Atharv_Patawar_A4_LogReg_Task.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statsmodels.api as sm\n",
        "import scipy.stats as st\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.mlab as mlab\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q1 Import the dataset and try to understand what information each column is giving. What is our target variable? Is this a classification or regression problem?\n",
        "\n",
        "Dataset link: https://drive.google.com/file/d/1A-iAsI3MUU6o9oh5AE1NG1kVU5KD60Ms/view?usp=sharing\n"
      ],
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      "source": [
        "Ans: This is a classification problem and the target variable is TenYearCHD"
      ],
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      "id": "Qm0cjT4BZWal"
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    {
      "cell_type": "code",
      "source": [
        "#Import dataset\n",
        "df=pd.read_csv('heart_study.csv')"
      ],
      "metadata": {
        "id": "Tk7yD3E2bPct"
      },
      "id": "Tk7yD3E2bPct",
      "execution_count": 3,
      "outputs": []
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        "df"
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      "outputs": [
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              "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
              "0        1   39        4.0              0         0.0     0.0   \n",
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              "4238     1   40        3.0              0         0.0     0.0   \n",
              "4239     0   39        3.0              1        30.0     0.0   \n",
              "\n",
              "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
              "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
              "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
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              "...               ...           ...       ...      ...    ...    ...    ...   \n",
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              "4239                0             0         0    196.0  133.0   86.0  20.91   \n",
              "\n",
              "      heartRate  glucose  TenYearCHD  \n",
              "0          80.0     77.0           0  \n",
              "1          95.0     76.0           0  \n",
              "2          75.0     70.0           0  \n",
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              "4237       80.0    107.0           0  \n",
              "4238       67.0     72.0           0  \n",
              "4239       85.0     80.0           0  \n",
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    {
      "cell_type": "markdown",
      "source": [
        "sex : male or female (Nominal)\n",
        "\n",
        "age : age of the patient (Continuous)\n",
        "\n",
        "education: maximum level of education of the patient\n",
        "\n",
        "currentSmoker : whether or not the patient is a current smoker (Nominal)\n",
        "\n",
        "cigsPerDay : the number of cigarettes that the person smoked on average in one day (Continuous)\n",
        "\n",
        "BPMeds : whether or not the patient was on blood pressure medication (Nominal)\n",
        "\n",
        "prevalentStroke : whether or not the patient had previously had a stroke (Nominal)\n",
        "\n",
        "prevalentHyp : whether or not the patient was Hypertension (Nominal)\n",
        "\n",
        "diabetes : whether or not the patient had diabetes (Nominal)\n",
        "\n",
        "totChol : total cholesterol level (Continuous)\n",
        "\n",
        "sysBP : systolic blood pressure (Continuous)\n",
        "\n",
        "diaBP : diastolic blood pressure (Continuous)\n",
        "\n",
        "BMI : Body Mass Index (Continuous)\n",
        "\n",
        "heartRate : heart rate (Continuous)\n",
        "\n",
        "glucose : glucose level (Continuous)\n",
        "\n",
        "TenYearCHD : 10 year risk of Coronary Heart Disease (CHD) (binary: 1 (Yes), 0 (No))"
      ],
      "metadata": {
        "id": "xQCirxWhT03R"
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      "id": "xQCirxWhT03R"
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q2. Are there any columns which give information about something totally irrelevant to whether a person has a heart disease or not? If yes, drop the column. Also rename the column 'male' to 'sex_male'."
