "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
        "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # nothing else to do\n",
        "    def transform(self, X, y=None):\n",
        "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
        "        population_per_household = X[:, population_ix] / X[:, household_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,\n",
        "                         bedrooms_per_room]\n",
        "        else:\n",
        "            return np.c_[X, rooms_per_household, population_per_household]\n",
        "\n",
        "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
        "housing_extra_attribs = attr_adder.transform(housing.values)\n",
        "housing_extra_attribs = pd.DataFrame(housing_extra_attribs, columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"])\n",
        "housing_extra_attribs.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9w-EIVw7dGg",
        "colab_type": "text"
      },
      "source": [
        "## Go to slide Custom Transformers - Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXWvP0w07dGh",
        "colab_type": "code",
        "outputId": "39d0812e-757c-46cc-cc30-b13ff96c9e13",
        "colab": {}
      },
      "source": [
        "# Feature Scaling - Min-max Scaling - Example\n",
        "# Creating DataFrame first\n",
        "\n",
        "s1 = pd.Series([1, 2, 3, 4, 5, 6], index=(range(6)))\n",
        "s2 = pd.Series([10, 9, 8, 7, 6, 5], index=(range(6)))\n",
        "df = pd.DataFrame(s1, columns=['s1'])\n",
        "df['s2'] = s2\n",
        "df \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>s1</th>\n",
              "      <th>s2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>9</td>\n",
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              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   s1  s2\n",
              "0   1  10\n",
              "1   2   9\n",
              "2   3   8\n",
              "3   4   7\n",
              "4   5   6\n",
              "5   6   5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSlY5QD87dKn",
        "colab_type": "code",
        "outputId": "7153ad5a-95e5-4260-f310-8756d58097f4",
        "colab": {}
      },
      "source": [
        "# Use Scikit-Learn minmax_scaling\n",
        "\n",
        "from mlxtend.preprocessing import minmax_scaling\n",
        "minmax_scaling(df, columns=['s1', 's2'])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <td>0.4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    s1   s2\n",
              "0  0.0  1.0\n",
              "1  0.2  0.8\n",
              "2  0.4  0.6\n",
              "3  0.6  0.4\n",
              "4  0.8  0.2\n",
              "5  1.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "a8tf-lK27dK2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Now let's build a pipeline for preprocessing the numerical attributes:\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        ('imputer', Imputer(strategy=\"median\")),\n",
        "        ('attribs_adder', CombinedAttributesAdder()),\n",
        "        ('std_scaler', StandardScaler()),\n",
        "    ])\n",
        "\n",
        "housing_num_tr = num_pipeline.fit_transform(housing_num)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhjcqMRU7dK8",
        "colab_type": "code",
        "outputId": "de2830a2-ecba-4299-9815-a2a578fa922e",
        "colab": {}
      },
      "source": [
        "housing_num_tr"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.31205452,\n",
              "        -0.08649871,  0.15531753],\n",
              "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.21768338,\n",
              "        -0.03353391, -0.83628902],\n",
              "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.46531516,\n",
              "        -0.09240499,  0.4222004 ],\n",
              "       ..., \n",
              "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.3469342 ,\n",
              "        -0.03055414, -0.52177644],\n",
              "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.02499488,\n",
              "         0.06150916, -0.30340741],\n",
              "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.22852947,\n",
              "        -0.09586294,  0.10180567]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KG-EafUz7dLD",
        "colab_type": "text"
      },
      "source": [
        "And a transformer to just select a subset of the Pandas DataFrame columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "tSsz_53W7dLE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "# Create a class to select numerical or categorical columns \n",
        "# since Scikit-Learn doesn't handle DataFrames yet\n",
        "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, attribute_names):\n",
        "        self.attribute_names = attribute_names\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "    def transform(self, X):\n",
        "        return X[self.attribute_names].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfLl6ogS7dLK",
        "colab_type": "text"
      },
      "source": [
        "Now let's join all these components into a big pipeline that will preprocess both the numerical and the categorical features:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "2KZ6zMdu7dLM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_attribs = list(housing_num)\n",
        "cat_attribs = [\"ocean_proximity\"]\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        ('selector', DataFrameSelector(num_attribs)),\n",
