Logistic_Regression_Bank_Data.ipynb

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        "#Logistic Regression Bank Data set"
      ],
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        "# Importing the libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/bank-full.csv\", sep=\";\")"
      ],
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        "id": "fWJRiYcR5iKB"
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      "execution_count": 32,
      "outputs": []
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      "source": [
        "df.head()"
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      "execution_count": 33,
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              "   age           job  marital  education default  balance housing loan  \\\n",
              "0   58    management  married   tertiary      no     2143     yes   no   \n",
              "1   44    technician   single  secondary      no       29     yes   no   \n",
              "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
              "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
              "4   33       unknown   single    unknown      no        1      no   no   \n",
              "\n",
              "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
              "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
              "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
              "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
              "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
              "4  unknown    5   may       198         1     -1         0  unknown  no  "
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\"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          1,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"apr\",\n          \"mar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 0,\n        \"max\": 4918,\n        \"num_unique_values\": 1573,\n        \"samples\": [\n          835,\n          1135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 63,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          41,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100,\n        \"min\": -1,\n        \"max\": 871,\n        \"num_unique_values\": 559,\n        \"samples\": [\n          249,\n          551\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 275,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          17,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poutcome\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"failure\",\n          \"success\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##EDA"
      ],
      "metadata": {
        "id": "NDQucRjz8PkO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MHbOnMsj5iCl",
        "outputId": "6a018938-dd35-46ee-f313-5b182c7c69b7"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45211 entries, 0 to 45210\n",
            "Data columns (total 17 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   age        45211 non-null  int64 \n",
            " 1   job        45211 non-null  object\n",
            " 2   marital    45211 non-null  object\n",
            " 3   education  45211 non-null  object\n",
            " 4   default    45211 non-null  object\n",
            " 5   balance    45211 non-null  int64 \n",
            " 6   housing    45211 non-null  object\n",
            " 7   loan       45211 non-null  object\n",
            " 8   contact    45211 non-null  object\n",
            " 9   day        45211 non-null  int64 \n",
            " 10  month      45211 non-null  object\n",
            " 11  duration   45211 non-null  int64 \n",
            " 12  campaign   45211 non-null  int64 \n",
            " 13  pdays      45211 non-null  int64 \n",
            " 14  previous   45211 non-null  int64 \n",
            " 15  poutcome   45211 non-null  object\n",
            " 16  y          45211 non-null  object\n",
            "dtypes: int64(7), object(10)\n",
            "memory usage: 5.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYi7xKGK5h_W",
        "outputId": "9d0699ef-67a3-45d5-8018-a3f7b3ffce9b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
              "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Knowing the categorical variables for columns ('job', 'marital', 'education', 'contact', 'poutcome')"
      ],
      "metadata": {
        "id": "9g8s68ZZ_CHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate\n",
        "data = {\n",
        "    'Variable': ['job', 'marital', 'education', 'contact', 'poutcome'],\n",
        "    'Unique Values': [\n",
        "        ', '.join(df['job'].unique()),\n",
        "        ', '.join(df['marital'].unique()),\n",
        "        ', '.join(df['education'].unique()),\n",
        "        ', '.join(df['contact'].unique()),\n",
        "        ', '.join(df['poutcome'].unique())]}\n",
        "print(tabulate(data, headers='keys', tablefmt='grid'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3j0EKgP-y2U",
        "outputId": "d6f029c7-12ea-4cf5-d762-43f4c6afa896"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------+--------------------------------------------------------------------------------------------------------------------------------------+\n",
            "| Variable   | Unique Values                                                                                                                        |\n",
            "+============+======================================================================================================================================+\n",
            "| job        | management, technician, entrepreneur, blue-collar, unknown, retired, admin., services, self-employed, unemployed, housemaid, student |\n",
            "+------------+--------------------------------------------------------------------------------------------------------------------------------------+\n",
            "| marital    | married, single, divorced                                                                                                            |\n",
            "+------------+--------------------------------------------------------------------------------------------------------------------------------------+\n",
            "| education  | tertiary, secondary, unknown, primary                                                                                                |\n",
            "+------------+--------------------------------------------------------------------------------------------------------------------------------------+\n",
            "| contact    | unknown, cellular, telephone                                                                                                         |\n",
            "+------------+--------------------------------------------------------------------------------------------------------------------------------------+\n",
            "| poutcome   | unknown, failure, other, success                                                                                                     |\n",
            "+------------+--------------------------------------------------------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Doing One-Hot Encoding of categrical variables"
      ],
      "metadata": {
        "id": "CHQrEEJlACc0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.get_dummies(df,columns=['job','marital','education','contact','poutcome'])"
      ],
      "metadata": {
        "id": "4fn2q6DE5h79"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option(\"display.max.columns\", None)\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "vEiqkwyy5h4P",
        "outputId": "eb35135b-e522-462c-da44-48eefe54b329"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age default  balance housing loan  day month  duration  campaign  \\\n",
              "0       58      no     2143     yes   no    5   may       261         1   \n",
              "1       44      no       29     yes   no    5   may       151         1   \n",
              "2       33      no        2     yes  yes    5   may        76         1   \n",
              "3       47      no     1506     yes   no    5   may        92         1   \n",
              "4       33      no        1      no   no    5   may       198         1   \n",
              "...    ...     ...      ...     ...  ...  ...   ...       ...       ...   \n",
