Credit_Scoring_Analysis_Using_Logistic_Regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/WanjohiChristopher/Intro-to-Machine-Learning-DSCJKUAT/blob/master/Credit_Scoring_Analysis_Using_Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## PROBLEM STATEMENT"
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        "### 1.Business Understanding\n",
        "\n",
        "<ol>A company in kenya named Tokyo wants to offer loans to its customers.To do so they requested a data scientist to perform analysis of their data to determine the credit worthiness of a customer based on historical data features whether customer will default or not.</ol>\n",
        "\n",
        "<ol>In order to do that i got upto task to do credit scoring analysis.I used CRISP-DM -CROSS INDUSTRY STANDARD PROCESS FOR DATA MINING APPROACH</ol>\n",
        "\n",
        "<ol>Dataset contains 11 columns:\n",
        "    <ul>job category 0-non-skilled,1-slightly skilled,-2-skilled,3-highly skilled</ul></ol>"
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        ""
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      "cell_type": "markdown",
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      "source": [
        "### 2.Data Understanding"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "YNh3ZdROq0ue",
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      "source": [
        "#importing required libraries\n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression,LinearRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC #SUPPORT VECTOR MACHINES\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,mean_squared_error,mean_absolute_error,r2_score,recall_score,precision_score,confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "07v5-aTvq0ui"
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      "source": [
        "#loading dataset\n",
        "df=pd.read_csv('german_credit_data (3).csv')"
      ]
    },
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              "   Unnamed: 0  Age     Sex  ...  Duration              Purpose  Risk\n",
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              "     Unnamed: 0  Age     Sex  ...  Duration              Purpose  Risk\n",
              "995         995   31  female  ...        12  furniture/equipment  good\n",
              "996         996   40    male  ...        30                  car  good\n",
              "997         997   38    male  ...        12             radio/TV  good\n",
              "998         998   23    male  ...        45             radio/TV   bad\n",
              "999         999   27    male  ...        45                  car  good\n",
              "\n",
              "[5 rows x 11 columns]"
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        "# previewing last 5 records\n",
        "df.tail()"
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              "\n",
              "  <div id=\"df-965135f1-7061-4efb-977a-da076bb657be\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Job</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Saving accounts</th>\n",
              "      <th>Checking account</th>\n",
              "      <th>Credit amount</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Purpose</th>\n",
              "      <th>Risk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>600</td>\n",
              "      <td>45</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>2329</td>\n",
              "      <td>7</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-965135f1-7061-4efb-977a-da076bb657be')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-965135f1-7061-4efb-977a-da076bb657be 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-965135f1-7061-4efb-977a-da076bb657be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Unnamed: 0  Age     Sex  Job  ... Credit amount Duration   Purpose  Risk\n",
              "600         600   45  female    2  ...          2329        7  radio/TV  good\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#sampling data\n",
        "df.sample()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "6aC0siZCq0u0",
        "outputId": "ebec4b9e-74c8-48dc-f076-e15ef9a23307"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8cdb752910>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#previewing target variable\n",
        "df['Risk'].value_counts().plot(kind='pie',autopct='green')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K41GCWuUq0u4",
        "outputId": "48523532-9301-4c56-da29-457f1d34e802"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 11 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   Unnamed: 0        1000 non-null   int64 \n",
            " 1   Age               1000 non-null   int64 \n",
            " 2   Sex               1000 non-null   object\n",
            " 3   Job               1000 non-null   int64 \n",
            " 4   Housing           1000 non-null   object\n",
            " 5   Saving accounts   817 non-null    object\n",
            " 6   Checking account  606 non-null    object\n",
