Google_GStore_Data_transaction_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Google GStore Data transaction prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "u-8Klk-I84XM"
      ],
      "toc_visible": true,
      "machine_shape": "hm",
      "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/shishir-joshi/notebooks/blob/master/Google_GStore_Data_transaction_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KuhAiPfZ3rJW",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from IPython.display import clear_output\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NFtnFm1T0kMf",
        "outputId": "c9c25806-5181-4098-b915-11f342bd625c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "try:\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(123)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJi8W4aCr43a",
        "colab_type": "code",
        "outputId": "90ea29d0-4c39-49f5-d33f-f1b02b783f13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "# Load dataset.\n",
        "data = pd.read_csv('https://storage.googleapis.com/sample_user_behavior_data/sample_user_data.csv')\n",
        "\n",
        "dftrain, dftest = train_test_split(data,test_size=0.3)\n",
        "dftrain, dfeval = train_test_split(dftrain,test_size=0.2)\n",
        "\n",
        "_=dftrain.pop('fullVisitorId')\n",
        "_=dftest.pop('fullVisitorId')\n",
        "_=dfeval.pop('fullVisitorId')\n",
        "\n",
        "y_train = dftrain.pop('totals_totalTransactionRevenue').to_frame()\n",
        "y_test = dftest.pop('totals_totalTransactionRevenue').to_frame()\n",
        "y_eval = dfeval.pop('totals_totalTransactionRevenue').to_frame()\n",
        "\n",
        "y_train.fillna(0,inplace=True)\n",
        "y_test.fillna(0,inplace=True)\n",
        "y_eval.fillna(0,inplace=True)\n",
        "\n",
        "dftrain.fillna(0,inplace=True)\n",
        "dftest.fillna(0,inplace=True)\n",
        "dfeval.fillna(0,inplace=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4259: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  **kwargs\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tElEFocc-aOo",
        "colab_type": "text"
      },
      "source": [
        "*NOTE:*\n",
        "\n",
        "*As the task is to predict whether a visitor will make a* *transaction or not, I have converted the target variable from* *numeric to binary(o = no revenue, 1 = some revenue)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XHwor1C-Y_j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "y_train.loc[y_train.totals_totalTransactionRevenue > 0, 'totals_totalTransactionRevenue'] = 1\n",
        "y_test.loc[y_test.totals_totalTransactionRevenue > 0, 'totals_totalTransactionRevenue'] = 1\n",
        "y_eval.loc[y_eval.totals_totalTransactionRevenue > 0, 'totals_totalTransactionRevenue'] = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxK3jliyFDKb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AoPiWsJALr-k"
      },
      "source": [
        "## Exploring the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUzp9neCkn3y",
        "colab_type": "text"
      },
      "source": [
        "Plotting visitors against the sum of transaction revenue per customer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKqQTQkwBICW",
        "colab_type": "code",
        "outputId": "794f3dac-d321-4955-cc91-56c7548de72f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "source": [
        "data['totals_totalTransactionRevenue'] = data['totals_totalTransactionRevenue'].astype('float')\n",
        "to_plot = data.groupby('fullVisitorId')['totals_totalTransactionRevenue'].sum().reset_index()\n",
        "fig=plt.figure(figsize=(10,10), dpi= 100, facecolor='w', edgecolor='k')\n",
        "colors = np.random.rand(to_plot.shape[0])\n",
        "plt.figure(figsize=(9,5))\n",
        "plt.scatter(range(to_plot.shape[0]), np.log(np.sort(to_plot['totals_totalTransactionRevenue'].values)), c=colors)\n",
        "plt.xlabel('index', fontsize=15)\n",
        "plt.ylabel('Log(TransactionRevenue)', fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5DMm4EwJCW6ySlpYuelNl+HuH6emsxJbM4ZlgYV6d5S/X3oTX6Sr1YZREscXK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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2ft79PCXu9R",
        "colab_type": "text"
      },
      "source": [
        "Plotting total time on site against Revenue"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CaVDmZtuDfux",
        "outputId": "e27fdf26-d5a3-4a30-e7b5-5479a254d265",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "fig=plt.figure(figsize=(10,10), dpi= 100, facecolor='w', edgecolor='k')\n",