      ],
      "metadata": {
        "id": "W04MdijOULuY"
      },
      "id": "W04MdijOULuY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ans: Yes education seems to be irrelevant, both from the correlation heatmap and logically otherwise"
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      },
      "id": "xE5sMKu0ZkZp"
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    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(df.corr())"
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      "id": "FIxbXHVJ0Ryr",
      "execution_count": 5,
      "outputs": [
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa707692f10>"
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop and/or rename columns\n",
        "df.drop(['education'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "-Tm933YkT2Tn"
      },
      "id": "-Tm933YkT2Tn",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "36MiGXqM02I_",
        "outputId": "d1dbae6d-ec7e-4556-c4ce-c69cb740ff5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "id": "36MiGXqM02I_",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      male  age  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
              "0        1   39              0         0.0     0.0                0   \n",
              "1        0   46              0         0.0     0.0                0   \n",
              "2        1   48              1        20.0     0.0                0   \n",
              "3        0   61              1        30.0     0.0                0   \n",
              "4        0   46              1        23.0     0.0                0   \n",
              "...    ...  ...            ...         ...     ...              ...   \n",
              "4235     0   48              1        20.0     NaN                0   \n",
              "4236     0   44              1        15.0     0.0                0   \n",
              "4237     0   52              0         0.0     0.0                0   \n",
              "4238     1   40              0         0.0     0.0                0   \n",
              "4239     0   39              1        30.0     0.0                0   \n",
              "\n",
              "      prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  \\\n",
              "0                0         0    195.0  106.0   70.0  26.97       80.0   \n",
              "1                0         0    250.0  121.0   81.0  28.73       95.0   \n",
              "2                0         0    245.0  127.5   80.0  25.34       75.0   \n",
              "3                1         0    225.0  150.0   95.0  28.58       65.0   \n",
              "4                0         0    285.0  130.0   84.0  23.10       85.0   \n",
              "...            ...       ...      ...    ...    ...    ...        ...   \n",
              "4235             0         0    248.0  131.0   72.0  22.00       84.0   \n",
              "4236             0         0    210.0  126.5   87.0  19.16       86.0   \n",
              "4237             0         0    269.0  133.5   83.0  21.47       80.0   \n",
              "4238             1         0    185.0  141.0   98.0  25.60       67.0   \n",
              "4239             0         0    196.0  133.0   86.0  20.91       85.0   \n",
              "\n",
              "      glucose  TenYearCHD  \n",
              "0        77.0           0  \n",
              "1        76.0           0  \n",
              "2        70.0           0  \n",
              "3       103.0           1  \n",
              "4        85.0           0  \n",
              "...       ...         ...  \n",
              "4235     86.0           0  \n",
              "4236      NaN           0  \n",
              "4237    107.0           0  \n",
              "4238     72.0           0  \n",
              "4239     80.0           0  \n",
              "\n",
              "[4240 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4920151-e0c3-4eea-9679-250d89d2f029\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>male</th>\n",
              "      <th>age</th>\n",
              "      <th>currentSmoker</th>\n",
              "      <th>cigsPerDay</th>\n",
              "      <th>BPMeds</th>\n",
              "      <th>prevalentStroke</th>\n",
              "      <th>prevalentHyp</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>totChol</th>\n",
              "      <th>sysBP</th>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={'male':'sex_male'},inplace=True)"
      ],
      "metadata": {
        "id": "-gNfzTZi040Y"
      },
      "id": "-gNfzTZi040Y",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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        "id": "Nh6o5qMb1jyJ",
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          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "id": "Nh6o5qMb1jyJ",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      sex_male  age  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
              "0            1   39              0         0.0     0.0                0   \n",
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              "4238         1   40              0         0.0     0.0                0   \n",
              "4239         0   39              1        30.0     0.0                0   \n",
              "\n",
              "      prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  \\\n",
              "0                0         0    195.0  106.0   70.0  26.97       80.0   \n",
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              "    <tr>\n",
              "      <th>4236</th>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>126.5</td>\n",
              "      <td>87.0</td>\n",
              "      <td>19.16</td>\n",
              "      <td>86.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4237</th>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>269.0</td>\n",
              "      <td>133.5</td>\n",
              "      <td>83.0</td>\n",
              "      <td>21.47</td>\n",
              "      <td>80.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4238</th>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>25.60</td>\n",
              "      <td>67.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4239</th>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>20.91</td>\n",
              "      <td>85.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
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              "<p>4240 rows × 15 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q3. Find the number of null values in each column and decide whether you can drop them or not. If you cannot drop them, use an appropriate method to deal with them"
      ],
      "metadata": {
        "id": "rJ8_1AZ6UmF6"
      },
      "id": "rJ8_1AZ6UmF6"
    },
    {
      "cell_type": "code",
      "source": [
