Understanding_Confusion_Matrix1.ipynb

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      "name": "Understanding_Confusion_Matrix1.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Blackman9t/Machine_Learning/blob/master/Understanding_Confusion_Matrix1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "iDhT4fAxgOzZ",
        "colab_type": "code",
        "outputId": "3b2daea9-7d60-41b5-f4d3-b2636ad92492",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(__doc__)\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import itertools\n",
        "from sklearn.metrics import roc_curve, auc\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Automatically created module for IPython interactive environment\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4MzCNjgh2W0",
        "colab_type": "text"
      },
      "source": [
        "Let's import our data set from github and read into a Pandas Data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuVVGqibiAwq",
        "colab_type": "code",
        "outputId": "4e47ee4c-43bd-4090-e50f-064ca5d75d90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "data_link = 'https://raw.githubusercontent.com/Blackman9t/Machine_Learning/master/diabetes.csv'\n",
        "\n",
        "diabetes_df = pd.read_csv(data_link)\n",
        "\n",
        "diabetes_df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PatientID</th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>PlasmaGlucose</th>\n",
              "      <th>DiastolicBloodPressure</th>\n",
              "      <th>TricepsThickness</th>\n",
              "      <th>SerumInsulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigree</th>\n",
              "      <th>Age</th>\n",
              "      <th>Diabetic</th>\n",
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              "  </thead>\n",
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            "text/plain": [
              "   PatientID  Pregnancies  PlasmaGlucose  ...  DiabetesPedigree  Age  Diabetic\n",
              "0    1354778            0            171  ...          1.213191   21         0\n",
              "1    1147438            8             92  ...          0.158365   23         0\n",
              "2    1640031            7            115  ...          0.079019   23         0\n",
              "3    1883350            9            103  ...          1.282870   43         1\n",
              "4    1424119            1             85  ...          0.549542   22         0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wy36kU00ijM8",
        "colab_type": "text"
      },
      "source": [
        "Let's check the shape and if any missing values exist"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fD2ShA1Qin4x",
        "colab_type": "code",
        "outputId": "7b7d5afc-f546-4723-9edd-8d52d522a52e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "diabetes_df.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrAm8bWLiqro",
        "colab_type": "code",
        "outputId": "c30e67fb-ab25-4a8c-c33c-d7bd5679a22a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "diabetes_df.isna().any()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PatientID                 False\n",
              "Pregnancies               False\n",
              "PlasmaGlucose             False\n",
              "DiastolicBloodPressure    False\n",
              "TricepsThickness          False\n",
              "SerumInsulin              False\n",
              "BMI                       False\n",
              "DiabetesPedigree          False\n",
              "Age                       False\n",
              "Diabetic                  False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PlahZq0mYx_",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the distribution of each column, this would help us choose the most ideal feature normalization method.<br>\n",
        "First let's define a method that plots the distribution of each feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVySOHK8msHN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_features(data):\n",
        "    plt.figure(figsize=(20, 10))\n",
        "    sns.set(font_scale=1.2)\n",
        "    sns.set_style('ticks') # change background to white background\n",
        "    plt.suptitle('Visualizing The Features Distribution', y=0.95)\n",
        "\n",
        "    plt.subplot(241)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.Age.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('Age')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    plt.subplot(242)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.Pregnancies.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('Pregnancies')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    plt.subplot(243)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.PlasmaGlucose.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('PlasmaGlucose')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    plt.subplot(244)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.DiastolicBloodPressure.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('DiastolicBP')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    plt.subplot(245)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.TricepsThickness.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('TricepsThickness')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    plt.subplot(246)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.SerumInsulin.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('SerumInsulin')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "\n",
        "    plt.subplot(247)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.BMI.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('BMI')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    plt.subplot(248)\n",
        "    color_list = ['gold','purple','brown']\n",
        "    data.DiabetesPedigree.plot(kind='hist', color='brown')\n",
        "    plt.xlabel('DiabetesPedigree')\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4PlJMg7JAsP",
        "colab_type": "code",
        "outputId": "fe969ce7-20d8-4422-c6de-7600ae4cbd1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        }
      },
      "source": [