              "45206   51      no      825      no   no   17   nov       977         3   \n",
              "45207   71      no     1729      no   no   17   nov       456         2   \n",
              "45208   72      no     5715      no   no   17   nov      1127         5   \n",
              "45209   57      no      668      no   no   17   nov       508         4   \n",
              "45210   37      no     2971      no   no   17   nov       361         2   \n",
              "\n",
              "       pdays  previous    y  job_admin.  job_blue-collar  job_entrepreneur  \\\n",
              "0         -1         0   no           0                0                 0   \n",
              "1         -1         0   no           0                0                 0   \n",
              "2         -1         0   no           0                0                 1   \n",
              "3         -1         0   no           0                1                 0   \n",
              "4         -1         0   no           0                0                 0   \n",
              "...      ...       ...  ...         ...              ...               ...   \n",
              "45206     -1         0  yes           0                0                 0   \n",
              "45207     -1         0  yes           0                0                 0   \n",
              "45208    184         3  yes           0                0                 0   \n",
              "45209     -1         0   no           0                1                 0   \n",
              "45210    188        11   no           0                0                 1   \n",
              "\n",
              "       job_housemaid  job_management  job_retired  job_self-employed  \\\n",
              "0                  0               1            0                  0   \n",
              "1                  0               0            0                  0   \n",
              "2                  0               0            0                  0   \n",
              "3                  0               0            0                  0   \n",
              "4                  0               0            0                  0   \n",
              "...              ...             ...          ...                ...   \n",
              "45206              0               0            0                  0   \n",
              "45207              0               0            1                  0   \n",
              "45208              0               0            1                  0   \n",
              "45209              0               0            0                  0   \n",
              "45210              0               0            0                  0   \n",
              "\n",
              "       job_services  job_student  job_technician  job_unemployed  job_unknown  \\\n",
              "0                 0            0               0               0            0   \n",
              "1                 0            0               1               0            0   \n",
              "2                 0            0               0               0            0   \n",
              "3                 0            0               0               0            0   \n",
              "4                 0            0               0               0            1   \n",
              "...             ...          ...             ...             ...          ...   \n",
              "45206             0            0               1               0            0   \n",
              "45207             0            0               0               0            0   \n",
              "45208             0            0               0               0            0   \n",
              "45209             0            0               0               0            0   \n",
              "45210             0            0               0               0            0   \n",
              "\n",
              "       marital_divorced  marital_married  marital_single  education_primary  \\\n",
              "0                     0                1               0                  0   \n",
              "1                     0                0               1                  0   \n",
              "2                     0                1               0                  0   \n",
              "3                     0                1               0                  0   \n",
              "4                     0                0               1                  0   \n",
              "...                 ...              ...             ...                ...   \n",
              "45206                 0                1               0                  0   \n",
              "45207                 1                0               0                  1   \n",
              "45208                 0                1               0                  0   \n",
              "45209                 0                1               0                  0   \n",
              "45210                 0                1               0                  0   \n",
              "\n",
              "       education_secondary  education_tertiary  education_unknown  \\\n",
              "0                        0                   1                  0   \n",
              "1                        1                   0                  0   \n",
              "2                        1                   0                  0   \n",
              "3                        0                   0                  1   \n",
              "4                        0                   0                  1   \n",
              "...                    ...                 ...                ...   \n",
              "45206                    0                   1                  0   \n",
              "45207                    0                   0                  0   \n",
              "45208                    1                   0                  0   \n",
              "45209                    1                   0                  0   \n",
              "45210                    1                   0                  0   \n",
              "\n",
              "       contact_cellular  contact_telephone  contact_unknown  poutcome_failure  \\\n",
              "0                     0                  0                1                 0   \n",
              "1                     0                  0                1                 0   \n",
              "2                     0                  0                1                 0   \n",
              "3                     0                  0                1                 0   \n",
              "4                     0                  0                1                 0   \n",
              "...                 ...                ...              ...               ...   \n",
              "45206                 1                  0                0                 0   \n",
              "45207                 1                  0                0                 0   \n",
              "45208                 1                  0                0                 0   \n",
              "45209                 0                  1                0                 0   \n",
              "45210                 1                  0                0                 0   \n",
              "\n",
              "       poutcome_other  poutcome_success  poutcome_unknown  \n",
              "0                   0                 0                 1  \n",
              "1                   0                 0                 1  \n",
              "2                   0                 0                 1  \n",
              "3                   0                 0                 1  \n",
              "4                   0                 0                 1  \n",
              "...               ...               ...               ...  \n",
              "45206               0                 0                 1  \n",
              "45207               0                 0                 1  \n",
              "45208               0                 1                 0  \n",
              "45209               0                 0                 1  \n",
              "45210               1                 0                 0  \n",
              "\n",
              "[45211 rows x 38 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e6fabb4-6242-4b4c-9236-a048f81f5cdf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>y</th>\n",