        "#Deal with null values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "wT_73n8EbWmm",
        "outputId": "901f9e40-d70f-462f-beb1-8342bd5d430c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "wT_73n8EbWmm",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex_male             0\n",
              "age                  0\n",
              "currentSmoker        0\n",
              "cigsPerDay          29\n",
              "BPMeds              53\n",
              "prevalentStroke      0\n",
              "prevalentHyp         0\n",
              "diabetes             0\n",
              "totChol             50\n",
              "sysBP                0\n",
              "diaBP                0\n",
              "BMI                 19\n",
              "heartRate            1\n",
              "glucose            388\n",
              "TenYearCHD           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['glucose'],axis=1,inplace=True)         #dropping glucose, too many null values"
      ],
      "metadata": {
        "id": "dtBMOg-s1tIi"
      },
      "id": "dtBMOg-s1tIi",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['cigsPerDay'].fillna(value=df['cigsPerDay'].mean(),inplace=True)        #filling cigsPerDay,totChol,BMI and heartRate with mean as they are continuous and have relatively few null values"
      ],
      "metadata": {
        "id": "M60bFK7h13U0"
      },
      "id": "M60bFK7h13U0",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['BPMeds'].fillna(value=df['BPMeds'].mode()[0],inplace=True)             #filling BPMeds with mode as it is nominal abd has relatively few null values"
      ],
      "metadata": {
        "id": "BY7kMx3s2yGd"
      },
      "id": "BY7kMx3s2yGd",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['totChol'].fillna(value=df['totChol'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "KGNW9Ik53GUy"
      },
      "id": "KGNW9Ik53GUy",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['BMI'].fillna(value=df['BMI'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "kQBSW1G43Sdk"
      },
      "id": "kQBSW1G43Sdk",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['heartRate'].fillna(value=df['heartRate'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "EeaecM4h3Zfc"
      },
      "id": "EeaecM4h3Zfc",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "VTgqibIW3j7d",
        "outputId": "87973ef5-5e32-424a-cea7-b79d9d988134",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "VTgqibIW3j7d",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex_male           0\n",
              "age                0\n",
              "currentSmoker      0\n",
              "cigsPerDay         0\n",
              "BPMeds             0\n",
              "prevalentStroke    0\n",
              "prevalentHyp       0\n",
              "diabetes           0\n",
              "totChol            0\n",
              "sysBP              0\n",
              "diaBP              0\n",
              "BMI                0\n",
              "heartRate          0\n",
              "TenYearCHD         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
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      "source": [
        "df"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "id": "93ZFfSDF3oTW",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex_male  age  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
              "0            1   39              0         0.0     0.0                0   \n",
              "1            0   46              0         0.0     0.0                0   \n",
              "2            1   48              1        20.0     0.0                0   \n",
              "3            0   61              1        30.0     0.0                0   \n",
              "4            0   46              1        23.0     0.0                0   \n",
              "...        ...  ...            ...         ...     ...              ...   \n",
              "4235         0   48              1        20.0     0.0                0   \n",
              "4236         0   44              1        15.0     0.0                0   \n",
              "4237         0   52              0         0.0     0.0                0   \n",
              "4238         1   40              0         0.0     0.0                0   \n",
              "4239         0   39              1        30.0     0.0                0   \n",
              "\n",
              "      prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  \\\n",
              "0                0         0    195.0  106.0   70.0  26.97       80.0   \n",
              "1                0         0    250.0  121.0   81.0  28.73       95.0   \n",
              "2                0         0    245.0  127.5   80.0  25.34       75.0   \n",
              "3                1         0    225.0  150.0   95.0  28.58       65.0   \n",
              "4                0         0    285.0  130.0   84.0  23.10       85.0   \n",
              "...            ...       ...      ...    ...    ...    ...        ...   \n",
              "4235             0         0    248.0  131.0   72.0  22.00       84.0   \n",
              "4236             0         0    210.0  126.5   87.0  19.16       86.0   \n",
              "4237             0         0    269.0  133.5   83.0  21.47       80.0   \n",
              "4238             1         0    185.0  141.0   98.0  25.60       67.0   \n",
              "4239             0         0    196.0  133.0   86.0  20.91       85.0   \n",
              "\n",
              "      TenYearCHD  \n",
              "0              0  \n",
              "1              0  \n",
              "2              0  \n",
              "3              1  \n",
              "4              0  \n",
              "...          ...  \n",
              "4235           0  \n",
              "4236           0  \n",
              "4237           0  \n",
              "4238           0  \n",
              "4239           0  \n",
              "\n",
              "[4240 rows x 14 columns]"
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              "      <td>21.47</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4238</th>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>25.60</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4239</th>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>20.91</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4240 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8adcc6a-8f1d-48b1-a466-ff529b576fec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8adcc6a-8f1d-48b1-a466-ff529b576fec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8adcc6a-8f1d-48b1-a466-ff529b576fec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q4. The following code is set up for you, replace 'dataframe' with the name of the your variable. What do you understand from this EDA? Why do some graphs have only two bars, at 0 and 1 and others have multiple?"