        "plot_features(diabetes_df)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HZuYNg391kpYkjuCSpA5ExPuAK4GzgZWBVYBvVGVrVmWnA+MoQappEfHu2iEu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\n4yLiVQPTJEnqnx122IEXX3x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kvrEvktQK7IvUbgwWqTu9TTJ/ZtOmUcAbgO2AcwaqUZLU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\n",
            "text/plain": [
              "<Figure size 1440x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWkfoi6FF7AG",
        "colab_type": "text"
      },
      "source": [
        "We can see that features like Age, DiabetesPedigree, SerumInsulin and BMI seem to be skewed to the right.<br>\n",
        "This is due to a lot of smaller values and a progressive distribution of fewer large values.<br>\n",
        "Let's make the distribution better by using the log of the values, rather than actual values in these features.<br>\n",
        "This is a part of the  feature-engineering process in Machine Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1uy0emlF5kY",
        "colab_type": "code",
        "outputId": "c33398d7-76ac-4638-8194-cda64e77e1fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "for i in diabetes_df.columns:\n",
        "    if i in ['Age', 'DiabetesPedigree', 'BMI', 'SerumInsulin']:\n",
        "        print(i)\n",
        "        diabetes_df[i] = diabetes_df[i].apply(np.log)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SerumInsulin\n",
            "BMI\n",
            "DiabetesPedigree\n",
            "Age\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wgOV2tyLJjQ",
        "colab_type": "text"
      },
      "source": [
        "Let's view the distribution again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4bVNsQRJjEz",
        "colab_type": "code",
        "outputId": "4abed7a8-95a6-4c7c-acb8-3455f2c9e661",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        }
      },
      "source": [
        "plot_features(diabetes_df)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tW7duVdOmTSusUQBwN8QiAOaAWATAHBCLAFQV5VqFcMSI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\nRERERCSp/QUhd6mDrPg7zAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYiYg3NkLN3c",
        "colab_type": "text"
      },
      "source": [
        "From the above plots, we can see an improvement to the distribution of our features.<br>\n",
        "This would help us achieve a better result when we train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vXTDeo5mDGQ",
        "colab_type": "text"
      },
      "source": [
        "Okay so now, let's look at the dataset that contains the names of the doctors treating our respective patients.<br>\n",
        "The raw file is in Github, let's read it to a Dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0J90XTBs0JW",
        "colab_type": "code",
        "outputId": "6cfab740-c3f6-4fba-f18d-9e309a10efd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "doctors_link = 'https://raw.githubusercontent.com/Blackman9t/Machine_Learning/master/doctors.csv'\n",
        "\n",
        "doctors_df = pd.read_csv(doctors_link, encoding='latin-1')\n",
        "# Note the above code line throws a UnicodeDecodeError except we encode the string in latin-1 as shown above.\n",
        "\n",
        "doctors_df.head()"
      ],
      "execution_count": 9,
      "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>PatientID</th>\n",
              "      <th>Physician</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000038</td>\n",
              "      <td>Jeanne Vestergaard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000069</td>\n",
              "      <td>Sheldon Comeaux</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000118</td>\n",
              "      <td>Brain Dulaney</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000183</td>\n",
              "      <td>Alaine Poisson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000326</td>\n",
              "      <td>Erik Collado</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PatientID           Physician\n",
              "0    1000038  Jeanne Vestergaard\n",
              "1    1000069     Sheldon Comeaux\n",
              "2    1000118       Brain Dulaney\n",
              "3    1000183      Alaine Poisson\n",
              "4    1000326        Erik Collado"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RG01OWeTs_rr",
        "colab_type": "code",
        "outputId": "7bb4937c-ae8e-4641-85be-f4782d2e3971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "doctors_df.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14895, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SlFJ3NsoHX-",
        "colab_type": "code",
        "outputId": "db678c31-5980-4e56-9463-ede275c4c8bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "doctors_df.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PatientID    0\n",
              "Physician    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPIhFJE-pU7C",
        "colab_type": "text"
      },
      "source": [
        "So we can see that the Doctor data is slightly smaller than the patients dataset.<br>\n",
        "This may be due to the fact that a few doctors saw more than one patient.<br>\n",
        "Let's left-join the diabetes_df and doctors_df data frames using the PatientID as the join key.<br>\n",
        "Left-join here is important so that we keep data for patients who may not have doctors names too."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lw7IvlJCpNxu",
        "colab_type": "code",
        "outputId": "8cdc4b1c-deac-41bf-be1d-3503e85f6758",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "diabetes_doctor_df = pd.merge(diabetes_df, doctors_df, how='left', on='PatientID' )\n",
        "\n",
        "diabetes_doctor_df.head()"
      ],
      "execution_count": 12,
      "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>PatientID</th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>PlasmaGlucose</th>\n",
              "      <th>DiastolicBloodPressure</th>\n",
              "      <th>TricepsThickness</th>\n",
              "      <th>SerumInsulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigree</th>\n",
              "      <th>Age</th>\n",
