Prodigy_DS_task3.ipynb

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        "<a href=\"https://colab.research.google.com/github/Vinithpr2004/PRODIGY_DS_3/blob/main/Prodigy_DS_task3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ]
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        "df = pd.read_csv(\"/content/bank-additional.csv\", delimiter=';')\n",
        "df.rename(columns={'y':'deposit'}, inplace=True)\n",
        "df.head()"
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              "   age          job  marital          education default  housing     loan  \\\n",
              "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
              "1   39     services   single        high.school      no       no       no   \n",
              "2   25     services  married        high.school      no      yes       no   \n",
              "3   38     services  married           basic.9y      no  unknown  unknown   \n",
              "4   47       admin.  married  university.degree      no      yes       no   \n",
              "\n",
              "     contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
              "0   cellular   may         fri  ...         2    999         0  nonexistent   \n",
              "1  telephone   may         fri  ...         4    999         0  nonexistent   \n",
              "2  telephone   jun         wed  ...         1    999         0  nonexistent   \n",
              "3  telephone   jun         fri  ...         3    999         0  nonexistent   \n",
              "4   cellular   nov         mon  ...         1    999         0  nonexistent   \n",
              "\n",
              "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  deposit  \n",
              "0         -1.8          92.893          -46.2      1.313       5099.1       no  \n",
              "1          1.1          93.994          -36.4      4.855       5191.0       no  \n",
              "2          1.4          94.465          -41.8      4.962       5228.1       no  \n",
              "3          1.4          94.465          -41.8      4.959       5228.1       no  \n",
              "4         -0.1          93.200          -42.0      4.191       5195.8       no  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      age         job  marital    education default housing loan    contact  \\\n",
              "4114   30      admin.  married     basic.6y      no     yes  yes   cellular   \n",
              "4115   39      admin.  married  high.school      no     yes   no  telephone   \n",
              "4116   27     student   single  high.school      no      no   no   cellular   \n",
              "4117   58      admin.  married  high.school      no      no   no   cellular   \n",
              "4118   34  management   single  high.school      no     yes   no   cellular   \n",
              "\n",
              "     month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
              "4114   jul         thu  ...         1    999         0  nonexistent   \n",
              "4115   jul         fri  ...         1    999         0  nonexistent   \n",
              "4116   may         mon  ...         2    999         1      failure   \n",
              "4117   aug         fri  ...         1    999         0  nonexistent   \n",
              "4118   nov         wed  ...         1    999         0  nonexistent   \n",
              "\n",
              "     emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \\\n",
              "4114          1.4          93.918          -42.7      4.958       5228.1   \n",
              "4115          1.4          93.918          -42.7      4.959       5228.1   \n",
              "4116         -1.8          92.893          -46.2      1.354       5099.1   \n",
              "4117          1.4          93.444          -36.1      4.966       5228.1   \n",
              "4118         -0.1          93.200          -42.0      4.120       5195.8   \n",
              "\n",
              "      deposit  \n",
              "4114       no  \n",
              "4115       no  \n",
              "4116       no  \n",
              "4117       no  \n",
              "4118       no  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>...</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>deposit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4114</th>\n",
              "      <td>30</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.6y</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>cellular</td>\n",
              "      <td>jul</td>\n",
              "      <td>thu</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.4</td>\n",
              "      <td>93.918</td>\n",
              "      <td>-42.7</td>\n",
              "      <td>4.958</td>\n",
              "      <td>5228.1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4115</th>\n",
              "      <td>39</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>jul</td>\n",
              "      <td>fri</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.4</td>\n",
              "      <td>93.918</td>\n",
              "      <td>-42.7</td>\n",
              "      <td>4.959</td>\n",
              "      <td>5228.1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4116</th>\n",
              "      <td>27</td>\n",
              "      <td>student</td>\n",
              "      <td>single</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>999</td>\n",
              "      <td>1</td>\n",
              "      <td>failure</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>92.893</td>\n",
              "      <td>-46.2</td>\n",
              "      <td>1.354</td>\n",
              "      <td>5099.1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4117</th>\n",
              "      <td>58</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>aug</td>\n",
              "      <td>fri</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.4</td>\n",
              "      <td>93.444</td>\n",
              "      <td>-36.1</td>\n",
              "      <td>4.966</td>\n",
              "      <td>5228.1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4118</th>\n",