      ],
      "metadata": {
        "id": "CIa2gyH9U17n"
      },
      "id": "CIa2gyH9U17n"
    },
    {
      "cell_type": "markdown",
      "source": [
        "This EDA plots every available feature with all other features, as some features are nominal, there are only two bars at 0 and 1"
      ],
      "metadata": {
        "id": "LDHsktoyaW6O"
      },
      "id": "LDHsktoyaW6O"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "2e63f28a",
      "metadata": {
        "id": "2e63f28a",
        "outputId": "9c213be0-3f1d-477f-f1ac-57a73d8cd1eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "def draw_histograms(dataframe, features, rows, cols):\n",
        "    fig=plt.figure(figsize=(20,20))\n",
        "    for i, feature in enumerate(features):\n",
        "        ax=fig.add_subplot(rows,cols,i+1)\n",
        "        dataframe[feature].hist(bins=20,ax=ax,facecolor='red')\n",
        "        ax.set_title(feature+\"Distribution\", color='blue')\n",
        "    fig.tight_layout()\n",
        "    plt.show()\n",
        "draw_histograms(df, df.columns, 6, 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q5. Using sns.countplot find out number of Positive cases and Negative cases in the dataset, i.e., in your target column."
      ],
      "metadata": {
        "id": "ejFQdFbDVZ_Z"
      },
      "id": "ejFQdFbDVZ_Z"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "80c234a7",
      "metadata": {
        "id": "80c234a7",
        "outputId": "4cf0ee05-205d-4ba5-c9af-7c9c5671df7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa704873a30>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Code\n",
        "sns.countplot(df['TenYearCHD'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q6a. Split the dataset into train and test and fit the training dataset in the Logistic Regression. "
      ],
      "metadata": {
        "id": "mj0ExaFaWZlH"
      },
      "id": "mj0ExaFaWZlH"
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, :-1].values  \n",
        "y = df.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "vn7It2bn42op"
      },
      "id": "vn7It2bn42op",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Split the dataset and fit the model\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 = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "x2p0VehmWDpD"
      },
      "id": "x2p0VehmWDpD",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scaling values so that they have equal importance\n",
        "from sklearn.preprocessing import StandardScaler          \n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "uzQftFF25IZ8"
      },
      "id": "uzQftFF25IZ8",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)   #fitting the model"
      ],
      "metadata": {
        "id": "ezmhIpRv5R0w",
        "outputId": "7ee29d6b-a4a9-4aab-a2be-7bdded61cd7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "ezmhIpRv5R0w",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)    #predictions"
      ],
      "metadata": {
        "id": "C4h7Ia3o5bgN"
      },
      "id": "C4h7Ia3o5bgN",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "1wjTFS1e5WVR",
        "outputId": "afe9e81c-29bb-44a7-bc4d-77f03271fdcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "1wjTFS1e5WVR",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[895   6]\n",
            " [145  14]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8575471698113207"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q6b. Does running the above code give a Convergence Warning? What could be the cause of this problem? Try to find out methods that can deal with this problem."