        "        ('imputer', Imputer(strategy=\"median\")),\n",
        "        ('attribs_adder', CombinedAttributesAdder()),\n",
        "        ('std_scaler', StandardScaler()),\n",
        "    ])\n",
        "\n",
        "cat_pipeline = Pipeline([\n",
        "        ('selector', DataFrameSelector(cat_attribs)),\n",
        "        ('cat_encoder', CategoricalEncoder(encoding=\"onehot-dense\")),\n",
        "    ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lhdWU8p7dLQ",
        "colab_type": "code",
        "outputId": "0780db74-3c82-488b-e664-5957da153711",
        "colab": {}
      },
      "source": [
        "num_pipeline.fit_transform(housing)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.31205452,\n",
              "        -0.08649871,  0.15531753],\n",
              "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.21768338,\n",
              "        -0.03353391, -0.83628902],\n",
              "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.46531516,\n",
              "        -0.09240499,  0.4222004 ],\n",
              "       ..., \n",
              "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.3469342 ,\n",
              "        -0.03055414, -0.52177644],\n",
              "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.02499488,\n",
              "         0.06150916, -0.30340741],\n",
              "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.22852947,\n",
              "        -0.09586294,  0.10180567]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "FMeoMINV7dLU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import FeatureUnion\n",
        "\n",
        "full_pipeline = FeatureUnion(transformer_list=[\n",
        "        (\"num_pipeline\", num_pipeline),\n",
        "        (\"cat_pipeline\", cat_pipeline),\n",
        "    ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7Xwu9Qa7dLW",
        "colab_type": "code",
        "outputId": "f9022d27-062f-490b-f2aa-dd7fb8f070a9",
        "colab": {}
      },
      "source": [
        "housing.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\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>17606</th>\n",
              "      <td>-121.89</td>\n",
              "      <td>37.29</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1568.0</td>\n",
              "      <td>351.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>339.0</td>\n",
              "      <td>2.7042</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18632</th>\n",
              "      <td>-121.93</td>\n",
              "      <td>37.05</td>\n",
              "      <td>14.0</td>\n",
              "      <td>679.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>306.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>6.4214</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14650</th>\n",
              "      <td>-117.20</td>\n",
              "      <td>32.77</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1952.0</td>\n",
              "      <td>471.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>2.8621</td>\n",
              "      <td>NEAR OCEAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3230</th>\n",
              "      <td>-119.61</td>\n",
              "      <td>36.31</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1847.0</td>\n",
              "      <td>371.0</td>\n",
              "      <td>1460.0</td>\n",
              "      <td>353.0</td>\n",
              "      <td>1.8839</td>\n",
              "      <td>INLAND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3555</th>\n",
              "      <td>-118.59</td>\n",
              "      <td>34.23</td>\n",
              "      <td>17.0</td>\n",
              "      <td>6592.0</td>\n",
              "      <td>1525.0</td>\n",
              "      <td>4459.0</td>\n",
              "      <td>1463.0</td>\n",
              "      <td>3.0347</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
              "18632    -121.93     37.05                14.0        679.0           108.0   \n",
              "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
              "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
              "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
              "\n",
              "       population  households  median_income ocean_proximity  \n",
              "17606       710.0       339.0         2.7042       <1H OCEAN  \n",
              "18632       306.0       113.0         6.4214       <1H OCEAN  \n",
              "14650       936.0       462.0         2.8621      NEAR OCEAN  \n",
              "3230       1460.0       353.0         1.8839          INLAND  \n",
              "3555       4459.0      1463.0         3.0347       <1H OCEAN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "ZsC1PYHJ7dLZ",
        "colab_type": "code",
        "outputId": "9ba75e9b-6909-43eb-9ea7-dcf2d023d3ed",
        "colab": {}
      },
      "source": [
        "housing_prepared = full_pipeline.fit_transform(housing)\n",
        "print(housing_prepared[0])\n",
        "print(housing_prepared[1])\n",
        "print(housing_prepared[2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1.15604281  0.77194962  0.74333089 -0.49323393 -0.44543821 -0.63621141\n",
            " -0.42069842 -0.61493744 -0.31205452 -0.08649871  0.15531753  1.          0.\n",
            "  0.          0.          0.        ]\n",
            "[-1.17602483  0.6596948  -1.1653172  -0.90896655 -1.0369278  -0.99833135\n",