            " 7   Credit amount     1000 non-null   int64 \n",
            " 8   Duration          1000 non-null   int64 \n",
            " 9   Purpose           1000 non-null   object\n",
            " 10  Risk              1000 non-null   object\n",
            "dtypes: int64(5), object(6)\n",
            "memory usage: 86.1+ KB\n"
          ]
        }
      ],
      "source": [
        "#checking information about data\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GztOGaeKq0u9",
        "outputId": "c9dd453e-ce1e-430c-913f-fb706df146f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Age', 'Sex', 'Job', 'Housing', 'Saving accounts',\n",
              "       'Checking account', 'Credit amount', 'Duration', 'Purpose', 'Risk'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "#checking columns\n",
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYpzBPlBq0vB",
        "outputId": "70ae4558-54aa-4e60-bf8c-7f7f7ad2d71c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#cheking shape or number of record in the data\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9HX8IpOq0vE",
        "outputId": "99ebe678-67f3-40a2-d74b-18c697aceabc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0           int64\n",
              "Age                  int64\n",
              "Sex                 object\n",
              "Job                  int64\n",
              "Housing             object\n",
              "Saving accounts     object\n",
              "Checking account    object\n",
              "Credit amount        int64\n",
              "Duration             int64\n",
              "Purpose             object\n",
              "Risk                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#checking datatypes of data\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5AcW6miEq0vI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c90f7bf9-1090-4093-f777-a26ace1fd616"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "#conver4ting column names intolower_case\n",
        "df.columns=df.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbdhCy_-q0vL"
      },
      "source": [
        "## 3.Data Preparation and Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKFnhPcCq0vM"
      },
      "source": [
        "### Data exploration and cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXlUPrefq0vN",
        "outputId": "88eb6ad3-d0db-4ab6-ffc8-a3e2c312d2e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unnamed:_0            0\n",
              "age                   0\n",
              "sex                   0\n",
              "job                   0\n",
              "housing               0\n",
              "saving_accounts     183\n",
              "checking_account    394\n",
              "credit_amount         0\n",
              "duration              0\n",
              "purpose               0\n",
              "risk                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#checking missing data\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxNQkFx5q0vR"
      },
      "source": [
        "obsv:There are mising valuea in savings and checking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hhiaBFyjq0vS"
      },
      "outputs": [],
      "source": [
        "# treating missing values with none to indicate no checking or saving account\n",
        "df['saving_accounts'].fillna('None',inplace=True)\n",
        "df['checking_account'].fillna('None',inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHss9AoZq0vW",
        "outputId": "97b23c03-8e65-4e77-ea3a-6eb93e98ba93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unnamed:_0          0\n",
              "age                 0\n",
              "sex                 0\n",
              "job                 0\n",
              "housing             0\n",
              "saving_accounts     0\n",
              "checking_account    0\n",
              "credit_amount       0\n",
              "duration            0\n",
              "purpose             0\n",
              "risk                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_7O6Xs8Cq0va"
      },
      "outputs": [],
      "source": [
        "#we drop unwanted columns 'unnamed'\n",
        "df.drop(columns=['unnamed:_0'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHrSCM_Jq0vd",
        "outputId": "eca9fd0f-a77b-489d-e66a-7b2016237bd0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
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              "      <th>purpose</th>\n",
              "      <th>risk</th>\n",
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              "      <td>67</td>\n",
              "      <td>male</td>\n",
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              "      <td>None</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
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              "      <td>radio/TV</td>\n",
              "      <td>bad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
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              "      <td>little</td>\n",
              "      <td>None</td>\n",