      ],
      "metadata": {
        "id": "40dI3XQSWrDh"
      },
      "id": "40dI3XQSWrDh"
    },
    {
      "cell_type": "markdown",
      "id": "2caf8c74",
      "metadata": {
        "id": "2caf8c74"
      },
      "source": [
        "Although there are several methods, we will use backward feature selection to solve this problem. You can learn more about feature selection using the below link. \n",
        "\n",
        "https://www.javatpoint.com/feature-selection-techniques-in-machine-learning\n",
        "\n",
        "Understand and run the following 4 cells which carry out backward feature selection for you. Make sure to replace 'df' by your dataframe variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "fff22648",
      "metadata": {
        "id": "fff22648",
        "outputId": "8082fd76-374e-45fc-eab3-9f9d254087f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   const  sex_male  age  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
              "0    1.0         1   39              0         0.0     0.0                0   \n",
              "1    1.0         0   46              0         0.0     0.0                0   \n",
              "2    1.0         1   48              1        20.0     0.0                0   \n",
              "3    1.0         0   61              1        30.0     0.0                0   \n",
              "4    1.0         0   46              1        23.0     0.0                0   \n",
              "\n",
              "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  TenYearCHD  \n",
              "0             0         0    195.0  106.0   70.0  26.97       80.0           0  \n",
              "1             0         0    250.0  121.0   81.0  28.73       95.0           0  \n",
              "2             0         0    245.0  127.5   80.0  25.34       75.0           0  \n",
              "3             1         0    225.0  150.0   95.0  28.58       65.0           1  \n",
              "4             0         0    285.0  130.0   84.0  23.10       85.0           0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25a35dd2-c108-44dd-b206-55a47f33f258\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>const</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>age</th>\n",
              "      <th>currentSmoker</th>\n",
              "      <th>cigsPerDay</th>\n",
              "      <th>BPMeds</th>\n",
              "      <th>prevalentStroke</th>\n",
              "      <th>prevalentHyp</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>totChol</th>\n",
              "      <th>sysBP</th>\n",
              "      <th>diaBP</th>\n",
              "      <th>BMI</th>\n",
              "      <th>heartRate</th>\n",
              "      <th>TenYearCHD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>26.97</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>28.73</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>127.5</td>\n",
              "      <td>80.0</td>\n",
              "      <td>25.34</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>28.58</td>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>285.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>23.10</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-25a35dd2-c108-44dd-b206-55a47f33f258');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from statsmodels.tools import add_constant as add_constant\n",
        "df_constant = add_constant(df)\n",
        "df_constant.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "c9ae3c88",
      "metadata": {
        "id": "c9ae3c88",
        "outputId": "a2519ff7-14aa-4c28-8c25-455027923487",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.379709\n",
            "         Iterations 7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                           Logit Regression Results                           \n",
              "==============================================================================\n",
              "Dep. Variable:             TenYearCHD   No. Observations:                 4240\n",
              "Model:                          Logit   Df Residuals:                     4226\n",
              "Method:                           MLE   Df Model:                           13\n",
              "Date:                Fri, 30 Dec 2022   Pseudo R-squ.:                  0.1086\n",
              "Time:                        14:01:54   Log-Likelihood:                -1610.0\n",
              "converged:                       True   LL-Null:                       -1806.1\n",
              "Covariance Type:            nonrobust   LLR p-value:                 9.574e-76\n",
              "===================================================================================\n",
              "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
              "-----------------------------------------------------------------------------------\n",
              "const              -7.7449      0.626    -12.365      0.000      -8.973      -6.517\n",
              "sex_male            0.5119      0.100      5.112      0.000       0.316       0.708\n",
              "age                 0.0627      0.006     10.169      0.000       0.051       0.075\n",
              "currentSmoker       0.0160      0.144      0.111      0.912      -0.266       0.298\n",
              "cigsPerDay          0.0207      0.006      3.628      0.000       0.010       0.032\n",
              "BPMeds              0.2374      0.219      1.085      0.278      -0.192       0.666\n",