              "      <th>job_admin.</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_housemaid</th>\n",
              "      <th>job_management</th>\n",
              "      <th>job_retired</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_services</th>\n",
              "      <th>job_student</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_unemployed</th>\n",
              "      <th>job_unknown</th>\n",
              "      <th>marital_divorced</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>education_primary</th>\n",
              "      <th>education_secondary</th>\n",
              "      <th>education_tertiary</th>\n",
              "      <th>education_unknown</th>\n",
              "      <th>contact_cellular</th>\n",
              "      <th>contact_telephone</th>\n",
              "      <th>contact_unknown</th>\n",
              "      <th>poutcome_failure</th>\n",
              "      <th>poutcome_other</th>\n",
              "      <th>poutcome_success</th>\n",
              "      <th>poutcome_unknown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
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              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
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              "      <th>3</th>\n",
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              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
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              "      <th>45208</th>\n",
              "      <td>72</td>\n",
              "      <td>no</td>\n",
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              "      <td>nov</td>\n",
              "      <td>1127</td>\n",
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              "      <th>45209</th>\n",
              "      <td>57</td>\n",
              "      <td>no</td>\n",
              "      <td>668</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>37</td>\n",
              "      <td>no</td>\n",
              "      <td>2971</td>\n",
              "      <td>no</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 38 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "                                                    [key], {});\n",
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              "        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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      .colab-df-generate:hover {\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1"
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          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0O1FA8Y5h04",
        "outputId": "33ee2739-71ac-4225-d6ff-40ded6bbb80e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45211 entries, 0 to 45210\n",
            "Data columns (total 38 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   age                  45211 non-null  int64 \n",
            " 1   default              45211 non-null  object\n",
            " 2   balance              45211 non-null  int64 \n",
            " 3   housing              45211 non-null  object\n",
            " 4   loan                 45211 non-null  object\n",
            " 5   day                  45211 non-null  int64 \n",
            " 6   month                45211 non-null  object\n",
            " 7   duration             45211 non-null  int64 \n",
            " 8   campaign             45211 non-null  int64 \n",
            " 9   pdays                45211 non-null  int64 \n",
            " 10  previous             45211 non-null  int64 \n",
            " 11  y                    45211 non-null  object\n",
            " 12  job_admin.           45211 non-null  uint8 \n",
            " 13  job_blue-collar      45211 non-null  uint8 \n",
            " 14  job_entrepreneur     45211 non-null  uint8 \n",
            " 15  job_housemaid        45211 non-null  uint8 \n",
            " 16  job_management       45211 non-null  uint8 \n",
            " 17  job_retired          45211 non-null  uint8 \n",
            " 18  job_self-employed    45211 non-null  uint8 \n",
            " 19  job_services         45211 non-null  uint8 \n",
            " 20  job_student          45211 non-null  uint8 \n",
            " 21  job_technician       45211 non-null  uint8 \n",
            " 22  job_unemployed       45211 non-null  uint8 \n",
            " 23  job_unknown          45211 non-null  uint8 \n",
            " 24  marital_divorced     45211 non-null  uint8 \n",
            " 25  marital_married      45211 non-null  uint8 \n",
            " 26  marital_single       45211 non-null  uint8 \n",
            " 27  education_primary    45211 non-null  uint8 \n",
            " 28  education_secondary  45211 non-null  uint8 \n",
            " 29  education_tertiary   45211 non-null  uint8 \n",
            " 30  education_unknown    45211 non-null  uint8 \n",
            " 31  contact_cellular     45211 non-null  uint8 \n",
            " 32  contact_telephone    45211 non-null  uint8 \n",
            " 33  contact_unknown      45211 non-null  uint8 \n",
            " 34  poutcome_failure     45211 non-null  uint8 \n",
            " 35  poutcome_other       45211 non-null  uint8 \n",
            " 36  poutcome_success     45211 non-null  uint8 \n",
            " 37  poutcome_unknown     45211 non-null  uint8 \n",
            "dtypes: int64(7), object(5), uint8(26)\n",
            "memory usage: 5.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Custom Binary Encoding of Binary Output variables for columns ('default' ,'housing' ,'loan' ,'y')"
      ],
      "metadata": {
        "id": "aSN0cirvB3bs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1['default'] = np.where(df1['default'].str.contains(\"yes\"), 1, 0)\n",
        "df1['housing'] = np.where(df1['housing'].str.contains(\"yes\"), 1, 0)\n",
        "df1['loan'] = np.where(df1['loan'].str.contains(\"yes\"), 1, 0)\n",
        "df1['y'] = np.where(df1['y'].str.contains(\"yes\"), 1, 0)\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "zhfPJhfI5htd",
        "outputId": "902c1fe0-589d-4f99-9660-a2c0c16e7dd3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  default  balance  housing  loan  day month  duration  campaign  \\\n",
              "0       58        0     2143        1     0    5   may       261         1   \n",
              "1       44        0       29        1     0    5   may       151         1   \n",
              "2       33        0        2        1     1    5   may        76         1   \n",
              "3       47        0     1506        1     0    5   may        92         1   \n",
              "4       33        0        1        0     0    5   may       198         1   \n",
              "...    ...      ...      ...      ...   ...  ...   ...       ...       ...   \n",
              "45206   51        0      825        0     0   17   nov       977         3   \n",
              "45207   71        0     1729        0     0   17   nov       456         2   \n",
              "45208   72        0     5715        0     0   17   nov      1127         5   \n",
              "45209   57        0      668        0     0   17   nov       508         4   \n",
              "45210   37        0     2971        0     0   17   nov       361         2   \n",
              "\n",
              "       pdays  previous  y  job_admin.  job_blue-collar  job_entrepreneur  \\\n",
              "0         -1         0  0           0                0                 0   \n",
              "1         -1         0  0           0                0                 0   \n",
              "2         -1         0  0           0                0                 1   \n",
              "3         -1         0  0           0                1                 0   \n",
              "4         -1         0  0           0                0                 0   \n",
              "...      ...       ... ..         ...              ...               ...   \n",
              "45206     -1         0  1           0                0                 0   \n",
              "45207     -1         0  1           0                0                 0   \n",
              "45208    184         3  1           0                0                 0   \n",
              "45209     -1         0  0           0                1                 0   \n",
              "45210    188        11  0           0                0                 1   \n",
