handsonml_Ch2.ipynb

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  "metadata": {
    "colab": {
      "name": "handsonml_Ch2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pacificlion/handsonml/blob/test/handsonml_Ch2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hc1xc5vauS-",
        "colab_type": "code",
        "outputId": "bfbb6f2c-661f-4d73-de82-77184e74b1d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "source": [
        "# Clone the entire repo.\n",
        "!git clone -l -s git://github.com/ageron/handson-ml.git cloned-repo\n",
        "%cd cloned-repo\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'cloned-repo'...\n",
            "warning: --local is ignored\n",
            "remote: Enumerating objects: 4, done.\u001b[K\n",
            "remote: Counting objects:  25% (1/4)\u001b[K\rremote: Counting objects:  50% (2/4)\u001b[K\rremote: Counting objects:  75% (3/4)\u001b[K\rremote: Counting objects: 100% (4/4)\u001b[K\rremote: Counting objects: 100% (4/4), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 1508 (delta 0), reused 1 (delta 0), pack-reused 1504\u001b[K\n",
            "Receiving objects: 100% (1508/1508), 83.37 MiB | 39.72 MiB/s, done.\n",
            "Resolving deltas: 100% (874/874), done.\n",
            "/content/cloned-repo/cloned-repo/cloned-repo\n",
            "01_the_machine_learning_landscape.ipynb\n",
            "02_end_to_end_machine_learning_project.ipynb\n",
            "03_classification.ipynb\n",
            "04_training_linear_models.ipynb\n",
            "05_support_vector_machines.ipynb\n",
            "06_decision_trees.ipynb\n",
            "07_ensemble_learning_and_random_forests.ipynb\n",
            "08_dimensionality_reduction.ipynb\n",
            "09_up_and_running_with_tensorflow.ipynb\n",
            "10_introduction_to_artificial_neural_networks.ipynb\n",
            "11_deep_learning.ipynb\n",
            "12_distributed_tensorflow.ipynb\n",
            "13_convolutional_neural_networks.ipynb\n",
            "14_recurrent_neural_networks.ipynb\n",
            "15_autoencoders.ipynb\n",
            "16_reinforcement_learning.ipynb\n",
            "book_equations.ipynb\n",
            "datasets\n",
            "docker\n",
            "extra_autodiff.ipynb\n",
            "extra_capsnets-cn.ipynb\n",
            "extra_capsnets.ipynb\n",
            "extra_gradient_descent_comparison.ipynb\n",
            "extra_tensorflow_reproducibility.ipynb\n",
            "future_encoders.py\n",
            "images\n",
            "index.ipynb\n",
            "LICENSE\n",
            "math_linear_algebra.ipynb\n",
            "ml-project-checklist.md\n",
            "README.md\n",
            "requirements.txt\n",
            "tensorflow_graph_in_jupyter.py\n",
            "tools_matplotlib.ipynb\n",
            "tools_numpy.ipynb\n",
            "tools_pandas.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hm8lUHsIbocT",
        "colab_type": "text"
      },
      "source": [
        "### datapath define"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSKBsq2JbnU9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "datapath = os.path.join(\"datasets\",\"lifesat\", \"\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MUCyUHNb7XD",
        "colab_type": "text"
      },
      "source": [
        "### import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G65QNhppcJvo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib \n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import sklearn.linear_model as linear_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJYzPaRKce-t",
        "colab_type": "text"
      },
      "source": [
        "### import data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mL6gxZAse_yB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=\",\");\n",
        "gdp_data =  pd.read_csv(datapath + \"gdp_per_capita.csv\", thousands=\",\", delimiter='\\t', encoding='latin1', na_values=\"n/a\");"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cvHiQYU6dTxQ",
        "colab_type": "text"
      },
      "source": [
        "### The code to download from github"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsMJvCrKWWQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import tarfile\n",
        "from six.moves import urllib\n",
        "\n",
        "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
        "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
        "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
        "\n",
        "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
        "    os.makedirs(housing_path, exist_ok=True)\n",
        "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
        "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
        "    housing_tgz = tarfile.open(tgz_path)\n",
        "    housing_tgz.extractall(path=housing_path)\n",
        "    housing_tgz.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gA-X5T48YLlL",
        "colab_type": "text"
      },
      "source": [
        "### load the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRVN-IheYiQC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fetch_housing_data()\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "def load_housing_data(housing_path=HOUSING_PATH):\n",
        "  csv_path = os.path.join(housing_path, \"housing.csv\")\n",
        "  return pd.read_csv(csv_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcEHVzbKZecJ",
        "colab_type": "code",
        "outputId": "d9958c7c-72b0-4bb5-d62b-672cc3180e3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pd.set_option('display.max_columns', None)  \n",
        "\n",
        "housing = load_housing_data()\n",
        "print(housing.head())\n",
        "print(housing.describe())\n",
        "print(housing.info())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
            "0    -122.23     37.88                41.0        880.0           129.0   \n",
            "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
            "2    -122.24     37.85                52.0       1467.0           190.0   \n",
            "3    -122.25     37.85                52.0       1274.0           235.0   \n",
            "4    -122.25     37.85                52.0       1627.0           280.0   \n",
            "\n",
            "   population  households  median_income  median_house_value ocean_proximity  \n",
            "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
            "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
            "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
            "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
            "4       565.0       259.0         3.8462            342200.0        NEAR BAY  \n",
            "          longitude      latitude  housing_median_age   total_rooms  \\\n",
            "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
            "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
            "std        2.003532      2.135952           12.585558   2181.615252   \n",
            "min     -124.350000     32.540000            1.000000      2.000000   \n",
            "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
            "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
            "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
            "max     -114.310000     41.950000           52.000000  39320.000000   \n",
            "\n",
            "       total_bedrooms    population    households  median_income  \\\n",
            "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
            "mean       537.870553   1425.476744    499.539680       3.870671   \n",
            "std        421.385070   1132.462122    382.329753       1.899822   \n",
            "min          1.000000      3.000000      1.000000       0.499900   \n",
            "25%        296.000000    787.000000    280.000000       2.563400   \n",
            "50%        435.000000   1166.000000    409.000000       3.534800   \n",
            "75%        647.000000   1725.000000    605.000000       4.743250   \n",
            "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
            "\n",
            "       median_house_value  \n",
            "count        20640.000000  \n",
            "mean        206855.816909  \n",
            "std         115395.615874  \n",
            "min          14999.000000  \n",
            "25%         119600.000000  \n",
            "50%         179700.000000  \n",
            "75%         264725.000000  \n",
            "max         500001.000000  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 10 columns):\n",
            "longitude             20640 non-null float64\n",
            "latitude              20640 non-null float64\n",
            "housing_median_age    20640 non-null float64\n",
            "total_rooms           20640 non-null float64\n",
            "total_bedrooms        20433 non-null float64\n",
            "population            20640 non-null float64\n",
            "households            20640 non-null float64\n",
            "median_income         20640 non-null float64\n",
            "median_house_value    20640 non-null float64\n",
            "ocean_proximity       20640 non-null object\n",
            "dtypes: float64(9), object(1)\n",
            "memory usage: 1.6+ MB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_mi2CtMZp9f",
        "colab_type": "text"
      },
      "source": [
        "#### value_counts function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PvlwRacbqjk",
        "colab_type": "code",
        "outputId": "db958fdc-5c98-4974-cec5-bd9e53d41314",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print(housing[\"ocean_proximity\"].value_counts())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<1H OCEAN     9136\n",
            "INLAND        6551\n",
            "NEAR OCEAN    2658\n",
            "NEAR BAY      2290\n",
            "ISLAND           5\n",
            "Name: ocean_proximity, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTqJsDf7bx1I",
        "colab_type": "text"
      },
      "source": [
        "### plot data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irymvni7eCu_",
        "colab_type": "code",
        "outputId": "4fb27fa9-b99a-4209-9214-741995a79907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "housing.hist(bins=50, figsize=(20,15))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LdvOeW5p/RdxyxngfIuqviYkJRvF1bcX2jrZ+tdfpbJIkSZJqJTMfAL4CvBiYHxFTmY+F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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JP4VuYTFegea",
        "colab_type": "code",
        "outputId": "0383e77d-dc50-47e9-bc6e-65306e0a77d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        " #lets run the code\n",
        "  housing[\"ocean_proximity\"].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<1H OCEAN     9136\n",
              "INLAND        6551\n",
              "NEAR OCEAN    2658\n",
              "NEAR BAY      2290\n",
              "ISLAND           5\n",