              "prevalentStroke     0.9916      0.441      2.248      0.025       0.127       1.856\n",
              "prevalentHyp        0.2205      0.128      1.720      0.085      -0.031       0.472\n",
              "diabetes            0.7725      0.219      3.533      0.000       0.344       1.201\n",
              "totChol             0.0018      0.001      1.781      0.075      -0.000       0.004\n",
              "sysBP               0.0148      0.004      4.214      0.000       0.008       0.022\n",
              "diaBP              -0.0038      0.006     -0.633      0.527      -0.015       0.008\n",
              "BMI                 0.0042      0.012      0.359      0.719      -0.019       0.027\n",
              "heartRate          -0.0005      0.004     -0.134      0.893      -0.008       0.007\n",
              "===================================================================================\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Logit Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>TenYearCHD</td>    <th>  No. Observations:  </th>  <td>  4240</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  4226</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    13</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Fri, 30 Dec 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.1086</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>14:01:54</td>     <th>  Log-Likelihood:    </th> <td> -1610.0</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -1806.1</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>9.574e-76</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>           <td>   -7.7449</td> <td>    0.626</td> <td>  -12.365</td> <td> 0.000</td> <td>   -8.973</td> <td>   -6.517</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sex_male</th>        <td>    0.5119</td> <td>    0.100</td> <td>    5.112</td> <td> 0.000</td> <td>    0.316</td> <td>    0.708</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>age</th>             <td>    0.0627</td> <td>    0.006</td> <td>   10.169</td> <td> 0.000</td> <td>    0.051</td> <td>    0.075</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>currentSmoker</th>   <td>    0.0160</td> <td>    0.144</td> <td>    0.111</td> <td> 0.912</td> <td>   -0.266</td> <td>    0.298</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>cigsPerDay</th>      <td>    0.0207</td> <td>    0.006</td> <td>    3.628</td> <td> 0.000</td> <td>    0.010</td> <td>    0.032</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>BPMeds</th>          <td>    0.2374</td> <td>    0.219</td> <td>    1.085</td> <td> 0.278</td> <td>   -0.192</td> <td>    0.666</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>prevalentStroke</th> <td>    0.9916</td> <td>    0.441</td> <td>    2.248</td> <td> 0.025</td> <td>    0.127</td> <td>    1.856</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>prevalentHyp</th>    <td>    0.2205</td> <td>    0.128</td> <td>    1.720</td> <td> 0.085</td> <td>   -0.031</td> <td>    0.472</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>diabetes</th>        <td>    0.7725</td> <td>    0.219</td> <td>    3.533</td> <td> 0.000</td> <td>    0.344</td> <td>    1.201</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>totChol</th>         <td>    0.0018</td> <td>    0.001</td> <td>    1.781</td> <td> 0.075</td> <td>   -0.000</td> <td>    0.004</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sysBP</th>           <td>    0.0148</td> <td>    0.004</td> <td>    4.214</td> <td> 0.000</td> <td>    0.008</td> <td>    0.022</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>diaBP</th>           <td>   -0.0038</td> <td>    0.006</td> <td>   -0.633</td> <td> 0.527</td> <td>   -0.015</td> <td>    0.008</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>BMI</th>             <td>    0.0042</td> <td>    0.012</td> <td>    0.359</td> <td> 0.719</td> <td>   -0.019</td> <td>    0.027</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>heartRate</th>       <td>   -0.0005</td> <td>    0.004</td> <td>   -0.134</td> <td> 0.893</td> <td>   -0.008</td> <td>    0.007</td>\n",
              "</tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "st.chisqprob = lambda chisq, df: st.chi2.sf(chisq, df)\n",
        "cols = df_constant.columns[:-1]\n",
        "model = sm.Logit(df.TenYearCHD, df_constant[cols])\n",
        "result = model.fit()\n",
        "result.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "9b196481",
      "metadata": {
        "id": "9b196481",
        "outputId": "050c4e51-0323-420e-da1b-ea2c21461c9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                           Logit Regression Results                           \n",
              "==============================================================================\n",
              "Dep. Variable:             TenYearCHD   No. Observations:                 4240\n",
              "Model:                          Logit   Df Residuals:                     4233\n",
              "Method:                           MLE   Df Model:                            6\n",
              "Date:                Fri, 30 Dec 2022   Pseudo R-squ.:                  0.1063\n",
              "Time:                        14:01:54   Log-Likelihood:                -1614.1\n",
              "converged:                       True   LL-Null:                       -1806.1\n",
              "Covariance Type:            nonrobust   LLR p-value:                 7.480e-80\n",
