boston_housing_pickle.ipynb

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  "metadata": {
    "colab": {
      "name": "boston_housing_pickle.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "KI83JYvffyK1",
        "fovOM9U-iRnV",
        "aOv5V5vfj2fT",
        "fzgovOmks6qJ"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/noahgift/boston_housing_pickle/blob/master/boston_housing_pickle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XplJekvedlT",
        "colab_type": "text"
      },
      "source": [
        "# Pickling sklearn Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AiV60fMZes0G",
        "colab_type": "text"
      },
      "source": [
        "## Ingest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnCqYlfCd_I1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy\n",
        "from numpy import arange\n",
        "from matplotlib import pyplot\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from pandas import read_csv\n",
        "from pandas import set_option\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LV_voGAYfREn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "boston_housing = \"https://raw.githubusercontent.com/noahgift/boston_housing_pickle/master/housing.csv\"\n",
        "names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
        "df = read_csv(boston_housing, delim_whitespace=True, names=names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_RzfzsbfcV1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f45f6849-a45f-4d94-ca21-f0244a525385"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
              "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
              "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
              "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
              "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  MEDV  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     17.8  392.83   4.03  34.7  \n",
              "3     18.7  394.63   2.94  33.4  \n",
              "4     18.7  396.90   5.33  36.2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KI83JYvffyK1",
        "colab_type": "text"
      },
      "source": [
        "## EDA\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "detBthVTgorP",
        "colab_type": "text"
      },
      "source": [
        "* CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n",
        "* RM - average number of rooms per dwelling\n",
        "* TAX - full-value property-tax rate per $10,000\n",
        "* PTRATIO - pupil-teacher ratio by town\n",
        "* Bk is the proportion of blacks by town\n",
        "\n",
        "* LSTAT - `%` lower status of the population\n",
        "* MEDV - Median value of owner-occupied homes in $1000's"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCbu5OWDfzoI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "a41b8bbf-72a1-4c94-dbb0-804e846860df"
      },
      "source": [
        "prices = df['MEDV']\n",
        "df = df.drop(['CRIM','ZN','INDUS','NOX','AGE','DIS','RAD'], axis = 1)\n",
        "features = df.drop('MEDV', axis = 1)\n",
        "df.head()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>CHAS</th>\n",
              "      <th>RM</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>6.575</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>6.421</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>7.185</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>6.998</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>7.147</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHAS     RM    TAX  PTRATIO       B  LSTAT  MEDV\n",
              "0     0  6.575  296.0     15.3  396.90   4.98  24.0\n",
              "1     0  6.421  242.0     17.8  396.90   9.14  21.6\n",
              "2     0  7.185  242.0     17.8  392.83   4.03  34.7\n",
              "3     0  6.998  222.0     18.7  394.63   2.94  33.4\n",
              "4     0  7.147  222.0     18.7  396.90   5.33  36.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fovOM9U-iRnV",
        "colab_type": "text"
      },
      "source": [
        "## Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQyqmgoEieit",
        "colab_type": "text"
      },
      "source": [
        "### Split Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPn72X5LiStX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split-out validation dataset\n",
        "array = df.values\n",
        "X = array[:,0:6]\n",
        "Y = array[:,6]\n",
        "validation_size = 0.20\n",
        "seed = 7\n",
        "X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state=seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUDuyhYupEU2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "0eb67535-8ca5-47b6-91d1-594686c7ef86"
      },
      "source": [
        "for sample in list(X_validation)[0:2]:\n",
        "    print(f\"X_validation {sample}\")"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_validation [  1.      6.395 666.     20.2   391.34   13.27 ]\n",
            "X_validation [  0.      5.895 224.     20.2   394.81   10.56 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eWMiShkirWb",
        "colab_type": "text"
      },
      "source": [
        "### Tune scaled GBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxmMBV1yihEq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "ce66c0c0-1bbf-4873-e2c7-cdbdcc88b1f4"
      },
      "source": [
        "# Test options and evaluation metric using Root Mean Square error method\n",
        "num_folds = 10\n",
        "seed = 7\n",
        "RMS = 'neg_mean_squared_error'\n",
        "scaler = StandardScaler().fit(X_train)\n",
        "rescaledX = scaler.transform(X_train)\n",
        "param_grid = dict(n_estimators=numpy.array([50,100,150,200,250,300,350,400]))\n",
        "model = GradientBoostingRegressor(random_state=seed)\n",
        "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
        "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=RMS, cv=kfold)\n",
        "grid_result = grid.fit(rescaledX, Y_train)\n",
        "\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best: -11.830068 using {'n_estimators': 200}\n",
            "-12.479635 (6.348297) with: {'n_estimators': 50}\n",