              "\n",
              "       job_housemaid  job_management  job_retired  job_self-employed  \\\n",
              "0                  0               1            0                  0   \n",
              "1                  0               0            0                  0   \n",
              "2                  0               0            0                  0   \n",
              "3                  0               0            0                  0   \n",
              "4                  0               0            0                  0   \n",
              "...              ...             ...          ...                ...   \n",
              "45206              0               0            0                  0   \n",
              "45207              0               0            1                  0   \n",
              "45208              0               0            1                  0   \n",
              "45209              0               0            0                  0   \n",
              "45210              0               0            0                  0   \n",
              "\n",
              "       job_services  job_student  job_technician  job_unemployed  job_unknown  \\\n",
              "0                 0            0               0               0            0   \n",
              "1                 0            0               1               0            0   \n",
              "2                 0            0               0               0            0   \n",
              "3                 0            0               0               0            0   \n",
              "4                 0            0               0               0            1   \n",
              "...             ...          ...             ...             ...          ...   \n",
              "45206             0            0               1               0            0   \n",
              "45207             0            0               0               0            0   \n",
              "45208             0            0               0               0            0   \n",
              "45209             0            0               0               0            0   \n",
              "45210             0            0               0               0            0   \n",
              "\n",
              "       marital_divorced  marital_married  marital_single  education_primary  \\\n",
              "0                     0                1               0                  0   \n",
              "1                     0                0               1                  0   \n",
              "2                     0                1               0                  0   \n",
              "3                     0                1               0                  0   \n",
              "4                     0                0               1                  0   \n",
              "...                 ...              ...             ...                ...   \n",
              "45206                 0                1               0                  0   \n",
              "45207                 1                0               0                  1   \n",
              "45208                 0                1               0                  0   \n",
              "45209                 0                1               0                  0   \n",
              "45210                 0                1               0                  0   \n",
              "\n",
              "       education_secondary  education_tertiary  education_unknown  \\\n",
              "0                        0                   1                  0   \n",
              "1                        1                   0                  0   \n",
              "2                        1                   0                  0   \n",
              "3                        0                   0                  1   \n",
              "4                        0                   0                  1   \n",
              "...                    ...                 ...                ...   \n",
              "45206                    0                   1                  0   \n",
              "45207                    0                   0                  0   \n",
              "45208                    1                   0                  0   \n",
              "45209                    1                   0                  0   \n",
              "45210                    1                   0                  0   \n",
              "\n",
              "       contact_cellular  contact_telephone  contact_unknown  poutcome_failure  \\\n",
              "0                     0                  0                1                 0   \n",
              "1                     0                  0                1                 0   \n",
              "2                     0                  0                1                 0   \n",
              "3                     0                  0                1                 0   \n",
              "4                     0                  0                1                 0   \n",
              "...                 ...                ...              ...               ...   \n",
              "45206                 1                  0                0                 0   \n",
              "45207                 1                  0                0                 0   \n",
              "45208                 1                  0                0                 0   \n",
              "45209                 0                  1                0                 0   \n",
              "45210                 1                  0                0                 0   \n",
              "\n",
              "       poutcome_other  poutcome_success  poutcome_unknown  \n",
              "0                   0                 0                 1  \n",
              "1                   0                 0                 1  \n",
              "2                   0                 0                 1  \n",
              "3                   0                 0                 1  \n",
              "4                   0                 0                 1  \n",
              "...               ...               ...               ...  \n",
              "45206               0                 0                 1  \n",
              "45207               0                 0                 1  \n",
              "45208               0                 1                 0  \n",
              "45209               0                 0                 1  \n",
              "45210               1                 0                 0  \n",
              "\n",
              "[45211 rows x 38 columns]"
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 38 columns</p>\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Find and Replace Encoding for \"month\" categorical varaible"
      ],
      "metadata": {
        "id": "r00tP_lPDGHn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1['month'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggynkuwC5hmN",
        "outputId": "cfc959cd-71dd-4a56-be10-47bdf198be89"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "may    13766\n",
              "jul     6895\n",
              "aug     6247\n",
              "jun     5341\n",
              "nov     3970\n",
              "apr     2932\n",
              "feb     2649\n",
              "jan     1403\n",
              "oct      738\n",
              "sep      579\n",
              "mar      477\n",
              "dec      214\n",
              "Name: month, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order={'month':{'jan':1,'feb':2,'mar':3,'apr':4,'may':5,'jun':6,'jul':7,'aug':8,'sep':9,'oct':10,'nov':11,'dec':12}}"
      ],
      "metadata": {
        "id": "7NtfiFMe5hjF"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df1.replace(order)"
      ],
      "metadata": {
        "id": "w2Q-UBt95hfd"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "7g0tVJjA5hb2",
        "outputId": "9be417d0-479c-42c4-f4d9-5c3a1aa546fe"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  default  balance  housing  loan  day  month  duration  campaign  \\\n",
              "0       58        0     2143        1     0    5      5       261         1   \n",
              "1       44        0       29        1     0    5      5       151         1   \n",