        "to_plot = data.groupby('totals_timeOnSite')['totals_totalTransactionRevenue'].sum().reset_index()\n",
        "colors = np.random.rand(to_plot.shape[0])\n",
        "plt.figure(figsize=(9,5))\n",
        "plt.scatter(range(to_plot.shape[0]), (np.sort(to_plot['totals_totalTransactionRevenue'].values)), c=colors)\n",
        "plt.xlabel('index', fontsize=15)\n",
        "plt.ylabel('Time on Site', fontsize=15)\n",
        "# dftrain.totals_timeOnSite.hist(figsize=(5,4),bins=4)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w+osf5v5vbyNVLTC0qcpsR5TK15MMbS2yZf1xjjzUR/raEumTlvhgkUTSIQx9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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K13l3tw_YWXS",
        "colab_type": "code",
        "outputId": "6c1a88b8-3d2e-497a-e1d4-7b90c238104d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        }
      },
      "source": [
        "plt.scatter(data['totals_timeOnSite'],data['totals_totalTransactionRevenue'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f2183350e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6XY2BUw7V6dl9OjObqdv/X+k7jx7Tpm13r3VH1euWuvX+J3T3vierTig61SOzEE5KqQ2s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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7h4IWL8PavwX",
        "colab_type": "text"
      },
      "source": [
        "Which channels bring the most visitors:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-WazAq30MO5J",
        "outputId": "1bd620f1-cff5-438c-f5e8-027f219b4b43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "dftrain.channelGrouping.value_counts().plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jaedOVp0LnClJqd5+PdxlA/ByCq89gUP63AkzM9siWQcYQEQsApYCJ0XEPOAG4GFJ7cDN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\nnFYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3h7n2aJ3a5EH",
        "colab_type": "text"
      },
      "source": [
        "Plotting the revenue from specific data sources, \n",
        "direct and google seem to dominate the total revenue streams"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Eh3KW5oYkaNS",
        "outputId": "7d882b6e-78ba-4382-9456-d274769764a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        }
      },
      "source": [
        "ind = np.arange(len(data.groupby('trafficSource_source')))\n",
        "width = 5\n",
        "fig=plt.figure(figsize=(8,8), dpi= 80, facecolor='w', edgecolor='k')\n",
        "fig.suptitle('Revenue from traffic sources', fontsize=20)\n",
        "plt.ylabel('Traffic Source', fontsize=18)\n",
        "plt.xlabel('Revenue', fontsize=16)\n",
        "to_plot = data.groupby('trafficSource_source')['totals_totalTransactionRevenue'].sum().reset_index()\n",
        "to_plot = to_plot.sort_values(by='totals_totalTransactionRevenue')[::-1][:20]\n",
        "plt.yticks(ind[:20], to_plot['trafficSource_source'], rotation = -360)\n",
        "\n",
        "bar = plt.barh(ind[:20], to_plot['totals_totalTransactionRevenue'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ph4WFRY23ge4+jhElItja2irhc8GCBbhy5YrS5u/vjyZNmuD48eM4depUjfsuHZP6zTff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\ntnIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRZ5WgCC0Jf8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VdrpM01elqfk",
        "colab_type": "text"
      },
      "source": [
        "###Dataset Summary Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "15PLelXBlxEW",
        "outputId": "31692473-c622-4940-a909-29acc331bd3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        }
      },
      "source": [
        "dftrain.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>visitNumber</th>\n",
              "      <th>date</th>\n",
              "      <th>visitStartTime</th>\n",
              "      <th>totals_bounces</th>\n",
              "      <th>totals_pageviews</th>\n",
              "      <th>totals_timeOnSite</th>\n",
              "      <th>totals_transactions</th>\n",
              "      <th>trafficSource_source</th>\n",
              "      <th>trafficSource_medium</th>\n",
              "      <th>trafficSource_campaign</th>\n",
              "      <th>device_deviceCategory</th>\n",
              "      <th>device_operatingSystem</th>\n",
              "      <th>device_mobileDeviceModel</th>\n",
              "      <th>geoNetwork_city</th>\n",
              "      <th>channelGrouping</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>156944</th>\n",
              "      <td>1</td>\n",
              "      <td>20170603</td>\n",
              "      <td>1496557210</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>(direct)</td>\n",
              "      <td>(none)</td>\n",
              "      <td>(not set)</td>\n",
