Bank_marketing_preprocessing.ipynb

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        "<a href=\"https://colab.research.google.com/github/muajnstu/Bank-Marketing-using-rough-set-approach/blob/main/Bank_marketing_preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url='https://raw.githubusercontent.com/muajnstu/Bank-Marketing-using-rough-set-approach/main/bank.csv'\n",
        "df=pd.read_csv(url)\n",
        "df.head()"
      ],
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      "execution_count": 106,
      "outputs": [
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          "data": {
            "text/plain": [
              "   age         job  marital  education default  balance housing loan  contact  \\\n",
              "0   59      admin.  married  secondary      no     2343     yes   no  unknown   \n",
              "1   56      admin.  married  secondary      no       45      no   no  unknown   \n",
              "2   41  technician  married  secondary      no     1270     yes   no  unknown   \n",
              "3   55    services  married  secondary      no     2476     yes   no  unknown   \n",
              "4   54      admin.  married   tertiary      no      184      no   no  unknown   \n",
              "\n",
              "   day month  duration  campaign  pdays  previous poutcome deposit  \n",
              "0    5   may      1042         1     -1         0  unknown     yes  \n",
              "1    5   may      1467         1     -1         0  unknown     yes  \n",
              "2    5   may      1389         1     -1         0  unknown     yes  \n",
              "3    5   may       579         1     -1         0  unknown     yes  \n",
              "4    5   may       673         2     -1         0  unknown     yes  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11162,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 18,\n        \"max\": 95,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          54,\n          47,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"self-employed\",\n          \"unknown\",\n          \"admin.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"married\",\n          \"single\",\n          \"divorced\"\n      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{\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 63,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          31,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108,\n        \"min\": -1,\n        \"max\": 854,\n        \"num_unique_values\": 472,\n        \"samples\": [\n          294,\n          148\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\": 58,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          30,\n          14\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          \"other\",\n          \"success\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deposit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 106
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        "df.describe()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yBtnEJ-Lh68_",
        "outputId": "d6719d28-1b1e-4253-f67c-980361c426a3"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age       balance           day      duration      campaign  \\\n",
              "count  11162.000000  11162.000000  11162.000000  11162.000000  11162.000000   \n",
              "mean      41.231948   1528.538524     15.658036    371.993818      2.508421   \n",
              "std       11.913369   3225.413326      8.420740    347.128386      2.722077   \n",
              "min       18.000000  -6847.000000      1.000000      2.000000      1.000000   \n",
              "25%       32.000000    122.000000      8.000000    138.000000      1.000000   \n",
              "50%       39.000000    550.000000     15.000000    255.000000      2.000000   \n",
              "75%       49.000000   1708.000000     22.000000    496.000000      3.000000   \n",
              "max       95.000000  81204.000000     31.000000   3881.000000     63.000000   \n",
              "\n",
              "              pdays      previous  \n",
              "count  11162.000000  11162.000000  \n",
              "mean      51.330407      0.832557  \n",
              "std      108.758282      2.292007  \n",
              "min       -1.000000      0.000000  \n",
              "25%       -1.000000      0.000000  \n",
              "50%       -1.000000      0.000000  \n",
              "75%       20.750000      1.000000  \n",
              "max      854.000000     58.000000  "
            ],
            "text/html": [
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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>balance</th>\n",
              "      <th>day</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "      <td>11162.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.231948</td>\n",
              "      <td>1528.538524</td>\n",
              "      <td>15.658036</td>\n",
              "      <td>371.993818</td>\n",
              "      <td>2.508421</td>\n",
              "      <td>51.330407</td>\n",
              "      <td>0.832557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.913369</td>\n",
              "      <td>3225.413326</td>\n",
              "      <td>8.420740</td>\n",
              "      <td>347.128386</td>\n",
              "      <td>2.722077</td>\n",
              "      <td>108.758282</td>\n",
              "      <td>2.292007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>-6847.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>550.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>1708.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>20.750000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>81204.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>3881.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>854.000000</td>\n",
              "      <td>58.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  }\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",
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              "    }\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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3931.9910823606933,\n        \"min\": 11.913369192215526,\n        \"max\": 11162.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          41.231947679627304,\n          39.0,\n          11162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28556.87170826913,\n        \"min\": -6847.0,\n        \"max\": 81204.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1528.5385235620856,\n          550.0,\n          11162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3941.2684170092043,\n        \"min\": 1.0,\n        \"max\": 11162.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.658036194230425,\n          15.0,\n          11162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3883.5837945353724,\n        \"min\": 2.0,\n        \"max\": 11162.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          371.99381831213043,\n          255.0,\n          11162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3942.6210107711095,\n        \"min\": 1.0,\n        \"max\": 11162.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11162.0,\n          2.508421429851281,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3905.092718695694,\n        \"min\": -1.0,\n        \"max\": 11162.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11162.0,\n          51.33040673714388,\n          854.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3943.27625599434,\n        \"min\": 0.0,\n        \"max\": 11162.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11162.0,\n          0.8325568894463358,\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_TIz31iiAa-",
        "outputId": "1158bb58-29db-437f-88eb-f44d83ddf839"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11162, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-k3F1cHylOd5",
        "outputId": "839215e6-8cff-4eb5-86d4-49be7d754756"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          0\n",
              "job          0\n",
              "marital      0\n",
              "education    0\n",
              "default      0\n",
              "balance      0\n",
              "housing      0\n",