              "2       33        0        2        1     1    5      5        76         1   \n",
              "3       47        0     1506        1     0    5      5        92         1   \n",
              "4       33        0        1        0     0    5      5       198         1   \n",
              "...    ...      ...      ...      ...   ...  ...    ...       ...       ...   \n",
              "45206   51        0      825        0     0   17     11       977         3   \n",
              "45207   71        0     1729        0     0   17     11       456         2   \n",
              "45208   72        0     5715        0     0   17     11      1127         5   \n",
              "45209   57        0      668        0     0   17     11       508         4   \n",
              "45210   37        0     2971        0     0   17     11       361         2   \n",
              "\n",
              "       pdays  previous  y  job_admin.  job_blue-collar  job_entrepreneur  \\\n",
              "0         -1         0  0           0                0                 0   \n",
              "1         -1         0  0           0                0                 0   \n",
              "2         -1         0  0           0                0                 1   \n",
              "3         -1         0  0           0                1                 0   \n",
              "4         -1         0  0           0                0                 0   \n",
              "...      ...       ... ..         ...              ...               ...   \n",
              "45206     -1         0  1           0                0                 0   \n",
              "45207     -1         0  1           0                0                 0   \n",
              "45208    184         3  1           0                0                 0   \n",
              "45209     -1         0  0           0                1                 0   \n",
              "45210    188        11  0           0                0                 1   \n",
              "\n",
              "       job_housemaid  job_management  job_retired  job_self-employed  \\\n",
              "0                  0               1            0                  0   \n",
              "1                  0               0            0                  0   \n",
              "2                  0               0            0                  0   \n",
              "3                  0               0            0                  0   \n",
              "4                  0               0            0                  0   \n",
              "...              ...             ...          ...                ...   \n",
              "45206              0               0            0                  0   \n",
              "45207              0               0            1                  0   \n",
              "45208              0               0            1                  0   \n",
              "45209              0               0            0                  0   \n",
              "45210              0               0            0                  0   \n",
              "\n",
              "       job_services  job_student  job_technician  job_unemployed  job_unknown  \\\n",
              "0                 0            0               0               0            0   \n",
              "1                 0            0               1               0            0   \n",
              "2                 0            0               0               0            0   \n",
              "3                 0            0               0               0            0   \n",
              "4                 0            0               0               0            1   \n",
              "...             ...          ...             ...             ...          ...   \n",
              "45206             0            0               1               0            0   \n",
              "45207             0            0               0               0            0   \n",
              "45208             0            0               0               0            0   \n",
              "45209             0            0               0               0            0   \n",
              "45210             0            0               0               0            0   \n",
              "\n",
              "       marital_divorced  marital_married  marital_single  education_primary  \\\n",
              "0                     0                1               0                  0   \n",
              "1                     0                0               1                  0   \n",
              "2                     0                1               0                  0   \n",
              "3                     0                1               0                  0   \n",
              "4                     0                0               1                  0   \n",
              "...                 ...              ...             ...                ...   \n",
              "45206                 0                1               0                  0   \n",
              "45207                 1                0               0                  1   \n",
              "45208                 0                1               0                  0   \n",
              "45209                 0                1               0                  0   \n",
              "45210                 0                1               0                  0   \n",
              "\n",
              "       education_secondary  education_tertiary  education_unknown  \\\n",
              "0                        0                   1                  0   \n",
              "1                        1                   0                  0   \n",
              "2                        1                   0                  0   \n",
              "3                        0                   0                  1   \n",
              "4                        0                   0                  1   \n",
              "...                    ...                 ...                ...   \n",
              "45206                    0                   1                  0   \n",
              "45207                    0                   0                  0   \n",
              "45208                    1                   0                  0   \n",
              "45209                    1                   0                  0   \n",
              "45210                    1                   0                  0   \n",
              "\n",
              "       contact_cellular  contact_telephone  contact_unknown  poutcome_failure  \\\n",
              "0                     0                  0                1                 0   \n",
              "1                     0                  0                1                 0   \n",
              "2                     0                  0                1                 0   \n",
              "3                     0                  0                1                 0   \n",
              "4                     0                  0                1                 0   \n",
              "...                 ...                ...              ...               ...   \n",
              "45206                 1                  0                0                 0   \n",
              "45207                 1                  0                0                 0   \n",
              "45208                 1                  0                0                 0   \n",
              "45209                 0                  1                0                 0   \n",
              "45210                 1                  0                0                 0   \n",
              "\n",
              "       poutcome_other  poutcome_success  poutcome_unknown  \n",
              "0                   0                 0                 1  \n",
              "1                   0                 0                 1  \n",
              "2                   0                 0                 1  \n",
              "3                   0                 0                 1  \n",
              "4                   0                 0                 1  \n",
              "...               ...               ...               ...  \n",
              "45206               0                 0                 1  \n",
              "45207               0                 0                 1  \n",