              "      <th>Diabetic</th>\n",
              "      <th>Physician</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1354778</td>\n",
              "      <td>0</td>\n",
              "      <td>171</td>\n",
              "      <td>80</td>\n",
              "      <td>34</td>\n",
              "      <td>3.135494</td>\n",
              "      <td>3.772984</td>\n",
              "      <td>0.193254</td>\n",
              "      <td>3.044522</td>\n",
              "      <td>0</td>\n",
              "      <td>Dan Drayton</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1147438</td>\n",
              "      <td>8</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>47</td>\n",
              "      <td>3.583519</td>\n",
              "      <td>3.055913</td>\n",
              "      <td>-1.842853</td>\n",
              "      <td>3.135494</td>\n",
              "      <td>0</td>\n",
              "      <td>Anthony Frizzell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1640031</td>\n",
              "      <td>7</td>\n",
              "      <td>115</td>\n",
              "      <td>47</td>\n",
              "      <td>52</td>\n",
              "      <td>3.555348</td>\n",
              "      <td>3.725971</td>\n",
              "      <td>-2.538072</td>\n",
              "      <td>3.135494</td>\n",
              "      <td>0</td>\n",
              "      <td>Gordon Fredrickson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1883350</td>\n",
              "      <td>9</td>\n",
              "      <td>103</td>\n",
              "      <td>78</td>\n",
              "      <td>25</td>\n",
              "      <td>5.717028</td>\n",
              "      <td>3.387173</td>\n",
              "      <td>0.249100</td>\n",
              "      <td>3.761200</td>\n",
              "      <td>1</td>\n",
              "      <td>Chad Corbitt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1424119</td>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>59</td>\n",
              "      <td>27</td>\n",
              "      <td>3.555348</td>\n",
              "      <td>3.751961</td>\n",
              "      <td>-0.598670</td>\n",
              "      <td>3.091042</td>\n",
              "      <td>0</td>\n",
              "      <td>Zachary Fellows</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PatientID  Pregnancies  ...  Diabetic           Physician\n",
              "0    1354778            0  ...         0         Dan Drayton\n",
              "1    1147438            8  ...         0    Anthony Frizzell\n",
              "2    1640031            7  ...         0  Gordon Fredrickson\n",
              "3    1883350            9  ...         1        Chad Corbitt\n",
              "4    1424119            1  ...         0     Zachary Fellows\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKUdBM_wtMlE",
        "colab_type": "text"
      },
      "source": [
        "Now we can see the doctors column appended and can tell which doctor treated a patient.<br>\n",
        "Let's view the shape and also check the number and columns with null values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZoyhLr5tnCh",
        "colab_type": "code",
        "outputId": "f963c6f7-59b4-427b-893a-04b0200c44ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "diabetes_doctor_df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVrVwD_nt0DE",
        "colab_type": "code",
        "outputId": "61691fec-8e13-46ce-acd6-35d853e6a22b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "diabetes_doctor_df.isna().sum()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PatientID                 0\n",
              "Pregnancies               0\n",
              "PlasmaGlucose             0\n",
              "DiastolicBloodPressure    0\n",
              "TricepsThickness          0\n",
              "SerumInsulin              0\n",
              "BMI                       0\n",
              "DiabetesPedigree          0\n",
              "Age                       0\n",
              "Diabetic                  0\n",
              "Physician                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ts9Qxqcft5Jd",
        "colab_type": "text"
      },
      "source": [
        "We can see no missing values, reason could very well be that some doctors treated multiple patients."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZUqsBVhtiCp",
        "colab_type": "text"
      },
      "source": [
        "Let's check the number of unique doctors in the doctor list"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lX_Pxqw2tHL8",
        "colab_type": "code",
        "outputId": "cb691f19-287c-49e7-8f6c-ee1d88185553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "diabetes_doctor_df.Physician.nunique()\n",
        "\n",
        "# We can see that there are 109 doctors who treated these 15000 patients"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "109"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xscYfjYC0LsS",
        "colab_type": "text"
      },
      "source": [
        "Let's also check the number of unique patients in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_5RIFmWz78a",
        "colab_type": "code",
        "outputId": "325a40af-1731-4e93-98c9-81b7901183eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "diabetes_doctor_df.PatientID.nunique()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14895"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtylrfUE0ZEB",
        "colab_type": "text"
      },
      "source": [
        "It now makes sense,<br>\n",
        "There are 14895 unique patients and the doctors records have entries for exactly 14895 patients.<br>\n",
        "The fact that the patients data set has 15000 entries is simply because some patients had multiple entries.<br>\n",
        "Since we merged the doctors and patients on the PatientID column, the merger rightly assigns each doctor to the patients they treated, even though we have only 109 unique doctors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUtG2XtXtpod",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQ3LCTeHujMP",
        "colab_type": "code",
