logisticregression.ipynb

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  "metadata": {
    "colab": {
      "name": "logisticregression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPJYHs6eJg7QweSgo4L0d1M"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQoWgaFQAzyh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Libraries\n",
        "import pandas as pd\n",
        "import pylab as pl\n",
        "import numpy as np\n",
        "import scipy.optimize as opt\n",
        "from sklearn import preprocessing\n",
        "%matplotlib inline \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kX4LkQeGBeOq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9c6fa2e4-ad8f-4b10-af97-8d62be075cab"
      },
      "source": [
        "#Dataset\n",
        "!wget -O ChurnData.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/ChurnData.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-18 19:01:49--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/ChurnData.csv\n",
            "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 36144 (35K) [text/csv]\n",
            "Saving to: ‘ChurnData.csv’\n",
            "\n",
            "\rChurnData.csv         0%[                    ]       0  --.-KB/s               \rChurnData.csv       100%[===================>]  35.30K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-03-18 19:01:50 (2.80 MB/s) - ‘ChurnData.csv’ saved [36144/36144]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMomWQjgBjma",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "f679c045-5c61-46e3-8cbb-731da15901dd"
      },
      "source": [
        "#Load the dataset\n",
        "churn_df = pd.read_csv(\"ChurnData.csv\")\n",
        "churn_df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tenure</th>\n",
              "      <th>age</th>\n",
              "      <th>address</th>\n",
              "      <th>income</th>\n",
              "      <th>ed</th>\n",
              "      <th>employ</th>\n",
              "      <th>equip</th>\n",
              "      <th>callcard</th>\n",
              "      <th>wireless</th>\n",
              "      <th>longmon</th>\n",
              "      <th>tollmon</th>\n",
              "      <th>equipmon</th>\n",
              "      <th>cardmon</th>\n",
              "      <th>wiremon</th>\n",
              "      <th>longten</th>\n",
              "      <th>tollten</th>\n",
              "      <th>cardten</th>\n",
              "      <th>voice</th>\n",
              "      <th>pager</th>\n",
              "      <th>internet</th>\n",
              "      <th>callwait</th>\n",
              "      <th>confer</th>\n",
              "      <th>ebill</th>\n",
              "      <th>loglong</th>\n",
              "      <th>logtoll</th>\n",
              "      <th>lninc</th>\n",
              "      <th>custcat</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.40</td>\n",
              "      <td>20.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.25</td>\n",
              "      <td>35.7</td>\n",
              "      <td>42.00</td>\n",
              "      <td>211.45</td>\n",
              "      <td>125.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.482</td>\n",
              "      <td>3.033</td>\n",
              "      <td>4.913</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>288.80</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.246</td>\n",
              "      <td>3.240</td>\n",
              "      <td>3.497</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.30</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>157.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.841</td>\n",
              "      <td>3.240</td>\n",
              "      <td>3.401</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.05</td>\n",
              "      <td>45.00</td>\n",
              "      <td>50.1</td>\n",
              "      <td>23.25</td>\n",
              "      <td>64.9</td>\n",
              "      <td>239.55</td>\n",
              "      <td>1873.05</td>\n",
              "      <td>880.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.800</td>\n",
              "      <td>3.807</td>\n",
              "      <td>4.331</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.10</td>\n",
              "      <td>22.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>47.45</td>\n",
              "      <td>166.10</td>\n",
              "      <td>145.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.960</td>\n",
              "      <td>3.091</td>\n",
              "      <td>4.382</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tenure   age  address  income   ed  ...  loglong  logtoll  lninc  custcat  churn\n",
              "0    11.0  33.0      7.0   136.0  5.0  ...    1.482    3.033  4.913      4.0    1.0\n",
              "1    33.0  33.0     12.0    33.0  2.0  ...    2.246    3.240  3.497      1.0    1.0\n",
              "2    23.0  30.0      9.0    30.0  1.0  ...    1.841    3.240  3.401      3.0    0.0\n",
              "3    38.0  35.0      5.0    76.0  2.0  ...    1.800    3.807  4.331      4.0    0.0\n",
              "4     7.0  35.0     14.0    80.0  2.0  ...    1.960    3.091  4.382      3.0    0.0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOizd1CxBpWK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "dcf6064a-17c4-4155-9693-54bf27299c7f"
      },
      "source": [
        "#Lets select some features for the modeling\n",