              "45208               0                 1                 0  \n",
              "45209               0                 0                 1  \n",
              "45210               1                 0                 0  \n",
              "\n",
              "[45211 rows x 38 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
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              "      <th>marital_divorced</th>\n",
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              "      <th>marital_single</th>\n",
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              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>668</td>\n",
              "      <td>0</td>\n",
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              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>508</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>2971</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>361</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 38 columns</p>\n",
              "</div>\n",
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              "        document.querySelector('#df-295fdb7e-9820-4a16-8f67-bd0c846ce24c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
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              "        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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fd7164e0-a3ff-4fdf-b07c-99e326c3ad07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0d4c4eed-c565-4899-bda1-6da087821a1a\">\n",
              "    <style>\n",
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              "        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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0d4c4eed-c565-4899-bda1-6da087821a1a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYDz6KQT5hYV",
        "outputId": "3a503c52-fd50-4e51-dadd-715e7c30ac5c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45211 entries, 0 to 45210\n",
            "Data columns (total 38 columns):\n",
            " #   Column               Non-Null Count  Dtype\n",
            "---  ------               --------------  -----\n",
            " 0   age                  45211 non-null  int64\n",
            " 1   default              45211 non-null  int64\n",
            " 2   balance              45211 non-null  int64\n",
            " 3   housing              45211 non-null  int64\n",
            " 4   loan                 45211 non-null  int64\n",
            " 5   day                  45211 non-null  int64\n",
            " 6   month                45211 non-null  int64\n",
            " 7   duration             45211 non-null  int64\n",
            " 8   campaign             45211 non-null  int64\n",
            " 9   pdays                45211 non-null  int64\n",
            " 10  previous             45211 non-null  int64\n",
            " 11  y                    45211 non-null  int64\n",
            " 12  job_admin.           45211 non-null  uint8\n",
            " 13  job_blue-collar      45211 non-null  uint8\n",
            " 14  job_entrepreneur     45211 non-null  uint8\n",
            " 15  job_housemaid        45211 non-null  uint8\n",
            " 16  job_management       45211 non-null  uint8\n",
            " 17  job_retired          45211 non-null  uint8\n",
            " 18  job_self-employed    45211 non-null  uint8\n",
            " 19  job_services         45211 non-null  uint8\n",
            " 20  job_student          45211 non-null  uint8\n",
            " 21  job_technician       45211 non-null  uint8\n",
            " 22  job_unemployed       45211 non-null  uint8\n",
            " 23  job_unknown          45211 non-null  uint8\n",
            " 24  marital_divorced     45211 non-null  uint8\n",
            " 25  marital_married      45211 non-null  uint8\n",
            " 26  marital_single       45211 non-null  uint8\n",
            " 27  education_primary    45211 non-null  uint8\n",
            " 28  education_secondary  45211 non-null  uint8\n",
            " 29  education_tertiary   45211 non-null  uint8\n",
            " 30  education_unknown    45211 non-null  uint8\n",
            " 31  contact_cellular     45211 non-null  uint8\n",
            " 32  contact_telephone    45211 non-null  uint8\n",
            " 33  contact_unknown      45211 non-null  uint8\n",
            " 34  poutcome_failure     45211 non-null  uint8\n",
            " 35  poutcome_other       45211 non-null  uint8\n",
            " 36  poutcome_success     45211 non-null  uint8\n",
            " 37  poutcome_unknown     45211 non-null  uint8\n",
            "dtypes: int64(12), uint8(26)\n",
            "memory usage: 5.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's do some Visualization"
      ],
      "metadata": {
        "id": "UPhFXURyEuUl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Age: Trying to find some strange values or null values"
      ],
      "metadata": {
        "id": "u8YgFrJyE-a1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Min age: ', df1['age'].max())\n",
        "print('Max age: ', df1['age'].min())\n",
        "print('Null Values: ', df1['age'].isnull().any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7N_LretdE-OF",
        "outputId": "e6c2e72d-0a29-4500-99a3-66d6e37a1e34"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min age:  95\n",
            "Max age:  18\n",
            "Null Values:  False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "plt.figure(figsize=(20, 8))\n",
        "sns.countplot(x='age', data=df1, palette='viridis')\n",
        "plt.xlabel('Age', fontsize=15)\n",
        "plt.ylabel('Count', fontsize=15)\n",
        "plt.title('Age Count Distribution', fontsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "EcrPx8KxE-K-",
        "outputId": "b9ed42af-32c2-44b5-f4df-78986c2ac237"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objs as go\n",
        "# Box Plot\n",
        "fig1 = go.Figure()\n",
        "fig1.add_trace(go.Box(y=df1['age'], name='Age Distribution'))\n",
        "fig1.update_layout(title='Age Distribution (Box Plot)',\n",
        "                   xaxis_title='People Age',\n",
        "                   yaxis_title='Age',\n",
        "                   font=dict(size=12))\n",
        "fig1.show()\n",
        "\n",
        "# Distribution Plot\n",
        "fig2 = px.histogram(df1, x='age', title='Age Distribution (Distribution Plot)', nbins=20)\n",
        "fig2.update_xaxes(title_text='Age', tickfont=dict(size=12))\n",
        "fig2.update_yaxes(title_text='Occurrence', tickfont=dict(size=12))\n",
        "fig2.update_layout(font=dict(size=12))\n",
        "fig2.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1W2s8g_SE-Eb",
        "outputId": "95f5d52e-466c-4060-d9a7-4e9a4ec1f27d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Quartiles\n",
        "print('1º Quartile: ', df1['age'].quantile(q = 0.25))\n",
        "print('2º Quartile: ', df1['age'].quantile(q = 0.50))\n",
        "print('3º Quartile: ', df1['age'].quantile(q = 0.75))\n",
        "print('4º Quartile: ', df1['age'].quantile(q = 1.00))\n",
        "#Calculate the outliers:\n",
        "  # Interquartile range, IQR = Q3 - Q1\n",
        "  # lower 1.5*IQR whisker = Q1 - 1.5 * IQR\n",
        "  # Upper 1.5*IQR whisker = Q3 + 1.5 * IQR\n",
        "\n",
        "print('Ages above: ', df1['age'].quantile(q = 0.75) +\n",
        "                      1.5*(df1['age'].quantile(q = 0.75) - df1['age'].quantile(q = 0.25)), 'are outliers')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkFytTysE9bz",
        "outputId": "f2201ad6-93e6-41ec-9df4-b0905101ac68"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1º Quartile:  33.0\n",
            "2º Quartile:  39.0\n",
            "3º Quartile:  48.0\n",
            "4º Quartile:  95.0\n",
            "Ages above:  70.5 are outliers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see that Ages above 70 are outliers"
      ],
      "metadata": {
        "id": "BKF9vbGdIFh0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Numerber of outliers: ', df1[df1['age'] > 70.5]['age'].count())\n",
        "print('Number of clients: ', len(df1))\n",
        "#Outliers in %\n",
        "print('Outliers are:', round(df1[df1['age'] > 70.5]['age'].count()*100/len(df1),2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_iSJ-wnEmmC",
        "outputId": "aeb27570-a1e8-41c1-bb29-bf145727bf9a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerber of outliers:  487\n",