            " -1.02222705  1.33645936  0.21768338 -0.03353391 -0.83628902  1.          0.\n",
            "  0.          0.          0.        ]\n",
            "[ 1.18684903 -1.34218285  0.18664186 -0.31365989 -0.15334458 -0.43363936\n",
            " -0.0933178  -0.5320456  -0.46531516 -0.09240499  0.4222004   0.          0.\n",
            "  0.          0.          1.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEDWcu5W7dLh",
        "colab_type": "text"
      },
      "source": [
        "# Select and train a model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5eXakkp7dLh",
        "colab_type": "code",
        "outputId": "b924cda6-95f1-4bdb-d5ae-85d895c6196b",
        "colab": {}
      },
      "source": [
        "# Train a Linear Regression model\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u__WJ9PE7dLk",
        "colab_type": "code",
        "outputId": "80ed88ed-7703-4451-9b59-b7b047da8d72",
        "colab": {}
      },
      "source": [
        "# Let's try the full pipeline on a few training instances\n",
        "\n",
        "some_data = housing.iloc[:5]\n",
        "some_labels = housing_labels.iloc[:5]\n",
        "some_data_prepared = full_pipeline.transform(some_data)\n",
        "\n",
        "print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions: [ 210644.60459286  317768.80697211  210956.43331178   59218.98886849\n",
            "  189747.55849879]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvYQ04Vm7dLp",
        "colab_type": "text"
      },
      "source": [
        "Compare against the actual values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrJxWrr-7dLp",
        "colab_type": "code",
        "outputId": "bfe5f5c2-4b05-44d7-bfab-659ee89fa568",
        "colab": {}
      },
      "source": [
        "# Print the actual values\n",
        "\n",
        "print(\"Labels:\", list(some_labels))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fi7XUxnS7dLt",
        "colab_type": "code",
        "outputId": "2cf507fb-6b2c-46c3-9e14-55e88c3f6f0a",
        "colab": {}
      },
      "source": [
        "# Calculate the RMSE in Linear Regression Model\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\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",
        "lin_rmse"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68628.198198489234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQvglTIT7dLx",
        "colab_type": "code",
        "outputId": "c981dca2-235b-4720-9daf-bd15be018328",
        "colab": {}
      },
      "source": [
        "# Train a model using Decision Tree\n",
        "\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "tree_reg = DecisionTreeRegressor(random_state=42)\n",
        "tree_reg.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
              "           max_leaf_nodes=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",
              "           presort=False, random_state=42, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hj8UHHMy7dL0",
        "colab_type": "code",
        "outputId": "8dbcf8c6-dac2-4cc8-ba19-ae4e72bc1a39",
        "colab": {}
      },
      "source": [
        "# Calculate RMSE in Decision Tree model\n",
        "\n",
        "housing_predictions = tree_reg.predict(housing_prepared)\n",
        "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "tree_rmse = np.sqrt(tree_mse)\n",
        "tree_rmse"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "Ej1AuyOe7dL3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Performs K-fold cross-validation\n",
        "# Randomly splits the training set into 10 distinct subsets called folds\n",
        "# Then it trains and evaluates the Decision Tree model 10 times By\n",
        "# Picking a different fold for evaluation every time and training on the other 9 folds\n",
        "# The result is an array containing the 10 evaluation scores\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "tree_reg = DecisionTreeRegressor(random_state=42)\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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNtZq6Po7dL5",
        "colab_type": "code",
        "outputId": "e9f640ce-b22a-4d9b-8d19-b62159ebca1c",
        "colab": {}
      },
      "source": [
        "# Look at the score of cross-validation of DecisionTreeRegressor\n",
        "\n",
        "def display_scores(scores):\n",
        "    print(\"Scores:\", scores)\n",
        "    print(\"Mean:\", scores.mean())\n",
        "    print(\"Standard deviation:\", scores.std())\n",
        "\n",
        "display_scores(tree_rmse_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [ 70232.0136482   66828.46839892  72444.08721003  70761.50186201\n",
            "  71125.52697653  75581.29319857  70169.59286164  70055.37863456\n",
            "  75370.49116773  71222.39081244]\n",
            "Mean: 71379.0744771\n",
            "Standard deviation: 2458.31882043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AG_3DRf67dL8",
        "colab_type": "code",
        "outputId": "41419e69-c0a5-419f-ee55-90dee63b8e38",
        "colab": {}
      },
      "source": [
        "# Now compute the same score for Linear Regression\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)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [ 66782.73843989  66960.118071    70347.95244419  74739.57052552\n",
            "  68031.13388938  71193.84183426  64969.63056405  68281.61137997\n",
            "  71552.91566558  67665.10082067]\n",
            "Mean: 69052.4613635\n",