        "churn_df = churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip',   'callcard', 'wireless','churn']]\n",
        "#Changing churn float to int \n",
        "churn_df['churn'] = churn_df['churn'].astype('int')\n",
        "#Show the new table\n",
        "churn_df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>tenure</th>\n",
              "      <th>age</th>\n",
              "      <th>address</th>\n",
              "      <th>income</th>\n",
              "      <th>ed</th>\n",
              "      <th>employ</th>\n",
              "      <th>equip</th>\n",
              "      <th>callcard</th>\n",
              "      <th>wireless</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tenure   age  address  income   ed  employ  equip  callcard  wireless  churn\n",
              "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0      1\n",
              "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0      1\n",
              "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0      0\n",
              "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0      0\n",
              "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szWrmYIcCiLi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "d20ce08c-2076-43ce-e8f4-3bd0e8fd9f95"
      },
      "source": [
        "#turn into array\n",
        "X = np.asarray(churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip']])\n",
        "X[0:5]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 11.,  33.,   7., 136.,   5.,   5.,   0.],\n",
              "       [ 33.,  33.,  12.,  33.,   2.,   0.,   0.],\n",
              "       [ 23.,  30.,   9.,  30.,   1.,   2.,   0.],\n",
              "       [ 38.,  35.,   5.,  76.,   2.,  10.,   1.],\n",
              "       [  7.,  35.,  14.,  80.,   2.,  15.,   0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRDAC8n5CpOr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "68d5231d-79a8-409e-f1f2-736a48783476"
      },
      "source": [
        "y = np.asarray(churn_df['churn'])\n",
        "y [0:5]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyOj4he0CuJe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "b5c226d6-7bd4-47f3-b890-9983cb91f760"
      },
      "source": [
        "#Normalize the dateset\n",
        "from sklearn import preprocessing\n",
        "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
        "X[0:5]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.13518441, -0.62595491, -0.4588971 ,  0.4751423 ,  1.6961288 ,\n",
              "        -0.58477841, -0.85972695],\n",
              "       [-0.11604313, -0.62595491,  0.03454064, -0.32886061, -0.6433592 ,\n",
              "        -1.14437497, -0.85972695],\n",
              "       [-0.57928917, -0.85594447, -0.261522  , -0.35227817, -1.42318853,\n",
              "        -0.92053635, -0.85972695],\n",
              "       [ 0.11557989, -0.47262854, -0.65627219,  0.00679109, -0.6433592 ,\n",
              "        -0.02518185,  1.16316   ],\n",
              "       [-1.32048283, -0.47262854,  0.23191574,  0.03801451, -0.6433592 ,\n",
              "         0.53441472, -0.85972695]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGteOXq0C6IB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "08f0197a-4db0-4979-ab13-f82abadd48c0"
      },
      "source": [
        "#Train/Test\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
        "print ('Train set:', X_train.shape,  y_train.shape)\n",
        "print ('Test set:', X_test.shape,  y_test.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train set: (160, 7) (160,)\n",
            "Test set: (40, 7) (40,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOllhx5nDU1D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "0e1e3f26-0834-4539-bc0a-20d2d4044c72"
      },
      "source": [
        "#Modeling (Logistic Regression with Scikit-learn)\n",
        "#C parameter indicates inverse of regularization strength which must be a positive float\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
        "LR"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PksKkkEDjH6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "70c6b151-7e96-43bf-cf6f-e37511fc769c"
      },
      "source": [
        "#Now we can predict using our test set\n",
        "yhat = LR.predict(X_test)\n",
        "yhat"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qi4Q3w_0DmmM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "e65fa816-80ec-4628-b513-90b41f0cbe9c"
      },
      "source": [
        "#predict_proba returns estimates for all classes, ordered by the label of classes. \n",
        "#So, the first column is the probability of class 1, P(Y=1|X), and second column is probability of class 0, P(Y=0|X)\n",
        "yhat_prob = LR.predict_proba(X_test)\n",
        "yhat_prob"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.54132919, 0.45867081],\n",
              "       [0.60593357, 0.39406643],\n",
              "       [0.56277713, 0.43722287],\n",
              "       [0.63432489, 0.36567511],\n",
              "       [0.56431839, 0.43568161],\n",
              "       [0.55386646, 0.44613354],\n",
              "       [0.52237207, 0.47762793],\n",
              "       [0.60514349, 0.39485651],\n",
              "       [0.41069572, 0.58930428],\n",
              "       [0.6333873 , 0.3666127 ],\n",
              "       [0.58068791, 0.41931209],\n",
              "       [0.62768628, 0.37231372],\n",