              "Name: ocean_proximity, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4AURf-rlM3dx",
        "colab_type": "text"
      },
      "source": [
        "### split the data into training and test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tk_STFO709Uu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "def split_train_test(data, test_ratio):\n",
        "  shuffled_indices = np.random.permutation(len(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",
        "  print(shuffled_indices)\n",
        "  print(test_indices)\n",
        "  print(train_indices)\n",
        "  return data.iloc[train_indices], data.iloc[test_indices]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNPLQcAg2FuP",
        "colab_type": "text"
      },
      "source": [
        "### call data splitter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXh3mXOS2LS3",
        "colab_type": "code",
        "outputId": "b7b05832-2659-4fb8-9b30-189358824201",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "train_set, test_set = split_train_test(housing,0.2)\n",
        "print(len(train_set), \"train + \", len(test_set), \"test\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20046  3024 15663 ...  5390   860 15795]\n",
            "[20046  3024 15663 ... 18086  2144  3665]\n",
            "[14196  8267 17445 ...  5390   860 15795]\n",
            "16512 train +  4128 test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WrcBYd82eHY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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",
        "  ids = data[id_column]\n",
        "  in_test_set = ids.apply(lambda id_: test_set_check(id_,test_ratio, hash))\n",
        "  return data.loc[~in_test_set], data.loc[in_test_set]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ua6wqWRH-znF",
        "colab_type": "text"
      },
      "source": [
        "#### add an index column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHWxKu4O_E3g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing_with_id = housing.reset_index()\n",
        "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2,\"index\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oku4OLHJ_Ygy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TLgz_rvA_mk",
        "colab_type": "text"
      },
      "source": [
        "#### scikit train test split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZsUKWWEBXMH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YX1rjGJFBnTx",
        "colab_type": "text"
      },
      "source": [
        "### income category"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ee6dTH4ENgQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"]/1.5)\n",
        "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nk_kBDBOErd0",
        "colab_type": "code",
        "outputId": "d0fcfd6f-4aa0-4429-e33b-25ae106c4feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "housing[\"income_cat\"].value_counts()/len(housing)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0    0.350581\n",
              "2.0    0.318847\n",
              "4.0    0.176308\n",
              "5.0    0.114438\n",
              "1.0    0.039826\n",
              "Name: income_cat, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPXRryD_EzOT",
        "colab_type": "text"
      },
      "source": [
        "### Stratified sampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOT9JI80FhS1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
        "  strat_train_set = housing.loc[train_index]\n",
        "  strat_test_set = housing.loc[test_index]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5qOEfqHGJ4d",
        "colab_type": "text"
      },
      "source": [
        "### test the distribution in housing dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zV72MfAVp0I",
        "colab_type": "code",
        "outputId": "b35bde98-0cdb-40d8-dfc5-0a011f114466",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "print(strat_train_set[\"income_cat\"].value_counts()/len(strat_train_set))\n",
        "print(strat_test_set[\"income_cat\"].value_counts()/len(strat_test_set))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.0    0.350594\n",
            "2.0    0.318859\n",
            "4.0    0.176296\n",
            "5.0    0.114402\n",
            "1.0    0.039850\n",
            "Name: income_cat, dtype: float64\n",
            "3.0    0.350533\n",
            "2.0    0.318798\n",
            "4.0    0.176357\n",
            "5.0    0.114583\n",
            "1.0    0.039729\n",
            "Name: income_cat, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBkEqL8KV0ST",
        "colab_type": "text"
      },
      "source": [
        "#### remove unused column income_cat for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBGcjCnAnd4H",
        "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": "cpVJlcoDoIz4",
        "colab_type": "text"
      },
      "source": [
        "## Visualize the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBXv7nbWoNwH",
        "colab_type": "text"
      },
      "source": [
        "### visualize graphical data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqABAskFoU9Y",
        "colab_type": "code",
        "outputId": "9ea19d1b-39b5-4a72-aa0b-fe261cd0b6c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "housing = strat_train_set.copy()\n",
        "housing.plot(kind=\"scatter\", x=\"longitude\",y=\"latitude\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1419e7cdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BPdHqEjST94IhUGDpF/tx8Z8+Y2ElBiMAzDEYcOkFvQI+n2Tj8cqtRRiV08uf\nL7g5DMcAAKfb3JqO3Xe/qjfMSdQ3tsHGB5fsqG9sU4zDNGLEgDQsmJIb054HI5yes/mVQLmXrPTE\nkKfEyXlgZSU7OTAYBrAcgwHS/GKj6JBIKY780Ibb/7XdH/O+83Kv1o8zBINFAPRItEFUzTpwekQ8\nuaEWFw06Dws31CqqlUbl9DKcbSwv8aw+2mQ4nlTO1gM/YPvhRtNrjjZWjkNNw2mcON2uORXIu7/n\nT8nFI+9Xh/kuhCmtMhgGsD6GAKytPIqHVleBEOLPHyTZLBrdHwlv2IaGPHv5yty++Lj2uKnX2i0E\nj07Ow/y11X6nZeUJnruxABTwl7y6BAEihW6VVLzDE8DCc96ksirxoe53WLH1EBasq4FIaUjaUFYO\n2PoH1uPA6N6wPoYooJbFAOD/s77uD4ebR2brTicLhFmnAHjVWR9dU60I+3AEyO3fA5OXlHa6XlJE\n8FVMyQM9yXYegkg13eIzRw7CVfn9UNPQhF+9/pWpJDQAPBfGwCUG41yBOYYgqBvM5H/WqyTKy0xD\nopVDW5BBOR1BfQiwcBwqdRrAuirqrIh6Hrcar+RIH/zq0kH455ZDQe9flJ2G46fb4WhxMufAYOjA\nks8hIE8CS2WjdguHJBsPu4XD9KIsPLCqMqpOQY9Wl4Afzrg0jsrCecNbqXYLbDzp9Kojs3hUzXnq\nedxG3DVhqCkdqIrDTXjiw92sOonBMIA5BpOoyybXVh71hS2o939UxNvbjmjyCxYT3/CwPskdXt/z\nn+71C9RJwnPPTy/Ef38/HreNHQJCCKxmFgNza44V1hDUUTNS7Ljj8uBlxnLufodVJzEYalgoyQR6\n+kQPrqoEBfEld6UEqdIpSANijjSewWNra3QTwYlWDj+7sA/2njjQoTVaOQ75mWmaIT51x5uxZOM+\n06WdNp7AG8yJj5AUR+Af/mOGWaOH4NXNB0JKun9acww3FYdXasxgdEfiaG8Yv+jpE7mE4BU/0oCY\nmcWD8NqvfooEna14m1vEK5s75hS89/H4nYE0yGdN5VFc82JpSPX+/2/0YNgs8RNzsvE8/r2jHnNX\nV+Fzn4BhIDJS7HjuxgKfgzNH3fetHVkig9HtYCcGExiN6FRj4QCe4xRS3FIIJC+zR/QGIQCgVHlz\n6ZSjNwnNyhNQnfLOJN/AHDO9D7Gi2enBE77JbO9W1OPCvsn4+L7LA14jDfu5evFmUyeHq/L6RmKp\nDEa3gZ0YTCAfmpNk5XVfY+GABVPz8d/fe7V85AN7pKazeZNyffeI/NcuUIqahib/7zUNTeB0hOls\nPME1+f2g175yxiXg7yGW2saEDLR4AAAgAElEQVSaPcdbTZ0ccvqm4u7xQ4O+Ljs9EUWDMzTd5QzG\nuQw7MZgk0IhOALBbeCzcUIvUBItiYph6zsK8ybmw8xweXVONMxGvXiL+95yzukqTCLdZOIwb1gtr\nqr4zvENXaIf7pPY4CrPTDQceScwozsbijfsCnhqONzuxYushTXc5m/rGOJdhJ4YQUI/olM8qbnUJ\nmjkKelo/C9fXonBgTwhR6Dj/oaXd/55qp2C3cHjwimH4uFZ/NnJXokeCVVMhpkdGih2PTc0LeC8L\nT7BgXY2pWRgMxrkCcwxhMLVwAMrmjseCqXlIsStDS3IxO6OhOq0uAbPHBQ9zhMq9K3dh3pqvNe+Z\nZPUK/p2XbIv4e8aavqlW/Kv8kK4h1wsHzSwehCeuy4eNJ0i0akNrboHCakKQUIKFnBjnAswxhElG\nih3jhvfRNGPJ5ygYTR3LSk/EjOLskGWjzfDh18fRppq1IK3h/IykiL9frPnFRQN1ne2K8sOGp4iZ\nIwdh/pQ8CCL81UoJVg4JVg7zp+RqTm/qWRiSM1ix9ZCpkwqD0dVhOYYOoDdVTF6JFOj5ZV9+q3Eq\nkUJj6ASK363YDrcggiPewT5dlVc27QfPq0uHRSwt2Qenh8r6TKqQ278HcvqmwtHixMINtYqyXVGk\n+PDuMcjpm4pUu8Xw71DKEfHkrKCffNY2mwjH6I4wx9BB1EJ7aiOh9/yKrYcMx25GAkK1SeR4KkHt\nCB4K9Eu24uQZt0LufNmm/YqZzy6B4poXS/GswehUu4X3G3qjv0N5jkgPo/GrDEZXhzmGCKAW2gv0\nvKPFiUfXhDtDwBzx0bMcPeqbnLhtzGCMGdoLAEFmWgKWflGneZ3LN/Rn/ezRQUen6v0d6inoBroH\ng9FdYI4hxtQ0nI6LSWldnVc3H8DyLYf8zYTTi7Lw9rYjmtJUKdkfKORnhFFjo5EEOIPRXWCOIeYw\nrxAJKLxT7qR+kuVbDutWUki7+oKBPQOG/PTQyxHNm5SL/AFppu/BYHRFmGOIMXmZabDypEtOVot3\n1Ht7u4XTFAOEasyD5ZAYjO4IK1eNMWdF3rrGV28hwKghGYhCZW1UkXo3ItHBLBcmZDDOBbqGdepm\nTC0cgC0Pj8dlF5zX2UvRwBEolEk9FCjb7+hyJa4iKDLTElgzGoMRBswxdBIZKXa8+D8XwR5PU3EA\nWDkSkkx3PMIDGH9hb0x6cTNrRmMwwiBqVokQkkAI2UYIqSKE1BBCFvgen0AI2UEIqSSElBJCcqK1\nhngnI8V+VnfJrq/aGmucXdwpAN6xSR9WH4fTQ+NG/4hJaTC6EtFMPjsBjKeUthBCrABKCSEfAXgJ\nwLWU0m8IIb8D8EcAv47iOuIaKblZsvtElBRXuw42Xnta4QkiUt7bmc1oaoVdpt7KiHeidmKgXlp8\nv1p9P9T308P3eBqAhmitoatgpLt0LmHlpJGiSvRmSoRDZzWj6SnsdvbphcEIhinHQAgZRgj5nBBS\n7ft9BCHkjyau4wkhlQBOAPiUUloOYBaADwkh9QBuAfCUwbW3E0IqCCEV33//vdnP02XJSLFj/pTA\nEtHxBAGQao/cgVOkFMTX4yHPu7gj5CynF2Upus9jFdYxUtg1Um9lMOIBsyeGVwA8DMANAJTSXQBu\nCnYRpVSglBYCyAJwCSEkH8B9AK6hlGYB+AeA5w2uXUYpLaKUFvXu3dvkMrs2M0cOwhPX58Nm8eYc\n7HE0e1kNBfDnX/wYkRpGJ9Cz+Q2PICLZFtnD7MqKejhanFhTeTRmCqmOFiea2txwCYHlOBiMeMPs\nli+JUrqNKI/1HqMXq6GUniKElAC4GkCB7+QAAO8C+I/Z+5wLzCwehKvy+qG+sQ3JNh5XvrApbiU0\nPq5uQDRSIgKFRrjObuHw2JRc/PGD6rC+DyvHoabhtD+sE4pCqjSaNZQGN+8UvV3gOQKPIMLKEyRY\neNNyHAxGZ2LWMZwkhFwAn54DIWQaAOP5kN7X9Abg9jmFRABXAFgEII0QMoxSutf32Ddhr76bInXo\nVh05hQQrh1ZXfCak1319PGr3lht/K0/wzDRvwvab705j+dbDId/PJQjY/30zeFXOIlhSOpzEsaPF\niQdXVSm62wmlWDrzJ8jLTGNOgRH3mHUMdwJYBmA4IeQogAMAbg5yTX8AbxBCeHhDVisppesJIbcB\neI8QIgJoBPCb8Jbe/clKT4zb00Ksye3fA3XHm/HOV0dCvpaDdwbFs5/s9UttS7hFEck2HlVHTmlO\nBPLEcSgnjJqGJo3kiTQiPFynEM6phcEIF1OOgVK6H8BEQkgyAI5S2mziml0AfqLz+PsA3g91oeci\nchE3jgBOt3hOOgq3QHHlC5tACDGs3OIJwYisVOw8ctrwHm7hrFNItvEQKMX0oixMXlKqeyLQk902\nV/aqnxt65j978EOLE9ddNDDIJ1bCyl0ZsSagYyCE3G/wOACAUqqbOGZEDrWI29KN+/D6fw919rJi\njkABUK1T4Alwy8hBePurw6g7cQYEWv1adSAu2c5jwZQ8FA7siUkvblZMfpOfCAKNZg1EXmYPWLiz\npwSJrxtO496Vu7DoP7ux5Q9X+B8PdBoI99TCYHSEYKUfqb6fIgC/BTDA93MHgIuiuzSGhFzELS3R\n2tnLiTve/uqIv8vZzIHK6RYwbngffFh9DE6Pdn6DVEoqndgSrBxS7RYkWDlTieOMFDuen14Im0Ez\n+3enXfhghzckFqxKipW7MjqDgCcGSqkkY7EJwEVSCIkQ8hiADVFfHUPD9y2uzl5CXGG38EaRG0MI\nIWhsdWFpic7UN0FQnAjCld2eWjgAPZOs+PXrX+nOf1v/9TGMGdYn6Gkg3FMLg9ERzBaL9wUgt0gu\n32OMGJOX2SP4izqB/ql2JBttkSMARwBeR/vb6REgBGmCUwsVugWKl76o0w1NFWVrFW/Dld3Oy0wz\n9FmTf9zP1Gkg3FMLg9ERzDqG5QC2EUIe850WygG8EbVVMQz5eV6/mL7fLcUDsfimQr9hsls48Cpr\nZ7cQ/O/owZqKn0hB4K0qshKtIec4gken5CLByiHJqnVMSVYOVMcBvLezQVcwsGy/A5f++XO8+Pk+\n1B1v7lCHdEaKHX+5qVDzeP8eNlx30UDD04BUJSW979TCASibOx5vzirG+tmjMSgjmUlqMKIK0ftH\no/tCQi4CMMb36yZK6c6orUpFUVERraioiNXbxT0vfr4Pz326Nybvde+EHNx7xYVwtDixovwwlpbs\nA3xdygm+tud5k3KxcEOtpiktFqTaLXhzVjGSbTxK607izx/t9o/7BIAEK4ebfjoQ/wwzYW/jAMIR\nzB43FDOKs8PaqTtanPjXloPYeeQUri/MVFQlra08qhgdOr0oCysr6nUrkF7+8ls88/EeWHgCSuHv\n7ZDeg5WzMtQQQrZTSotCvs6MYyCEZOs9TikNvdMoDJhjUOJoceLSP38es7kJ04sGYO5VP8KoRRsV\nxt/GE3x49xi0ugTc/Go5mp2mm+EjynUFmVhb1QALTyCIFBx3tst4elEW3v2qXuEswsXGE8yfmoeZ\nxYMisOqzSEY92cZj8pJSxXecYOVQNnc8Fn30DVZuVyamrTzB1ocnoLTuJCtnZegSrmMwG0raAGC9\n7+dzAPsBfBTqmzEiQ0aKHc/eWBCzcZsrK45iy7cnNfFwu4VHq8ubrFXrAcWSD6oaIAJwCRQC9eYQ\nls78CdbPHo2VFZFxCvDd/5H3q7Fiq/7pI1xxPimH0eoSdHMOW751aJwC4P2cW751MPVWRsQx5Rgo\npT+mlI7w/QwFcAmALdFdGiMQUwsH4KtHJuKp6/Mxa/RgvHbrxZrYfyT59856uARt17AUupg9Tjtv\nyc6TTpsVvWHXd7qGFoA/BBYu89dWo+64ssczEuJ8RjmHkt3G0iMnW5ysnJURccL6F0Ip3QGgOMJr\nYYRIRoodNxUPwh8n56LFJYCLohX+YvdJeGR+wcJBUR0zozhbowZLOIJP7h2Lx6bkxnyG7LsV9Sg/\n4NAYWruFYNktF+OJ6/L9CXVriA7MIwLXvFjqN/5mZy4EO1HoVSDNm5SLdbv0ZckIgPzMHqyclRFx\nTEliqDqgOXib2875ATvxgmSY1Po8kUQEFOWdPMdhVE4v/+/eMaUFikTq0zeMQE7f1LPXGyCVos68\nJBuJdh6vfLkfngh8lKf/sxtzrhyO5z/bq1jT2GF9AADFg89Dad1JPPnhNwh17IPLI+LBVVXomWTF\n6TaPZqCQWjrDrKyFum+ivrENdgsPl6DN39h4gptf36absGYJaEZHMCuilyr7swfenMN7kV8OIxz0\nNH1i9b4ZKXZ/8nRUTi+UzR2vqY5pdQmwWzjDWP9lF5wHG8+DI8ANP8nCrNFD8Nwne/HWto7VNnhE\n4NlP92L+lFzkZ6b51ySvsOIJF3YS3yVQ/O8/K3T7KOS79lBlLSR1Xfm99HAKFBAoVlbUY/3s0f58\nD3MKjI5i1jHUUkpXyR8ghNwIYJXB6xkxRC82HW2cHhEbdx/HQUdr0J1wsLBGad0P/j+//t9DmF40\nAB/s1B5IrTwJ+VTk8oh4bG0N3p5VjIwUu39OwlknFbz3gueA56aNwJz3vtY4ET2nYLcom9DCF+NT\nCilaOQ5OQQShVNGDYeU4tLoEFAzsGfSzMBhmMFuuuoNSelGwx6IFK1cNjrwe3iWI8AixUWJVi8VJ\n5ZVqgxeJ3ot+qTa8OWskVu+ox9+/3B/y9TYOACGGJ4QkG492l6B77rJZOFwxvA82VB8L+B4JFg7L\nbi3C2GFnpw46WpyaUl+j78kIMyWtwe7Feh3OPcItVw2mrno1gGsADCCELJY91QMhTHBjRB95bLqp\nzY07V+yISV+BOjpktBOeUZyNv36+V/P6UDjW7MJVL2wCCTPJ7p13pO8ULBww/sJe+KT2hK7jcHlE\nfLb7hK5qqhyRUo1siXrXH04eQB5emn5xlmJYkXyetRFMupsRCsFCSQ0AKgBMBbBd9ngzvLObGXGE\nZDwcLc6Yh5Ykol0R46FANI5CHhFYH2QindMj+mdc80R/GfOn5Oka6XDF+NQ4WpxYub1e8djKinrc\nM2GY4T2ZdDcjVAJWEVJKqyilbwC4gFL6huzn35TSxhitkREi6rJHC4eY9RMM6Jmga2zqG9uQaFXu\nQ5KsPLLStK/trN4HM0gRHJ4juGd8Dmw8QZKNg5Un+O3lQ3BVvrGWVbhifHLCkeFm0t2MUAkWSlpJ\nKZ0OYCchWgUzSumIqK2M0SHUO1QAGLvoM7S6o/u+335/Bi98ugf3XnGh4nG9BLkIijV3jUHl4Uas\n2l6PzLQEzCgehPKDP+CR96uju9AOYrfwGP+jvrj1svN9FU51eHPLYfyj7GBUwzThyHAz6W5GqARM\nPhNC+lNKvyOE6IrDUEpjMkqMJZ8jx/m/j80Yje1/nKjZGa/YeggL1tXAynMQKA1oQFdsPYR5H1TH\nuADXPFLCF0CHE8uhohbeM+OIwrmG0fWJSvKZUiq1XP6OUjpX9YaLAMzVXsWIZw4+NQm5f9yAM1HO\nS9c0nFZU5qypPIqFG2phs3j7BuZPyQ1omFISLLBaOHCEQBBF/O7yC7D0i2+j2sRnFg7AnZd7JUCM\nwjE1DaeRlmhV9E5EqiIonHxFpHIcjHODjpSr7opVKImdGCJP3fFm/KPsIN7edjgqu/Llv/mpv8M4\n1HJNo9fPm5SLR9fUQDD4b5YjCLmDOVwIACsPjB7aCxt3n9Q8b7dwsPHBpbQjhdrxsNJUBhC9ctXf\nAvgdgCGEkF2yp1IBlIX6Zoz4IadvKp74xY+xooPdxXpYeYK8zDT/76E2eBm9Pn9AGrY9MgEPra7U\nNcYWArhi5BgoAJcA3XUA3gomqYlu+Rbvd2ymIigcg64uRZ1+cRZWbo+uI2J0b4KVq74Fr7z2nwH8\nXvZ4M6X0B/1LGF2FwR3MN/DEK59E4f2xEIDnOTwzTVmjH2ryM9jr75lwIfZ/34qDDmUYxyVvtLNw\naI+Q3HakMXKK4fQa6JWiSj0OrDSVES7BylWbKKUHKaX/40s0t8FrA1KMhvcwugaf1x4zaPUyj0C9\n4njSfTiOYMNdozXGzEg1tL6xTVdp1GjOcWndSb+09bHTTtw2ejAm/qgPElXbmwQrh7vG52hmPccL\nek7RrEKrGr1SVDWsNJURKmbVVacAeB5AJoATAAYB+AZAXvSWxogma6siL47Lc977Ti3I9KuqSsiT\nn9VHm7BwQ23AnbFeua2Ud5B2wv8qP4T1s0djct1JyPVb290iFm+swy9/6o3tE99jHMwoI0UfvU7l\ncPWUzOhksdJURqiY3VL9CcBIAHsppYMBTACwNWqrYkSdHw9IC/6iEGlzUyzeWIeJf9mER9d8rXk+\nI8WOrPRE/3zoYDtjeUOYUZNWq0vA0zeM0MyCcHpEv+roO7dfik/vGwtrBwf0RIp3vzqCTXtPKD5z\nuL0GeqerWy/N1py2YhlGCneSHSN+MKuu6qaUOgghHCGEo5SWEEJeiOrKGFHl+ouy8KcPd0ft/su3\nHMZ5STbccun5CqMUyZ2xZDgLBvZEzyQr7nhzB864zp4JJMchzTWYNzkXC9fXAhSdmn9weijueHMH\nRFkvR0f0lPRKUe+ZMKxTqpKYJlP3wKxjOEUISQGwCcAKQsgJAK3RWxYj2mSk2PHAFcNCUjy18gQ2\nC4dWp7mAzAuf1+GFz+uw+KZCv3Ho6M7YyHDmZaZpJLDdoojqo0345bIt/mumFmTiPZ35ybFGcmDy\nxHBHeg3UMxzUv8cCpsnUfTDrGK4F0A6vcN5MAGkAHo/WohixYUZxNpaU7INTNS6taFBP7Dx8yi8S\nxwFYeF0+rsrvp8gRmN15P7Cy0m8cIr0zliitOwlB5nCsPPGfEOSGamVFvea+nYmV41DT0IS0RJv/\nM3VVI9qRuROM+MKUY6CUyk8Hb0RpLYwYIx/HKQoiXKJ3JnJ1w2k8fm0+Bp6XCIAgL7OH/x+2FPe/\nKr8fahpO47blFYaT2SRE0Ttm88aLs1A0OCMiO2Mpji2dNOa+t0shh80RYGB6YsxnTRthNMGu3SPg\ntuUVsPF8lw+9ME2m7kMwraRm6AvYEwCUUtpD57mIwzqfo0vd8WZc82IpXDLDpe5MNmq8kmvwtHuE\noJIVY3Iy8K9ZIzu0Xnkcu9XpQf+0BJxsbodTZpNS7RZcMjgdn+/+vkPvFSkIUYzMBuCd2SyIVCHf\nHW2dpWjDNJnii2hpJaUGep7RPWh1CbDznMIxyEMAgRKK6t3/ve/sxOY6h+F7ba5zoOKAA0WDM8Ja\nq14c+2hTu+Z1To9H1ymMHJyOrQdirxhPqfcUk2DlIYgUE4b3waffHNfMdJB/711R1oJpMnUP4uWk\nzehEAoUAzDReyctK/zVrJF679WLwAYYqbNqnLyNhpswxWEOXjfPuuqeM0N+lVhzsvDEiIgXaXAIE\nUcSn3xzXPV1J3/uayqP+Zr5RizZibWXnJ8zNEom5E4zOJWqOgRCSQAjZRgipIoTUEEIW+B4nhJAn\nCCF7CSHfEELujtYaGOYw6jQO1D8QqJO2V2oCkqy84fNjh/bSPGbWEAZr6LqgbyrK5o7HuOF9dJ/3\ndLI4K4V3WpyeU7DxBE/f4NWlDKcL+lyB9UlEH7NVSeHgBDCeUtpCCLECKCWEfATgRwAGAhhOKRUJ\nIfr/ghkxxSgEEEpCUT6w3sh4j8nJ0ISRQilzlJzYve9U6qrCzvzpQO/JZevBoJ/ZyhO4BWqYGI4l\nNguHD+8ajZy+qag6copV9xjA+iRiQ9QcA/VmtVt8v1p9PxTAbwHMoJSKvtediNYaGKGhVypptrxU\no/Apk5p2ekSMu7A3bhszWDe3EEqZo6PFiUEZyfjkvrG4dkkpWmXS3GkJPG6+bDAqDjhQHiSPYLcQ\nvHJrETLTEtHqEhQyHS5B0JTwRoskG+9vdJNkRFh1jz6sTyJ2RPPEAEIID2A7gBwASyml5YSQCwD8\nkhByPYDvAdxNKd2nc+3tAG4HgOxsptfXmQRLKOr9g5XkKKTO43C7muXo7RZPn3Fhza7vcO2I/rj5\nssEAjHMYFo4g0Xq2LFSaFwHAX4IrfcayupO4+51K819SmPzqskGYNXqIpjkt3F6P7gzrk4gdUXUM\nlFIBQCEhpCeA9wkh+QDsANoppUWEkF8AeB3AGJ1rlwFYBnjLVaO5TkZwAjVeGf2DbXUJKBjY09S9\ngxlCo91i2dzxfocgMXZoLyzeWKd5n5dvvgi9UhMMHZX8M0rO8KkPv0H5QQd+1LcHSvadVFRuRYJ/\nlB3ErNFDNBVIrLpHCztJxY6oOgYJSukpQkgJgKsA1AP4t++p9wH8IxZrYESPcP/Byo2hnpqq1MAm\nT4Kb2S0O7p3ibbSRPUYAFGanh9xM98z0Qv9EuUg7BcD7GVaUH8bfvqjTxM3ljqorlq5GGnaSih1R\ncwyEkN7wiu+dIoQkArgCwCIAHwAYB+AAgJ8BMC/Ww4hLwvkHa5RENOqbGJXTS+N8XIK+86lvbEOK\n3YJm59nB1il2S9ghBz2nFClcgoilPlkS+Ukot38PtLoEJNt4fFh9DEtL6vyjQqXvI1RHoedcuprD\nYSep2GBq5nNYNyZkBLzyGTy8ZbErKaWP+8JKKwBkw5ucvoNSWhXoXqzzuWtg1sjozXS2Wzj89/fj\nAcBwPnRZ3Uk8sKrKX+pp4YDnpxdqqlJCnTFt5nOp7ydVNIVLst3b6Hbn5TlYtmm/wonZeQJKCDgC\nxXtKWDiA5ziFowhWmaPnbCnAKny6OVHpfO4IlNJdAH6i8/gpAJOi9b6MzsOsAJzeDtzpEfFW+WGM\nHdYbPFE2x3G+a0bl9IK8b84j6lelhHOCkZfaqhPmeve78/IcLP2iTtdwq+EAWCwc7D5DPm9SLvIH\npPlPO0u/UOZDnII0LFUfjwh4xLMzpYNV5ujlZx5aXQWAwOk5+9jd71QiMy0h7K50RvchJjkGBkN+\nmshKT4RL0BrUJSX7vLLeLqWs9xm3iNfL9uM3o4bAxvNwes7uruXNduEmb6XdNBUpnAJFgm+gTyDp\nDwB4caOmmE4Xq8U78tSoQkvudJweARxHTDkc9XcQSnGAKFK4Ra3zmfbyVtx6aTYev/bHpt+f0f1g\njoERVRwtTryyeT9eK90PG89D8NXszx6Xo5kFYeE4PP0f/eFBayq/w/kZybpJbvXMBb3kbaD1Sbtp\nCenP6p24/H7jnyuBy0QoycIBz0wr0Iw6lSN3Osk2HpOXlAa9rxyXIARM9OsXBxjfb/mWw7h15PkB\n1xzvdLXcSbzBtJIYUWNN5VFc8sRn+PuX++EWvGJ9krzD1fn9NOM4292CRlROzpKSOtw/cZhCumPe\n5FzTo0L1CKS9ZCT98eT6Guz//kzQez/082Eo/8NERdzeSM5B0hfK6ZuKeZNzYbNwsPHGelNyfjN6\nMOob2ww/s1ryxMYT/6nIiMojp0y9dzzSlXWm4gV2YmBEBUeLE3NWV+kaep4QNDS1Y/a4od7wEc/D\nJQjeCWwBHIMgAs9+sgfzp+T5Y/QdbXoKpL2kV3LraHHi1bKDQe9r4YBWlwf3vrMDe75rRmqiFT/J\nTsfaqgZYec5/clIne9dUHsXC9bWwcgQugYIDAtZCWXmC10sP4M0thwMmkEM9lRSa6D+JR1h3dGRg\nJwZGVKhvbANP9P/zand7h9O8vOlbiCIwvG8KhmQkB3QKEi6BYuGGWn+IoKNNT/LdtN23Q0+wcgoh\nQQlHixPrqo5CJzSvwSMCf/tiPzbX/YATrW58e/IMVu84CpdA/Senh1ZXKXb5cqPW6vLOtuANTg12\nCwe7hQOlFE4PNXVakp9Knr5hBKwG9x47tFeXDSOFI/rI0MJODIyokJWeCIHq73U5zlsNI1VofnU4\ntLAFB4KahtMYO6x3RJqe1LtpvSSxlKAWzXgFkzg9FG+VH8ZdE4YC0E8SJ1h4zBqdjddKD8DCEXhE\nigd/fiGKh2Sgqc2NO1fsUJS6BjstSbH3UTm98NHdY3DFXzZpGgH/8svCiH3GWMO6oyMDcwyMqCCN\nDb1/ZaV/5KaFI7ht7GD8a8shuAUh8A0CcMZ34nhmmjdsEommp0CJam9YbFdUFFgXb9yHGcXZAU8/\ns8YMwawxQ3Sb00Ixgnq9DH+9qRAPrqoCIQSUUjx7Y0GXDrmw7ujIELUGt0jCGty6Lo4WJ2oaTgOg\nyMtMAwBc+lRk5CViNQZz8ef78Pyn0WvQ/+3PhmDu1T8CEPpoTLOvD9T0B6DbVfAYVSWda9VKcdfg\nxmAA3h3c2GG9FY/Nn5KLR96v7vC9w1XWDMU4OFqcWFpi3K9gtxDcWpyNV8oOhbQGOS99uR9Z5yVh\nZvEgjMrphWW3FEFypMHWZ/a0pBem4glBye4TGDe8jymxw66E3gmQzXIwD3MMjJgzs3gQWts9ePIj\n/Z4Fszg9IpJtxpPi9AjVONQ3tmma6uT88qcD8ciUfDhFiuVbDoe0FjmPrqkBKPwzIeSaSDUNTTja\n2AanR8ToHG1iOFAYrO54MyqPnML5GUmasFOrS8Bj62rwxzXVIRnJrrjrZtVKocFCSYxOY0X5Icxf\nU41wo0pWjoD3jcM0s2sOR0NJ7xo58usrDjhw0yvl8ISZoFbrL/HEm6hXazKZ7Uyes7oKKyvq/b+P\nHJyOyvom8IRousvNhuW66q676sgp3PxquSJRn2q34M1Zxd3utCQn3FASK1dldBoziwfh9V9fgiC9\nVoa4RYp2t4j7V1bisqeCNzSFU8qYkWLH5B/3N3ye+O4LAFYLj8QAs66DYeGU5aMC1Z8NvXzLYdQd\nb1Y85mhxYn1VA/5ZdgB1x5ux7MtvFU4BALYeaMT/jR2CBVPzkGJXrtNMSad8193VZlGzaqXQYKEk\nRqeSl9kDvE9cLlzMisqFaxwO/WDc5dzuPnu93v154jXwwRrVeAJvg59JPq455g8prak8ivtXVpm6\nfsnGOrx7+0hN8r/dI8r2q98AABzDSURBVMDtCVwp1pUnqLFqpdBgjoHRqaj/wTo9HrjCr2QFYGys\nwjUO147oj68O6s+Qlm/yM1LsmDc5FwvW1cLKEwgi1YS5Dnzfglc278dn35zwd4XzxNs7cMhxRqMf\nFQyplNa0U6HAjNe2geO8HosjgOg7mQQT0NNzfC5BRFObG44Wpyn12s7MS7BZDuZhOQZGXCA3HI+v\nq8Gaqu/CvpeZvEGoxqHgsf+gqV3rseRxain+zhMCtyBi/pQ8zBw5yHANNQ1NAAjyMnsgI8WOuuPN\nmPiXTabW89l9Y5GebEPJ7hN45IPqiPZYfHbfWMPOZ3l5bLtHAKUUdgvv/7zFg8/D0pJ9qD56GvkD\neuDOcUNR891p3cFL8WSgY+m4Yvle4eYYmGNgxCW/+Uc5Nu45GdI1HACbT8oiGgnRl0v24c8fK3f0\n8l6Ajg4HqjpyCr9ctkUzwKhgQA9sO3S2O3x6URay0pOwtKQOFk6bSDZC+n6CSXo/O20EphUNNHxe\ncmq3La+A0xPcfvAcUZxogg0aivXpIpYJ9Vgn71kfA6Nbcc/EC1Fa5zAlbS1BAayfPTpqOj//N24o\n+qcn6Yaiqo6c0u0TkIe0pNLRQp9ekRq9XAchwEu3FKGx1YXKI6fwQ6sLz326x2+QjdK+Y3IysO3g\nDyCEQBC83dM3XJRlStI7mIBeRoodaYk2WDgOTgR3SuowV6CcUKwNZyzLWPXe68FVVcjt3yPutKmY\nY2DEJVnpif44uFk4wPTuWQ8zO1WjOLVe/L3VJaC6oQkFA3vi0Q++xvKtZ/sc9GL5gXIgGSl2pCfb\nMGrRRlO79HsmDMXg3imadarv/9NB6dhc51Csy4yRykpPhFtn2FI4yCuiYt1rEMuEen1jG6jKSboE\nimteLMWz0+Kr7Jc5BkZcIjeSHoGa6g2g0N91A8GNfig7VXlDmfy+8ybl4pEPlB3dC9fX4sI+KQqn\nABgPw5laOAC5/XvonixqGk6Dg7kZDY+8/zXumjAUl16gNKp6goH3TBBw0HHG8CRj9B3Mn5Kn+byB\nSLbz8AiiP9ktIVWGdUbVUyzLWJNtvG9sqxKXR4y7ZjvmGBhxy9TCAWhu92DBuhokWQnOuAM7h5k+\nMTo5jhYnVpQfxtKSuoAx7XB2qmpncuflOUix82hxnj21WDkOm/bp50oqj5zSGGIjB7Wm8mhIQn57\nTrRi9tuV4Ajwwi8LFZ83I8WO0rqTmvcJNZwxc+QgfN/cjhc+rwv62kQrhwVT8jBueB+U1Z00rAyL\nda9BLMtYW10CEgxyPPFW9sscAyNucbQ4sXBDLVwCNVXC+tPB5/mvq2k4jY3fHMfyLYf8+0+jPodw\ndqp6zmRJSR3UQyWcHgEFWWm691DH8o0cVG7/Hpj7XnjqriIFHlhZidz+Pfxy4kDkQjbjhvfFS19+\nGzS8RQGMG94n4Dzuzuo1iFUZayAHF2/NdswxMOIWPYMdiDnv7UJzuwfz1waW2VAb/XDCCXprs/Ec\nbh87BEu/qAMVKZwCBccR3Pn2TozJyQgayzdyUJU6ie1QcIvAz1/YhAQLD4GKmD1uKHiiDEkFcoQV\nBxz4uPY4+qbaMaxfD2SmJaChqR0ARWZaIgghkDtECwdcdkEGyuocsFt5iL5pdfJ7G+k7dVavgZn5\n4JF4D8nxAd7mSDtPQDgSd812zDEw4pZAYzf14DmCx9YF115yegSF0Q9np2rkTGYUZ+Pq/H645sVS\nANQfNvjqUCNW/99Iw1i+o8WJpjY32lXdx+0eAYUDe3aoMxzwnhzOuL33fv7TvZpheXJHKM+b3PPO\nTpTKHJoaDsDonF7YcsABm2pkqXQfKZchb4ILlPOJhZGOFsFyWWaGQsUDzDEw4ha5weYIwZkg8SS3\nQGEhHNxBdtZTRmQiI8Xu/0fs9ghweUQ8dX0+qhua0a+HHT2TbAENmXxtPCFwCSJuKhronz1h5zmF\n7ISV42C18Lr9AVJewaIjmCcIFB/XHMNVuX3xQQea/uToBX3mTcr1l4vOWV0FnnBweQQEK4ASAWyq\n8+ZQBFHA768e7s9nGOUyKNAlhfiCYbaAoSs4Ptbgxoh7JKN87zs7cMBhLPT2h6uHK2r8jXjq+nzs\nPtaMN7YcCjpm+pLz03HpkAy8vHm/7j/4l7/8Fov+s1sxB5qDVxVVXkklb3aTOxkAuOypjVGZDmeW\nZDuPt2aNRLKNx5UvbAqlQliXJ67Px8ziQbrKtHYLAUAUn1fdCBjqvIx46KAOR7k3FrAGN0a3Rdph\nlTw0Huf/foPua5JtPIqHZOCZaQW4953KgGeGh9+vDuoQJLYdbMQ2n06SOlH73vZ6/FlnpoQI+GdD\n85x3RvX9E4ehvrENL3y6B29tOwIbz0EEcHF2eqc6BcBbLll9tAnz11Z32CkA3tkSV+X1Q31jmyaX\nwRMO6opbeX4jlLLheJIA72ipbbw4OAnmGBhdioNPTcKD7+7A6p3KsIpAKbLSE1EwsCdG5fTCtS9u\nRn2Tfl9wR21fu1vEkxtq8d7OhqCvFURAANUMJWrzOYP/7jeO38cKUaR4dE1knALg7XSuaWjCkR/a\nNA2HAhUBqvQMUn4jlLLheBu805F+iHhycBJsHgOjy/HsLy/C4psKkWDlkGq3IMGnjyQveyx9eCJe\nmD4CnLl+sJAx4xRijYUAQzKSQr7OZuEi5hQk9hxrxoJ1NZrHH7jiQjw6ORc2niDZxiv+7kKZlxHO\nbI1oIuWcjP6bNCJeZ1ywEwOjS2KmrPG6iwaC4zh/tZFLECCINOyJcfFOTu8k/On6ETjjFiHNjP5P\nzTE8ttZroN0ChZUjcGu0iyKfZ1z0nz26933yo93gCZBo4+ESKOZPycWonF6oOnIKyTbe9K47Gh3L\nHQ3nhFNqG68zLljymdHtkf+DL6s7iXveqexwOKkr8NufeYXz5D0HrS4B1UebFLOl503KxWPranSn\nxUUbtdLq9KIsrKyo15Xolso7pf9fUX4QKyvOTuubOLwPbr1sEPIy0xRG1YzB76xwTqCkNYAO5x2Y\n7DaDYRJHixP3vVtpKFXRXUnggHYRsBJgzNBeuKBvCkYOzsD3zU68vOnbgBVfMVujlcP62aP9DuC9\nHfV4rfQAAK/gnNRKZ7dwhkl7K0/w3I0FfimRYAa/syuK5DMuIl3SyxwDgxEikgz2+RlJ+EfZQWyo\nPqZ4fmpBP0y7OBuf1x7Dpn3fIz3JhosHnYd/lR+C2y2aEJxmhIo0+OigoxX3BakuC4TdwmHDXaMx\neUlpUINfdeQUbn61HM1Oj2YdBUEkyCOFuoQ5Uo4q7spVCSEJADYBsPveZzWldL7s+cUAfkMpTYnW\nGhiMQOT0TfV3IBcNzsB9x5vxcY3XOVyZ18//3NhhvRXX3XH5BdjyrQOz394Z2wWfAzgFEW6PgPvf\nDd8pAF5drH+UHTQVv4+lwqoR8qY3vdkesc47RDP57AQwnlLaQgixAiglhHxEKd1KCCkCkB7F92Yw\nQkbuKAKRkWLH5IJMfPT1d5pTBiM07DwBJQQc8ZYBU0rxP6+WR6RKatX2wyBEWbmkZ/A7S7zPiHhw\nVFErV6VeWny/Wn0/lBDCA3gGwJxovTeDEQsevy4f1mjVw54jUELw0oyf+If+uAVqOglut3DgA3z9\ndosFs8flmCohnVo4AGVzx+PNWcUomzs+5n0EjhYnqo6c8suwhFP6GkmimmPwOYHtAHIALKWUziWE\n3AOAo5T+hRDSYiaUxHIMjHhlbeVR3PtuJaJQ8XlOYOO8neKhlhD/9vIhmDV6CMrqTuL+lZW610ey\nuidS6FVIGSXII9ENHdfJZ0JITwDvA5gP4EkAl1NKPYEcAyHkdgC3A0B2dvbFhw4divo6GYxwcLQ4\n8eLGfXjjv2e1l/okW3H7zy6Ao9WF18sOwsITnHEK50SZbDQhAP50XT5mjhzkf8zR4sSWbx0o2XMc\n63cdMxzIZESs5Cj0HMConF5RrYiKa8cAAISQR+H9e/0tgHbfw9kA