            "-12.102737 (6.441597) with: {'n_estimators': 100}\n",
            "-11.843649 (6.631569) with: {'n_estimators': 150}\n",
            "-11.830068 (6.559724) with: {'n_estimators': 200}\n",
            "-11.879805 (6.512414) with: {'n_estimators': 250}\n",
            "-11.895362 (6.487726) with: {'n_estimators': 300}\n",
            "-12.008611 (6.468623) with: {'n_estimators': 350}\n",
            "-12.053759 (6.453899) with: {'n_estimators': 400}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIWfjJc4jDqv",
        "colab_type": "text"
      },
      "source": [
        "### Fit Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7tAtchiivRS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "76bd0c03-9337-48c0-9f86-91cabc05941c"
      },
      "source": [
        "# prepare the model\n",
        "scaler = StandardScaler().fit(X_train)\n",
        "rescaledX = scaler.transform(X_train)\n",
        "model = GradientBoostingRegressor(random_state=seed, n_estimators=400)\n",
        "model.fit(rescaledX, Y_train)\n",
        "# transform the validation dataset\n",
        "rescaledValidationX = scaler.transform(X_validation)\n",
        "predictions = model.predict(rescaledValidationX)\n",
        "print(\"Mean Squared Error: \\n\")\n",
        "print(mean_squared_error(Y_validation, predictions))"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error: \n",
            "\n",
            "26.326748591395717\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOv5V5vfj2fT",
        "colab_type": "text"
      },
      "source": [
        "### Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xac8_GtCjHZj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "3c9db672-37cf-4d1a-fb85-4dea3b409916"
      },
      "source": [
        "predictions=predictions.astype(int)\n",
        "evaluate = pd.DataFrame({\n",
        "        \"Org House Price\": Y_validation,\n",
        "        \"Pred House Price\": predictions\n",
        "    })\n",
        "evaluate[\"difference\"] = evaluate[\"Org House Price\"]-evaluate[\"Pred House Price\"]\n",
        "evaluate.head()"
      ],
      "execution_count": 153,
      "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>Org House Price</th>\n",
              "      <th>Pred House Price</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21.7</td>\n",
              "      <td>21</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.5</td>\n",
              "      <td>19</td>\n",
              "      <td>-0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.2</td>\n",
              "      <td>20</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.4</td>\n",
              "      <td>19</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.8</td>\n",
              "      <td>9</td>\n",
              "      <td>-0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Org House Price  Pred House Price  difference\n",
              "0             21.7                21         0.7\n",
              "1             18.5                19        -0.5\n",
              "2             22.2                20         2.2\n",
              "3             20.4                19         1.4\n",
              "4              8.8                 9        -0.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ESdxsa1jWeD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "c9ee3855-786f-4d99-d6c7-6ff7eaa51faf"
      },
      "source": [
        "evaluate.describe()"
      ],
      "execution_count": 154,
      "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>Org House Price</th>\n",
              "      <th>Pred House Price</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.573529</td>\n",
              "      <td>22.117647</td>\n",
              "      <td>0.455882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.033622</td>\n",
              "      <td>8.758921</td>\n",
              "      <td>5.154438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.300000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>-34.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.350000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>-0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.800000</td>\n",
              "      <td>20.500000</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24.800000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>2.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Org House Price  Pred House Price  difference\n",
              "count       102.000000        102.000000  102.000000\n",
              "mean         22.573529         22.117647    0.455882\n",
              "std           9.033622          8.758921    5.154438\n",
              "min           6.300000          8.000000  -34.100000\n",
              "25%          17.350000         17.000000   -0.800000\n",
              "50%          21.800000         20.500000    0.600000\n",
              "75%          24.800000         25.000000    2.200000\n",
              "max          50.000000         56.000000   22.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VprC6zwplMzg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDIBaFIHld3y",
        "colab_type": "text"
      },
      "source": [
        "## Adhoc Predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2XbKOHynPsV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "9f23be81-b537-4bac-f594-7fae1fcff72e"
      },
      "source": [
        "actual_sample = df.head(1)\n",
        "actual_sample"
      ],
      "execution_count": 155,
      "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>CHAS</th>\n",
              "      <th>RM</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>6.575</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.9</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHAS     RM    TAX  PTRATIO      B  LSTAT  MEDV\n",
              "0     0  6.575  296.0     15.3  396.9   4.98  24.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uk37hVZsl1so",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "211f8b79-2737-4a52-fade-3df086077ca2"
      },
      "source": [
        "adhoc_predict = actual_sample[[\"CHAS\", \"RM\", \"TAX\", \"PTRATIO\", \"B\", \"LSTAT\"]]\n",