              "      <td>34</td>\n",
              "      <td>management</td>\n",
              "      <td>single</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>wed</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>93.200</td>\n",
              "      <td>-42.0</td>\n",
              "      <td>4.120</td>\n",
              "      <td>5195.8</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-c8282fa3-ca84-4d4c-912d-40ce49272e8f 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-c8282fa3-ca84-4d4c-912d-40ce49272e8f');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-602dd454-cf25-4d32-860c-bbe807a61db1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-602dd454-cf25-4d32-860c-bbe807a61db1')\"\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",
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              "    </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",
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              "\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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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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-602dd454-cf25-4d32-860c-bbe807a61db1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeFBvqXuNaJ_",
        "outputId": "a04735a2-4f42-4577-e7d6-643892a94829"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4119 entries, 0 to 4118\n",
            "Data columns (total 21 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             4119 non-null   int64  \n",
            " 1   job             4119 non-null   object \n",
            " 2   marital         4119 non-null   object \n",
            " 3   education       4119 non-null   object \n",
            " 4   default         4119 non-null   object \n",
            " 5   housing         4119 non-null   object \n",
            " 6   loan            4119 non-null   object \n",
            " 7   contact         4119 non-null   object \n",
            " 8   month           4119 non-null   object \n",
            " 9   day_of_week     4119 non-null   object \n",
            " 10  duration        4119 non-null   int64  \n",
            " 11  campaign        4119 non-null   int64  \n",
            " 12  pdays           4119 non-null   int64  \n",
            " 13  previous        4119 non-null   int64  \n",
            " 14  poutcome        4119 non-null   object \n",
            " 15  emp.var.rate    4119 non-null   float64\n",
            " 16  cons.price.idx  4119 non-null   float64\n",
            " 17  cons.conf.idx   4119 non-null   float64\n",
            " 18  euribor3m       4119 non-null   float64\n",
            " 19  nr.employed     4119 non-null   float64\n",
            " 20  deposit         4119 non-null   object \n",
            "dtypes: float64(5), int64(5), object(11)\n",
            "memory usage: 675.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3WL1sPLNaMc",
        "outputId": "d1524c6c-c186-443e-a3ed-f0815d6cbe52"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4119, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-x_XmixNaqt",
        "outputId": "701b31bd-fb87-4c6e-f7e3-23700f163b5a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
              "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'deposit'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7gUxl0mqREVd",
        "outputId": "269a0f0b-b93a-4985-da27-9b57b2a49584"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 int64\n",
              "job                object\n",
              "marital            object\n",
              "education          object\n",
              "default            object\n",
              "housing            object\n",
              "loan               object\n",
              "contact            object\n",
              "month              object\n",
              "day_of_week        object\n",
              "duration            int64\n",
              "campaign            int64\n",
              "pdays               int64\n",
              "previous            int64\n",
              "poutcome           object\n",
              "emp.var.rate      float64\n",
              "cons.price.idx    float64\n",
              "cons.conf.idx     float64\n",
              "euribor3m         float64\n",
              "nr.employed       float64\n",
              "deposit            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxvM-8T_RETX",
        "outputId": "3854552f-4a59-44d0-9cba-9eb60578ef6b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "object     11\n",
              "int64       5\n",
              "float64     5\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YeZMWdBFREQJ",
        "outputId": "19061aae-c4ac-4589-b5ac-22199f0c83e5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "job               0\n",
              "marital           0\n",
              "education         0\n",
              "default           0\n",
              "housing           0\n",
              "loan              0\n",
              "contact           0\n",
              "month             0\n",
              "day_of_week       0\n",
              "duration          0\n",
              "campaign          0\n",
              "pdays             0\n",
              "previous          0\n",
              "poutcome          0\n",
              "emp.var.rate      0\n",
              "cons.price.idx    0\n",
              "cons.conf.idx     0\n",
              "euribor3m         0\n",
              "nr.employed       0\n",
              "deposit           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0q8OzMQCRl9V",
        "outputId": "27ed279e-202e-439f-ac6b-f99645db4e80"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
              "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'deposit'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = df.select_dtypes(include='object').columns\n",
        "print(cat_cols)\n",
        "\n",
        "num_cols = df.select_dtypes(exclude='object').columns\n",
        "print(num_cols)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QPoWCPWhREDt",