            "Number of clients:  45211\n",
            "Outliers are: 1.08 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating some values to evaluate this independent variable\n",
        "print('MEAN:', round(df1['age'].mean(), 1))\n",
        "# A low standard deviation indicates that the data points tend to be close to the mean or expected value\n",
        "# A high standard deviation indicates that the data points are scattered\n",
        "print('STD :', round(df1['age'].std(), 1))\n",
        "# I think the best way to give a precise insight about dispersion is using the CV (coefficient of variation) (STD/MEAN)*100\n",
        "#    cv < 15%, low dispersion\n",
        "#    cv > 30%, high dispersion\n",
        "print('CV  :', round(df1['age'].std()*100/df1['age'].mean(), 1), ', High middle dispersion')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SqN2vOJIuNi",
        "outputId": "90a15c08-022d-4619-db75-3516374d6ba2"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEAN: 40.9\n",
            "STD : 10.6\n",
            "CV  : 25.9 , High middle dispersion\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusion about AGE, in my opinion due to almost high dispersion and just looking at this this graph we cannot conclude if age have a high effect to our variable y, need to keep searching for some pattern. high middle dispersion means we have people with all ages and maybe all of them can subscript a term deposit, or not. The outliers was calculated, so my thinking is fit the model with and without them"
      ],
      "metadata": {
        "id": "rdGbQ6OLJLNj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Job: What kind of jobs clients this bank have, if you cross jobs with default, loan or housing, there is no relation"
      ],
      "metadata": {
        "id": "F_-RQ3hbJY5j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the count of each job category\n",
        "job_counts = df1[['job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid', 'job_management',\n",
        "                  'job_retired', 'job_self-employed', 'job_services', 'job_student', 'job_technician',\n",
        "                  'job_unemployed', 'job_unknown']].sum()\n",
        "\n",
        "# Convert the counts into a DataFrame\n",
        "job_counts_df = pd.DataFrame({'Job': job_counts.index, 'Count': job_counts.values})\n",
        "\n",
        "fig = px.bar(job_counts_df, x='Job', y='Count', title='Job Count Distribution')\n",
        "fig.update_xaxes(title_text='Job', tickfont=dict(size=12))\n",
        "fig.update_yaxes(title_text='Count', tickfont=dict(size=12))\n",
        "fig.update_layout(title_font=dict(size=20), font=dict(size=12))\n",
        "fig.show()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "48iANqs-IuFl",
        "outputId": "a99dcae7-4413-4a67-8997-852a01fd2b1c"
      },
      "execution_count": 59,
      "outputs": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Martial: What kind of 'marital clients' this bank have, if you cross marital with default, loan or housing, there is no relation"
      ],
      "metadata": {
        "id": "lENyxo2oK7DC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the count of each marital status category\n",
        "marital_counts = df1[['marital_divorced', 'marital_married', 'marital_single']].sum()\n",
        "\n",
        "# Convert the counts into a DataFrame\n",
        "marital_counts_df = pd.DataFrame({'Marital Status': marital_counts.index, 'Count': marital_counts.values})\n",
        "\n",
        "# Create a bar plot using Plotly\n",
        "fig = px.bar(marital_counts_df, x='Marital Status', y='Count', title='Marital Status Count Distribution')\n",
        "fig.update_xaxes(title_text='Marital Status', tickfont=dict(size=12))\n",
        "fig.update_yaxes(title_text='Count', tickfont=dict(size=12))\n",
        "fig.update_layout(title_font=dict(size=20), font=dict(size=12))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Za_wuNeBIt8i",
        "outputId": "0476197e-6e8f-413d-8fcf-8d014809f93a"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Education: What kind of 'education clients this bank have, if you cross education with default, loan or housing, there is no relation"
      ],
      "metadata": {
        "id": "-5C0rr8pLiDy"
      }
    },
    {
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      "source": [
        "# Calculate the count of each education category\n",
        "education_counts = df1[['education_primary', 'education_secondary', 'education_tertiary', 'education_unknown']].sum()\n",
        "\n",
        "# Convert the counts into a DataFrame\n",
        "education_counts_df = pd.DataFrame({'Education Level': education_counts.index, 'Count': education_counts.values})\n",
        "\n",
        "# Create a bar plot using Plotly\n",
        "fig = px.bar(education_counts_df, x='Education Level', y='Count', title='Education Level Count Distribution')\n",
        "fig.update_xaxes(title_text='Education Level', tickfont=dict(size=12))\n",
        "fig.update_yaxes(title_text='Count', tickfont=dict(size=12))\n",
        "fig.update_layout(title_font=dict(size=20), font=dict(size=12))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "glpqimmgLZwK",
        "outputId": "0fa5ab0a-6409-4811-ae51-fa7bd90c7c5e"
      },
      "execution_count": 64,
      "outputs": [
        {
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          "metadata": {}
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    {
      "cell_type": "markdown",
      "source": [
        "# DEFAULT, HOUSING, LOAN"
      ],
      "metadata": {
        "id": "Plj11KsHM2Zz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting occurrences for each category in default, housing, and loan variables\n",
        "default_counts = df1['default'].value_counts()\n",
        "housing_counts = df1['housing'].value_counts()\n",
        "loan_counts = df1['loan'].value_counts()\n",
        "\n",
        "# Create a bar plot for Default\n",
        "fig1 = go.Figure(go.Bar(\n",
        "    x=default_counts.index,\n",
        "    y=default_counts.values,\n",
        "    text=default_counts.values,\n",
        "    textposition='auto',\n",
        "    marker=dict(color='blue')\n",
        "))\n",
        "fig1.update_layout(title='Default Count Distribution',\n",
        "                   xaxis_title='Default',\n",
        "                   yaxis_title='Count',\n",
        "                   font=dict(size=12))\n",
        "fig1.show()\n",
        "\n",
        "# Create a bar plot for Housing\n",
        "fig2 = go.Figure(go.Bar(\n",
        "    x=housing_counts.index,\n",
        "    y=housing_counts.values,\n",
        "    text=housing_counts.values,\n",
        "    textposition='auto',\n",
        "    marker=dict(color='green')\n",
        "))\n",
        "fig2.update_layout(title='Housing Count Distribution',\n",
        "                   xaxis_title='Housing',\n",
        "                   yaxis_title='Count',\n",
        "                   font=dict(size=12))\n",
        "fig2.show()\n",
        "\n",
        "# Create a bar plot for Loan\n",
        "fig3 = go.Figure(go.Bar(\n",
        "    x=loan_counts.index,\n",
        "    y=loan_counts.values,\n",
        "    text=loan_counts.values,\n",
        "    textposition='auto',\n",
        "    marker=dict(color='orange')\n",
        "))\n",
        "fig3.update_layout(title='Loan Count Distribution',\n",
        "                   xaxis_title='Loan',\n",
        "                   yaxis_title='Count',\n",
        "                   font=dict(size=12))\n",
        "fig3.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Uhz1nhmyLZs6",
        "outputId": "0a21776e-c670-4c86-8a86-64ae1b288ad2"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "source": [
        "# BANK CLIENTS CONCLUSION"
      ],
      "metadata": {
        "id": "B1phxocBPd9I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The ages dont mean to much, has a medium dispersion and dont make sense relate with other variables will not tell any insight\n",
        "\n",
        "Jobs, Marital and Education i think the best analisys is just the count of each variable, if we related with the other ones its is not conclusive, all this kind of variables has yes, unknown and no for loan, default and housing.\n",
        "\n",
        "Default, loan and housing, its just to see the distribution of people."