              "      <td>2096</td>\n",
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              "      <td>good</td>\n",
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              "      <td>male</td>\n",
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              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>bad</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad16c275-570b-4c9c-8638-1dccdbd61b19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ad16c275-570b-4c9c-8638-1dccdbd61b19 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-ad16c275-570b-4c9c-8638-1dccdbd61b19');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age     sex  job housing  ... credit_amount duration              purpose  risk\n",
              "0   67    male    2     own  ...          1169        6             radio/TV  good\n",
              "1   22  female    2     own  ...          5951       48             radio/TV   bad\n",
              "2   49    male    1     own  ...          2096       12            education  good\n",
              "3   45    male    2    free  ...          7882       42  furniture/equipment  good\n",
              "4   53    male    2    free  ...          4870       24                  car   bad\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YxaNMDeq0vg",
        "outputId": "f03cd6d8-bc1c-43be-b470-ab9e5214b6b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#checking duplicated values\n",
        "df.duplicated().any()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzE9_FCCq0vj"
      },
      "source": [
        "###### no duplicates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6A_QtoWnq0vk",
        "outputId": "c94aab27-31ee-4b66-9d8e-4997ee40702f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  53\n",
              "sex                   2\n",
              "job                   4\n",
              "housing               3\n",
              "saving_accounts       5\n",
              "checking_account      4\n",
              "credit_amount       921\n",
              "duration             33\n",
              "purpose               8\n",
              "risk                  2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "#checking unique values\n",
        "df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YpXAPC6q0vn",
        "outputId": "bf7ae409-5a8f-42c3-8507-64e607f9579d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8cdb752850>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pqvp8VR2pqpurqkb7P1JVc1V1/+j788/FGwUAgHH19a9/fc01AIyDJ3Mk0h8mufoJe3NJ/mFr7SeS/Kckv7rkvi+31i4fff3Skv0PJPnFJJeNvk6/5juT3NVauyzJXaM1AAAAAGNkMCK11g4meeQJe7OttYXR8tNJLl7rNarqhUkubK19up362IkPJXnt6O7rkpz+DNOZJfsAAPC0cOWVVy5bT05OdpoEAFa3EddE+u+THFiyfklVfbaq7q6qPaO9XUmOLnnM0dFekuxsrT04uv1QklU/iqKqrq+qQ1V16Pjx4xswOgAA9HfDDTesuQaAcbCuiFRV/1uShST/ZrT1YJIXt9Z+MsmvJPmjqrrwyb7e6Ciltsb9H2yt7W6t7XaxQQAANpOJiYll3wFg3Jz1/0JV1X+X5B8n+W9H8SettcdaaydGt+9N8uUkL01yLMtPebt4tJckD49Odzt92ts3znYmAAA4H83MzCyLSDMzMwPPAICn3llFpKq6Osn/kuTa1tp3l+zvqKoLRrf/QU5dQPsro9PVvl1VLx99Kttbk3x89LT9SaZHt6eX7AMAwNPC3NxcFhZOXXJ0YWEhs7OznScCgJUGI1JVfTjJnyd5WVUdraq3Jfm/kjwnyVxV3VdVvzd6+N4kh6vqviR/kuSXWmunL8r9y0n+IMmRnDpC6fR1lN6XZLKq7k9y5WgNAABPG5OTk9myZUuSZMuWLZmamuo8EQCsVKMz0c47u3fvbocOHeo9BgAArNv8/Hze8IY3ZHFxMRMTE/nYxz6Wbdu29R4LgKehqrq3tbb7TPe5ah8AAIyB0/+4e77+Iy8Am5+IBAAAnd1yyy3LItItt9zSeSIAWElEAgCAzu66665l6zvvvLPTJACwOhEJAAA6e+IpbE5pA2AciUgAANDZlVdeuWw9OTnZaRIAWJ2IBAAAnb3xjW9ctn7Tm97UaRIAWJ2IBAAAnd12223L1vv37+80CQCsTkQCAIDOZmdnl63vuOOOTpMAwOpEJAAA6Gznzp1rrgFgHIhIAADQ2cMPP7zmGgDGgYgEAACd7d27d9n6Fa94RadJAGB1IhIAAAAAg0QkAADo7ODBg8vWd999d6dJAGB1IhIAAHTmwtoAnA9EJAAA6MyFtQE4H4hIAADQ2dTU1LL1VVdd1WkSAFidiAQAAJ1dc801y9bXXnttp0kAYHUiEgAAdPbHf/zHy9Yf/ehHO00CAKsTkQAAoLM777xz2Xpubq7TJACwOhEJAAA6q6o11wAwDkQkAADo7NWvfvWy9ZVXXtlpEgBYnYgEAACd3XDDDZmYOPWn+cTERG644YbOEwHASiISAAB0tn379uzatStJsmvXrmzbtq3zRACwkogEAACdzc/P56GHHkqSPPzwwzlx4kTniQBgJREJAAA6m5mZ+f7t1tqyNQCMCxEJAAA6m5uby8mTJ5MkJ0+ezOzsbOeJAGAlEQkAADqbnJzM1q1bkyRbt27N1NRU54kAYCURCQAAOpuenk5VJTn16WzT09OdJwKAlUQkAADobPv27dm3b1+qKvv27fPpbACMJREJAADGwDXXXJNnPvOZufbaa3uPAgBnJCIBAMAYuO222/Ld7343+/fv7z0KAJyRiAQAAJ3Nz8/n9ttvT2stn/jEJ3LixIneIwHACiISAAB0NjMzk5MnTyZJTp48mZmZmc4TAcBKIhIAAHR2xx13LFt/8pOf7DQJAKxORAIAgM62bNmy5hoAxoGIBAAAnf3t3/7tmmsAGAciEgAAdPbsZz97zTUAjAMRCQAAOltYWFhzDQDjQEQCAIDOrrrqqmXrq6++utMkALA6EQkAADqbnp5OVSVJqirT09OdJwKAlUQkAAAYA0sjEgCMIxEJAAA6m5mZyeLiYpJkcXExMzMznScCgJVEJAAA6Gx2dnbZ+o477ug0CQCsTkQCAIDOdu7cueYaAMaBiAQAAJ09/PDDa64BYByISAAA0NnU1NSy9VVXXdVpEgBYnYgEAACdXXPNNcvW1157badJAGB1IhIAAHR22223paqSJFWV/fv3d54IAFYSkQAAoLO5ubm01pIkrbUVn9YGAONARAIAgM727NmzbL13795OkwDA6kQkAAAAAAaJSAAA0NnBgweXre++++5OkwDA6kQkAADobOfOnWuuAWAciEgAANDZQw89tOYaAMaBiAQAAJ294AUvWHMNAONARAIAgM4ciQTA+UBEAgCAznbs2LHmGgDGgYgEAACdHTt2bM01AIyDJxWRqurWqvpGVX1hyd6PVNVcVd0/+v780X5V1c1VdaSqDlfVP1rynOnR4++vqukl+z9VVZ8fPefmqqqNfJMAADDOJiYm1lwDwDh4sv/r9IdJrn7C3juT3NVauyzJXaN1kuxLctno6/okH0hORackv5HkZ5JckeQ3Toen0WN+ccnznvizAABg03r1q1+9bH3llVd2mgQAVvekIlJr7WCSR56wfV2SmdHtmSSvXbL/oXbKp5M8r6pemOSqJHOttUdaa48mmUty9ei+C1trn26ttSQfWvJaAACw6b3xjW9ctn7Tm97UaRIAWN16jpPd2Vp7cHT7oSQ7R7d3JfnakscdHe2