              "===================================================================================\n",
              "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
              "-----------------------------------------------------------------------------------\n",
              "const              -7.9005      0.372    -21.230      0.000      -8.630      -7.171\n",
              "sex_male            0.4848      0.097      4.999      0.000       0.295       0.675\n",
              "age                 0.0650      0.006     10.985      0.000       0.053       0.077\n",
              "cigsPerDay          0.0212      0.004      5.513      0.000       0.014       0.029\n",
              "prevalentStroke     1.0687      0.436      2.449      0.014       0.213       1.924\n",
              "diabetes            0.7937      0.217      3.660      0.000       0.369       1.219\n",
              "sysBP               0.0173      0.002      8.664      0.000       0.013       0.021\n",
              "===================================================================================\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Logit Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>TenYearCHD</td>    <th>  No. Observations:  </th>  <td>  4240</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  4233</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     6</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Fri, 30 Dec 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.1063</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>14:01:54</td>     <th>  Log-Likelihood:    </th> <td> -1614.1</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -1806.1</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>7.480e-80</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>           <td>   -7.9005</td> <td>    0.372</td> <td>  -21.230</td> <td> 0.000</td> <td>   -8.630</td> <td>   -7.171</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sex_male</th>        <td>    0.4848</td> <td>    0.097</td> <td>    4.999</td> <td> 0.000</td> <td>    0.295</td> <td>    0.675</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>age</th>             <td>    0.0650</td> <td>    0.006</td> <td>   10.985</td> <td> 0.000</td> <td>    0.053</td> <td>    0.077</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>cigsPerDay</th>      <td>    0.0212</td> <td>    0.004</td> <td>    5.513</td> <td> 0.000</td> <td>    0.014</td> <td>    0.029</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>prevalentStroke</th> <td>    1.0687</td> <td>    0.436</td> <td>    2.449</td> <td> 0.014</td> <td>    0.213</td> <td>    1.924</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>diabetes</th>        <td>    0.7937</td> <td>    0.217</td> <td>    3.660</td> <td> 0.000</td> <td>    0.369</td> <td>    1.219</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sysBP</th>           <td>    0.0173</td> <td>    0.002</td> <td>    8.664</td> <td> 0.000</td> <td>    0.013</td> <td>    0.021</td>\n",
              "</tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "def back_feature_elem (data_frame, dep_var, col_list):\n",
        "    while len(col_list)>0 :\n",
        "        model = sm.Logit(dep_var,data_frame[col_list])\n",
        "        result = model.fit(disp=0)\n",
        "        largest_pvalue = round(result.pvalues,3).nlargest(1)\n",
        "        if largest_pvalue[0]<(0.05):\n",
        "            return result\n",
        "            break\n",
        "        else:\n",
        "            col_list = col_list.drop(largest_pvalue.index)\n",
        "result = back_feature_elem(df_constant, df.TenYearCHD, cols)\n",
        "result.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "a2a9efac",
      "metadata": {
        "id": "a2a9efac",
        "outputId": "24a0cce4-173a-46e8-90f5-e1f57e05b7ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 CI 95%(2.5%)  CI 95%(97.5%)  Odds Ratio  pvalue\n",
            "const                0.000179       0.000768    0.000371   0.000\n",
            "sex_male             1.342792       1.963893    1.623915   0.000\n",
            "age                  1.054868       1.079628    1.067177   0.000\n",
            "cigsPerDay           1.013739       1.029117    1.021399   0.000\n",
            "prevalentStroke      1.237931       6.847906    2.911569   0.014\n",
            "diabetes             1.445885       3.382922    2.211632   0.000\n",
            "sysBP                1.013462       1.021418    1.017432   0.000\n"
          ]
        }
      ],
      "source": [
        "params = np.exp(result.params)\n",
        "conf = np.exp(result.conf_int())\n",
        "conf['OR'] = params\n",
        "pvalue = round(result.pvalues,3)\n",
        "conf['pvalue'] = pvalue\n",
        "conf.columns = ['CI 95%(2.5%)','CI 95%(97.5%)', 'Odds Ratio', 'pvalue']\n",
        "print((conf))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns=list(conf.index)      #creating a list of the selected columns"
      ],
      "metadata": {
        "id": "IAEmwpcS9JUN"
      },
      "id": "IAEmwpcS9JUN",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns.remove('const')       #removing columns with a low coef value\n",
        "columns.remove('age')\n",
        "columns.remove('cigsPerDay')\n",
        "columns.remove('sysBP')"
      ],
      "metadata": {
        "id": "EBpZwJOWF33G"
      },
      "id": "EBpZwJOWF33G",
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns.append('TenYearCHD')       #appending the target column"