            "Standard deviation: 2731.6740018\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBi7P6j57dL_",
        "colab_type": "code",
        "outputId": "a7a5a2b3-ce5d-4f8a-d242-6974c793ccf3",
        "colab": {}
      },
      "source": [
        "# Let's train one more model using Random Forests\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "forest_reg = RandomForestRegressor(random_state=42)\n",
        "forest_reg.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
              "           max_features='auto', max_leaf_nodes=None,\n",
              "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "           min_samples_leaf=1, min_samples_split=2,\n",
              "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
              "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1svV3ihi7dMB",
        "colab_type": "code",
        "outputId": "9d35c471-1e02-4400-f86b-14a413a22ccf",
        "colab": {}
      },
      "source": [
        "# Calculate RMSE in Random Forest model\n",
        "\n",
        "housing_predictions = forest_reg.predict(housing_prepared)\n",
        "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "forest_rmse = np.sqrt(forest_mse)\n",
        "forest_rmse"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21941.911027380233"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAulTPwK7dMH",
        "colab_type": "code",
        "outputId": "d055490d-9361-4e6b-e583-b5c4e8caa9b5",
        "colab": {}
      },
      "source": [
        "# Cross Validation in Random Forest model\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
        "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
        "forest_rmse_scores = np.sqrt(-forest_scores)\n",
        "display_scores(forest_rmse_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [ 51650.94405471  48920.80645498  52979.16096752  54412.74042021\n",
            "  50861.29381163  56488.55699727  51866.90120786  49752.24599537\n",
            "  55399.50713191  53309.74548294]\n",
            "Mean: 52564.1902524\n",
            "Standard deviation: 2301.87380392\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfRgQ0Rw7dMK",
        "colab_type": "text"
      },
      "source": [
        "### Fine-tune the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVG5u4TF7dML",
        "colab_type": "code",
        "outputId": "3dcab152-8ff5-46ea-d401-5405eaf40c77",
        "colab": {}
      },
      "source": [
        "param_grid = [\n",
        "    # try 12 (3×4) combinations of hyperparameters\n",
        "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
        "    # then try 6 (2×3) combinations with bootstrap set as False\n",
        "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
        "]\n",
        "\n",
        "count = 0;\n",
        "for i in [3, 10, 30]:\n",
        "    for j in  [2, 4, 6, 8]:\n",
        "        print(\" I am goin to try %d <> %d\" % (i, j) )\n",
        "        count += 1\n",
        "\n",
        "for k in [False]:\n",
        "    for i in [3, 10]:\n",
        "        for j in  [2, 3, 4]:\n",
        "            print(\" I am goin to try %d <> %d <> %s\" % (i, j, k) )\n",
        "            count += 1\n",
        "print(count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " I am goin to try 3 <> 2\n",
            " I am goin to try 3 <> 4\n",
            " I am goin to try 3 <> 6\n",
            " I am goin to try 3 <> 8\n",
            " I am goin to try 10 <> 2\n",
            " I am goin to try 10 <> 4\n",
            " I am goin to try 10 <> 6\n",
            " I am goin to try 10 <> 8\n",
            " I am goin to try 30 <> 2\n",
            " I am goin to try 30 <> 4\n",
            " I am goin to try 30 <> 6\n",
            " I am goin to try 30 <> 8\n",
            " I am goin to try 3 <> 2 <> False\n",
            " I am goin to try 3 <> 3 <> False\n",
            " I am goin to try 3 <> 4 <> False\n",
            " I am goin to try 10 <> 2 <> False\n",
            " I am goin to try 10 <> 3 <> False\n",
            " I am goin to try 10 <> 4 <> False\n",
            "18\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gS27VDGi7dMO",
        "colab_type": "code",
        "outputId": "d4c140f6-90fd-4ceb-fcba-962b36387033",
        "colab": {}
      },
      "source": [
        "# GridSearchCV\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = [\n",
        "    # try 12 (3×4) combinations of hyperparameters\n",
        "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
        "    # then try 6 (2×3) combinations with bootstrap set as False\n",
        "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
        "]\n",
        "\n",
        "forest_reg = RandomForestRegressor(random_state=42)\n",
        "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
        "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
        "                           scoring='neg_mean_squared_error')\n",
        "grid_search.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise',\n",
              "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
              "           max_features='auto', max_leaf_nodes=None,\n",
              "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "           min_samples_leaf=1, min_samples_split=2,\n",
              "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
              "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
              "       fit_params=None, iid=True, n_jobs=1,\n",
              "       param_grid=[{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}],\n",