        "outputId": "e4d4a64c-719a-411d-9ae1-7996035d7f6d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
            "       'month', 'day_of_week', 'poutcome', 'deposit'],\n",
            "      dtype='object')\n",
            "Index(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
            "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "dilcSuTdRZXe",
        "outputId": "5503e46c-f57b-4068-a728-aa9f95c24896"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age     duration     campaign        pdays     previous  \\\n",
              "count  4119.000000  4119.000000  4119.000000  4119.000000  4119.000000   \n",
              "mean     40.113620   256.788055     2.537266   960.422190     0.190337   \n",
              "std      10.313362   254.703736     2.568159   191.922786     0.541788   \n",
              "min      18.000000     0.000000     1.000000     0.000000     0.000000   \n",
              "25%      32.000000   103.000000     1.000000   999.000000     0.000000   \n",
              "50%      38.000000   181.000000     2.000000   999.000000     0.000000   \n",
              "75%      47.000000   317.000000     3.000000   999.000000     0.000000   \n",
              "max      88.000000  3643.000000    35.000000   999.000000     6.000000   \n",
              "\n",
              "       emp.var.rate  cons.price.idx  cons.conf.idx    euribor3m  nr.employed  \n",
              "count   4119.000000     4119.000000    4119.000000  4119.000000  4119.000000  \n",
              "mean       0.084972       93.579704     -40.499102     3.621356  5166.481695  \n",
              "std        1.563114        0.579349       4.594578     1.733591    73.667904  \n",
              "min       -3.400000       92.201000     -50.800000     0.635000  4963.600000  \n",
              "25%       -1.800000       93.075000     -42.700000     1.334000  5099.100000  \n",
              "50%        1.100000       93.749000     -41.800000     4.857000  5191.000000  \n",
              "75%        1.400000       93.994000     -36.400000     4.961000  5228.100000  \n",
              "max        1.400000       94.767000     -26.900000     5.045000  5228.100000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7bbf8e09-f84d-44e2-a289-3c051ef19630\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "      <td>4119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.113620</td>\n",
              "      <td>256.788055</td>\n",
              "      <td>2.537266</td>\n",
              "      <td>960.422190</td>\n",
              "      <td>0.190337</td>\n",
              "      <td>0.084972</td>\n",
              "      <td>93.579704</td>\n",
              "      <td>-40.499102</td>\n",
              "      <td>3.621356</td>\n",
              "      <td>5166.481695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.313362</td>\n",
              "      <td>254.703736</td>\n",
              "      <td>2.568159</td>\n",
              "      <td>191.922786</td>\n",
              "      <td>0.541788</td>\n",
              "      <td>1.563114</td>\n",
              "      <td>0.579349</td>\n",
              "      <td>4.594578</td>\n",
              "      <td>1.733591</td>\n",
              "      <td>73.667904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.400000</td>\n",
              "      <td>92.201000</td>\n",
              "      <td>-50.800000</td>\n",
              "      <td>0.635000</td>\n",
              "      <td>4963.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.800000</td>\n",
              "      <td>93.075000</td>\n",
              "      <td>-42.700000</td>\n",
              "      <td>1.334000</td>\n",
              "      <td>5099.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>181.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>93.749000</td>\n",
              "      <td>-41.800000</td>\n",
              "      <td>4.857000</td>\n",
              "      <td>5191.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>317.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>93.994000</td>\n",
              "      <td>-36.400000</td>\n",
              "      <td>4.961000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.000000</td>\n",
              "      <td>3643.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>94.767000</td>\n",
              "      <td>-26.900000</td>\n",
              "      <td>5.045000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7bbf8e09-f84d-44e2-a289-3c051ef19630')\"\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",
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              "  </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-7bbf8e09-f84d-44e2-a289-3c051ef19630 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-7bbf8e09-f84d-44e2-a289-3c051ef19630');\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-7625bfe8-8b3f-4ce7-bcd5-51854cc56dbf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7625bfe8-8b3f-4ce7-bcd5-51854cc56dbf')\"\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",
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              "    </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",
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              "      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-7625bfe8-8b3f-4ce7-bcd5-51854cc56dbf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</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\": 1442.663440584843,\n        \"min\": 10.313361547199813,\n        \"max\": 4119.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.11361981063365,\n          38.0,\n          4119.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1718.2926546408369,\n        \"min\": 0.0,\n        \"max\": 4119.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          256.7880553532411,\n          181.0,\n          4119.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"campaign\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1453.9532184971567,\n        \"min\": 1.0,\n        \"max\": 4119.