9lNKcwJdy04MjK6AkYGRy0+r\nq2QAb0iEAvjdz4bgxZJvIbDjhy5WnuChn1+I4iEZhjtulyBg9rihmKEzzU+PSPYvBHIwRiWxy24p\nwp0rdkStIioeq5J6A3BTSk8RQhIBXAFgEaW0n+w1LcGcAoPRVTCSU5Y/LpfqdgsiHlQZuiG9U/DQ\n6l0gvmQs4yxuwas5lWT1GvFZo70NfGrNpKVf1GFGcXbQ+0VSbymYgzHqcAZopyea9YhmVVJ/AG/4\n8gwcgJWU0vVRfD8GI+4JJpsgf17d2cvwcsbnMF/6cj9e+nI/7Lz5aXRyIiVHYcbBGFUa5WWmxVVF\nlETUHAOldBeAnwR5DethYJxzBBvUIj1fMLAQ1UdPo/a75hiuruvhVFUxmd1xR6os1IyDCVQS21nj\nTAPBRPQYjDjmyty+zDGYJNVuCWnHHan+BbMORj3Ws2TPCdz7zg5MGZGJCbn94sIhSDBJDAYjjnG0\nOHHxnz7r7GV0Ce6bkIObLz0/ZAMbiaokPb2jQAOG7nmnUvHYhX2T8fF9l4f13oGIu+Qzg8HoOBkp\ndiy+qRB3qwwJQ8v2w6dw86VeSYlQjHwoM5iNnIjZcJCjxYkH3tX+Xe453orPa49hQm4/natiD3MM\nDEacIxmde97egdJvf+js5UQFvTkRoZJs4zFq0caoNIoBwSuPzDiYmobThs2Qn9QeZ46BwWCYQzJs\nf/2fi7yDd9bUaIzoH64ZDrdHxEdfH0X1d62dtNLw6ahTAICNe76H06OsDGpu9yhmT4Tbp9DR0lZH\nixMryg9jycZ9huKAlw45L+R1RQvmGBiMOEZvl7r1DxMUg2vkO+E7xw+Fo8WJVzd7Szm7C4lWDr+6\ndBAqDjbihzMu7D95RvG8lQPU6VJRpP4BRB3tU+hIaeuayqOYs3qX4fwIwNv5O7h3cAHHWMFmPjMY\ncYrRPODGVhcAID3ZhoKBPTWGKSPFjrlX/wjb/zgRT12fj+xObpYKlSSbd6KbHJECl+X0xsu3FmHV\nHZfBblH2LhBC4BKUhtelI8jHEYI3/nsQdcdDq/QKt7RV+jsM5BQAwG7lOr2pTQ5zDAxGnKI38J6K\nFNe8WIqbXy3HqEUbsbbSuAEuI8WOm4oHYdPc8Xji+nzYeIJEKx/SGpJtsTcRIgXmT831q4taeQKP\nIOKON7fjsqc2oqzuJJ6ZVqBQHx0ztJepe59xCVi8sQ4T/7IJj6752vSawlU81fs7lJNs5ztFPTUY\nrFyVwYhT9PR11IQiuCZPwv7q1a2oPtYS9BpplGZHsfEEhABX5ffDmsrvNM9bOCDRatEkjWsamvCb\nf36lUE+18gRbH54AAP6Q2jWLSzUnBjN8dt9Y/wwOM0nqUBPZRn+HdgvBo5Pz/n97dx8kVXXmcfz7\ndA/T4gyiGUQXJgvRCWWA8LJgGRKLKNGUSRSNrhhjfKlEzbLrZhP3BbaIlXXRypsm0WAZrYrZSpbC\nELMYxGhKExJ1gCSAIxleVhFFB1bQUYkDZobpOfvHvT3Tt6d7uvv27e4Z+vepmpI+9/bt8zB4zu1z\nz3kO0yeOLeuiNk1XFTnGZC7A6u5NEotZoJEpJoVD+qyZdV/+KCt/v5db1rQPuVNaQyJOV3fpm5j2\nJB2JuhgtJ4+hof4gh3sGrtmQiHPvVX/D2NH1gUYy9d/MUZijScf2/YeYP2U8TY0Jnnv1berrjJ4Q\n1Xxm9xsc7knSvu8Qyx/dQV3M6Ek6vnbRVK46a9Kg84uZ2po6P/132JPs46ZzWwpO8lct6hhEhrHM\n1bIXrngmcLyUhGtXnTWJC6adSsdb7/KbXQe469e7A8dvv2Q6yx/dEbrumbp7+1ix/gW87yEDepNe\nzqBsDeXGFztzXG3gGs0njaY35Kymrz+2i1FxG9T5LVvTDo5Aeu+whmPKi3zUMYgMc9mys0aVcG0g\nL9OJXDNvMtv3HwKMaRNOoKkxwbOvvMVDW6NL5Be3GIvOnMhPNr7Sv31nsg9+vqVjUDrtzq5uHmh9\nOcs1YNqEEwIx3HLhVK8xL6YuMaO7t4+0jNcBtz6ynQumR5OqothvGtWmjkFkBCnn3WdTY4L5U8YH\nyva+eSTH2eEcOZrkvza8Eijr7fPSadfHgFisfxin4613qY/HBs3o+cyZg1NqT58wtn/6bjoD4jEG\nPaNYvnAat/9yV2AfhEx1MSsq02qlNvypBM1KEhlhUnf4lWh8Lp7xV2X/jJSePujp7WPZmnZWbtqb\ndYoowMNt+wbNyGo+aTTJjIk0o+LGE1+Zz3cWzQrMJrrz8pmcP+1UDvfk7hTAG/pqqC9sFtcv/DoV\nMltsJFDHICI5fe7D7+O4KrQStz6yHaB/imhDYqCBPtyT7F/T0dnVDWSfTnrn5TNpOWUMC2dNpHXJ\nAv77+rNoXbKAhbMm8tLrXXn36Y7FjAtXPJO3kc+13iRVt5FIQ0kiMqSfLv4IF9/TWtHPHBX3Zlul\nhs7W7zrI19ZuDwwVZc7IGmqYLXOM/6kX3shbh6P+Arl8q6Wj2vBnONE3BhEZUvNJo4lXuKVIOtc/\n26qpMcG5Z4wfNFSUbUZWocNsM5vHFlyXVCOfS1Qb/gwn6hhEZEhNjQmWXHBGRT6rLkbWlcBNjQkW\nzWkOnLtobnPoO/JxY44r+Nyu7l4OvduTc2go7Kpo8Iahnnv17WE37KShJBHJ68b5p/PA03t47Z2e\nsn6OmbHuprP7VyOndHZ1s3pLR6Bs9eYO/uljU0JtzLNj/6GCz3fAtT/6I4m6WM7srMXOFktlW71n\n/W7q46Vlfi0HdQwiktfuA++UvVMAqK+LDZpy2tnVzfpdB6mLBRfGZRvHzzdlNJWttthMQM7R/1A5\n1/OGQtcqrNy0l/94pJ2jfpip6bhhM7+WgzoGEcmr7dW3K/I5R3u9sflUA59KVRE3G9RhZI7jZ6Yo\nv+VTUwO5iNJnD4VV6kPllZv2suzh7AvxhtMDa3UMIpLXrPeeWJHPSfY57nryeVZv6aAuNjhVBXi5\nlXqTjn84p6W/LNtGOssebqcxEae3z/Gty2Ywqalh0OyhYpXyULmzq5tb1+VOMTKcHljr4bOI5NVy\nyhjOnFT+ziHp4MebXuEvR/uydwr1cS6eORFw3P/Unv7FZLnSW3d1D6x5aKiPZ10wV6jUM4awd/Te\nSm7LeqzUa0dNHYOIFOTrl86I/Jp1MTiurvBmqLevj59v7aC71wUWk+Vr9EfFvGcXqdlDxxe5L8Xi\nj57GhqULSno4nCvZXxTXjpo6BhEpSMspY7hm3uA8RaW4Yf5pmclWs2qo9za0uenc9w/a3S2z0c+W\nxiI1TJNaBf2Dq+eQKKBDqo8bt396Oks+8YGS7+bTp7U2JOLU18Uiu3bUtFGPiBRl94F3+MdVW9j5\n2uGC35OIQ+bIUKIuxoalC2jd/UYgY+yiuc2s3twx8BD5wqlMnzC2f/w9c+Ob9M2K+h9a7z/E8nU7\nAlloM+/I17bt6//cbMn0/vn8KWXZN6GSyfbCbtSjjkFEQuns6mbObU/mPa/l5OO5c9HsIRvrzMZy\nqMYzvUEfav5/sTuyPf38Qda07WP2e0/i6nmTh91dfBjqGESk4ta27eNLD7blPG7A5q+eF9hjIYq7\n5WMpxXU5aWtPEam41IrfL63cSutLbwaO1ceNOy6fOWQyu7BG2sY3I406BhEpSVNjgpVfnNf/Wnfz\nI586BhGJlO7mRz5NVxURkQB1DCIiEqCOQUREAtQxiIhIgDoGEREJGBEL3MzsdWBvBT5qHJB/l/Bj\nR63FC7UXc63FC4o53STn3MnFXmxEdAyVYmabw6wSHKlqLV6ovZhrLV5QzFHQUJKIiASoYxARkQB1\nDEH3V7sCFVZr8ULtxVxr8YJiLpmeMYiISIC+MYiISEBNdgxmdrmZbTezPjObm1Z+vpltMbM/+f9d\nkOW9a82svbI1Ll2xMZvZ8Wb2qJnt8t/3jerVvnhhfsdmNscv321md5tZAZtODh9DxNxkZuvNrMvM\nVmS850o/5m1m9riZjat8zcMJGW+9md1vZs/7/7Yvq3zNwwsTc9o5BbddNdkxAO3ApcBTGeVvABc5\n5z4IXAv8JP2gmV0KdFWkhtELE/MdzrkzgNnAR8zsExWpaTTCxHsvcAPwfv/nggrUM0q5Yv4LcAvw\nL+mFZlYH3AWc65ybAWwDbqpAPaNSVLy+ZcBB59wUYCrwu7LWMHphYi667arJtNvOuZ0AmTeEzrln\n015uB0abWcI5121mjcDNwI3A6krVNSohYj4CrPfP6TGzrUBzhapbsmLjBd4DnOCc2+S/78fAJcBj\nFalwBIaI+TDwjJm1ZLzF/J8GM+sETgB2V6CqkQgRL8DngTP88/oYYQvhwsQcpu2q1W8MhbgM2Oqc\n6/ZfLwfuBI5Ur0pllxkzAGZ2InAR8Ouq1Kp80uOdCHSkHevwy45ZzrmjwGLgT8B+vDvoH1a1UmXk\n/zsGWG5mW83sZ2Z2SlUrVRlFt13H7DcGM3sSODXLoWXOuV/kee804JvAx/3Xs4DTnXNfMbPJEVc1\nMlHGnFZeB6wC7nbO7YmqrlEoR7zDXSkxZ7nWKLyOYTawB/g+8O/AbaXWMypRxovX3jUDG5xzN5vZ\nzcAdwNUlVjNSEf+OQ7Vdx2zH4Jw7L8z7zKwZWANc45x70S+eB8w1s5fx/s7Gm9lvnXPnRFHXqEQc\nc8r9wAvOue+VWr+oRRzvPoJDZc1+2bASNuYcZvnXfBHAzFYDSyO8fskijrcT7675f/zXPwO+EOH1\nIxFxzKHaLg0lpfG/aj4KLHXOtabKnXP3OucmOOcmA2cDzw+3TiGsXDH7x24DxgJfrkbdymGI3/H/\nAX82sw/5s5GuAYq9Ix1p9gFTzSyVZO18YGcV61NWzlu09Qhwjl/0MWBH1SpUAaHbLudczf0An8Yb\nQ+4GDgC/8su/ChwG2tJ+xme8dzLQXu0Yyh0z3h2zw2soUuXXVzuOcv6Ogbl4sz5eBFbgLwAdKT+5\nYvaPvQy8iTczpQOY6pf/nf873obXaDZVO44yxzsJb0bPNrxnZn9d7TjKHXPa8YLbLq18FhGRAA0l\niYhIgDoGEREJUMcgIiIB6hhERCRAHYOIiASoY5CaYGaRJz80s4VmttT/8yVmNjXENX6bniVTZDhQ\nxyASknNurXMulY78ErxcQyIjnjoGqSnm+baZtfv7EFzhl5/j370/5OfpX5naj8HMPumXbfH3aVjn\nl19nZivM7MPAQuDbZtZmZqenfxMws3F+SgLMbLSZPWhmO81sDTA6rW4fN7ONaQneGiv7tyPiOWZz\nJYnkcClejqCZwDjgj2aWym0/G5iGl2m0FW8Pis3AfcB859xLZrYq84LOuQ1mthZY55x7CAanRU6z\nGDjinPuAmc0Atvrnj8NblX2ec+6wmS3BS5X8n1EELVIMdQxSa84GVjnnksABM/sdcCbwZ+APzrkO\nADNrw0sh0AXscc695L9/FV5e+7DmA3cDOOe2mdk2v/xDeENRrX6nUg9sLOFzREJTxyAyIH0fiiSl\n/f/Ry8BQ7XEFnG/AE865K0v4TJFI6BmD1JqngSvMLO5nFZ0P/GGI8/8XOC0tl/0VOc57BxiT9vpl\nYI7/579NK38K+CyAmU0HZvjlm/CGrlr8Yw1mNqWAeEQip45Bas0avMyazwG/Af7NOfdarpOdc+8C\nfw88bmZb8DqAQ1lOfRD4VzN71sxOx9sAZrGZPYv3LCPlXqDRzHbiPT/Y4n/O68B1wCp/eGkj/haU\nIpWm7KoieZhZo3Ouy5+ldA/exkXfrXa9RMpF3xhE8rvBfxi9HW/jovuqXB+RstI3BhERCdA3BhER\nCVDHICIiAeoYREQkQB2DiIgEqGMQEZEAdQwiIhLw/1XuLkik+ULcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJR52p_KoZN6",
        "colab_type": "text"
      },
      "source": [
        "#### use low alpha value to show density"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcWBpRsWovRA",
        "colab_type": "code",
        "outputId": "d06a3203-12dc-4e0f-cac4-c8d47f91675c",
        "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 0x7f14190a7780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "xXvxUv7ioyma",
        "colab_type": "text"
      },
      "source": [
        "### use matplotlib color and thickness plot options"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ukP3TpxtoTf",
        "colab_type": "code",
        "outputId": "95404ff3-8ae8-44b2-a445-f10c03f99861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "housing.plot(kind=\"scatter\", x=\"longitude\",y=\"latitude\", alpha=0.4, s=housing[\"population\"]/100, label=\"population\", c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True)\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f1419014b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7d/dsNxSSRCIQEyO4cgas3Qb+ACTFwYzxUFamRGTKFEhKUkK1YAFs2QIf7Yat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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-LXXwJNu9-R",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xay_aiDpxLWy",
        "colab_type": "text"
      },
      "source": [
        "### Looking for correlations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ngy9A-CZxRMT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corr_matrix = housing.corr()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khYBgiAVxUwx",
        "colab_type": "code",
        "outputId": "27dbaa9a-628b-4e78-9ad2-214fb8b7a944",
        "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": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j708uvsWyKzp",
        "colab_type": "code",
        "outputId": "ba1e2665-3b1f-4dfb-c472-0398c28483f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        }
      },
      "source": [
        "from pandas.plotting import scatter_matrix\n",
        "\n",
        "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
        "\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 0x7f14190143c8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418f8d588>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418f36ac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418f66080>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1418f0d5f8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418f32b70>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418ee6128>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418e8c6d8>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1418e8c710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418e661d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418e0c748>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418db6cc0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1418de7278>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418d8e7f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418d36d68>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1418d67320>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CHgnLskkEPHz95DJPTayRr+l4FYnffOM4nzswR66icfdgjIpmMpMpYzuuDnq5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7wwWUJHcm6lTtXxCcKIIJJESPrTBhnbwBACL4g7EpLJYrOjqQ5iyVShQcOLGMwk+Cd//grzZYdD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xnIhJuLaLxcb0P3GnBs64jywskZNE3jd969kd50CE1o9KVD2K7klaE5/vQHZyg37IXPdqRE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\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HgV2ac086kITd3olXIwjkRGO6PkOJCJqqko5qxAyNqKExEDd4pdBiqWyyUbdkgyjdRltV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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": "markdown",
      "metadata": {
        "id": "Bsh9naXzzGV5",
        "colab_type": "text"
      },
      "source": [
        "### plot median income vs median house value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBQUkWDs24jx",
        "colab_type": "code",
        "outputId": "c3f89d72-9937-4680-bbfb-c9725441fd31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "housing.plot(kind=\"scatter\",x=\"median_income\", y=\"median_house_value\", alpha=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1418b93438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9mdNaR6Qkujunj+Lm+TBCVIngRhNecHMjp25MKKrsLDHXccf1Yr1yPT4/5vPu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\n1qGwCceyLMs6FP8fqmYnnwQWP6YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_cWIzLC3FJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing['rooms_per_households'] = 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\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pItv0KvZ3h1S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corr_matrix = housing.corr()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9xddm_v4GjQ",
        "colab_type": "code",
        "outputId": "62f57d4f-e13f-4441-ba80-1a3f9af00106",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "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",