ttX/0DPsrVNX1VXWoqg4dP358HaMDAMD4uO2225at9+/f32kSAFjdhpxsPTqCqG3Eaw38nA+21na31nb7xAoAADaLubm5ZevZ2dlOkwDA6tYTkR4enYqW0fdvjPaPJXnRksddPNpba//iM+wDAMDTwp49e5at9+7d22kSAFjdeiLS/iSnP2FtOsnHl+y/dfQpbS9P8q3RaW93JJmqquePLqg9leSO0X3frqqXjz6V7a1LXgsAAACAMfCkIlJVfTjJnyd5WVUdraq3JXlfksmquj/JlaN1ktye5CtJjiT5/SS/nCSttUeS/PMknxl9vWe0l9Fj/mD0nC8nObD+twYAAOeHe+65Z9n64MGDnSYBgNVteTIPaq29ZZW7Xv3EjdH1kd6+yuvcmuTWM+wfSvIPn8wsAACw2UxOTuYTn/hEFhYWsmXLlkxNTfUeCQBW2JALawMAAGdveno6p67skExMTGR6enrgGQDw1BORAACgs+3bt+cFL3hBkmTnzp3Ztm1b54kAYCURCQAAOpufn8+xY6c+oPjYsWM5ceJE54kAYCURCQAAOrvllluyuLiYJFlcXMwtt9zSeSIAWElEAgCAzu68885l67m5uU6TAMDqRCQAAOjs9FFIq60BYByISAAA0NnpT2ZbbQ0A40BEAgCAzvbu3bts/YpXvKLTJACwOhEJAAAAgEEiEgAAdHbPPfcsWx88eLDTJACwOhEJAAA6c00kAM4HIhIAAHT20z/908vWV1xxRadJAGB1IhIAAHT2wAMPrLkGgHEgIgEAQGcPPvjgsvXXv/71TpMAwOpEJAAAAAAGiUgAANDZz/7szy5b/9zP/VynSQBgdSISAAB0duGFF665BoBxICIBAEBnBw8eXLa+++67O00CAKsTkQAAoLPt27evuQaAcSAiAQBAZ8eOHVtzDQDjQEQCAIDOFhcX11wDwDgQkQAAAAAYJCIBAEBnF1xwwZprABgHIhIAAHS2Z8+eZeu9e/d2mgQAViciAQBAZ894xjPWXAPAOBCRAACgs3vuuWfZ+uDBg50mAYDViUgAANDZ5OTk96+DdMEFF2RqaqrzRACwkogEAACdTU9Pp7WWJGmtZXp6uvNEALCSiAQAAGNgcXFx2XcAGDciEgAAdHbLLbesuQaAcSAiAQBAZ3feeeey9dzcXKdJAGB1IhIAAHT2+OOPr7kGgHEgIgEAQGenP5lttTUAjAMRCQAAOrviiiuWrV/+8pd3mgQAViciAQBAZ1/72teWrb/61a92mgQAViciAQBAZ0ePHl1zDQDjQEQCAIDOnvWsZ625BoBxICIBAEBn3/ve99ZcA8A4EJEAAKCzxcXFNdcAMA5EJAAAAAAGiUgAANCZayIBcD4QkQAAoLPHH398zTUAjAMRCQAAOnvFK16xbP3KV76yzyAAsAYRCQAAOnvsscfWXAPAOBCRAACgs4MHDy5b33333Z0mAYDViUgAANDZ4uLimmsAGAciEgAAAACDRCQAAOjsmc985pprABgHIhIAAHT2d3/3d2uuAWAciEgAANBZVa25BoBxsKX3AAAA8GTdfPPNOXLkSO8xNtyFF16YRx99dNn6pptu6jjRuXHppZduyvcF8HThSCQAAOjsoosuWnMNAOPAkUgAAJw3NvNRLNddd10effTRXH311fm1X/u13uMAwAoiEgAAjIGLLroof//3f58bbrih9ygAcEZOZwMAgDGwdevWXHbZZdm2bVvvUQDgjEQkAAAAAAaJSAAAAAAMEpEAAAAAGHTWEamqXlZV9y35+nZV/bOqendVHVuy//NLnvOrVXWkqv66qq5asn/1aO9IVb1zvW8KAAAAgI111p/O1lr76ySXJ0lVXZDkWJI/TfJPk/xOa+1fLX18Vf1Ykjcn+fEkFyW5s6peOrr7d5NMJjma5DNVtb+19qWznQ0AAACAjXXWEekJXp3ky621r1bVao+5LslHWmuPJfmbqjqS5IrRfUdaa19Jkqr6yOixIhIAAADAmNioayK9OcmHl6zfUVWHq+rWqnr+aG9Xkq8teczR0d5q+ytU1fVVdaiqDh0/fnyDRgcAAABgyLojUlX9UJJrk/zxaOsDSX40p051ezDJb6/3Z5zWWvtga213a233jh07NuplAQAAABiwEaez7UvyH1trDyfJ6e9JUlW/n+Tfj5bHkrxoyfMuHu1ljX0AAAAAxsBGnM72liw5la2qXrjkvtcl+cLo9v4kb66qZ1TVS5JcluQvknwmyWVV9ZLRUU1vHj0WAAAAgDGxriORqupZOfWpajcs2f4XVXV5kpbkgdP3tda+WFUfzakLZi8keXtr7fHR67wjyR1JLkhya2vti+uZCwAAAICNta6I1Fr7f5Nse8LeL6zx+Pcmee8Z9m9Pcvt6ZgEAAADg3NmoT2cDAAAAYBMTkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYtO6IVFUPVNXnq+q+qjo02vuRqpqrqvtH358/2q+qurmqjlTV4ar6R0teZ3r0+Puranq9cwEAAACwcTbqSKRXtdYub63tHq3fmeSu1tplSe4arZNkX5LLRl/XJ/lAcio6JfmNJD+T5Iokv3E6PAEAAADQ37k6ne26JDOj2zNJXrtk/0PtlE8neV5VvTDJVUnmWmuPtNYeTTKX5OpzNBsAAAAAP6CNiEgtyWxV3VtV14/2drbWHhzdfijJztHtXUm+tuS5R0d7q+0vU1XXV9Whqjp0/PjxDRgdAAAAgCdjywa8xn/VWjtWVf9Zkrmq+quld7bWWlW1Dfg5aa19MMkHk2T37t0b8poAAAAADFv3kUittWOj799I8qc5dU2jh0enqWX0/Rujhx9L8qIlT794tLfaPgAAAABjYF0RqaqeVVXPOX07yVSSLyTZn+T0J6xNJ/n46Pb+JG8dfUrby5N8a3Ta2x1Jpqrq+aMLak+N9gAAAAAYA+s9nW1nkj+tqtOv9UettU9W1WeSfLSq3pbkq0neNHr87Ul+PsmRJN9N8k+TpLX2SFX98ySfGbG88hwAABCySURBVD3uPa21R9Y5GwAAAAAbZF0RqbX2lST/xRn2TyR59Rn2W5K3r/Jatya5dT3zAAAAAHBubMSnswEAAACwyYlIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGDQlt4DAACwcW6++eYcOXKk9xichfvvvz9JctNNN3WehLN16aWX+u8HbGoiEgDAJnLkyJF89oufTZ7XexJ+YIunvn322Gf7zsHZ+WbvAQDOPREJAGCzeV6y+MrF3lPA08rEn7lSCLD5+U0HAAAAwCARCQAAAIBBIhIAAAAAg0QkAAAAAAaJSAAAAAAMEpEAAAAAGCQiAQAAADBIRAIAAABgkIgEAAAAwCARCQAAAIBBIhIAAAAAg0QkAAAAAAaJSAAAAAAMEpEAAAAAGCQiAQAAADBIRAIAAABgkIgEAAAAwCARCQAAAIBBIhIAAAAAg0QkAAAAAAaJSAAAAAAMEpEAAAAAGCQiAQAAADBIRAIAAABgkIgEAAAAwCARCQAAAIBBIhIAAAAAg0QkAAAAAAaJSAAAAAAMEpEAAAAAGCQiAQAAADBIRAIAAABgkIgEAAAAwCARCQAAAIBBZx2RqupFVfWpqvpSVX2xqv7H0f67q+pYVd03+vr5Jc/51ao6UlV/XVVXLdm/erR3pKreub63BAAAAMBG27KO5y4k+Z9ba/+xqp6T5N6qmhvd9zuttX+19MFV9WNJ3pzkx5NclOTOqnrp6O7fTTKZ5GiSz1TV/tbal9YxGwAAAAAb6KwjUmvtwSQPjm5/p6r+MsmuNZ5yXZKPtNYeS/I3VXUkyRWj+4601r6SJFX1kdFjRSQAAACAMbEh10SqqkuS/GSS/zDaekdVHa6qW6vq+aO9XUm+tuRpR0d7q+2f6edcX1WHqurQ8ePHN2J0AAAAAJ6EdUekqnp2ko8l+WettW8n+UCSH01yeU4dqfTb6/0Zp7XWPtha291a271jx46NelkAAAAABqznmkipqq05FZD+TWvt3yVJa+3hJff/fpJ/P1oeS/KiJU+/eLSXNfYBAAAAGAPr+XS2SvKvk/xla+3/WLL/wiUPe12SL4xu70/y5qp6RlW9JMllSf4iyWeSXFZVL6mqH8qpi2/vP9u5AAAAANh46zkS6b9M8gtJPl9V9432fi3JW6rq8iQtyQNJbkiS1toXq+qjOXXB7IUkb2+tPZ4kVfWOJHckuSDJra21L65jLgCAp62jR48m30om/mxDLn0JPFnfTI62o72nADin1vPpbP9PkjrDXbev8Zz3JnnvGfZvX+t5AAAAAPS1rmsiAQAwXi6++OIcr+NZfOVi71HgaWXizyZy8a6Le48BcE45zhkAAACAQSISAAAAAINEJAAAAAAGiUgAAAAADBKRAAAAABgkIgEAAAAwSEQCAAAAYJCIBAAAAMAgEQkAAACAQSISAAAAAINEJAAAAAAGiUgAAAAADBKRAAAAABgkIgEAAAAwSEQCAAAAYJCIBAAAAMAgEQkAAACAQSISAAAAAINEJAAAAAAGiUgAAAAADBKRAAAAABgkIgEAAAAwSEQCAAAAYNCW3gMAALDBvplM/Jl/Kzzv/O3o+7O7TsHZ+maSXb2HADi3RCQAgE3k0ksv7T0CZ+n+++9Pkly267LOk3BWdvn/P2DzE5EAADaRm266qfcInKXT/+1uvvnmzpMAwJk5zhkAAACAQSISAAAAAINEJAAAAAAGiUgAAAAADBKRAAAAABgkIgEAAAAwSEQCAAAAYJCIBAAAAMAgEQkAAACAQSISAAAAAINEJAAAAAAGiUgAAAAADBKRAAAAABgkIgEAAAAwSEQCAAAAYJCIBAAAAMAgEQkAAACAQSISAAAAAINEJAAAAAAGiUgAAAAADBKRAAAAABgkIgEAAAAwSEQCAAAAYJCIBAAAAMAgEQkAAACAQSISAAAAAIO29B4AAACerJtvvjlHjhzpPcY5cf/99ydJbrrpps6TnDuXXnrppn5/AJudiAQAAGPgh3/4h3uPAABrEpHY1F7zmtfkO9/5Tp773Ofmtttu6z0OALBOjmIB+MHMz8/nN3/zN/Pud78727Zt6z0