              "      <td>tablet</td>\n",
              "      <td>Android</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>Direct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191023</th>\n",
              "      <td>1</td>\n",
              "      <td>20170630</td>\n",
              "      <td>1498806257</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>youtube.com</td>\n",
              "      <td>referral</td>\n",
              "      <td>(not set)</td>\n",
              "      <td>mobile</td>\n",
              "      <td>Android</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>Social</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304487</th>\n",
              "      <td>1</td>\n",
              "      <td>20170129</td>\n",
              "      <td>1485702353</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>quora.com</td>\n",
              "      <td>referral</td>\n",
              "      <td>(not set)</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>Social</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350015</th>\n",
              "      <td>1</td>\n",
              "      <td>20170708</td>\n",
              "      <td>1499511926</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>google</td>\n",
              "      <td>organic</td>\n",
              "      <td>(not set)</td>\n",
              "      <td>mobile</td>\n",
              "      <td>Android</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>(not set)</td>\n",
              "      <td>Organic Search</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174144</th>\n",
              "      <td>1</td>\n",
              "      <td>20170226</td>\n",
              "      <td>1488141142</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>google</td>\n",
              "      <td>organic</td>\n",
              "      <td>(not set)</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>Organic Search</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        visitNumber      date  ...                geoNetwork_city  channelGrouping\n",
              "156944            1  20170603  ...  not available in demo dataset           Direct\n",
              "191023            1  20170630  ...                        Chennai           Social\n",
              "304487            1  20170129  ...  not available in demo dataset           Social\n",
              "350015            1  20170708  ...                      (not set)   Organic Search\n",
              "174144            1  20170226  ...  not available in demo dataset   Organic Search\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "j2hiM4ETmqP0",
        "outputId": "1e1a207f-b8c2-476f-bb29-d8d5a3315eb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "source": [
        "dftrain.describe(include='all')"
      ],
      "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>visitNumber</th>\n",
              "      <th>date</th>\n",
              "      <th>visitStartTime</th>\n",
              "      <th>totals_bounces</th>\n",
              "      <th>totals_pageviews</th>\n",
              "      <th>totals_timeOnSite</th>\n",
              "      <th>totals_transactions</th>\n",
              "      <th>trafficSource_source</th>\n",
              "      <th>trafficSource_medium</th>\n",
              "      <th>trafficSource_campaign</th>\n",
              "      <th>device_deviceCategory</th>\n",
              "      <th>device_operatingSystem</th>\n",
              "      <th>device_mobileDeviceModel</th>\n",
              "      <th>geoNetwork_city</th>\n",
              "      <th>channelGrouping</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>260233.000000</td>\n",
              "      <td>2.602330e+05</td>\n",
              "      <td>2.602330e+05</td>\n",
              "      <td>260233.000000</td>\n",
              "      <td>260233.000000</td>\n",
              "      <td>260233.000000</td>\n",
              "      <td>260233.000000</td>\n",
              "      <td>260233</td>\n",
              "      <td>260233</td>\n",
              "      <td>260233</td>\n",
              "      <td>260233</td>\n",
              "      <td>260233</td>\n",
              "      <td>260233</td>\n",
              "      <td>260233</td>\n",
              "      <td>260233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>184</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>435</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(direct)</td>\n",
              "      <td>(none)</td>\n",
              "      <td>(not set)</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>not available in demo dataset</td>\n",