              "rooms_per_households        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": 225
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlAB6jwa4NyE",
        "colab_type": "text"
      },
      "source": [
        "### convert to clean training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vl34i-gh5UE1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
        "housing_labels = strat_train_set[\"median_house_value\"].copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHZLdgom5kMn",
        "colab_type": "code",
        "outputId": "5338a03d-4d0f-4e76-d8de-ad7131fe5d6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "strat_train_set.head()"
      ],
      "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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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  median_house_value  \\\n",
              "17606       710.0       339.0         2.7042            286600.0   \n",
              "18632       306.0       113.0         6.4214            340600.0   \n",
              "14650       936.0       462.0         2.8621            196900.0   \n",
              "3230       1460.0       353.0         1.8839             46300.0   \n",
              "3555       4459.0      1463.0         3.0347            254500.0   \n",
              "\n",
              "      ocean_proximity  \n",
              "17606       <1H OCEAN  \n",
              "18632       <1H OCEAN  \n",
              "14650      NEAR OCEAN  \n",
              "3230           INLAND  \n",
              "3555        <1H OCEAN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGJxDX50744A",
        "colab_type": "text"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XKZNJbR8F5A",
        "colab_type": "text"
      },
      "source": [
        "### drop na options"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_1r0ZF_8IWB",
        "colab_type": "code",
        "outputId": "4cbd1906-71b2-4589-87b7-20f96f42cc24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "housing.dropna(subset=[\"total_bedrooms\"]) #option 1: remove rows\n",
        "housing.drop(\"total_bedrooms\", axis=1) #option2: remove attribute\n",
        "median = housing[\"total_bedrooms\"].median()\n",
        "housing[\"total_bedrooms\"].fillna(median) #option 3\n",
        "print(median)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "433.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3zU32Cg8heY",
        "colab_type": "text"
      },
      "source": [
        "### use imputer instance to remove missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Zi1e8El_ySF",
        "colab_type": "code",
        "outputId": "4eeef5b3-a728-40d9-8ba2-451a5063a2f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn.preprocessing import Imputer\n",
        "\n",
        "imputer= Imputer(strategy=\"median\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doiHXuOCABol",
        "colab_type": "text"
      },
      "source": [
        "### remove non-numeric data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15bnLb5nAQog",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing_num = housing.drop(\"ocean_proximity\", axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WIL8CeIAWdf",
        "colab_type": "text"
      },
      "source": [
        "#### calculate median using imputer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVSVfN8AAfWO",
        "colab_type": "code",
        "outputId": "b9f788d3-0dc3-4ade-8e5c-4d3faa394bf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "imputer.fit(housing_num)\n",
        "print(imputer.statistics_)\n",
        "print(housing_num.median().values)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-118.51     34.26     29.     2119.5     433.     1164.      408.\n",
            "    3.5409]\n",
            "[-118.51     34.26     29.     2119.5     433.     1164.      408.\n",
            "    3.5409]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHWvqN_6C4nA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = imputer.transform(housing_num)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6KX2L69DQlX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing_tr = pd.DataFrame(X, columns=housing_num.columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNgwPZsRI6jO",
        "colab_type": "text"
      },
      "source": [
        "### Handling Text and Categorical Attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgIDZZclDaUX",
        "colab_type": "code",
        "outputId": "363cc6dc-02de-405a-bc1d-15e319866e87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "housing_cat = housing[\"ocean_proximity\"]\n",
        "housing_cat_encoded = encoder.fit_transform(housing_cat)\n",
        "print(housing_cat_encoded)\n",
        "print(encoder.classes_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 4 ... 1 0 3]\n",
            "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjuPZEfkJTpL",
        "colab_type": "text"
      },
      "source": [
        "### One Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ey4G_63UJznj",
        "colab_type": "code",
        "outputId": "da0f5b83-e1d4-4f02-da91-9b0e3635c69f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "encoder = OneHotEncoder()\n",
        "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
        "# print(housing_cat_1hot)\n",
        "\n",
        "print(housing_cat.shape)\n",
        "print(housing_cat_encoded.reshape(-1,1).shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(16512,)\n",
            "(16512, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RAeYuSgNNpb",
        "colab_type": "code",
        "outputId": "13c16b1b-ce33-4804-b3ee-a3794e2d5ee8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "housing_cat_1hot.toarray()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1.],\n",
              "       ...,\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jilms9ouN9VO",
        "colab_type": "text"
      },
      "source": [
        "### LabelBinarizer "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCNhL-9iOae9",
        "colab_type": "text"
      },
      "source": [
        "#### applies integer to category and one-hotEncoding together"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yU-AyA5nOktF",
        "colab_type": "code",
        "outputId": "d756622b-c040-439f-cab7-beebdbc8050f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "from sklearn.preprocessing import LabelBinarizer\n",
        "encoder = LabelBinarizer()\n",
        "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
        "housing_cat_1hot"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 1],\n",
              "       ...,\n",
              "       [0, 1, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 1, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cujQCh2ePPja",
        "colab_type": "text"
      },
      "source": [
        "## Custom Transformers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYzGxLJwQv4h",
        "colab_type": "code",
        "outputId": "22defa7f-17eb-436a-df15-27294e5c22be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\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):\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, bedrooms_per_room]\n",
        "    else:\n",
        "      return np.c_[X, rooms_per_household, population_per_household]\n",
        "   \n",
        "  \n",
        "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
        "housing_extra_attribs = attr_adder.transform(housing.values)\n",
        "housing_extra_attribs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-121.89, 37.29, 38.0, ..., '<1H OCEAN', 4.625368731563422,\n",
              "        2.094395280235988],\n",
              "       [-121.93, 37.05, 14.0, ..., '<1H OCEAN', 6.008849557522124,\n",
              "        2.7079646017699117],\n",
              "       [-117.2, 32.77, 31.0, ..., 'NEAR OCEAN', 4.225108225108225,\n",
              "        2.0259740259740258],\n",
              "       ...,\n",
              "       [-116.4, 34.09, 9.0, ..., 'INLAND', 6.34640522875817,\n",
              "        2.742483660130719],\n",
              "       [-118.01, 33.82, 31.0, ..., '<1H OCEAN', 5.50561797752809,\n",
              "        3.808988764044944],\n",
              "       [-122.45, 37.77, 52.0, ..., 'NEAR BAY', 4.843505477308295,\n",
              "        1.9859154929577465]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEeyOrMnTZR2",
        "colab_type": "text"
      },
      "source": [
        "### Transformation Pipelines"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UK6Gtig5aQ_Y",
        "colab_type": "text"
      },
      "source": [
        "### DataFrameSelector class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AF2Onxq0aQXT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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": "py19QGePh8xM",
        "colab_type": "text"
      },
      "source": [
        "Custom LabelBinarizer class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsLOAGXuh9Vy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CustomLabelBinarizer(BaseEstimator, TransformerMixin):\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "    def transform(self, X):\n",
        "        return LabelBinarizer().fit_transform(X);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GGbDVZZV3Va",
        "colab_type": "code",
        "outputId": "73e1a69a-c7aa-488b-fec8-6656e5030121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import FeatureUnion\n",
        "\n",
        "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",
        "    ('labelBinarizer',OneHotEncoder(sparse=False))\n",
        "])\n",
        "# housing_tr = num_pipeline.fit_transform(housing_num)\n",
        "\n",
        "full_pipeline = FeatureUnion(transformer_list=[\n",
        "    (\"num_pipeline\", num_pipeline),\n",
        "    (\"cat_pipeline\",cat_pipeline)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIvz3lWmcOVh",
        "colab_type": "text"
      },
      "source": [
        "### run the combined pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXUi7zZcW8g1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing_prepared = full_pipeline.fit_transform(housing)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "le1LVcfImSGs",
        "colab_type": "code",
        "outputId": "7ea31e17-d2ee-4c9a-94d3-ac863151a673",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(housing_cat.shape)\n",
        "print(DataFrameSelector(cat_attribs).fit_transform(housing).shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(16512,)\n",
            "(16512, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TG0BjLUxeksT",
        "colab_type": "text"
      },
      "source": [
        "## Select and Train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgAsg7A5_elV",
        "colab_type": "text"
      },
      "source": [
        "### Linear Regression (underfitting)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NCa74DOXAsN",
        "colab_type": "code",