O5znXRGJT+853vpMk+da3vtV5EgAAgKfe61//+nzuc5/L6173ut6jsAmISGxar3nNa5atr7nmmk6TAJvN3r17v/8FADCu5ufnl61PnDjRaRI2i7E5na2qrk7yfya5IMkftNbe13mkp43NeoHK00chnfatb31rUx4C7wKVAADAmbz+9a9ftn7d616XgwcPdpqGzaBaa71nSFVdkOQ/JZlMcjTJZ5K8pbX2pdWes3v37nbo0KGnZL6bb745Bw4ceEp+Vg+PPfZYFhcXe4+x4c70niYmNt/BdxMTE3nGM57Re4xzZt++fSIZY+VMRx/5YwwAzm+b9R/W77vvvhV7l19+eYdJzi3/sL6xqure1truM903LkciXZHkSGvtK0lSVR9Jcl2SVSMSwPluswfq7373uxmHf6h4KmzG09qqKs985jN7j3HOCNQAPxh/t2weZwpL57vPfe5zm/r/Psfp75ZxiUi7knxtyfpokp954oOq6vok1yfJi1/84qdmspz6FJBx+Q92LjydqvtP/MRPdJjk3FLdAQBgfS644AJnZ5zHNuN7GlfjcjrbG5Jc3Vr7H0brX0jyM621d6z2nKfydDbOX0uPDnC6CbARnM4GAJwv/N3C2VjrdLZxyXXHkrxoyfri0R6sy3Oe85wkyXOf+9zOkwAAADy1nhiMBCTWa1xOZ/tMksuq6iU5FY/enOS/6TsSm8EnPvGJ3iMAm8zBgwcd5QgAwNPSWESk1tpCVb0jyR1JLkhya2vti53HAgAAgPOaf/BiI41FREqS1trtSW7vPQcADPHHGAAAT0fjck0kAAAAAMaYiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEEiEgAAAACDRCQAAAAABolIAAAAAAwSkQAAAAAYJCIBAAAAMEhEAgAAAGCQiAQAAADAIBEJAAAAgEHVWus9w1mpquNJvtp7Ds4L25PM9x4C2HT8bgHOBb9bgHPB7xZ+EP95a23Hme44byMSPFlVdai1trv3HMDm4ncLcC743QKcC363sFGczgYAAADAIBEJAAAAgEEiEk8HH+w9ALAp+d0C/1979x/rdVXHcfz5Iiyoa1DBWpYGc5YVP66CTBFKXX+5ZZY52kzDWq0ypTUy1+/ZMk225sAfq61mWUZYFMFCCwKFYqAgP8xLLaW1fs4gBFEifPXHOVe/3l3ul4sXvvd+eT22u/v5fM75nHs+d/d+7jnvz/tzbhwNubdExNGQe0sMiKyJFBERERERERERTSUTKSIiIiIiIiIimkoQKSIiIiIiIiIimkoQKSIioglJv+2j7DxJS49lfyIiIuL4IukrkuYOQDujJX2iYf8kSfe82Hbj+JEgUkRERBO2p7e6DxExNEjaWz8/NzGT1Cnpwtb27PBJ+lyr+xARR07S8D6KRwPPBZFs/832+45+r6JdJIgUbUHSzyQ9JOkRSR+txz4s6Q+S1kv6tqQF9fhYST+RtKF+nNva3kfEYCdpr4qbJW2TtFXSrIYqr5S0TNJ2SXdIyt/XiDbSZELWqx4Ts05gyASRgASRIgYBSZ+v85k1wJvrsVWSptbtMZJ21O3ZkpZIWgmskNQhaYWkjXXc8u7a7I3AqZIeruOacZK21TZGSPpurb9J0vkNbf9U0nJJf5T0jWP8rYhBpN9/ECMGqQ/Z3ilpJLBB0jLgi8CZwB5gJbC51r0F+KbtNZJOAe4F3tKKTkfEkPJeykRwMjCGcq+5v5ZNA94K/BlYXusmNTxiCJF0BTAXMLAFOAg8A5wBrJV0K3ArMBbYB3zEdpek8cAPgQ7g5w3tjQOWUsYi1wMjJc0Avm57YS9ffxpljDICeBq40vZ2SbOBi4FXAKcB84CXApcD+4EL6xioE7gDeDnwJ8rYaJekVcBc2w9KGgM8aHtcbfeiWv9UYLHtayXdWPv6MPCI7cte3Hc2Io6EpCnA+yljj+HARuChJqedCUyq94ThwHtsP1l/99dJWgJcB0yw3Vm/zriG868CbHuipNOB+yS9qZZ1Uu6H+4Htkubb/stAXGsMLXlSGu3iGkmbgXXAyZSB1WrbO20fABY11H0nsKAOjpZQMgg6jnmPI2KomQHcbfug7X8Cq4Gzatl624/ZPgjcXetGxBAh6W3AF4ALbE8G5tSiNwDTbX8a+BZwte0plGDTbbXOLcDtticCf+/Ztu3/Al8CFtru7C2AVHUBM22fUevf0FA2gRKcPgv4GrCv1vsdcEWt8z3gs7YnAVuBLx/GpXcCs4CJwCxJJ9u+Dni69jUBpIjWmUkJ7u6z/SRl3tLMr2zvrNsCbpC0Bfg18HrgtU3OnwHcBWC7i/JwrDuItML2btvPAL8H3tivq4m2kUykGPIknUcJDJ1je1994tbFobOLhgFn1xtgRMRAcJP9iBjcLgAW2X4CoD7Fpx47WB82TQcW1eMAL6ufzwUuqdvfB246wj6MAu6UdBrlHnJCQ9lvbO8B9kjaDfyiHt8KTJI0Chhte3U9ficvfIB2KCts7waQ1D0pTGZBxOD2P55PBhnRo+yphu3LKJmTU2wfqK+99azfH/sbtg+SWMJxK5lI0Q5GAbtqAOl04GxKyvc7JL2qpnJe0lD/PuDq7p2a/h0R0cwDlCf1L5E0Fng7sL6WTZM0vq6FNAtY06pORsSA6p6QDQP+U7Nzuj8aH1YNROD4q5Rg0QTgXbxwstc4eXu2Yf9Zmk/k+ppwZlIYMXjdD1wsaaSkEyn3BYAdwJS63deC2KOAf9UA0vk8nzm0BzjxEOc8QAk+UV9jOwXYfsRXEG0pQaRoB8uB4ZIepSwUtw74KyUNfD2wlnKz3V3rXwNMlbSlPnX72DHvcUQMNQYWU9ZJ2UxZZ+1a2/+o5RuABcCjwOO1bkQMHSuBSyW9BkDSqxsL66skj0u6tJZL0uRavJaybgnUyVcv+pq0dRtFGb8AzO5P52s20S5JM+uhyymv3MLhTzgbHZB0QvNqEXG02N4ILKSMO35JGWtAWRft45I2UdZoPJQfUOY8WymvvXbVdv9NWedtm6Sbe5xzGzCsnrMQmG17PxENZCfjPtqTpA7be2sm0mLgO7YzsYuIfqmTyo228+5/RBuT9EHgM5SMnE318FLb99Ty8cDtwOsor5r9yPb1vSys/SnbHd0La9ueUINS99bzDrWw9jmU19CeApYBH2hYAHuq7U/Wejvq/hONZT0W1n6MsjD3rpql/eN6XX21uxSYZ3uVpJsoi25vzLpIERHRKEGkaFuS5lHWShpBeYVtjvMDHxH9IOkkYBUw3/b8FncnIiIiIqKlEkSKiIiIiIiIiIimsnheRERERMQxIulKYE6Pw2ttX9WK/kRERPRHMpEiIiIiIiIiIqKp/He2iIiIiIiIiIhoKkGkiIiIiIiIiIhoKkGkiIiIiIiIiIhoKkGkiIiIiIiIiIho6v8t7jfX96ikUAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#checking outliers\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.boxplot(data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wrYYPNoq0vq"
      },
      "source": [
        "#### credit amount has outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4FsC1vHDq0vr"
      },
      "outputs": [],
      "source": [
        "#treating outliers\n",
        "#from scipy import stats\n",
        "#import numpy as np\n",
        "#z = np.abs(stats.zscore(df['credit amount']))\n",
        "#print(z)\n",
        "#threshold = 5\n",
        "#print(np.where(z > 5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02FroDXPq0vu"
      },
      "source": [
        "## Univariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "m1uHE1rWq0vu",
        "outputId": "c3a982ee-ae32-40db-da38-13873e5c7202"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8498de54-0223-4442-93b7-67221ff159af\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.546000</td>\n",
              "      <td>1.904000</td>\n",
              "      <td>3271.258000</td>\n",
              "      <td>20.903000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.375469</td>\n",
              "      <td>0.653614</td>\n",
              "      <td>2822.736876</td>\n",
              "      <td>12.058814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1365.500000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2319.500000</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3972.250000</td>\n",
              "      <td>24.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>18424.000000</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        \n",