              "      <td>Organic Search</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>104926</td>\n",
              "      <td>104924</td>\n",
              "      <td>250031</td>\n",
              "      <td>175487</td>\n",
              "      <td>95968</td>\n",
              "      <td>260233</td>\n",
              "      <td>150305</td>\n",
              "      <td>130422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.345221</td>\n",
              "      <td>2.017042e+07</td>\n",
              "      <td>1.492489e+09</td>\n",
              "      <td>0.501405</td>\n",
              "      <td>3.763377</td>\n",
              "      <td>138.774314</td>\n",
              "      <td>0.014157</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.014187</td>\n",
              "      <td>2.008002e+02</td>\n",
              "      <td>5.283608e+06</td>\n",
              "      <td>0.499999</td>\n",
              "      <td>6.279368</td>\n",
              "      <td>371.015187</td>\n",
              "      <td>0.132951</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.017010e+07</td>\n",
              "      <td>1.483258e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.017022e+07</td>\n",
              "      <td>1.487893e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.017042e+07</td>\n",
              "      <td>1.492441e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.017061e+07</td>\n",
              "      <td>1.497097e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>394.000000</td>\n",
              "      <td>2.017073e+07</td>\n",
              "      <td>1.501571e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>358.000000</td>\n",
              "      <td>11848.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          visitNumber  ...  channelGrouping\n",
              "count   260233.000000  ...           260233\n",
              "unique            NaN  ...                8\n",
              "top               NaN  ...   Organic Search\n",
              "freq              NaN  ...           130422\n",
              "mean         2.345221  ...              NaN\n",
              "std         10.014187  ...              NaN\n",
              "min          1.000000  ...              NaN\n",
              "25%          1.000000  ...              NaN\n",
              "50%          1.000000  ...              NaN\n",
              "75%          1.000000  ...              NaN\n",
              "max        394.000000  ...              NaN\n",
              "\n",
              "[11 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_1NwYqGwDjFf",
        "outputId": "63a6a8de-db68-4bbb-cb27-8f9316e55f8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dftrain.shape[0], dfeval.shape[0], dftest.shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(260233, 65059, 139412)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "krkRHuMp3rJn"
      },
      "source": [
        "## Create feature columns and input functions\n",
        "The Gradient Boosted Trees Classifier needs utilize both numeric and categorical features. \n",
        "Need to make define feature columns as categorical and numeric features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNLu0jp85t0V",
        "colab_type": "code",
        "outputId": "232b1b8b-b1e2-4e72-c14b-c4db0253e6da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "dftrain.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "visitNumber                   int64\n",
              "date                          int64\n",
              "visitStartTime                int64\n",
              "totals_bounces              float64\n",
              "totals_pageviews            float64\n",
              "totals_timeOnSite           float64\n",
              "totals_transactions         float64\n",
              "trafficSource_source         object\n",
              "trafficSource_medium         object\n",
              "trafficSource_campaign       object\n",
              "device_deviceCategory        object\n",
              "device_operatingSystem       object\n",
              "device_mobileDeviceModel     object\n",
              "geoNetwork_city              object\n",
              "channelGrouping              object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "upaNWxcF3rJn",
        "colab": {}
      },
      "source": [
        "fc = tf.feature_column\n",
        "CATEGORICAL_COLUMNS = ['trafficSource_source','trafficSource_medium',\n",
        "       'trafficSource_campaign', 'device_deviceCategory',\n",
        "       'device_operatingSystem', 'device_mobileDeviceModel', 'geoNetwork_city',\n",