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4119.0,\n          2.537266326778344,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pdays\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1263.8913705723112,\n        \"min\": 0.0,\n        \"max\": 4119.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          960.4221898519058,\n          999.0,\n          191.92278580077644\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1455.9478568492361,\n        \"min\": 0.0,\n        \"max\": 4119.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.19033746054867687,\n          6.0,\n          0.5417883234290308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"emp.var.rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1456.269931723342,\n        \"min\": -3.4,\n        \"max\": 4119.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4119.0,\n          0.08497208060208788,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cons.price.idx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1428.2747426919952,\n        \"min\": 0.5793488049889662,\n        \"max\": 4119.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          93.57970429715951,\n          93.749,\n          4119.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cons.conf.idx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1468.2282271845525,\n        \"min\": -50.8,\n        \"max\": 4119.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -40.49910172371935,\n          -41.8,\n          4119.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"euribor3m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1455.1668677113134,\n        \"min\": 0.635,\n        \"max\": 4119.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.621355668851663,\n          4.857,\n          4119.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nr.employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1780.2498655236282,\n        \"min\": 73.66790355721253,\n        \"max\": 5228.1,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4119.0,\n          5166.481694586065,\n          5191.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "3Q0CV0RBRZdz",
        "outputId": "db5c8c3e-92b8-4a05-dc47-44a8690de836"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           job  marital          education default housing  loan   contact  \\\n",
              "count     4119     4119               4119    4119    4119  4119      4119   \n",
              "unique      12        4                  8       3       3     3         2   \n",
              "top     admin.  married  university.degree      no     yes    no  cellular   \n",
              "freq      1012     2509               1264    3315    2175  3349      2652   \n",
              "\n",
              "       month day_of_week     poutcome deposit  \n",
              "count   4119        4119         4119    4119  \n",
              "unique    10           5            3       2  \n",
              "top      may         thu  nonexistent      no  \n",
              "freq    1378         860         3523    3668  "
            ],
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              "      <td>4119</td>\n",
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              "      <td>cellular</td>\n",
              "      <td>may</td>\n",
              "      <td>thu</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1012</td>\n",
              "      <td>2509</td>\n",
              "      <td>1264</td>\n",
              "      <td>3315</td>\n",
              "      <td>2175</td>\n",
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              "      <td>2652</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\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",
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              "\n",
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              "      --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",
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              "    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",
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              "\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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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-28e9e810-84ac-4a09-a719-ff65fd0bb566 button');\n",
              "      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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12,\n          \"1012\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          \"2509\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8,\n          \"1264\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"3315\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"2175\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"3349\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contact\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"2652\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10,\n          \"1378\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          \"860\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poutcome\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"3523\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deposit\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"3668\",\n          \"4119\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(figsize=(10,10),color='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "JJWnb3xHRZir",
        "outputId": "8bbe5209-55db-4735-a626-a42fb74fe7a8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the number of rows and columns for subplots\n",
        "num_plots = len(cat_cols)\n",
        "num_rows = (num_plots + 1) // 2  # Add 1 and divide by 2 to round up for odd numbers\n",
        "num_cols = 2\n",
        "\n",
        "# Create a new figure\n",
        "plt.figure(figsize=(20, 25))  # Adjust the figure size as needed\n",
        "\n",
        "# Loop through each feature and create a countplot\n",
        "for i, feature in enumerate(cat_cols, 1):\n",
        "    plt.subplot(num_rows, num_cols, i)\n",
        "    sns.countplot(x=feature, data=df, palette='Wistia')\n",