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              "\n",
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              "          document.querySelector('#df-8498de54-0223-4442-93b7-67221ff159af 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-8498de54-0223-4442-93b7-67221ff159af');\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",
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              "  "
            ],
            "text/plain": [
              "               age          job  credit_amount     duration\n",
              "count  1000.000000  1000.000000    1000.000000  1000.000000\n",
              "mean     35.546000     1.904000    3271.258000    20.903000\n",
              "std      11.375469     0.653614    2822.736876    12.058814\n",
              "min      19.000000     0.000000     250.000000     4.000000\n",
              "25%      27.000000     2.000000    1365.500000    12.000000\n",
              "50%      33.000000     2.000000    2319.500000    18.000000\n",
              "75%      42.000000     2.000000    3972.250000    24.000000\n",
              "max      75.000000     3.000000   18424.000000    72.000000"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "#checking summary statistics for numericals\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "idKxcVbNq0vy",
        "outputId": "186ac791-3797-4de6-d0e1-fdb1623551b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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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>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>risk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>car</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>690</td>\n",
              "      <td>NaN</td>\n",
              "      <td>713</td>\n",
              "      <td>603</td>\n",
              "      <td>394</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>337</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.546000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.904000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3271.258000</td>\n",
              "      <td>20.903000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.375469</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.653614</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2822.736876</td>\n",
              "      <td>12.058814</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1365.500000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2319.500000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3972.250000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18424.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b936113e-bcbf-4f58-b5c1-6ab8a24ebd92')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b936113e-bcbf-4f58-b5c1-6ab8a24ebd92 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-b936113e-bcbf-4f58-b5c1-6ab8a24ebd92');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                age   sex          job  ...     duration purpose  risk\n",
              "count   1000.000000  1000  1000.000000  ...  1000.000000    1000  1000\n",
              "unique          NaN     2          NaN  ...          NaN       8     2\n",
              "top             NaN  male          NaN  ...          NaN     car  good\n",
              "freq            NaN   690          NaN  ...          NaN     337   700\n",
              "mean      35.546000   NaN     1.904000  ...    20.903000     NaN   NaN\n",
              "std       11.375469   NaN     0.653614  ...    12.058814     NaN   NaN\n",
              "min       19.000000   NaN     0.000000  ...     4.000000     NaN   NaN\n",
              "25%       27.000000   NaN     2.000000  ...    12.000000     NaN   NaN\n",
              "50%       33.000000   NaN     2.000000  ...    18.000000     NaN   NaN\n",
              "75%       42.000000   NaN     2.000000  ...    24.000000     NaN   NaN\n",
              "max       75.000000   NaN     3.000000  ...    72.000000     NaN   NaN\n",
              "\n",
              "[11 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "#checking summary statistics for numericals and non-numeric vales\n",
        "df.describe(include='all')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "kcoZT-zDq0wA",
        "outputId": "7c2d983c-e06d-48a8-d984-14ffecbd9be1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8cd8d78d10>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#performing correlation between diffrent varaibles\n",
        "corr=df.corr()\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(corr,annot=True,cmap='gist_rainbow',vmin=-1,vmax=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPBrRqoKq0wF"
      },
      "source": [
        " credit amount and duration has highest correlaion hencestrongly vorrelated"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "_HxGgm29q0wG",