        "outputId": "099363e3-5dbf-464a-89ba-ab68b3e27d14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "diabetes_doctor_df.isna().sum()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PatientID                 0\n",
              "Pregnancies               0\n",
              "PlasmaGlucose             0\n",
              "DiastolicBloodPressure    0\n",
              "TricepsThickness          0\n",
              "SerumInsulin              0\n",
              "BMI                       0\n",
              "DiabetesPedigree          0\n",
              "Age                       0\n",
              "Diabetic                  0\n",
              "Physician                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqesEjlvwu7o",
        "colab_type": "text"
      },
      "source": [
        "### Preparing The Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcBhfmN8w6Bf",
        "colab_type": "text"
      },
      "source": [
        "As is often the case with machine learning of any kind, some data preparation is required before you can\n",
        "use the data to train a model.<br>\n",
        "We shall normalize the features so that features that have large values do not dominate the training.<br>\n",
        "Looking at the shapes of the distribution of each feature, those with a roughly normal distribution bell-shape will be normalized using the Zscore method.<br>\n",
        "While those with varying large and low values will be normalized using the Min-Max method."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRNm0Ccq3YKH",
        "colab_type": "text"
      },
      "source": [
        "**1. Z-Score or Standard Score**_  \n",
        "_For each value here, we subtract the average or mean..._  \n",
        "_And then divide by the Standard deviation._  \n",
        "_This gives a range between minus 3 and 3, but can be more or less_  \n",
        "\n",
        "$Xnew =$ $Xold - mean \\over STD(sigma)$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bSw1UjuF3gEq",
        "colab_type": "text"
      },
      "source": [
        "_**2. Min-Max Method**_  \n",
        "_This method takes each value and subtracts the min and then divides by the range(max - min)..._  \n",
        "_The resultant values range between zero(0) and one(1)_\n",
        "\n",
        "$Xnew =$ $Xold - Xmin \\over Xmax - Xmin$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMfJe09V3a7e",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kokc-lzf4Lj2",
        "colab_type": "text"
      },
      "source": [
        "Now let's apply these methods to the selected columns using the apply method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dH-o3IJ04bDf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in diabetes_doctor_df.columns[:-2]:\n",
        "    mean = diabetes_doctor_df[i].mean()\n",
        "    std = diabetes_doctor_df[i].std()\n",
        "    mini = diabetes_doctor_df[i].min()\n",
        "    maxi = diabetes_doctor_df[i].max()\n",
        "    \n",
        "    # if columns are not Age or Pregnancies, apply the Z_score norm method\n",
        "    if i not in ['Age', 'Pregnancies']:\n",
        "        diabetes_doctor_df[i] = diabetes_doctor_df[i].apply(lambda x: (x - mean) / std)\n",
        "    \n",
        "    # Else if columns are either Age or Pregnancies, then apply the Min-Max norm method\n",
        "    else:\n",
        "        diabetes_doctor_df[i] = diabetes_doctor_df[i].apply(lambda x: (x - mini) / (maxi - mini))\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYPtSab0OWPf",
        "colab_type": "code",
        "outputId": "bdd0de3b-8e80-43c2-b680-7d2af5a7ffa4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "diabetes_doctor_df.head()"
      ],
      "execution_count": 19,
      "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>PatientID</th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>PlasmaGlucose</th>\n",
              "      <th>DiastolicBloodPressure</th>\n",
              "      <th>TricepsThickness</th>\n",
              "      <th>SerumInsulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigree</th>\n",
              "      <th>Age</th>\n",
              "      <th>Diabetic</th>\n",
              "      <th>Physician</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.512160</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.974335</td>\n",
              "      <td>0.523867</td>\n",
              "      <td>0.356286</td>\n",
              "      <td>-1.364665</td>\n",
              "      <td>1.165284</td>\n",
              "      <td>1.755479</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>Dan Drayton</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.228971</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>-0.495806</td>\n",
              "      <td>1.299582</td>\n",
              "      <td>1.249406</td>\n",
              "      <td>-0.908607</td>\n",
              "      <td>-1.076800</td>\n",
              "      <td>-0.637939</td>\n",
              "      <td>0.070017</td>\n",
              "      <td>0</td>\n",
              "      <td>Anthony Frizzell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.474010</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.223349</td>\n",
              "      <td>-1.445258</td>\n",
              "      <td>1.592914</td>\n",
              "      <td>-0.937283</td>\n",
              "      <td>1.018286</td>\n",
              "      <td>-1.455161</td>\n",
              "      <td>0.070017</td>\n",
              "      <td>0</td>\n",
              "      <td>Gordon Fredrickson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.315206</td>\n",
              "      <td>0.642857</td>\n",
              "      <td>-0.151863</td>\n",
              "      <td>0.404526</td>\n",
              "      <td>-0.262028</td>\n",
              "      <td>1.263156</td>\n",
              "      <td>-0.041044</td>\n",
              "      <td>1.821124</td>\n",
              "      <td>0.551595</td>\n",
              "      <td>1</td>\n",
              "      <td>Chad Corbitt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.272436</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>-0.714680</td>\n",
              "      <td>-0.729213</td>\n",
              "      <td>-0.124625</td>\n",
              "      <td>-0.937283</td>\n",