              "       [0.47559883, 0.52440117],\n",
              "       [0.4267593 , 0.5732407 ],\n",
              "       [0.66172417, 0.33827583],\n",
              "       [0.55092315, 0.44907685],\n",
              "       [0.51749946, 0.48250054],\n",
              "       [0.485743  , 0.514257  ],\n",
              "       [0.49011451, 0.50988549],\n",
              "       [0.52423349, 0.47576651],\n",
              "       [0.61619519, 0.38380481],\n",
              "       [0.52696302, 0.47303698],\n",
              "       [0.63957168, 0.36042832],\n",
              "       [0.52205164, 0.47794836],\n",
              "       [0.50572852, 0.49427148],\n",
              "       [0.70706202, 0.29293798],\n",
              "       [0.55266286, 0.44733714],\n",
              "       [0.52271594, 0.47728406],\n",
              "       [0.51638863, 0.48361137],\n",
              "       [0.71331391, 0.28668609],\n",
              "       [0.67862111, 0.32137889],\n",
              "       [0.50896403, 0.49103597],\n",
              "       [0.42348082, 0.57651918],\n",
              "       [0.71495838, 0.28504162],\n",
              "       [0.59711064, 0.40288936],\n",
              "       [0.63808839, 0.36191161],\n",
              "       [0.39957895, 0.60042105],\n",
              "       [0.52127638, 0.47872362],\n",
              "       [0.65975464, 0.34024536],\n",
              "       [0.5114172 , 0.4885828 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McxU2Th8D5eh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "477fc7c3-c8e1-45e3-8c01-fa55e5e71e46"
      },
      "source": [
        "#Evaluation\n",
        "'''\n",
        "Lets try jaccard index for accuracy evaluation. we can define jaccard as the size of the intersection divided by the size of the union of two label sets. \n",
        "If the entire set of predicted labels for a sample strictly match with the true set of labels, \n",
        "then the subset accuracy is 1.0; otherwise it is 0.0.\n",
        "'''\n",
        "from sklearn.metrics import jaccard_similarity_score\n",
        "jaccard_similarity_score(y_test, yhat)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.75"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3XzqtDbEaP2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "45424c2a-d63f-4adf-c1ff-9c68eb01529b"
      },
      "source": [
        "#Confusion Matrix\n",
        "#Another way of looking at accuracy of classifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import itertools\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "print(confusion_matrix(y_test, yhat, labels=[1,0]))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 6  9]\n",
            " [ 1 24]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huLIftzmEneJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "4b6c3ef8-7710-4957-fd4c-246a31f41fe5"
      },
      "source": [
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test, yhat, labels=[1,0])\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['churn=1','churn=0'],normalize= False,  title='Confusion matrix')\n",
        "\n",
        "'''\n",
        "Look at first row. The first row is for customers whose actual churn value in test set is 1. As you can calculate, out of 40 customers, \n",
        "the churn value of 15 of them is 1. And out of these 15, the classifier correctly predicted 6 of them as 1, and 9 of them as 0.\n",
        "\n",
        "It means, for 6 customers, the actual churn value were 1 in test set, and classifier also correctly predicted those as 1.\n",
        "However, while the actual label of 9 customers were 1, the classifier predicted those as 0, which is not very good. \n",
        "We can consider it as error of the model for first row.\n",
        "\n",
        "What about the customers with churn value 0? \n",
        "Lets look at the second row. It looks like there were 25 customers whom their churn value were 0.\n",
        "\n",
        "The classifier correctly predicted 24 of them as 0, and one of them wrongly as 1.\n",
        "So, it has done a good job in predicting the customers with churn value 0. \n",
        "A good thing about confusion matrix is that shows the model’s ability to correctly predict or separate the classes. \n",
        "In specific case of binary classifier, such as this example, \n",
        "we can interpret these numbers as the count of true positives, false positives, true negatives, and false negatives.\n",
        "'''"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[ 6  9]\n",
            " [ 1 24]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2XrfeYVg7dvryT+sdgrVj2ay53VJuQ9yqR8SutQzEzKyUSoYA1UpFA+DNzOqp4QbAm5nl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\nPxDw0ltEcTFWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edh6UKCfEqhf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "d9655562-d05f-4734-cee0-6205aa768221"
      },
      "source": [
        "print (classification_report(y_test, yhat))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.96      0.83        25\n",
            "           1       0.86      0.40      0.55        15\n",
            "\n",
            "    accuracy                           0.75        40\n",
            "   macro avg       0.79      0.68      0.69        40\n",
            "weighted avg       0.78      0.75      0.72        40\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}