        "       'channelGrouping']\n",
        "NUMERIC_COLUMNS = ['visitNumber', 'date', 'visitStartTime', 'totals_bounces', 'totals_pageviews', 'totals_timeOnSite',\n",
        "                   'totals_transactions']\n",
        "\n",
        "def one_hot_cat_column(feature_name, vocab):\n",
        "  return tf.feature_column.indicator_column(\n",
        "      tf.feature_column.categorical_column_with_vocabulary_list(feature_name,\n",
        "                                                 vocab))\n",
        "\n",
        "feature_columns = []\n",
        "for feature_name in NUMERIC_COLUMNS:\n",
        "  feature_columns.append(tf.feature_column.numeric_column(feature_name))\n",
        "  \n",
        "for feature_name in CATEGORICAL_COLUMNS:\n",
        "  # Need to one-hot encode categorical features.\n",
        "  vocabulary = dftrain[feature_name].unique()\n",
        "  # print(vocabulary)\n",
        "  feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9dquwCQB3rJp",
        "outputId": "0c385588-2756-41e3-ec43-127e8806cb53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# For Boosted Tree need to define tf.Data dataset in input function\n",
        "# Use entire batch since this is such a small dataset.\n",
        "NUM_EXAMPLES = len(y_train)\n",
        "print(NUM_EXAMPLES)\n",
        "def make_input_fn(X, y, n_epochs=None, shuffle=True):\n",
        "  def input_fn():\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))\n",
        "    if shuffle:\n",
        "      dataset = dataset.shuffle(NUM_EXAMPLES)\n",
        "    # For training, cycle thru dataset as many times as need (n_epochs=None).\n",
        "    dataset = dataset.repeat(n_epochs)\n",
        "    # In memory training doesn't use batching.\n",
        "    dataset = dataset.batch(NUM_EXAMPLES)\n",
        "    return dataset\n",
        "  return input_fn\n",
        "\n",
        "# Training and evaluation input functions.\n",
        "train_input_fn = make_input_fn(dftrain, y_train)\n",
        "test_input_fn = make_input_fn(dftest, y_test, shuffle=False, n_epochs=1)\n",
        "eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "260233\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HttfNNlN3rJr"
      },
      "source": [
        "## Train and evaluate the model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mrp_lgnaVQI3",
        "colab_type": "text"
      },
      "source": [
        "###Training a Basic Decision Tree classifier first"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEEOV6j4TOI9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import tree"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPdIaztbcMJs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqIrw90DWK01",
        "colab_type": "code",
        "outputId": "2eb79bdf-9b0a-42d9-bc6b-ffc3a362ee4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "train_tree_input = tf.keras.layers.DenseFeatures(feature_columns)(dict(dftrain)).numpy()\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "clf = clf.fit(train_tree_input,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.0.0/python3.6/tensorflow_core/python/feature_column/feature_column_v2.py:4276: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n",
            "WARNING:tensorflow:From /tensorflow-2.0.0/python3.6/tensorflow_core/python/feature_column/feature_column_v2.py:4331: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g98FWbBiVhaG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "eval_tree_input = tf.keras.layers.DenseFeatures(feature_columns)(dict(dfeval)).numpy()\n",
        "eval_preds = clf.predict(eval_tree_input)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f55bZODWVhnG",
        "colab_type": "code",
        "outputId": "4a668c8c-76eb-4f87-878d-388c3727fd28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "pd.DataFrame(\n",
        "    confusion_matrix(y_eval, eval_preds),\n",
        "    columns=['Predicted No Transaction', 'Predicted Transaction'],\n",
        "    index=['True No Transaction', 'True Transaction']\n",
        ")"
      ],
      "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>Predicted No Transaction</th>\n",
              "      <th>Predicted Transaction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>True No Transaction</th>\n",
              "      <td>64145</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True Transaction</th>\n",
              "      <td>0</td>\n",
              "      <td>914</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Predicted No Transaction  Predicted Transaction\n",
              "True No Transaction                     64145                      0\n",