              "      <td>1.099549</td>\n",
              "      <td>0.824581</td>\n",
              "      <td>0.035804</td>\n",
              "      <td>0</td>\n",
              "      <td>Zachary Fellows</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PatientID  Pregnancies  ...  Diabetic           Physician\n",
              "0  -0.512160     0.000000  ...         0         Dan Drayton\n",
              "1  -1.228971     0.571429  ...         0    Anthony Frizzell\n",
              "2   0.474010     0.500000  ...         0  Gordon Fredrickson\n",
              "3   1.315206     0.642857  ...         1        Chad Corbitt\n",
              "4  -0.272436     0.071429  ...         0     Zachary Fellows\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glrVj-wWQ9Wk",
        "colab_type": "text"
      },
      "source": [
        "Now that we have prepared the data set, we will use it to train and evaluate a classifier machine\n",
        "learning model. Typically, when training a supervised learning model, in which the training data includes\n",
        "known label values, we split the data into a training set with which to train the model, and a test set\n",
        "with which to validate predictions generated by the trained model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SXwGGcBEJl8",
        "colab_type": "text"
      },
      "source": [
        "Before we go any furteher, let's check the number of observations each class of Diabetics or Non-Diabetics has<br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TTEkwcmD48w",
        "colab_type": "code",
        "outputId": "0fc067a7-443e-45e7-d916-2c494fd24f18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "diabetes_doctor_df.Diabetic.value_counts()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10000\n",
              "1     5000\n",
              "Name: Diabetic, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2nkD1PnqFMTz",
        "colab_type": "text"
      },
      "source": [
        "Let's visualize this distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-xqbIxEFKI_",
        "colab_type": "code",
        "outputId": "d6e867a9-ac0e-4858-c854-3c6236461819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "x = diabetes_doctor_df.Diabetic.replace(to_replace=[0, 1], value=['Non-Diabetics','Diabetics'])\n",
        "sns.countplot(x)\n",
        "plt.title('Count of Diabetics and Non-Diabetics')\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zX7x4cWXWISIiYqZc34Pavn0727Zt48KFCxQWFpq1TZw4sUILExGRO1uZA2r2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\nSj2uEPYKWhcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6V6WZsYEfBN",
        "colab_type": "text"
      },
      "source": [
        "We can see that out of the 15,000 observations in our dataset, the Non-Diabetic class has 10,000 entries, while Diabetic class has only 5,000 entries.<br>\n",
        "This is an imbalanced Dataset and the risk involved is that our Model may learn one class features more than the other.<br>\n",
        "We need to find a way to balance the Dataset for a more unbiased and reliable classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6AJqd9wDTnjP",
        "colab_type": "text"
      },
      "source": [
        "To prove the point, let's go ahead to use this imbalanced data set to train a model and make predictions.<br>\n",
        "Then we would balance the data set and train a new model and see the difference."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8f6vQ7rDTjTx",
        "colab_type": "text"
      },
      "source": [
        "Before we do the split, let's select only columns that matter by deselecting PatientID and Physician columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sbh6IhSqQ8IH",
        "colab_type": "code",
        "outputId": "ca2cf5f2-dbbb-4a95-f63d-7d433fc96b7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "feature_matrix = diabetes_doctor_df.iloc[:,1:-2]\n",
        "feature_matrix.head()"
      ],
      "execution_count": 22,
      "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>Pregnancies</th>\n",
              "      <th>PlasmaGlucose</th>\n",
              "      <th>DiastolicBloodPressure</th>\n",
              "      <th>TricepsThickness</th>\n",
              "      <th>SerumInsulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigree</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.974335</td>\n",
              "      <td>0.523867</td>\n",
              "      <td>0.356286</td>\n",
              "      <td>-1.364665</td>\n",
              "      <td>1.165284</td>\n",
              "      <td>1.755479</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.571429</td>\n",
              "      <td>-0.495806</td>\n",
              "      <td>1.299582</td>\n",
              "      <td>1.249406</td>\n",
              "      <td>-0.908607</td>\n",
              "      <td>-1.076800</td>\n",
              "      <td>-0.637939</td>\n",
              "      <td>0.070017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.223349</td>\n",
              "      <td>-1.445258</td>\n",
              "      <td>1.592914</td>\n",
              "      <td>-0.937283</td>\n",
              "      <td>1.018286</td>\n",
              "      <td>-1.455161</td>\n",
              "      <td>0.070017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.642857</td>\n",
              "      <td>-0.151863</td>\n",
              "      <td>0.404526</td>\n",
              "      <td>-0.262028</td>\n",
              "      <td>1.263156</td>\n",
              "      <td>-0.041044</td>\n",
              "      <td>1.821124</td>\n",
              "      <td>0.551595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.071429</td>\n",
              "      <td>-0.714680</td>\n",
              "      <td>-0.729213</td>\n",
              "      <td>-0.124625</td>\n",
              "      <td>-0.937283</td>\n",
              "      <td>1.099549</td>\n",
              "      <td>0.824581</td>\n",
              "      <td>0.035804</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  PlasmaGlucose  ...  DiabetesPedigree       Age\n",
              "0     0.000000       1.974335  ...          1.755479  0.000000\n",
              "1     0.571429      -0.495806  ...         -0.637939  0.070017\n",