              "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
              "       scoring='neg_mean_squared_error', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DP5GrND7dMT",
        "colab_type": "code",
        "outputId": "b3d25e04-80e9-47a6-a0a9-9a7da28ae504",
        "colab": {}
      },
      "source": [
        "# The best hyperparameter combinations\n",
        "\n",
        "grid_search.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_features': 8, 'n_estimators': 30}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCdwPO8O7dMW",
        "colab_type": "code",
        "outputId": "835fb641-1923-4bbd-ad37-724cd0b09099",
        "colab": {}
      },
      "source": [
        "# Get the best estimator\n",
        "\n",
        "grid_search.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
              "           max_features=8, max_leaf_nodes=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=30, n_jobs=1, oob_score=False, random_state=42,\n",
              "           verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "sfHvDd5R7dMc",
        "colab_type": "code",
        "outputId": "1d0adb5c-4568-4599-fc41-b8ca82da4dbb",
        "colab": {}
      },
      "source": [
        "# Let's look at the score of each hyperparameter combination tested during the grid search\n",
        "\n",
        "cvres = grid_search.cv_results_\n",
        "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
        "    print(np.sqrt(-mean_score), params)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63647.854446 {'max_features': 2, 'n_estimators': 3}\n",
            "55611.5015988 {'max_features': 2, 'n_estimators': 10}\n",
            "53370.0640736 {'max_features': 2, 'n_estimators': 30}\n",
            "60959.1388585 {'max_features': 4, 'n_estimators': 3}\n",
            "52740.5841667 {'max_features': 4, 'n_estimators': 10}\n",
            "50374.1421461 {'max_features': 4, 'n_estimators': 30}\n",
            "58661.2866462 {'max_features': 6, 'n_estimators': 3}\n",
            "52009.9739798 {'max_features': 6, 'n_estimators': 10}\n",
            "50154.1177737 {'max_features': 6, 'n_estimators': 30}\n",
            "57865.3616801 {'max_features': 8, 'n_estimators': 3}\n",
            "51730.0755087 {'max_features': 8, 'n_estimators': 10}\n",
            "49694.8514333 {'max_features': 8, 'n_estimators': 30}\n",
            "62874.4073931 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
            "54643.4998083 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
            "59437.8922859 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
            "52735.3582936 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
            "57490.0168279 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
            "51008.2615672 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbG8YpKv7dMf",
        "colab_type": "text"
      },
      "source": [
        "### Go to slide Randomized Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "X2936iSL7dMj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# RandomizedSearchCV\n",
        "\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import randint\n",
        "\n",
        "param_distribs = {\n",
        "        'n_estimators': randint(low=1, high=200),\n",
        "        'max_features': randint(low=1, high=8),\n",
        "    }\n",
        "\n",
        "forest_reg = RandomForestRegressor(random_state=42)\n",
        "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
        "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
        "rnd_search.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNX2DeF57dMm",
        "colab_type": "text"
      },
      "source": [
        "### Go to Slide Analyze the Best Models and Their Errors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "DBH7Mk9v7dMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# See the importance score of each attribute in GridSearchCV\n",
        "\n",
        "feature_importances = grid_search.best_estimator_.feature_importances_\n",
        "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
        "cat_encoder = cat_pipeline.named_steps[\"cat_encoder\"]\n",
        "cat_one_hot_attribs = list(cat_encoder.categories_[0])\n",
        "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
        "sorted(zip(feature_importances, attributes), reverse=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoyKgKwm7dMo",
        "colab_type": "text"
      },
      "source": [
        "### Go to slide Evaluate model on the Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "NlhROpSS7dMp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Evaluate model on the Test Set\n",
        "\n",
        "final_model = grid_search.best_estimator_\n",
        "\n",
        "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
        "y_test = strat_test_set[\"median_house_value\"].copy()\n",
        "\n",
        "X_test_prepared = full_pipeline.transform(X_test)\n",
        "final_predictions = final_model.predict(X_test_prepared)\n",
        "\n",
        "final_mse = mean_squared_error(y_test, final_predictions)\n",
        "final_rmse = np.sqrt(final_mse)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "CcAjFNYs7dMq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final_rmse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZiBS7FG7dMs",
        "colab_type": "text"
      },
      "source": [
        "Congratulations! You already know quite a lot about Machine Learning. :)"
      ]
    }
  ]
}