              "True Transaction                            0                    914"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGtTQS6P5dmF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_tree_input = tf.keras.layers.DenseFeatures(feature_columns)(dict(dftest)).numpy()\n",
        "test_preds = clf.predict(test_tree_input)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "71e14dfc-8768-4fb2-a5f4-5fe2619671f7",
        "id": "zmFyUY6j9JNA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "pd.DataFrame(\n",
        "    confusion_matrix(y_test, test_preds),\n",
        "    columns=['Predicted No Transaction', 'Predicted Transaction'],\n",
        "    index=['True No Transaction', 'True Transaction']\n",
        ")"
      ],
      "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>Predicted No Transaction</th>\n",
              "      <th>Predicted Transaction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>True No Transaction</th>\n",
              "      <td>137552</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True Transaction</th>\n",
              "      <td>0</td>\n",
              "      <td>1860</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Predicted No Transaction  Predicted Transaction\n",
              "True No Transaction                    137552                      0\n",
              "True Transaction                            0                   1860"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xEH6NxK5d2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRT6F6rvZDmS",
        "colab_type": "code",
        "outputId": "02ef39fb-f08f-493c-9d81-bfcadb98d992",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy_score(y_test, test_preds)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BarkNXwA3rJu"
      },
      "source": [
        "### Boosted Trees model. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tgEzMtlw3rJu",
        "outputId": "3e2f5feb-2e48-444e-e58d-f16cb20cd1d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        }
      },
      "source": [
        "# Since data fits into memory, use entire dataset per layer. It will be faster.\n",
        "# Above one batch is defined as the entire dataset.\n",
        "n_batches = 1\n",
        "\n",
        "est = tf.estimator.BoostedTreesClassifier(feature_columns,\n",
        "                                          n_batches_per_layer=n_batches)\n",
        "\n",
        "est.train(train_input_fn, max_steps=70)\n",
        "\n",
        "# Eval.\n",
        "result = est.evaluate(eval_input_fn)\n",
        "clear_output()\n",
        "print(pd.Series(result))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy                 1.000000\n",
            "accuracy_baseline        0.985951\n",
            "auc                      1.000000\n",
            "auc_precision_recall     1.000000\n",
            "average_loss             0.000440\n",
            "label/mean               0.014049\n",
            "loss                     0.000440\n",
            "precision                1.000000\n",
            "prediction/mean          0.014476\n",
            "recall                   1.000000\n",
            "global_step             70.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAUKFn8ioo9X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1nljKKg9rYB",
        "colab_type": "text"
      },
      "source": [
        "Plotting the performance on test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6zmIjTr73rJ4",
        "outputId": "9b6bd832-e618-4fc9-e31f-19a63d78a11d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "pred_dicts = list(est.predict(test_input_fn))\n",
        "\n",
        "probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\n",
        "\n",
        "# print(pred_dicts)\n",
        "ind = [0.0,1.0]\n",
        "plt.xticks(ind, ('Wont', 'Will'))\n",
        "\n",
        "probs.plot(kind='hist', bins=2, title='predicted probabilities', colors='green')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmphh2lhufz/model.ckpt-70\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/plotting/_matplotlib/core.py:203: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
            "  \"'colors' is being deprecated. Please use 'color'\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ze6SamZnZVqoNl90j4o32mTS9R22GZGZmvV214fJ7SUe0z0g6Eniri/ZdkvQ/JK2S9Jik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\nAzUxU/1sDikIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BhGdm3c5xrW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oWqB1xg5yT8",
        "colab_type": "text"
      },
      "source": [
        "##Plotting the ROC for both\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mBUaNN1BzJHG"