              "2     0.500000       0.223349  ...         -1.455161  0.070017\n",
              "3     0.642857      -0.151863  ...          1.821124  0.551595\n",
              "4     0.071429      -0.714680  ...          0.824581  0.035804\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAOjoX3rluz6",
        "colab_type": "text"
      },
      "source": [
        "let's also define our label variable "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghv9W1Spl6hn",
        "colab_type": "code",
        "outputId": "93dafa86-2dab-49c5-fddb-a98615205432",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "label = diabetes_doctor_df.Diabetic\n",
        "\n",
        "label.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    1\n",
              "4    0\n",
              "Name: Diabetic, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbi9YdKRTzjO",
        "colab_type": "code",
        "outputId": "64006101-bd10-42a9-c037-394aa4c1e5bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "feature_matrix.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0C42ux8TUKDG",
        "colab_type": "code",
        "outputId": "fe38cdf3-9fd1-4922-e3b4-432e9af2a490",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(feature_matrix, label, test_size=0.3, random_state=1234)\n",
        "\n",
        "print('X_train shape is',X_train.shape)\n",
        "print('X_test shape is',X_test.shape)\n",
        "print('y_train shape is',y_train.shape)\n",
        "print('y_test shape is',y_test.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape is (10500, 8)\n",
            "X_test shape is (4500, 8)\n",
            "y_train shape is (10500,)\n",
            "y_test shape is (4500,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1DZRrpgm-O1",
        "colab_type": "text"
      },
      "source": [
        "Let's build the Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wt-xORLlpYHv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import log_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CoGU06nCqMGw",
        "colab_type": "text"
      },
      "source": [
        "Let's define a function that plots the Confusion-Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_i2i663qSyn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\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')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfP3DcOej1pm",
        "colab_type": "text"
      },
      "source": [
        "Lets define a function that plots the ROC Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOKwzx0Ljr2s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_roc_chart(model):\n",
        "     # calculate the fpr and tpr for all thresholds of the classification\n",
        "    probs = model.predict_proba(X_test)\n",
        "    preds = probs[:,1]\n",
        "    fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
        "    roc_auc = metrics.auc(fpr, tpr)\n",
        "\n",
        "    # method I: plt\n",
        "    plt.figure(figsize=(8,6))\n",
        "    plt.title('Receiver Operating Characteristic')\n",
        "    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "    plt.legend(loc = 'lower right')\n",
        "    plt.plot([0, 1], [0, 1],'r--')\n",
        "    plt.xlim([0, 1])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.show()\n",
        "    return probs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSrj-S1Fqc-B",
        "colab_type": "text"
      },
      "source": [
        "Let's define a function that returns the best decison tree classifier model and outputs its parameters and a plot of the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRlK5jEBJ4Ng",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set max depth range limit for DTree model to iterate through from 1 to 100 to find best parameters\n",
        "\n",
        "\n",
        "def best_decision_tree_classifier(X_train, X_test, y_train, y_test):\n",
        "    max_depth = 100\n",
        "    accuracy_dict={'max_depth':0, 'val_acc':0, 'f1_Score':0, 'log_loss':0}\n",
        "\n",
        "    for i in range(1, max_depth+1):\n",
        "            # Let's instantiate a model\n",
        "            decision_model = DecisionTreeClassifier(criterion='entropy', max_depth = i)\n",
        "\n",
        "            # Let's train the model\n",
        "            decision_model.fit(X_train, y_train)\n",
        "\n",
        "            # Let's make prediction on the test data\n",
        "            y_hat = decision_model.predict(X_test)\n",
        "\n",
        "            # Let's measure accuracy of predictions on test data\n",
        "            val_accu = round(metrics.accuracy_score(y_test, y_hat),4)\n",
        "\n",
        "            # Let's Measure the F1 Score\n",
        "            val_f1 = round(f1_score(y_test, y_hat, average='weighted'),4)\n",
        "\n",
        "            # Let's Measure Logloss\n",
        "            logloss = round(log_loss(y_test, y_hat, normalize=True),4)\n",
        "\n",
        "            if accuracy_dict['val_acc'] < val_accu or accuracy_dict['f1_Score'] < val_f1:\n",
        "                accuracy_dict['max_depth'] = i\n",
        "                accuracy_dict['val_acc'] = val_accu\n",
        "                accuracy_dict['f1_Score'] = val_f1\n",
        "                accuracy_dict['log_loss'] = logloss\n",
        "                decision_model = decision_model\n",
        "                \n",
        "            # Compute confusion matrix\n",
        "            Dtrees_cnf_matrix = confusion_matrix(y_test, y_hat, labels=[1,0])\n",
        "            np.set_printoptions(precision=2)\n",
        "            \n",
        "            TP = Dtrees_cnf_matrix[0,0]\n",
        "            FP = Dtrees_cnf_matrix[0,1]\n",
        "            FN = Dtrees_cnf_matrix[1,0]\n",
        "            TN = Dtrees_cnf_matrix[1,1]\n",
        "            \n",
        "            accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
        "            recall = TP / (TP + FN)\n",
        "            precision = TP / (TP + FP)\n",
        "            \n",
        "    # Plot non-normalized confusion matrix\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.set(font_scale=1.3)\n",