        "outputId": "e3b18e35-e6ec-46eb-9b5c-1efd369c390e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8cd03eaf10>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g/CUoycxRaewajJrTS2OtWlLEjLR47nplt9tRjIk4k+2DeBJ4FUgCPq6ql6nqo6r6VSAlmAHNxPqHRunsH47KAhEX4+GGM2bxenUrW+s73Y5jTESZ7BHE3aq6UFX/XVUPgn8dJQBVrQhaOjMpf+2gjnc5iTuuXVFKSnwMv3qlxu0oxkSUyRaI7wfY9uZUBjHHrrE7ukYwjZeWEMu1K0pZu+Ug+9r63I5jTMQ4YoEQkXwRWQYkishSETnFuZ2L/3STCQGNXQPEx3hIT4ze8QI3nFGGAPe8Vut2FGMixkTDXP8Gf8d0MXD7mO3dwLeClMkcpUMd1NE8zLMgPZHLlhTy6Pp9fOOCeWQkRc9wX2OC5YhHEKr6G1U9D/isqp435naZqv5hmjKaI1BVGrsGorb/Yaybz55N//Aov33LFvEzZioc8QhCRD6tqr8FykTk1vH7VfX2AE8z06i5Z5C+odGo7X8Ya0F+GueU53L/G3v4/FmzSYjwVW2NCbaJOqmTna8pQGqAm3HZzoYeIHo7qMf7wtmzaekZ5MlNtoifMcfriEcQqvor5+t3pyeOOVpVjd2AFYhDTpuTzeKiNO5+tYarK0rwRNHMcmOm2mQnyv1IRNJEJFZEnheRZhH5dLDDmYntbOgmOc5LSrxdkRX8i/jdfPYcapp7eW5Ho9txjAlrk50HcZGqdgEfw78W01zgfwUrlJm8qsZuO3oY56OL8ynOTOQumzhnzHGZbIE49PH0UuBxVbU1DUKAz6fssgJxmBivhxvPnMWGPe1s3NPudhxjwtZkC8TTIlIJLAOeF5FcYCB4scxk1Hf002sjmAK6qqKE9MRYW8TPmOMw2eW+bwNOBypUdRjoBVYFM5iZ2M73O6htDsR4yfExXLdyJn/e3khNc4/bcYwJS0fTs7kA/3yIsc95YIrzmKMQ7SOYHlq394j7UxNi8Irwj7/fwuVLi7h2Rek0JTMmMkyqQIjIg8AcYDMw6mxWrEC4amdDN4XpCTYh7EOkJsSytDSDTXvbueCEPLfjGBN2JnsEUQEsVFUNZhhzdKoaeyjPt/mKR3Lm3Fw21LXzVk0rXzhnjttxjAkrk+2k3grkBzOIOTojoz52N/Uwf4YViCPJTY3nhII03qppo3dwxO04xoSVyRaIHGC7iDwrImsO3YIZzBxZXWsfQ6M+yq1ATOic8lz6h0f5zZt1bkcxJqxM9hTTd4IZwhy9QyOY5uen8t5+m5ZyJCVZScyfkcpdr9Rw3cqZpCZE73UzjDkakx3m+jL+GdSxzv31wDtBzGUmUNXQjQjMzbNLgk/GBSfMoKNvmPter3M7ijFhY7JrMd0EPAH8ytlUBDwVrFBmYjsbuynLTrYRTJNUlJnIhQtncPerNXT2Dbsdx5iwMNk+iK8AZwBdAKq6C7Bxgy6qauymfIYdPRyNWy8sp3tghF+/Zms0GTMZky0Qg6o6dOiBM1nOhry6ZGB4lLqWXhvBdJROKEjj0hMLuPe1Wlp7Bt2OY0zIm2yBeFlEvgUkisiFwOPA/wQvljmS3c09+BSbA3EMvnlhOQMjPn7y3C63oxgT8iZbIG4DmoEtwBeAtcA/ByuUObL3RzDZEcRRm5uXwqdXlPK7dXve/zkaYwKb7CgmH/5O6S+r6hWqerfNqnZPVUMPsV6hLCd54sbmMN+4oJyU+Bj+z9PbsV9jYz7cEQuE+H1HRFqAKqDKuZrct6cnnglkZ2M3c3JTiPVO9gDQjJWZHMfXLyjn1V0tvFTV7HYcY0LWRBPlvol/9NKpqloLICKzgV+KyDdV9Y5gBzSHq2ropqIs0+0YYWfs6q+xXiEnJY7/9cR7fP38eXgDXLvaVn810W6ij6DXAdccKg4AqloDfBr4TDCDmcC6B4ap7+i3JTaOU4zHw0cXF9DSM8hru+wowphAJioQsaraMn6jqjYDtl6BC3Y1+S9+Yx3Ux29BQRqLCtN4vrKJlm4b9mrMeBMViKFj3GeCZGfDX9dgMsfvspMLifEKf9i0H591WBvzARMViJNFpCvArRs4caIXF5GLRaRKRKpF5LYA++NF5FFn/zoRKXO2XygiG0Vki/P1I8fyj4tEVY3dJMV5KcpIdDtKREhNiOWjiwuoa+1jfV2b23GMCSlHLBCq6lXVtAC3VFU94ikmEfECdwKXAAuBa0Rk4bhmNwLtqjoXuAP4obO9Bfi4qp4IXA88ePT/tMi0s7GbeTNS8QToVDXHZtnMTObkJvPM1gY6+uzA2JhDgjlOcjlQrao1zjIdjwCrxrVZBfzGuf8EcL6IiKpuUtUDzvZt+Gdwxwcxa9ioauhhvq3BNKVEhMuXFqPAoxv2MeqzU03GQHALRBGwb8zj/c62gG1UdQToBLLHtfkk8I6qRn0vYmvPIC09gzaCKQiykuP42yWF7Gnt44XKJrfjGBMSJnvBIFeIyCL8p50u+pD9NwM3A5SWRv6Y9Z2Nzggm66AOiiUlmVQ39fJSVROzc22WujHBPIKoB0rGPC52tgVs46wQmw60Oo+LgSeBz6jq7kDfQFXvUtUKVa3Izc2d4vihp6qhC8COIILospMLyU6J57H1+2i2oa8mygWzQKwH5onILBGJA1YD469jvQZ/JzTAFcALqqoikgH8CbhNVV8PYsawUtXYTWZSLHmp1h0TLHExHq5ZXkL/8Chf+u1GBkdG3Y5kjGuCViCcPoVbgGeBHcBjqrpNRL4nIpc5ze4BskWkGrgV/6qxOM+bC3xbRDY7t6i/QNGOg93Mz09FxEYwBVNBeiJXLCtmw552/vnJrbagn4laQe2DUNW1+JcGH7vt22PuDwBXBnje94HvBzNbuPH5lJ2N3VxVUTJxY3PcTirOIC8tgf/7/C7KZ6Ry09mz3Y5kzLQL6U5q81f72/vpGxplgXVQT5tvnD+P6qZu/u3/7WBmdhIXLcp3O5Ix08rWiw4TlU4HtY1gmj4ej/DjK5dwUnEGtzy8ibdqWt2OZMy0sgIRJiqdNZhsBNP0Sozzct9nT6U0K4nP/2YDW+s73Y5kzLSxAhEmqhq6Kc1KIjnezgpOt6zkOB68cTnpibFcf+/b7G7ucTuSMdPCCkSYqGzosv4HFxWkJ/LgjcsBuOaut6ixImGigBWIMDAwPEptS68VCJfNzk3hoZtWMupTVt/1lh1JmIhnBSIMVDf14FOYn5/mdpSoNz8/lYdv9heJa6xImAhnBSIMVNpFgkJK+Qx/kfCpHUmYyGYFIgxUHuwiPsZDWXaS21GMo3xGKg/ftBJ1ikR1U7fbkYyZcjYkJgxUNXYzb0YKMV6r59PpoXV7J2zzqRUzuee1Wlbd+QafP3MW37ywfBqSGTM97C9OGKhs6Gb+DOt/CEUz0hL4/Fmz8Ajc/WoNOw52uR3JmCljBSLEtfYM0tw9aCOYQlheagI3nTWbGI9wzd1v2WQ6EzHsFFOIq3I6qBcUWIEIZTkp8dx01mzuea2WK/77DT53xiyKMyfuM7p2ReRf6MqELzuCCHE7bART2Mh2ikRirJd7X69lX1uf25GMOS5WIELctgOd5KbGk5ea4HYUMwmZyXHcdNZskuJirEiYsGcFIsRtP9DF4kLroA4nGUmHioSX+9+o42Bnv9uRjDkmViBC2MDwKLuaelhclO52FHOU0hNjufHM2cR6hXtfr7PrW5uwZAUihFU2dDPqUxbZEURYykqO48Yz/Veiu/f1Wtp7h1xOZMzRsQIRwg4Nl1xUaEcQ4So3NZ7PnVHG0IiPe16vpat/2O1IxkyaFYgQtu1AJ+mJsRRnJrodxRyHgvREPnt6GT2DI9z7ei39Q6NuRzJmUqxAhLCt9V0sLkpDRNyOYo5TSVYS162cSWvvEA++tYfhUZ/bkYyZkBWIEDU86qOqoZvFdnopYszJTeGKZcXUtfby+Mb9+FTdjmTMEVmBCFG7GnsYGvWxyEYwRZSTizO4ZHE+W+s7WbvlIGpFwoQwKxAhausBfwe1zYGIPGfOzeGMOdm8sbuVX79a63YcYz6UrcUUorbVd5Ic56UsO9ntKGaKiQiXnFhA58AIP1i7gxnpCVx2cqHbsYw5jBWIELX1QBcLC9PweKyDOhJ5RLhyWTFJcV7+7rHN5KTEcfqcHLdjGfMBdoopBI36lO0Humz+Q4SL9Xq4+7oKZuUk84UHNtq1JEzIsQIRgmpbeukfHrUlNqJAelIs99+wnKR4Lzfct54DHbZukwkdViBC0LZDHdRF1kEdDQozErn/huX0Do7w2fveptNmW5sQYQUiBL23v5P4GA9zclPcjmKmyQkFafzqM8uobenl5gc2MDhis62N+6xAhKBNe9s5sSidWK+9PdHk9Dk5/NeVJ7Outo1bH3sXn8/mSBh32SimEDM04mPrgS4+s3Km21HMNHho3d7Dtl28KJ8/vXeQjt4hLj3pg8Nf7RKlZjpZgQgxlQ1dDI34WFqa6XYU45Kz5uXQ2T/M67tbSU+K48y5NvzVuMMKRIjZtLcDgCWlGS4nMW4RES49qYCugWHWbjlIUqyXU2baBwYz/YJ6kltELhaRKhGpFpHbAuyPF5FHnf3rRKTM2Z4tIi+KSI+I/DyYGUPN5n0d5KbGU5hu16COZh4RrqooYW5uCr9/Zz9bnGuDGDOdglYgRMQL3AlcAiwErhGRheOa3Qi0q+pc4A7gh872AeBfgL8PVr5QtWlvO0tLMmyJb0Os18OnV86kNCuJR9fvpbLBJtKZ6RXMI4jlQLWq1qjqEPAIsGpcm1XAb5z7TwDni4ioaq+qvoa/UESNtt4h6lr77PSSeV9cjIfrTy+jID2Rh9bt5cWqJrcjmSgSzD6IImDfmMf7gRUf1kZVR0SkE8gGWoKYK2Str2sDoKN3OODoFhOdEmK93HB6Gfe+UcsXHtjIz69dykWL8t2OZaJAWA+0F5GbRWSDiGxobm52O85x21DXRlyMxy4xag6TFB/DjWfM5oTCNL78u3f403sH3Y5kokAwC0Q9UDLmcbGzLWAbEYkB0oHWyX4DVb1LVStUtSI3N/c447pvfV07JxenE2MT5EwAiXFefnvjcpaUZPDVh9+xo0wTdMH8S7QemCcis0QkDlgNrBnXZg1wvXP/CuAFjdJLbPUNjbC1vpNTy7LcjmJCWGpCLL/53HLOLs/lW09u4cd/rrKr0pmgCVqBUNUR4BbgWWAH