        "    plt.title(f'Bar Plot of {feature}')\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel('Count')\n",
        "    plt.xticks(rotation=45)\n",
        "\n",
        "# Adjust layout to prevent overlap of subplots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "t21OZreMRD7F",
        "outputId": "1733d75c-6b30-476c-8094-d61d3ba1e4a7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2500 with 11 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind='box', subplots=True, layout=(2,5),figsize=(20,10),color='#7b3f00')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "_Ek0Wiv4SYiA",
        "outputId": "0e6b3f9d-c65a-49ba-be9e-82e1d196c6de"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Exclude non-numeric columns\n",
        "numeric_df = df.drop(columns=cat_cols)\n",
        "\n",
        "# Compute the correlation matrix\n",
        "corr = numeric_df.corr()\n",
        "\n",
        "# Print the correlation matrix\n",
        "print(corr)\n",
        "\n",
        "# Filter correlations with absolute value >= 0.90\n",
        "corr = corr[abs(corr) >= 0.90]\n",
        "\n",
        "sns.heatmap(corr,annot=True,cmap='Set3',linewidths=0.2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4vkVnMKVSYkr",
        "outputId": "5c860547-4db4-43dd-92a9-15a9a14dd600"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     age  duration  campaign     pdays  previous  \\\n",
            "age             1.000000  0.041299 -0.014169 -0.043425  0.050931   \n",
            "duration        0.041299  1.000000 -0.085348 -0.046998  0.025724   \n",
            "campaign       -0.014169 -0.085348  1.000000  0.058742 -0.091490   \n",
            "pdays          -0.043425 -0.046998  0.058742  1.000000 -0.587941   \n",
            "previous        0.050931  0.025724 -0.091490 -0.587941  1.000000   \n",
            "emp.var.rate   -0.019192 -0.028848  0.176079  0.270684 -0.415238   \n",
            "cons.price.idx -0.000482  0.016672  0.145021  0.058472 -0.164922   \n",
            "cons.conf.idx   0.098135 -0.034745  0.007882 -0.092090 -0.051420   \n",
            "euribor3m      -0.015033 -0.032329  0.159435  0.301478 -0.458851   \n",
            "nr.employed    -0.041936 -0.044218  0.161037  0.381983 -0.514853   \n",
            "\n",
            "                emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
            "age                -0.019192       -0.000482       0.098135  -0.015033   \n",
            "duration           -0.028848        0.016672      -0.034745  -0.032329   \n",
            "campaign            0.176079        0.145021       0.007882   0.159435   \n",
            "pdays               0.270684        0.058472      -0.092090   0.301478   \n",
            "previous           -0.415238       -0.164922      -0.051420  -0.458851   \n",
            "emp.var.rate        1.000000        0.755155       0.195022   0.970308   \n",
            "cons.price.idx      0.755155        1.000000       0.045835   0.657159   \n",
            "cons.conf.idx       0.195022        0.045835       1.000000   0.276595   \n",
            "euribor3m           0.970308        0.657159       0.276595   1.000000   \n",
            "nr.employed         0.897173        0.472560       0.107054   0.942589   \n",
            "\n",
            "                nr.employed  \n",
            "age               -0.041936  \n",
            "duration          -0.044218  \n",
            "campaign           0.161037  \n",
            "pdays              0.381983  \n",
            "previous          -0.514853  \n",
            "emp.var.rate       0.897173  \n",
            "cons.price.idx     0.472560  \n",
            "cons.conf.idx      0.107054  \n",
            "euribor3m          0.942589  \n",
            "nr.employed        1.000000  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()\n",
        "df1.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNOlRbTfUhwF",
        "outputId": "f74d04ec-8255-4d38-b258-1aae0e4c5355"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
              "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'deposit'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "lb = LabelEncoder()\n",
        "df_encoded = df1.apply(lb.fit_transform)\n",
        "df_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "YCpSy8RhSYnW",
        "outputId": "07b79916-1e61-41d8-f27d-76f0ad128516"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  job  marital  education  default  housing  loan  contact  month  \\\n",
              "0      12    1        1          2        0        2     0        0      6   \n",
              "1      21    7        2          3        0        0     0        1      6   \n",
              "2       7    7        1          3        0        2     0        1      4   \n",
              "3      20    7        1          2        0        1     1        1      4   \n",
              "4      29    0        1          6        0        2     0        0      7   \n",
              "...   ...  ...      ...        ...      ...      ...   ...      ...    ...   \n",
              "4114   12    0        1          1        0        2     2        0      3   \n",
              "4115   21    0        1          3        0        2     0        1      3   \n",
              "4116    9    8        2          3        0        0     0        0      6   \n",
              "4117   40    0        1          3        0        0     0        0      1   \n",
              "4118   16    4        2          3        0        2     0        0      7   \n",
              "\n",
              "      day_of_week  ...  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
              "0               0  ...         1     20         0         1             3   \n",
              "1               0  ...         3     20         0         1             8   \n",
              "2               4  ...         0     20         0         1             9   \n",
              "3               0  ...         2     20         0         1             9   \n",
              "4               1  ...         0     20         0         1             7   \n",