      ],
      "metadata": {
        "id": "NsqHYhUpPLgv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Building"
      ],
      "metadata": {
        "id": "KRh435h_QyiI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividing our data into input and output variables\n",
        "x=pd.concat([df1.iloc[:,0:11],df1.iloc[:,12:]],axis=1)\n",
        "y=df1.iloc[:,11]"
      ],
      "metadata": {
        "id": "_UjvUWxLOBm5"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic regression model\n",
        "classifier=LogisticRegression()\n",
        "classifier.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
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        "id": "zX-v2z_KO9P0",
        "outputId": "257e3251-8d17-4b4e-851d-4b5aeb853937"
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      "execution_count": 76,
      "outputs": [
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              "LogisticRegression()"
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            ]
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          "metadata": {},
          "execution_count": 76
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      "cell_type": "markdown",
      "source": [
        "## Model Predictions"
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      "metadata": {
        "id": "012Gc4NSRA-B"
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      "source": [
        "# Predict for x dataset\n",
        "y_pred=classifier.predict(x)\n",
        "y_pred"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSkM2GViO9Mc",
        "outputId": "160f222a-8386-41aa-fc75-7ade3f1af3a3"
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      "execution_count": 77,
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          "data": {
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              "array([0, 0, 0, ..., 1, 0, 1])"
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      "source": [
        "y_pred_df=pd.DataFrame({'actual_y':y,'y_pred_prob':y_pred})\n",
        "y_pred_df"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "rCf9j8kUO9JI",
        "outputId": "8d33878a-fb5e-4993-c0dd-30b43d56d631"
      },
      "execution_count": 78,
      "outputs": [
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          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45206</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45207</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45208</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45209</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45210</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45211 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aaca4465-a73d-45a6-b0a0-a66595adc4e4')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-aaca4465-a73d-45a6-b0a0-a66595adc4e4 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-aaca4465-a73d-45a6-b0a0-a66595adc4e4');\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",
              "\n",
              "\n",
              "<div id=\"df-e09d2e6b-ba45-4ff8-a270-c55a22dc4f85\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e09d2e6b-ba45-4ff8-a270-c55a22dc4f85')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e09d2e6b-ba45-4ff8-a270-c55a22dc4f85 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_17295709-a0d7-41f3-8d6e-dffe505087fe\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('y_pred_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_17295709-a0d7-41f3-8d6e-dffe505087fe button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_pred_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y_pred_df",
              "summary": "{\n  \"name\": \"y_pred_df\",\n  \"rows\": 45211,\n  \"fields\": [\n    {\n      \"column\": \"actual_y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y_pred_prob\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing Model Accuracy"
      ],
      "metadata": {
        "id": "4HuyYPLiRcsg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix for the model accuracy\n",
        "confusion_matrix = confusion_matrix(y,y_pred)\n",
        "confusion_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKdSiKM3RL7g",
        "outputId": "d769c396-939c-4059-ff6d-f2bc294e936f"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[39032,   890],\n",
              "       [ 3979,  1310]])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The model accuracy is calculated by (a+d)/(a+b+c+d)\n",
        "(39107+1282)/(39107+815+4007+1282)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZgfutT0RLxp",
        "outputId": "652ec0c4-6c7d-4cca-c9fb-bdb356f557dc"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8933445400455642"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy: 89.3%"
      ],
      "metadata": {
        "id": "yY6ygWRkSrDn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# As accuracy = 0.8933, which is greater than 0.5; Thus [:,1] Threshold value>0.5=1 else [:,0] Threshold value<0.5=0\n",
        "classifier.predict_proba(x)[:,1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VyIC8biTRkbS",
        "outputId": "5f7e12aa-3bd6-4383-b100-c602d5b0f99a"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.05129922, 0.0323888 , 0.02551381, ..., 0.73333881, 0.0773174 ,\n",
              "       0.54630385])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC Curve plotting and finding AUC value"
      ],
      "metadata": {
        "id": "94KurG8JR7E5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "\n",
        "# Compute ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y, classifier.predict_proba(x)[:,1])\n",
        "auc = roc_auc_score(y, y_pred)\n",
        "\n",
        "# Create a ROC curve plot using Plotly\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x=fpr, y=tpr, mode='lines', line=dict(color='red'), name='ROC Curve'))\n",
        "fig.add_trace(go.Scatter(x=[0, 1], y=[0, 1], mode='lines', line=dict(color='black', dash='dash'), name='Random Guess'))\n",
        "\n",
        "fig.update_layout(title='ROC Curve',\n",
        "                  xaxis_title='False Positive Rate',\n",
        "                  yaxis_title='True Positive Rate',\n",
        "                  font=dict(size=12),\n",
        "                  showlegend=True)\n",
        "fig.show()\n",
        "\n",
        "print('AUC Accuracy:', auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "rtIUiZDkRkYg",
        "outputId": "d8722ad7-8327-4d5e-ca77-0bf374e98bcf"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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            "AUC Accuracy: 0.6126951999583155\n"
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      "source": [
        "In conclusion, while the logistic regression model demonstrates a reasonable level of accuracy in predicting outcomes, the modest AUC value suggests that there is room for improvement."
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