        "outputId": "3ef3a95a-a22a-4842-e085-6dc7571d298b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "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=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRyJhUzpZxMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "some_data = housing.iloc[:5]\n",
        "some_labels = housing_labels.iloc[:5]\n",
        "some_data_prepared = full_pipeline.transform(some_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogsCJfGoo9Uq",
        "colab_type": "code",
        "outputId": "9a7d6424-6caf-4be0-b26b-c156d3c3de38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "some_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxNbCQ78o_ZX",
        "colab_type": "code",
        "outputId": "86ed42e4-ed6c-4223-dedb-22b5f4a42276",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "some_data_prepared"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.15604281,  0.77194962,  0.74333089, -0.49323393, -0.44543821,\n",
              "        -0.63621141, -0.42069842, -0.61493744, -0.31205452, -0.08649871,\n",
              "         0.15531753,  1.        ,  0.        ,  0.        ,  0.        ,\n",
              "         0.        ],\n",
              "       [-1.17602483,  0.6596948 , -1.1653172 , -0.90896655, -1.0369278 ,\n",
              "        -0.99833135, -1.02222705,  1.33645936,  0.21768338, -0.03353391,\n",
              "        -0.83628902,  1.        ,  0.        ,  0.        ,  0.        ,\n",
              "         0.        ],\n",
              "       [ 1.18684903, -1.34218285,  0.18664186, -0.31365989, -0.15334458,\n",
              "        -0.43363936, -0.0933178 , -0.5320456 , -0.46531516, -0.09240499,\n",
              "         0.4222004 ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
              "         1.        ],\n",
              "       [-0.01706767,  0.31357576, -0.29052016, -0.36276217, -0.39675594,\n",
              "         0.03604096, -0.38343559, -1.04556555, -0.07966124,  0.08973561,\n",
              "        -0.19645314,  0.        ,  1.        ,  0.        ,  0.        ,\n",
              "         0.        ],\n",
              "       [ 0.49247384, -0.65929936, -0.92673619,  1.85619316,  2.41221109,\n",
              "         2.72415407,  2.57097492, -0.44143679, -0.35783383, -0.00419445,\n",
              "         0.2699277 ,  1.        ,  0.        ,  0.        ,  0.        ,\n",
              "         0.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ro7JhP8zpDL2",
        "colab_type": "code",
        "outputId": "29e277eb-4259-486f-a968-731efe7b75bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(lin_reg.predict(some_data_prepared))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
            " 189747.55849879]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0rz9DjPpL0j",
        "colab_type": "code",
        "outputId": "bafb6f7b-0fb6-43da-bd49-04f15d02de07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "lin_reg.coef_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-55650.4116403 , -56716.45236929,  13732.83841856,  -1933.1277138 ,\n",
              "         7330.04062103, -45708.26306673,  45455.47519691,  74714.39134154,\n",
              "         6605.12802802,   1042.95709453,   9249.75886697, -18016.52432168,\n",
              "       -55219.15208555, 110357.78363967, -22479.84008184, -14642.2671506 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cLYd7VinprTX",
        "colab_type": "code",
        "outputId": "626f2d5d-9fef-4319-ffb3-6da681f74187",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "lin_reg.intercept_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "236914.99732815978"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Jv2EclCpwQ3",
        "colab_type": "text"
      },
      "source": [
        "### predict mean square error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FBSpWRZp8y3",
        "colab_type": "code",
        "outputId": "58996b12-7944-40fc-f2b2-0cdef59eacc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "housing_predictions = lin_reg.predict(housing_prepared)\n",
        "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "lin_rmse = np.sqrt(lin_mse)\n",
        "lin_rmse"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68628.19819848923"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FlfpJ56qUPy",
        "colab_type": "text"
      },
      "source": [
        "### Decision Tree Classifier (overfitting)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqW5inxR9lLN",
        "colab_type": "code",
        "outputId": "fa1779b6-06d6-48e3-92ad-03c4db1f14e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "tree_reg = DecisionTreeRegressor()\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=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHLShjcK95Go",
        "colab_type": "text"
      },
      "source": [
        "#### Test the decision tree classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLHvZpDW-VT5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "housing_predictions = tree_reg.predict(housing_prepared)\n",
        "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
        "tree_rmse = np.sqrt(tree_mse)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZFTrXEP-04z",
        "colab_type": "code",
        "outputId": "9e190c41-6754-47cf-8ced-cb809f69f3af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tree_rmse"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qm-X0VMI_acf",
        "colab_type": "text"
      },
      "source": [
        "### Cross Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMgYC1jnAEth",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(tree_reg, housing_prepared, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
        "rmse_scores = np.sqrt(-scores)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyfphSVzA37u",
        "colab_type": "code",
        "outputId": "e764769d-8925-4a6e-f47f-1a46950c8e06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "rmse_scores"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([69327.01708558, 65486.39211857, 71358.25563341, 69091.37509104,\n",
              "       70570.20267046, 75529.94622521, 69895.20650652, 70660.14247357,\n",
              "       75843.74719231, 68905.17669382])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOchshnrA9w4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def display_scores(scores):\n",
        "  print(\"Scores: \", scores)\n",
        "  print(\"mean score\",scores.mean())\n",
        "  print(\"Standard deviation\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJKU6sNNCEaB",
        "colab_type": "code",
        "outputId": "edc0c157-f78e-45a1-ea21-68d01e69f302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "display_scores(rmse_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores:  [69327.01708558 65486.39211857 71358.25563341 69091.37509104\n",
            " 70570.20267046 75529.94622521 69895.20650652 70660.14247357\n",
            " 75843.74719231 68905.17669382]\n",
            "mean score 70666.74616904806\n",
            "Standard deviation 2928.322738055112\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6B8IrX4gCK6l",
        "colab_type": "text"
      },
      "source": [
        "### Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_T9n4rSICekb",
        "colab_type": "code",
        "outputId": "380e502b-3abc-466b-c9f3-803ed259f152",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "clf = RandomForestRegressor()\n",
        "scores_rf = cross_val_score(clf, housing_prepared, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qGmGNSwCxvK",
        "colab_type": "code",
        "outputId": "d4d90cc9-d9e6-409d-ab26-3944ca190ccf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "rmse_rf = np.sqrt(-scores_rf)\n",
        "display_scores(rmse_rf)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores:  [53445.79752931 50228.21524731 51753.39062036 54736.20235366\n",
            " 51922.10966185 56522.25501082 52483.28421733 51662.07771931\n",
            " 55976.34901563 52118.52931523]\n",
            "mean score 53084.8210690824\n",
            "Standard deviation 1940.0725959484223\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wfmTho7DJfG",
        "colab_type": "text"
      },
      "source": [
        "### Save the model and hyperparams (need to call fit before using)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9g0xrqinEBR1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "\n",
        "joblib.dump(tree_reg, \"decision_tree_model.pkl\")\n",
        "\n",
        "tree_reg_loaded = joblib.load(\"decision_tree_model.pkl\")\n",
        "\n",
        "housing_predictions = tree_reg_loaded.predict(housing_prepared)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9OZlJU2EqSu",
        "colab_type": "text"
      },
      "source": [
        "### Grid Search "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w93NrtHdE6gm",
        "colab_type": "code",
        "outputId": "229e46ec-840e-4b89-d75c-b118c93219e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = [\n",
        "    {'n_estimators':[3,10,30],'max_features':[2,4,6,8]},\n",
        "    {'bootstrap':[False], 'n_estimators':[3,10],'max_features':[2,3,4]}\n",
        "]\n",
        "\n",
        "forest_reg = RandomForestRegressor()\n",
        "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\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-deprecating',\n",
              "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
              "                                             max_depth=None,\n",
              "                                             max_features='auto',\n",
              "                                             max_leaf_nodes=None,\n",
              "                                             min_impurity_decrease=0.0,\n",
              "                                             min_impurity_split=None,\n",
              "                                             min_samples_leaf=1,\n",
              "                                             min_samples_split=2,\n",
              "                                             min_weight_fraction_leaf=0.0,\n",
              "                                             n_estimators='warn', n_jobs=None,\n",
              "                                             oob_score=False, random_state=None,\n",
              "                                             verbose=0, warm_start=False),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
              "                          'n_estimators': [3, 10, 30]},\n",
              "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
              "                          'n_estimators': [3, 10]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzgROuEatM2h",
        "colab_type": "text"
      },
      "source": [
        "### Grid search best params"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mi1huQft-H0",
        "colab_type": "code",