        "adhoc_predict.head()"
      ],
      "execution_count": 156,
      "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>CHAS</th>\n",
              "      <th>RM</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>6.575</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.9</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHAS     RM    TAX  PTRATIO      B  LSTAT\n",
              "0     0  6.575  296.0     15.3  396.9   4.98"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaupM_q_R7Zv",
        "colab_type": "text"
      },
      "source": [
        "#### JSON workflow\n",
        "\n",
        "Useful for debugging Flask apps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGE0B355Puy_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4791d690-906a-4090-a9f3-8f044f479fa0"
      },
      "source": [
        "json_payload = adhoc_predict.to_json()\n",
        "json_payload"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'{\"CHAS\":{\"0\":0},\"RM\":{\"0\":6.575},\"TAX\":{\"0\":296.0},\"PTRATIO\":{\"0\":15.3},\"B\":{\"0\":396.9},\"LSTAT\":{\"0\":4.98}}'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjupnslOp-EZ",
        "colab_type": "text"
      },
      "source": [
        "#### scale input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5CCJPIDp9II",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "64680960-fd9c-4c0c-c34e-78242927efa1"
      },
      "source": [
        "scaler = StandardScaler().fit(adhoc_predict)\n",
        "scaled_adhoc_predict = scaler.transform(adhoc_predict)\n",
        "scaled_adhoc_predict"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.partial_fit(X, y)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMfbN--9lfyS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fbff9702-c75c-4342-b15e-0592385689b2"
      },
      "source": [
        "list(model.predict(scaled_adhoc_predict))"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[20.35373177134412]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qIswWaxsBMK",
        "colab_type": "text"
      },
      "source": [
        "## Pickling sklearn model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PROVvlbmsGsm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.externals import joblib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0JUY4-SsTvv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9b8b6719-72d7-4c57-95c5-982fc2378e07"
      },
      "source": [
        "joblib.dump(model, 'boston_housing_prediction.joblib')"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['boston_housing_prediction.joblib']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuMZrSiXseHX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c1f7631f-5747-4669-d32f-de7ea6c3e8e0"
      },
      "source": [
        "!ls -l"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 672\n",
            "-rw-r--r-- 1 root root 681425 May  5 00:35 boston_housing_prediction.joblib\n",
            "drwxr-xr-x 1 root root   4096 Apr 29 16:32 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzgovOmks6qJ",
        "colab_type": "text"
      },
      "source": [
        "## Unpickle and predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9Buj8J9s-Wg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = joblib.load('boston_housing_prediction.joblib')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wTBfMdkCtVtr"
      },
      "source": [
        "## Adhoc Predict From Pickle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "932d05cf-e880-4e35-d046-e122989fc89d",
        "id": "Um6vFnSttVts",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "actual_sample2 = df.head(5)\n",
        "actual_sample2"
      ],
      "execution_count": 164,
      "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>CHAS</th>\n",
              "      <th>RM</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>6.575</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>6.421</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>7.185</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>6.998</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>7.147</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHAS     RM    TAX  PTRATIO       B  LSTAT  MEDV\n",
              "0     0  6.575  296.0     15.3  396.90   4.98  24.0\n",
              "1     0  6.421  242.0     17.8  396.90   9.14  21.6\n",
              "2     0  7.185  242.0     17.8  392.83   4.03  34.7\n",
              "3     0  6.998  222.0     18.7  394.63   2.94  33.4\n",
              "4     0  7.147  222.0     18.7  396.90   5.33  36.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e61f7c5e-b787-4f46-a8e8-edf2c7e522db",
        "id": "wdhpAckstVty",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "adhoc_predict2 = actual_sample[[\"CHAS\", \"RM\", \"TAX\", \"PTRATIO\", \"B\", \"LSTAT\"]]\n",
        "adhoc_predict2.head()"
      ],
      "execution_count": 165,
      "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>CHAS</th>\n",
              "      <th>RM</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>6.575</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.9</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHAS     RM    TAX  PTRATIO      B  LSTAT\n",
              "0     0  6.575  296.0     15.3  396.9   4.98"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XJWYfCoJtVt1"
      },
      "source": [
        "#### scale input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "daebb00f-85c7-4d48-9157-dcb9bb356257",
        "id": "LRUac4uDtVt1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "scaler = StandardScaler().fit(adhoc_predict2)\n",
        "scaled_adhoc_predict2 = scaler.transform(adhoc_predict2)\n",
        "scaled_adhoc_predict2"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.partial_fit(X, y)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "36f52e67-dc3d-4235-b62c-555bc3ea5ade",
        "id": "VDt8yaUGtVt5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Use pickle loaded model\n",
        "list(clf.predict(scaled_adhoc_predict2))"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[20.35373177134412]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkwVbjd_tl5b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}