              "...           ...  ...       ...    ...       ...       ...           ...   \n",
              "4114            2  ...         0     20         0         1             9   \n",
              "4115            0  ...         0     20         0         1             9   \n",
              "4116            1  ...         1     20         1         0             3   \n",
              "4117            0  ...         0     20         0         1             9   \n",
              "4118            4  ...         0     20         0         1             7   \n",
              "\n",
              "      cons.price.idx  cons.conf.idx  euribor3m  nr.employed  deposit  \n",
              "0                  8              4        156            6        0  \n",
              "1                 18             16        207            8        0  \n",
              "2                 23              8        225           10        0  \n",
              "3                 23              8        222           10        0  \n",
              "4                 11              7        201            9        0  \n",
              "...              ...            ...        ...          ...      ...  \n",
              "4114              17              6        221           10        0  \n",
              "4115              17              6        222           10        0  \n",
              "4116               8              4        160            6        0  \n",
              "4117              13             17        229           10        0  \n",
              "4118              11              7        199            9        0  \n",
              "\n",
              "[4119 rows x 21 columns]"
            ],
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              "</table>\n",
              "<p>4119 rows × 21 columns</p>\n",
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              "\n",
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              "    }\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-ad58efda-1033-4759-8e08-2a09930854cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_de627e67-90e1-43e3-b51f-d74a282d7d77\">\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('df_encoded')\"\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_de627e67-90e1-43e3-b51f-d74a282d7d77 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_encoded');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_encoded"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_encoded['deposit'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EVB4k8ySYqB",
        "outputId": "dcff972c-a483-4e32-f1cd-304ae4166a28"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "deposit\n",
              "0    3668\n",
              "1     451\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df_encoded.drop('deposit',axis=1)  # independent variable\n",
        "y = df_encoded['deposit']              # dependent variable\n",
        "print(x.shape)\n",
        "print(y.shape)\n",
        "print(type(x))\n",
        "print(type(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vbabg267SYso",
        "outputId": "57a6ff60-e176-4439-c989-4419cdfbc0d0"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4119, 20)\n",
            "(4119,)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "pDap7ig_VM_a"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=1)\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0S2pAPOVNB2",
        "outputId": "a5057133-72b2-4cf0-bb7b-fb16925b55df"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3089, 20)\n",
            "(1030, 20)\n",
            "(3089,)\n",
            "(1030,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n"
      ],
      "metadata": {
        "id": "jTs8KrWWVk35"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_model(y_test,y_pred):\n",
        "    acc = accuracy_score(y_test,y_pred)\n",
        "    print('Accuracy_Score',acc)\n",
        "    cm = confusion_matrix(y_test,y_pred)\n",
        "    print('Confusion Matrix\\n',cm)\n",
        "    print('Classification Report\\n',classification_report(y_test,y_pred))\n",
        "\n",
        "\n",
        "def mscore(model):\n",
        "    train_score = model.score(x_train,y_train)\n",
        "    test_score = model.score(x_test,y_test)\n",
        "    print('Training Score',train_score)\n",
        "    print('Testing Score',test_score)"
      ],
      "metadata": {
        "id": "fBmyf53uVWh8"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(criterion='gini',max_depth=5,min_samples_split=10)\n",
        "dt.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "faXsXQZCVWkM",
        "outputId": "fa2bc308-e979-44f3-a40a-2a136ddd287b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5, min_samples_split=10)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, min_samples_split=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5, min_samples_split=10)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mscore(dt)\n",
        "\n",
        "\n",
        "ypred_dt = dt.predict(x_test)\n",
        "print(ypred_dt)\n",
        "\n",
        "\n",
        "\n",
        "eval_model(y_test,ypred_dt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnDP2x_uVWni",
        "outputId": "65841346-2822-4511-8ee7-423753ec27ae"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Score 0.9268371641307867\n",
            "Testing Score 0.9135922330097087\n",
            "[0 0 1 ... 0 0 0]\n",
            "Accuracy_Score 0.9135922330097087\n",
            "Confusion Matrix\n",
            " [[896  34]\n",
            " [ 55  45]]\n",
            "Classification Report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95       930\n",
            "           1       0.57      0.45      0.50       100\n",
            "\n",
            "    accuracy                           0.91      1030\n",
            "   macro avg       0.76      0.71      0.73      1030\n",
            "weighted avg       0.91      0.91      0.91      1030\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LXX0jp5DVNES"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}