        "    plot_confusion_matrix(Dtrees_cnf_matrix, \n",
        "                          classes=['Default=1','Non-Default=0'],\n",
        "                          normalize= False,  \n",
        "                          title='Confusion Matrix for Decision-Tree-Classifier')\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "    print('TP is:',TP,'FP is:',FP,'TN is:',TN,'FN is:',FN)\n",
        "    print()\n",
        "    print('Model Evaluation:')\n",
        "    print(accuracy_dict)\n",
        "    print()\n",
        "    plot_roc_chart(decision_model)\n",
        "    print()\n",
        "    print('accuracy is:',round(accuracy,2))\n",
        "    print('recall is:',round(recall,2))\n",
        "    print('precision is:',round(precision,2))\n",
        "    print()\n",
        "    \n",
        "    return decision_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZzLQQN1hkrC",
        "colab_type": "code",
        "outputId": "a04ae8be-cee3-4f84-f669-8e076e366e82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "decision_tree_classifier = best_decision_tree_classifier(X_train, X_test, y_train, y_test)\n",
        "decision_tree_classifier"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[1323  219]\n",
            " [ 236 2722]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MGXjz5g2srKwwZcqUAgf+JScnw8DAQKnLBQAMDQ0Vx3NVqFAhz9gFTU1NUSO0bWxsYGpq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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "TP is: 1323 FP is: 219 TN is: 2722 FN is: 236\n",
            "\n",
            "Model Evaluation:\n",
            "{'max_depth': 8, 'val_acc': 0.9093, 'f1_Score': 0.9097, 'log_loss': 3.1316}\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HLBbyrJ/+QwMk1kWCZVMEkY39oMsvZuebIqSeYhgGxnvZEAYEQvJUG4T6v1vn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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "accuracy is: 0.9\n",
            "recall is: 0.85\n",
            "precision is: 0.86\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=100,\n",
              "                       max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort=False,\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3gG2D7wqgw3",
        "colab_type": "text"
      },
      "source": [
        "##Note"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5e0gNw2ah8rz",
        "colab_type": "text"
      },
      "source": [
        "Notice the following about these metrics:<br>\n",
        "• The Confusion Matrix shows the number of True Positives and True Negatives (cases\n",
        "correctly classified) and False Negatives and False Positives (cases incorrectly classified).<br>\n",
        "• The AUC (Area Under the Curve) is the area under the ROC curve. A perfect classifier\n",
        "would have an AUC of 1.0, indicating no trade-off between True and False Positive Rates.<br>\n",
        "• Accuracy is the fraction of cases correctly classified.<br>\n",
        "• Recall, is the fraction of positive cases correctly classified from the total positive cases in the dataset. <br>\n",
        "• Precision is the fraction of positive cases correctly classified from all cases classified as positive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdTXUr1sxvUJ",
        "colab_type": "text"
      },
      "source": [
        "### Let's balance the dataset and train a new model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwSdHFGuUZPd",
        "colab_type": "text"
      },
      "source": [
        "We need to balance The Data set because in an imbalanced Data set Accuracy is not a reliable metric for the real performance of a classifier, because it will yield misleading results (that is, when the numbers of observations in different classes vary greatly).<br>\n",
        "For example, 70% non-defaulters and 30% defaulters, a particular classifier might classify all or most of the observations as Non-defaulters.<br> \n",
        "This is due to the classifier or model having a high recognition rate (sensitivity) for the dominant class.<br>\n",
        "[F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) too, would even be more unreliable in an imbalanced Data set.\n",
        "See [link](https://en.wikipedia.org/wiki/Confusion_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xkjzYL6U4lN",
        "colab_type": "text"
      },
      "source": [
        "**SMOTE - Synthetic Minority Over-sampling Technique** [link](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7Upg-LQUI-M",
        "colab_type": "code",
        "outputId": "69d94f65-35c4-4618-f6c1-154e23ee6fc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm = SMOTE(sampling_strategy='minority', random_state=19, k_neighbors=5)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoUQvT6bVdDW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "over_sampled_features, over_sampled_label = sm.fit_resample(feature_matrix, label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-G9_B5dVu0a",
        "colab_type": "code",
        "outputId": "68487a80-b8b1-4b36-8288-81c1730e3435",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('Shape of resampled feature set is:',over_sampled_features.shape)\n",
        "print('Shape of resampled target data is:',over_sampled_label.shape)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of resampled feature set is: (20000, 8)\n",
            "Shape of resampled target data is: (20000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYUGKtkCWFqg",
        "colab_type": "text"
      },
      "source": [
        "Let's concat the features and target back into one data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBvISEnpWHdF",
        "colab_type": "code",
        "outputId": "c44ad5a2-73e0-4a1e-87ad-3a8119b6286b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "over_sampled_df = pd.DataFrame(over_sampled_features, columns=['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age'])\n",
        "\n",
        "over_sampled_df['Diabetic'] = over_sampled_label\n",
        "\n",
        "over_sampled_df.head()"
      ],
      "execution_count": 34,
      "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>Pregnancies</th>\n",