        "outputId": "2c9db046-431d-4c70-e227-eab803bcdedd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "print(grid_search.best_params_)\n",
        "print(grid_search.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_features': 6, 'n_estimators': 30}\n",
            "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
            "                      max_features=6, 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=30,\n",
            "                      n_jobs=None, oob_score=False, random_state=None,\n",
            "                      verbose=0, warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0M1tLH1iuCjD",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj0HwMjwuu74",
        "colab_type": "code",
        "outputId": "e5be9c85-eaed-4490-e1bf-1512f22f5b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "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": [
            "65103.37852247545 {'max_features': 2, 'n_estimators': 3}\n",
            "55812.012618997505 {'max_features': 2, 'n_estimators': 10}\n",
            "52829.33026894664 {'max_features': 2, 'n_estimators': 30}\n",
            "60050.41492233346 {'max_features': 4, 'n_estimators': 3}\n",
            "52675.5448928722 {'max_features': 4, 'n_estimators': 10}\n",
            "50473.79089441315 {'max_features': 4, 'n_estimators': 30}\n",
            "58518.3274111494 {'max_features': 6, 'n_estimators': 3}\n",
            "52631.28884738872 {'max_features': 6, 'n_estimators': 10}\n",
            "50009.77682888977 {'max_features': 6, 'n_estimators': 30}\n",
            "58706.78887053464 {'max_features': 8, 'n_estimators': 3}\n",
            "52413.850993322616 {'max_features': 8, 'n_estimators': 10}\n",
            "50227.33402154076 {'max_features': 8, 'n_estimators': 30}\n",
            "61242.37837702333 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
            "54584.50556236228 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
            "61383.39998756611 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
            "52824.0921038068 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
            "58672.73349442919 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
            "51922.531390338845 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3agpgumvP9e",
        "colab_type": "text"
      },
      "source": [
        "### Randomized Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlbQ30L2_UpN",
        "colab_type": "code",
        "outputId": "7eb10ded-c70b-4ac9-b5e1-3224dcfb7ebf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "params = {'max_features': [2, 10], 'n_estimators': [3, 300],}\n",
        "\n",
        "random_search = RandomizedSearchCV(forest_reg, param_distributions=params,n_iter=7,scoring='neg_mean_squared_error')\n",
        "\n",
        "random_search.fit(housing_prepared, housing_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:266: UserWarning: The total space of parameters 4 is smaller than n_iter=7. Running 4 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\n",
              "                   estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                   criterion='mse',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators='warn',\n",
              "                                                   n_jobs=None, oob_score=False,\n",
              "                                                   random_state=None, verbose=0,\n",
              "                                                   warm_start=False),\n",
              "                   iid='warn', n_iter=7, n_jobs=None,\n",
              "                   param_distributions={'max_features': [2, 10],\n",
              "                                        'n_estimators': [3, 300]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
              "                   verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pITfGbbDAy6f",
        "colab_type": "text"
      },
      "source": [
        "### calculate mean scores for random search cv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8yMVfK7BDiI",
        "colab_type": "code",
        "outputId": "f3f11aaf-9e7f-4abc-e4f3-6e941756c1e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "print(random_search.best_params_)\n",
        "print(random_search.best_estimator_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_estimators': 300, 'max_features': 10}\n",
            "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
            "                      max_features=10, 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=300,\n",
            "                      n_jobs=None, oob_score=False, random_state=None,\n",
            "                      verbose=0, warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4HfKITubBF9U",
        "colab_type": "text"
      },
      "source": [
        "### random search cv results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXIsazGjBVlD",
        "colab_type": "code",
        "outputId": "5329d616-5957-4cfe-ede0-f4f89d80bb8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "rndm_cvres = random_search.cv_results_\n",
        "for mean_score, params in zip(rndm_cvres[\"mean_test_score\"], rndm_cvres[\"params\"]):\n",
        "  print(np.sqrt(-mean_score), params)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "65070.54985692122 {'n_estimators': 3, 'max_features': 2}\n",
            "52248.042954723365 {'n_estimators': 300, 'max_features': 2}\n",
            "59303.9031603218 {'n_estimators': 3, 'max_features': 10}\n",
            "49877.68232786823 {'n_estimators': 300, 'max_features': 10}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXNpQwS2BeIw",
        "colab_type": "code",
        "outputId": "82dbac65-ef48-473b-c4ee-0a848ed75906",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "rndm_cvres[\"mean_test_score\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.23417646e+09, -2.72985799e+09, -3.51695293e+09, -2.48778319e+09])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alvWCM77BcbY",
        "colab_type": "text"
      },
      "source": [
        "### Analyse the best models and their errors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLbbcKTNKB5W",
        "colab_type": "code",
        "outputId": "ad33bfec-2b3f-467a-983c-856975466b61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "feature_importances = grid_search.best_estimator_.feature_importances_\n",
        "print(feature_importances)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[8.17830874e-02 7.16965270e-02 4.22558122e-02 1.75555736e-02\n",
            " 1.67384586e-02 1.72036329e-02 1.60798708e-02 3.58305734e-01\n",
            " 5.22032548e-02 1.03474513e-01 7.43891055e-02 1.45494894e-02\n",
            " 1.23861310e-01 1.60016797e-04 3.59406289e-03 6.14955013e-03]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjTRz84QKOf0",
        "colab_type": "text"
      },
      "source": [
        "#### display importance scores next to their corresponding attribute names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CS4vUAODKxX4",
        "colab_type": "code",
        "outputId": "0e240c82-4f59-4ece-80fe-283685b4107e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
        "cat_one_hot_attribs = list(encoder.classes_)\n",
        "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
        "sorted(zip(feature_importances, attributes), reverse=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.3583057341317542, 'median_income'),\n",
              " (0.12386131026137194, 'INLAND'),\n",
              " (0.103474513475898, 'pop_per_hhold'),\n",
              " (0.0817830873662211, 'longitude'),\n",
              " (0.07438910552430406, 'bedrooms_per_room'),\n",
              " (0.07169652704892138, 'latitude'),\n",
              " (0.05220325477522301, 'rooms_per_hhold'),\n",
              " (0.042255812246158736, 'housing_median_age'),\n",
              " (0.017555573618533417, 'total_rooms'),\n",
              " (0.0172036329242136, 'population'),\n",
              " (0.01673845857425479, 'total_bedrooms'),\n",
              " (0.01607987080562857, 'households'),\n",
              " (0.014549489439349562, '<1H OCEAN'),\n",
              " (0.006149550125474585, 'NEAR OCEAN'),\n",
              " (0.003594062885533863, 'NEAR BAY'),\n",
              " (0.00016001679715923938, 'ISLAND')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KR99FoULUWI",
        "colab_type": "text"
      },
      "source": [
        "### Evaluating the system on test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9JYU4_IL2hK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MnqpRVdMPbI",
        "colab_type": "text"
      },
      "source": [
        "### Make the predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oioZ0nbJMT_r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXd7b3G-NAnU",
        "colab_type": "code",
        "outputId": "4a829528-9b53-4deb-dcf1-d4ef06eaad9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "final_rmse"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47347.40716894389"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NYXtf-9NDJ8",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8Wg3QIkbbdk",
        "colab_type": "text"
      },
      "source": [
        "### using svm regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7KHqhxNtbfdk",
        "colab_type": "code",
        "outputId": "ae9fc01d-9174-4f1f-fe05-7f1808e0a6f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "svr_reg = SVR()\n",
        "\n",
        "params_svr = [{'kernel':[\"rbf\",\"linear\"],'gamma':[\"scale\",\"auto\"], 'C': [1, 10, 100, 1000],}]\n",
        "grid_search_svm = GridSearchCV(svr_reg, param_grid =params_svr,scoring='neg_mean_squared_error')\n",
        "\n",
        "\n",
        "grid_search_svm.fit(housing_prepared, housing_labels)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
              "             estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
              "                           epsilon=0.1, gamma='auto_deprecated', kernel='rbf',\n",
              "                           max_iter=-1, shrinking=True, tol=0.001,\n",
              "                           verbose=False),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': ['scale', 'auto'],\n",
              "                          'kernel': ['rbf', 'linear']}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xWjqg_n7a_N",
        "colab_type": "text"
      },
      "source": [
        "### see the stats of grid search svr"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJtZd9N07amA",
        "colab_type": "code",
        "outputId": "116dba19-7295-4f95-bfb6-d17ba866b83b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "print(grid_search_svm.best_params_)\n",
        "print(grid_search_svm.best_estimator_)\n",
        "\n",
        "cvres = grid_search_svm.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": [
            "{'C': 1000, 'gamma': 'scale', 'kernel': 'linear'}\n",
            "SVR(C=1000, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