      ],
      "metadata": {
        "id": "RG_B9qDmGFy7"
      },
      "id": "RG_B9qDmGFy7",
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df[columns]"
      ],
      "metadata": {
        "id": "FYe8ddizFpru"
      },
      "id": "FYe8ddizFpru",
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "id": "RsrECuMBF_y7",
        "outputId": "e6a4e377-81d8-48c7-e678-272ff549233d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "id": "RsrECuMBF_y7",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex_male  prevalentStroke  diabetes  TenYearCHD\n",
              "0            1                0         0           0\n",
              "1            0                0         0           0\n",
              "2            1                0         0           0\n",
              "3            0                0         0           1\n",
              "4            0                0         0           0\n",
              "...        ...              ...       ...         ...\n",
              "4235         0                0         0           0\n",
              "4236         0                0         0           0\n",
              "4237         0                0         0           0\n",
              "4238         1                0         0           0\n",
              "4239         0                0         0           0\n",
              "\n",
              "[4240 rows x 4 columns]"
            ],
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              "\n",
              "  <div id=\"df-a268700c-03d6-4c53-8d82-18dddeaa0415\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <td>0</td>\n",
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              "<p>4240 rows × 4 columns</p>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q7. The above table mentions the columns with the highest importance. Create a new dataframe with just these columns and your target column. Split it into training and testing dataset and fit the training data into Logistic regression model. Also predict the y values for the test set."
      ],
      "metadata": {
        "id": "kB_Av3nKaDK4"
      },
      "id": "kB_Av3nKaDK4"
    },
    {
      "cell_type": "code",
      "source": [
        "#Split the new dataset and fit the model\n",
        "X = df1.iloc[:, :-1].values\n",
        "y = df1.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "kXQbeSsQbra-"
      },
      "id": "kXQbeSsQbra-",
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting\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 = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "sktHva2cGVcF"
      },
      "id": "sktHva2cGVcF",
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scaling for equal importance\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "sIBvXJdxGejn"
      },
      "id": "sIBvXJdxGejn",
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fitting the model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "cuh7zFaeGiAo",
        "outputId": "ae5b4167-2ecf-4436-9fbe-838da88a2a3b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "cuh7zFaeGiAo",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)    #predicting"
      ],
      "metadata": {
        "id": "DNpissRgGm1B"
      },
      "id": "DNpissRgGm1B",
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q8a. Create a confusion matrix for y test and y predcted values. Since this is prediction of heart disease which metric should be given more importance - Precision or Recall? Justify."
      ],
      "metadata": {
        "id": "5o18bJF7ajfp"
      },
      "id": "5o18bJF7ajfp"
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "af51ede7",
      "metadata": {
        "id": "af51ede7",
        "outputId": "8f024a03-4ace-4a28-ff91-b1a3f4d964f6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[900   1]\n",
            " [157   2]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8509433962264151"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "#Code for confusion matrix\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q8b. Using the above confusion matrix, how would you find out Accuracy, Precision and Recall values. Calculate them using the formulae which takes into account True Positive, False Positive, True Negative and False Negative values."
      ],
      "metadata": {
        "id": "2zGZNIB8b_-0"
      },
      "id": "2zGZNIB8b_-0"
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy = ',(cm[0][0]+cm[1][1])/(cm[0][0]+cm[0][1]+cm[1][0]+cm[1][1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wb_7fhU1U9qt",
        "outputId": "0054bde2-0574-4fa4-8774-8343e8f91286"
      },
      "id": "Wb_7fhU1U9qt",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.8509433962264151\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Precision = ',(cm[1][1])/(cm[0][1]+cm[1][1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8iXqTQSVvkl",
        "outputId": "74ad7ca5-8155-45a7-f82f-d042e8c8e42c"
      },
      "id": "d8iXqTQSVvkl",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision =  0.6666666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Recall = ',(cm[1][1])/(cm[1][0]+cm[1][1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pG2-PGF-WLSG",
        "outputId": "14a914f9-3423-4c80-bb86-e725c07f673b"
      },
      "id": "pG2-PGF-WLSG",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall =  0.012578616352201259\n"
          ]
        }
      ]
    }
  ],
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