              "loan         0\n",
              "contact      0\n",
              "day          0\n",
              "month        0\n",
              "duration     0\n",
              "campaign     0\n",
              "pdays        0\n",
              "previous     0\n",
              "poutcome     0\n",
              "deposit      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.select_dtypes(include='object').columns:\n",
        "    print(col)\n",
        "    print(df[col].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvumXHcUjB8m",
        "outputId": "9236c99b-ab2c-4d6b-a3d7-860ca9515cc3"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "job\n",
            "['admin.' 'technician' 'services' 'management' 'retired' 'blue-collar'\n",
            " 'unemployed' 'entrepreneur' 'housemaid' 'unknown' 'self-employed'\n",
            " 'student']\n",
            "marital\n",
            "['married' 'single' 'divorced']\n",
            "education\n",
            "['secondary' 'tertiary' 'primary' 'unknown']\n",
            "default\n",
            "['no' 'yes']\n",
            "housing\n",
            "['yes' 'no']\n",
            "loan\n",
            "['no' 'yes']\n",
            "contact\n",
            "['unknown' 'cellular' 'telephone']\n",
            "month\n",
            "['may' 'jun' 'jul' 'aug' 'oct' 'nov' 'dec' 'jan' 'feb' 'mar' 'apr' 'sep']\n",
            "poutcome\n",
            "['unknown' 'other' 'failure' 'success']\n",
            "deposit\n",
            "['yes' 'no']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['default', 'poutcome'], axis=1, inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "q34bpImYk2FR",
        "outputId": "093bd463-cb20-4a5b-ef50-11843f42eb5b"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age         job  marital  education  balance housing loan  contact  day  \\\n",
              "0   59      admin.  married  secondary     2343     yes   no  unknown    5   \n",
              "1   56      admin.  married  secondary       45      no   no  unknown    5   \n",
              "2   41  technician  married  secondary     1270     yes   no  unknown    5   \n",
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              "    async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11162,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 18,\n        \"max\": 95,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          54,\n          47,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"self-employed\",\n          \"unknown\",\n          \"admin.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"married\",\n          \"single\",\n          \"divorced\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"tertiary\",\n          \"unknown\",\n          \"secondary\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3225,\n        \"min\": -6847,\n        \"max\": 81204,\n        \"num_unique_values\": 3805,\n        \"samples\": [\n          3026,\n          1792,\n          566\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\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      \"column\": \"contact\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"unknown\",\n          \"cellular\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          10,\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\": 347,\n        \"min\": 2,\n        \"max\": 3881,\n        \"num_unique_values\": 1428,\n        \"samples\": [\n          597,\n          346\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 63,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          31,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108,\n        \"min\": -1,\n        \"max\": 854,\n        \"num_unique_values\": 472,\n        \"samples\": [\n          294,\n          148\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\": 58,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          30,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deposit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select relevant features and target\n",
        "x = df.drop(['deposit'], axis=1)\n",
        "y = df['deposit']"
      ],
      "metadata": {
        "id": "x3_imlvYqPP7"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling missing values\n",
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "4Y_iJzdcicxs"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Duplicates\n",
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "OwGNBNU4ihYx"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_features=[feature for feature in df.columns if ((df[feature].dtypes=='O') & (feature not in ['deposit']))]\n",
        "categorical_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgIx1WRNixuX",
        "outputId": "03409cf0-88b6-480b-de92-2e392e0415f2"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['job',\n",
              " 'marital',\n",
              " 'education',\n",
              " 'housing',\n",
              " 'loan',\n",
              " 'contact',\n",
              " 'month',\n",
              " 'poutcome']"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B-Z_IjPqo5Fh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df.copy()"
      ],
      "metadata": {
        "id": "tvzHZkLgjPbC"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_columns = ['job', 'marital', 'education', 'contact', 'month']\n",
        "for col in  cat_columns:\n",
        "    df2 = pd.concat([df2.drop(col, axis=1),pd.get_dummies(df2[col], prefix=col, prefix_sep='_',drop_first=True, dummy_na=False)], axis=1)"
      ],
      "metadata": {
        "id": "PJb0l_NXjkg7"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bool_columns = ['housing', 'loan', 'deposit']\n",
        "for col in  bool_columns:\n",
        "    df2[col+'_new']=df2[col].apply(lambda x : 1 if x == 'yes' else 0)\n",
        "    df2.drop(col, axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "QBc9XZn2jx6i"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "WN85Edk6j-2e",
        "outputId": "8250fe6b-cae1-4a6e-b2e6-80dbbe15ed08"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  balance  day  duration  campaign  pdays  previous  job_blue-collar  \\\n",
              "0   59     2343    5      1042         1     -1         0            False   \n",
              "1   56       45    5      1467         1     -1         0            False   \n",
              "2   41     1270    5      1389         1     -1         0            False   \n",
              "3   55     2476    5       579         1     -1         0            False   \n",
              "4   54      184    5       673         2     -1         0            False   \n",
              "\n",
              "   job_entrepreneur  job_housemaid  ...  month_jul  month_jun  month_mar  \\\n",
              "0             False          False  ...      False      False      False   \n",
              "1             False          False  ...      False      False      False   \n",
              "2             False          False  ...      False      False      False   \n",
              "3             False          False  ...      False      False      False   \n",
              "4             False          False  ...      False      False      False   \n",
              "\n",
              "   month_may  month_nov  month_oct  month_sep  housing_new  loan_new  \\\n",
              "0       True      False      False      False            1         0   \n",
              "1       True      False      False      False            0         0   \n",
              "2       True      False      False      False            1         0   \n",
              "3       True      False      False      False            1         0   \n",
              "4       True      False      False      False            0         0   \n",
              "\n",
              "   deposit_new  \n",
              "0            1  \n",
              "1            1  \n",
              "2            1  \n",
              "3            1  \n",
              "4            1  \n",
              "\n",
              "[5 rows x 39 columns]"
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              "<p>5 rows × 39 columns</p>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
            }
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    }
  ]
}