            "    kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\n",
            "118700.33783086088 {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}\n",
            "113570.79729869253 {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}\n",
            "118703.26770375816 {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}\n",
            "113570.79729869253 {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}\n",
            "116605.62613040932 {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}\n",
            "87119.08620015063 {'C': 10, 'gamma': 'scale', 'kernel': 'linear'}\n",
            "116629.97274418767 {'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}\n",
            "87119.08620015063 {'C': 10, 'gamma': 'auto', 'kernel': 'linear'}\n",
            "100495.464233206 {'C': 100, 'gamma': 'scale', 'kernel': 'rbf'}\n",
            "71923.97409919975 {'C': 100, 'gamma': 'scale', 'kernel': 'linear'}\n",
            "100399.20719137305 {'C': 100, 'gamma': 'auto', 'kernel': 'rbf'}\n",
            "71923.97409919975 {'C': 100, 'gamma': 'auto', 'kernel': 'linear'}\n",
            "72909.94812498326 {'C': 1000, 'gamma': 'scale', 'kernel': 'rbf'}\n",
            "70450.19366403717 {'C': 1000, 'gamma': 'scale', 'kernel': 'linear'}\n",
            "72283.19074751172 {'C': 1000, 'gamma': 'auto', 'kernel': 'rbf'}\n",
            "70450.19366403717 {'C': 1000, 'gamma': 'auto', 'kernel': 'linear'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JP3YSteM3IP_",
        "colab_type": "text"
      },
      "source": [
        "### Testing SVR with grid search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVrmu4zJ7Rin",
        "colab_type": "code",
        "outputId": "e9660453-e9cb-4abb-9f8b-47346d718d11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "final_model_svr = grid_search_svm.best_estimator_\n",
        "\n",
        "final_predictions = final_model_svr.predict(X_test_prepared)\n",
        "\n",
        "final_mse = mean_squared_error(y_test, final_predictions)\n",
        "final_rmse = np.sqrt(final_mse)\n",
        "print(final_rmse)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "68366.3364776461\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVix86VvAivE",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7B89YdJ9Pe-",
        "colab_type": "text"
      },
      "source": [
        "### Testing SVR with random search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWmcvzis72pV",
        "colab_type": "code",
        "outputId": "629b3745-bb97-499a-89a7-00f418c1f38b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "\n",
        "params_svr = {'kernel':[\"rbf\",\"linear\"],'gamma':[\"scale\",\"auto\"], 'C': [1, 10, 100, 1000],}\n",
        "random_search_svm = RandomizedSearchCV(svr_reg, param_distributions =params_svr,n_iter=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "\n",
        "random_search_svm.fit(housing_prepared, housing_labels)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\n",
              "                   estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
              "                                 epsilon=0.1, gamma='auto_deprecated',\n",
              "                                 kernel='rbf', max_iter=-1, shrinking=True,\n",
              "                                 tol=0.001, verbose=False),\n",
              "                   iid='warn', n_iter=5, n_jobs=None,\n",
              "                   param_distributions={'C': [1, 10, 100, 1000],\n",
              "                                        'gamma': ['scale', 'auto'],\n",
              "                                        'kernel': ['rbf', 'linear']},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
              "                   verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBfH0xzm9oFc",
        "colab_type": "code",
        "outputId": "cc906c85-047b-498b-e8df-e5f61434de5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "final_model_svr = random_search_svm.best_estimator_\n",
        "\n",
        "final_predictions = final_model_svr.predict(X_test_prepared)\n",
        "\n",
        "final_mse = mean_squared_error(y_test, final_predictions)\n",
        "final_rmse = np.sqrt(final_mse)\n",
        "print(final_rmse)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "68366.3364776461\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3O7aI8X_8cNe",
        "colab_type": "code",
        "outputId": "5c370042-b99d-4ef5-a44d-8f60ed30cf2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "attributes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['longitude',\n",
              " 'latitude',\n",
              " 'housing_median_age',\n",
              " 'total_rooms',\n",
              " 'total_bedrooms',\n",
              " 'population',\n",
              " 'households',\n",
              " 'median_income',\n",
              " 'rooms_per_hhold',\n",
              " 'pop_per_hhold',\n",
              " 'bedrooms_per_room',\n",
              " '<1H OCEAN',\n",
              " 'INLAND',\n",
              " 'ISLAND',\n",
              " 'NEAR BAY',\n",
              " 'NEAR OCEAN']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuR-HtgD_9K4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "most_important_features=['longitude',\n",
        " 'latitude',\n",
        " 'housing_median_age',\n",
        " 'total_rooms',\n",
        " 'total_bedrooms',\n",
        " 'population',\n",
        " 'households',\n",
        " 'median_income',\n",
        " 'rooms_per_hhold',\n",
        " 'pop_per_hhold',\n",
        " 'bedrooms_per_room']\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eznz-bOMBCFW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MostImportantSelector(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": "code",
      "metadata": {
        "id": "WUvo7VzpBJsG",
        "colab_type": "code",
        "outputId": "7bec37fd-db3c-40fb-8079-79b311998ab3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "num_attribs = list(housing_num)\n",
        "cat_attribs = [\"ocean_proximity\"]\n",
        "most_important_features=['longitude',\n",
        " 'latitude',\n",
        " 'housing_median_age',\n",
        " 'total_rooms',\n",
        " 'total_bedrooms',\n",
        " 'population',\n",
        " 'households',\n",
        " 'median_income']\n",
        "\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "    ('selector',DataFrameSelector(most_important_features)),\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",
        "    ('labelBinarizer',OneHotEncoder(sparse=False))\n",
        "])\n",
        "\n",
        "prediction_pipeline=Pipeline([\n",
        "          ('final_model',final_model)\n",
        "])\n",
        "\n",
        "full_pipeline = FeatureUnion(transformer_list=[\n",
        "    (\"num_pipeline\", num_pipeline),\n",
        "    (\"cat_pipeline\",cat_pipeline)\n",
        "])\n",
        "                                     \n",
        "full_pipeline_predict = Pipeline([\n",
        "    (\"full_pipeline\",full_pipeline),\n",
        "  (\"prediction_pipeline\",prediction_pipeline),\n",
        "])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNnnEtogCPxi",
        "colab_type": "code",
        "outputId": "65fdae7a-e8a5-4cdf-a3b3-097a2c76514d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "\n",
        "housing_prepared = full_pipeline_predict.fit_transform(housing)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
            "  import sys\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-305-32a83c895ea0>\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_prepared\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfull_pipeline_predict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    391\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\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;32m--> 393\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlast_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\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    394\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlast_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    385\u001b[0m         \"\"\"\n\u001b[1;32m    386\u001b[0m         \u001b[0mlast_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\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    388\u001b[0m         with _print_elapsed_time('Pipeline',\n\u001b[1;32m    389\u001b[0m                                  self._log_message(len(self.steps) - 1)):\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    315\u001b[0m                 \u001b[0mmessage_clsname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Pipeline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m                 \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_idx\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;32m--> 317\u001b[0;31m                 **fit_params_steps[name])\n\u001b[0m\u001b[1;32m    318\u001b[0m             \u001b[0;31m# Replace the transformer of the step with the fitted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0;31m# transformer. This is necessary when loading the transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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;32m--> 355\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\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    715\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\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;32m--> 716\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\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    717\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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    554\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-239-15f5f3bc9bc3>\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\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;32m----> 7\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattribute_names\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPDZEy8jFymY",
        "colab_type": "code",
        "outputId": "4ae083e4-b435-4b24-a500-38a1afedcaf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "housing.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 16512 entries, 17606 to 15775\n",
            "Data columns (total 9 columns):\n",
            "longitude             16512 non-null float64\n",
            "latitude              16512 non-null float64\n",
            "housing_median_age    16512 non-null float64\n",
            "total_rooms           16512 non-null float64\n",
            "total_bedrooms        16354 non-null float64\n",
            "population            16512 non-null float64\n",
            "households            16512 non-null float64\n",
            "median_income         16512 non-null float64\n",
            "ocean_proximity       16512 non-null object\n",
            "dtypes: float64(8), object(1)\n",
            "memory usage: 1.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8FlX9CFGXzW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}