              "      <th>PlasmaGlucose</th>\n",
              "      <th>DiastolicBloodPressure</th>\n",
              "      <th>TricepsThickness</th>\n",
              "      <th>SerumInsulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigree</th>\n",
              "      <th>Age</th>\n",
              "      <th>Diabetic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.974335</td>\n",
              "      <td>0.523867</td>\n",
              "      <td>0.356286</td>\n",
              "      <td>-1.364665</td>\n",
              "      <td>1.165284</td>\n",
              "      <td>1.755479</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.571429</td>\n",
              "      <td>-0.495806</td>\n",
              "      <td>1.299582</td>\n",
              "      <td>1.249406</td>\n",
              "      <td>-0.908607</td>\n",
              "      <td>-1.076800</td>\n",
              "      <td>-0.637939</td>\n",
              "      <td>0.070017</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.223349</td>\n",
              "      <td>-1.445258</td>\n",
              "      <td>1.592914</td>\n",
              "      <td>-0.937283</td>\n",
              "      <td>1.018286</td>\n",
              "      <td>-1.455161</td>\n",
              "      <td>0.070017</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.642857</td>\n",
              "      <td>-0.151863</td>\n",
              "      <td>0.404526</td>\n",
              "      <td>-0.262028</td>\n",
              "      <td>1.263156</td>\n",
              "      <td>-0.041044</td>\n",
              "      <td>1.821124</td>\n",
              "      <td>0.551595</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.071429</td>\n",
              "      <td>-0.714680</td>\n",
              "      <td>-0.729213</td>\n",
              "      <td>-0.124625</td>\n",
              "      <td>-0.937283</td>\n",
              "      <td>1.099549</td>\n",
              "      <td>0.824581</td>\n",
              "      <td>0.035804</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  PlasmaGlucose  ...       Age  Diabetic\n",
              "0     0.000000       1.974335  ...  0.000000         0\n",
              "1     0.571429      -0.495806  ...  0.070017         0\n",
              "2     0.500000       0.223349  ...  0.070017         0\n",
              "3     0.642857      -0.151863  ...  0.551595         1\n",
              "4     0.071429      -0.714680  ...  0.035804         0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7BQ2FxAYVNI",
        "colab_type": "code",
        "outputId": "43d1de5e-6397-40ed-f421-470faa0751ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "over_sampled_df.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtW0kwtuYoJD",
        "colab_type": "text"
      },
      "source": [
        "Let's visualize the new distribution "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiOIseGZYmlW",
        "colab_type": "code",
        "outputId": "c9004556-75f0-45eb-a303-14ff0787d690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "x = over_sampled_df.Diabetic.replace(to_replace=[0, 1], value=['Non-Diabetics','Diabetics'])\n",
        "sns.countplot(x)\n",
        "plt.title('Count of Diabetics and Non-Diabetics')\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f4v79+3Bzc8O8efPUar744gskJyfj22+/RVxcHAwNDdGmTRv07t1b/B107NgR//73v7Ft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\niEgyGFpERCQZDC0iIpIMhhYREUnG/wOCdvummxn8RQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_T_DyGXY_X6",
        "colab_type": "text"
      },
      "source": [
        "Okay so the above is a well balanced dataset. let's go on to train the model by first splitting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OO6-cjNtZE-T",
        "colab_type": "code",
        "outputId": "33297e1c-5df9-484d-c439-6fd757aca876",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(over_sampled_df.iloc[:,:-1], over_sampled_df.Diabetic, test_size=0.3, random_state=1234)\n",
        "\n",
        "print('X_train shape is',X_train.shape)\n",
        "print('X_test shape is',X_test.shape)\n",
        "print('y_train shape is',y_train.shape)\n",
        "print('y_test shape is',y_test.shape)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape is (14000, 8)\n",
            "X_test shape is (6000, 8)\n",
            "y_train shape is (14000,)\n",
            "y_test shape is (6000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umwXSX5iakyc",
        "colab_type": "text"
      },
      "source": [
        "Let's call the DecisionTreeFunction to build and classify and evaluate our model prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmTq9phbdMH7",
        "colab_type": "code",
        "outputId": "f7a57b87-99d7-409a-b4ed-839f76a97e0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "decision_tree_classifier = best_decision_tree_classifier(X_train, X_test, y_train, y_test)\n",
        "decision_tree_classifier"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[2773  245]\n",
            " [ 229 2753]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j4zF58mQkJyejSpUqaNiwIcLCwhS6PRo3boylS5di8eLF+Omnn2BsbIzZs2fL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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "TP is: 2773 FP is: 245 TN is: 2753 FN is: 229\n",
            "\n",
            "Model Evaluation:\n",
            "{'max_depth': 10, 'val_acc': 0.9257, 'f1_Score': 0.9257, 'log_loss': 2.5674}\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+aFJPznI0hBBCnkfepQtIS9oKxmgEo9ezHY5dUbLgQCp1cdU9hafrD3YhhJDa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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "accuracy is: 0.92\n",
            "recall is: 0.92\n",
            "precision is: 0.92\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=100,\n",
              "                       max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort=False,\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bP5X7_YsqpNY",
        "colab_type": "text"
      },
      "source": [
        "We can clearly see a boost in model performance by just balancing the dataset.<br>\n",
        "The model generalises better and is more accurate and confident in its classification tasks"
      ]
    }
  ]
}