      },
      "source": [
        "Plotting the Receiver Operating Characteristic (ROC) of the results\n",
        "\n",
        "Given the size of the dataset it is likely there is overfitting in the results, \n",
        "as such given more data and better models, a generalized classifier can be made."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpDUipEm8ZYl",
        "colab_type": "text"
      },
      "source": [
        "First for Basic Decision Tree:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "682e2525-38ed-4e07-be6a-1a090a25b976",
        "id": "5gHjOmkf8fAV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "# Gradienr Boosted Tree\n",
        "fpr, tpr, _ = roc_curve(y_test, test_preds)\n",
        "# fig=plt.figure(figsize=(10,10), dpi= 80, facecolor='w', edgecolor='k')\n",
        "\n",
        "plt.plot(fpr, tpr, color='purple', linewidth=4)\n",
        "plt.title('ROC curve for Basic Decision Tree')\n",
        "plt.xlabel('false positive rate')\n",
        "plt.ylabel('true positive rate')\n",
        "plt.xlim(0,)\n",
        "plt.ylim(0,)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5q/L41pJmlFj3dsCCiFgYEc8DlwCTG8ocCpwZEU8CRMSjKxO8mZmtujKXhqaRDupPAUTE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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NukJycT98hXc",
        "colab_type": "text"
      },
      "source": [
        "For Boosted Tree Classifier:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NzxghvVz3rJ6",
        "outputId": "bf4ef850-2d1d-4bf7-fb60-2f19bed5ebdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "# Gradienr Boosted Tree\n",
        "fpr, tpr, _ = roc_curve(y_test, probs)\n",
        "# fig=plt.figure(figsize=(10,10), dpi= 80, facecolor='w', edgecolor='k')\n",
        "\n",
        "plt.plot(fpr, tpr, color='green', linewidth=4)\n",
        "plt.title('ROC curve for Boosted Tree')\n",
        "plt.xlabel('false positive rate')\n",
        "plt.ylabel('true positive rate')\n",
        "plt.xlim(0,)\n",
        "plt.ylim(0,)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n9F6EMRFxDakKZwSppFG0hNRtdjvXkV5JeBgpKUDqSXUHSZvkOFeXtGmH9YuK\n5+XDhemt+/8NhZe4SNqqxLatAZwIbCD5JnCKpFl0Ls1+RtJdkmYDLwBXRMRvSPXjN0m6k/Rq03YX\n6KeBiUovDn8HqT0A0sX11LzNrfL0IcCP8vZmAWfEK18MMx04JjfiblycEREvkdoqds//EhGPkBLc\nhXlfN5ESWV+mkHqhvR14tDD9l8A+PY3FwKeBCblxey6pMdnMvY+a9ZD0VES84i4ds8HOJQIzs4Zz\nicDMrOFcIjAzazgnAjOzhnMiMDNrOCcCM7OGcyIwM2u4/weQSfDi5l0zIQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-8Klk-I84XM",
        "colab_type": "text"
      },
      "source": [
        "##rough"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYf1eBdMdp8F",
        "colab_type": "code",
        "outputId": "3394356c-5273-4b35-9bb0-77b1273ae1b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "\"\"\"\n",
        "Simple demo of a scatter plot.\n",
        "\"\"\"\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "N = 50\n",
        "x = np.random.rand(N)\n",
        "y = np.random.rand(N)\n",
        "colors = np.random.rand(N)\n",
        "area = np.pi * (5 * np.random.rand(N))**2  # 0 to 15 point radii\n",
        "\n",
        "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7uGl9B7a1ePemnUodYHjmBDHfe/NKjs700ZvcR1fNjYu2X6WWK71T1GO5XJ7x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\noCulVIXQgq6UUhVCC7pSSlWI/w/saeAqJrbg4QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ndq5Z3Qa5QJA",
        "colab_type": "code",
        "outputId": "f4c28d09-5308-4af4-fa5b-5aedb6b915ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# a stacked bar plot with errorbars\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "N = 5\n",
        "menMeans = (20, 35, 30, 35, 27)\n",
        "womenMeans = (25, 32, 34, 20, 25)\n",
        "menStd = (2, 3, 4, 1, 2)\n",
        "womenStd = (3, 5, 2, 3, 3)\n",
        "ind = np.arange(N)    # the x locations for the groups\n",
        "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
        "\n",
        "p1 = plt.bar(ind, menMeans, width, color='#d62728', yerr=menStd)\n",
        "p2 = plt.bar(ind, womenMeans, width,\n",
        "             bottom=menMeans, yerr=womenStd)\n",
        "\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Scores by group and gender')\n",
        "plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))\n",
        "plt.yticks(np.arange(0, 81, 10))\n",
        "plt.legend((p1[0], p2[0]), ('Men', 'Women'))\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I559/fv3+3nrrLVavXg3AiSeeSKdOnejUqRM777wzr7/+OjU1NX/X8VSiqecMPmzmAZgM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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}