8JiqbhOR74nIZU6ze4BsEakGbgXeHworInXA7cBnRWR/gBFQEWXzvg5GfGoFwkwoOT6Guz9TwdUVJfzshWr+/vH3GBrxuR3LRKCgTpRT1bXA2nHbvj3m/gBw5Yc8tyyY2ULN+tp2ROCUmZl2ftlMKNbr4T8+eSIFGQn85Lld1LX28stPnUJems2fMVPHZlKHiDdrWjghP430xFi3o5gQNr7fIS81gdWnlvD7d/Zzwe0vc+0K/7yJQ2ztJnM8rDc0BPQPjfLOng7OmJvtdhQThk4qzuCL58zB6xHufrWGN3a3WL+EmRJWIELAhj1tDI36ON0WZTPHqCA9ka+cN5d5eSk8/d5BHnhzDz2DI27HMmHOCkQIeL26lRiPsNw6qM1xSIqL4bqVM/nYSQVUN/fwsxd28UJlo9uxTBizAhEC3tzdwpKSDJLjrUvIHB8R4fQ5OXz53Dkkxnr53P0buPXRzXT0DbkdzYQhKxAu6+wbZkt9p51eMlOqID2RW86by9c+Mpc17x7ggttf4Y+b661vwhwVKxAue2VXMz6Fc8qtQJipFeP1cOtF8/njLWdQkJ7A1x/ZzDV3v8XOxm63o5kwYQXCZS9WNpGZFMuSErsgjAmORYXpPPWVM/jB5YvZcbCbS376Kv/y1FaauqNqsWRzDKxAuGjUp7y0s5lzynPxeuz6DyZ4vB7hUytm8uLfn8u1y0t5+O29nPufL/HjP1fR2T/sdjwToqxX1EXv7e+grXeI8xbkuR3FRKhAC/qdUJDG18+fx5+3N/KzF6q565UaVszK5vS52aQlxNrkOvM+KxAuerGyCY/AOeXhvxKtCS/ZKfFcs7yUczr6eWVXM6/uauaN3S2cVJxO+YwUls3MtKsaGisQbnpmWwMVZVlkJMW5HcVEqcKMRFafWsqFJwzyWnULm/d1cMV/v8m8vBQuP6WISxYXMCsn2e2YxiVWIFxS3dTNzhi0KGMAAA5QSURBVMYevnvZIrejGEN2SjyrlhRx8eJ8kuK8PLJ+Hz96poofPVPFgvxUzluQx8rZ2VTMzLT5OlHE3mmX/Om9BkTg4sV26UgTOuJjvFx9ailXn1pKfUc/z25t4JltDdz9Sg2/fGk3MR6hfEYqCwpSWZCfyvz8NHYc6CI1IWbSp6SsjyN8WIFwydotB6mYmckMW57ZhKiijEQ+d+YsPnfmLPqGRti4p523alrZUt/Fa7ta+MM7f71AZHyMh+yUOLKT48lJiSMnJZ7sFP/9pDj7MxOu7J1zwa7Gbqoau/nXj0f0NZBMBEmKi+GsebmcNe+vAyrae4eobOjmoXV7aOkZorV3kPqOfrbWdzJ2vnZirJeclDgKMxKZmZ3M2eU5FGUkWid4GLAC4YInNu4nxiN87KRCt6MYc8wyk+M4bU42tS29H9g+4vPR1jtEa88QLT2DtPYM0dwzyKa9HayrbeOxDfvISYljYUEaCwvSKM5KwnOEYmGnpNxjBWKaDY/6+P079Zy3II/c1Hi34xhzmEBzJ45GjMdDXmoCeakfPH066lMauwaoa+2lsqGb16pbeGVXC1nJcSwvy2KZdYCHHHs3ptnLVc209Axy5bJit6MYM628HqEwI5HCjEROn5ND/9AolQ1drK9r45ltDTy3o5ElJRmcNz+PzGQb+h0KrEBMs0fW+w+vbfa0iXaJcV6WlmaytDSThq4B1tW0snFPO+/sbWfZzCzOm59rc4RcZgViGtW19PJ8ZSO3nDeXWK8tg2XMIflpCaxaUsS58/N4qaqJDXXtbN7XztnluXzilCISYr1uR4xK9ldqGt3/Rh0xHuG6lTPdjmJMSEpPjGXVkiJuvbCc+TNSeX5HExfc/jJ/2W5XxnODFYhp0tk3zGMb9vHxkwvJs7kPxhxRZnIc166YyY1nziIpzstND2zg5gc2cLCz3+1oUcUKxDT59Ws19A2NctNZs92OYkzYmJObwp++dha3XbKAV3Y1c8GPX+a+12sZ9dmV8aaDFYhp0NIzyD2v1XLpSQWcUJDmdhxjwkqs18MXz5nDX755DhVlWXz3f7Zz+S9eZ2t9p9vRIp4ViGnwixd3MzA8yjcvKHc7ijFhqyQriftvOJWfXbOUAx0DXPbz1/j+09vpHRxxO1rEsgIRZDsOdvGbN+u4qqKEuXkpbscxJqyJCB8/uZDn/+4cVi8v5dev1XLh7S/znHViB4UViCDy+ZRvPbmF9MRY/vHiBW7HMSZipCfG8m+Xn8jvv3QaKQkxfP6BDXzxwY00dNp1tqeSFYgguuvVGjbt7eB/f/QEmxlqTBAsm5nF0189i3+4eD4vVvmHxN75YjXdA3ad7algE+WCZH1dG//5bBWXnljAJ04pcjuOMWFrMmtDZSTGcct5c/nTloP857NV3P1qDZ8/cxbXnVZGemLslHyPsaJlAUErEEFQ19LLl377DsWZifz7J0+0ZY2NmQbZKfF85rQy9rf3UdnQzX/9eSc/e6GaSxbnc9WpJayclY3HY/8Xj4YViClW39HPp+9Zx6jPxz3XryAtYeJPL8aYqVOcmcQ/XLyAbQc6efjtvfxx8wGe2nyAnJQ4Tp+Tw5nzclg2M5OZWUnE2JI3R2QFYgptre/kc/evp39olN9+fgVz81LdjmRM1FpUmM73//ZE/vnShTy7rYEXK5t4rbqVNe8eACDO62F2bjIlWUm09w6REh9DcnwMyfFeEmNjSIz1kBDrJSHOS2KslxiPRN3ZACsQU8DnU+57o44fPlNJTnIcT3zpdObnW3EwJhQkxHpZtaSIVUuKUFUqG7rZWt9JdVMPOxu72dfWx/72fvqGRjjSBG2vR0hLiCE9MY71dW0UZiRQkplEeX4q5TNSSYnAa1kE9V8kIhcDPwW8wK9V9T/G7Y8HHgCWAa3A1apa5+z7J+BGYBT4mqo+G8ysx8LnU17a2cR/PruTHQe7OH9BHv/xyZPsQkDGhCgR4YSCtMNWNHho3V58qgwMjdIzNMLAsI+B4VH6h0cZGB5lYGiUvuFRugdG6Ogb4u3aNhq6Bj6w5EdxZiILnGIxPz+VBflpzM5NDuuVm4NWIETEC9wJXAjsB9aLyBpV3T6m2Y1Au6rOFZHVwA+Bq0VkIbAaWAQUAs+JSLmqjgYr72SN+pQdB7v4y/ZG/ri5nrrWPkqyEvnp6iVcdnJh1B2CGhMpPCIkxceQNIkjgWtXlDLqU+rb+6lq7KaqoYuqxh6qGrp4qaqZEadwxHqFObkpLMhPZUFBGvPzUynLTqYoI5G4mNAvHME8glgOVKtqDYCIPAKsAsYWiFXAd5z7TwA/F/9f2FXAI6o6CNSKSLXzem8GI+ioTxkcGWVw2MfgiP+TQ8/gCO19Q7T1DtHQOUBNcy81LT1UNnTTPTCCCCwvy+KbF5ZzyeKCsHizjYkWx3vZ1MnweoTS7CRKs5O4cOGM97cPjfjY3dxDVUM3lQ3dVDZ0sa62jac2H3i/jQgUpCVQkpVEYUYiWclxZCXHke18TYmPeb/vIzHWS2Kcl4RYL7FewesRvOL/GuwPpMEsEEXAvjGP9wMrPqyNqo6ISCeQ7Wx/a9xzgzKZYNPedi7/xRsTtstJiWN2TgqXnVzIqWVZnDE3x04lGWMOExfjCXgaq6NviF1NPext7WNvWx/72vxf365to613iP7hoz9BIgJeET52UgE/Wb10qv4J7wvrXhURuRm42XnYIyJVx/AyOUDLRI32ABuP4cWnyKQyhgDLOXXCISOER84pz/ipqXyxvzrmnD8FfnrNMX/fD72CWTALRD1QMuZxsbMtUJv9IhIDpOPvrJ7Mc1HVu4C7jiekiGxQ1YrjeY1gC4eMYDmnUjhkhPDIGQ4ZITRzBvPE+XpgnojMEpE4/J3Oa8a1WQNc79y/AnhBVdXZvlpE4kVkFjAPeDuIWY0xxowTtCMIp0/hFuBZ/MNc71XVbSLyPWCDqq4B7gEedDqh2/AXEZx2j+Hv0B4BvhIKI5iMMSaaBLUPQlXXAmvHbfv2mPsDwJUf8twfAD8IZj7HcZ2imibhkBEs51QKh4wQHjnDISOEYE7xn9ExxhhjPsgG7xtjjAkoqgqEiNwrIk0isnXMtiwR+YuI7HK+ZrqcsUREXhSR7SKyTUS+Hmo5RSRBRN4WkXedjN91ts8SkXUiUi0ijzqDE1wnIl4R2SQiTzuPQy6niNSJyBYR2SwiG5xtIfOeO3kyROQJEakUkR0icloIZpzv/AwP3bpE5BshmPObzv+drSLysPN/KuR+L6OqQAD3AxeP23Yb8LyqzgOedx67aQT4O1VdCKwEvuIsPRJKOQeBj6jqycAS4GIRWYl/qZQ7VHUu0I5/KZVQ8HVgx5jHoZrzPFVdMmaoYyi95+Afbv+Mqi4ATsb/Mw2pjKpa5fwMl+Bf460PeJIQyikiRcDXgApVXYx/EM+hpYZC6/dSVaPqBpQBW8c8rgIKnPsFQJXbGcfl/SP+9axCMieQBLyDf5Z8CxDjbD8NeDYE8hXj/4PwEeBpQEI0Zx2QM25byLzn+Oco1eL0W4ZixgCZLwJeD7Wc/HUFiSz8A4WeBv4mFH8vo+0IIpAZqnrQud8AzDhS4+kkImXAUmAdIZbTOW2zGWgC/gLsBjpUdcRpErTlUY7ST4B/AHzO42xCM6cCfxaRjc4KARBa7/ksoBm4zzld92sRSSa0Mo63GnjYuR8yOVW1HvgvYC9wEOjEv1BDyP1eWoEYQ/2lOySGdYlICvB74Buq2jV2XyjkVNVR9R/GF+NfSHGBm3kCEZGPAU2q6uIqKZN2pqqeAlyC/7Ti2WN3hsB7HgOcAvxSVZcCvYw7TRMCGd/nnL+/DHh8/D63czr9H6vwF91CIJnDT32HBCsQ0CgiBQDO1yaX8yAisfiLw+9U9Q/O5pDLCaCqHcCL+A+JM5wlU+BDlkeZZmcAl4lIHfAI/tNMPyX0ch76VImqNuE/Z76c0HrP9wP7VXWd8/gJ/AUjlDKOdQnwjqo2Oo9DKecFQK2qNqvqMPAH/L+rIfd7aQXig8t9XI//nL9rRETwzzDfoaq3j9kVMjlFJFdEMpz7ifj7SHbgLxRXOM1c/1mq6j+parGqluE/3fCCqn6KEMspIskiknroPv5z51sJofdcVRuAfSIy39l0Pv6VDkIm4zjX8NfTSxBaOfcCK0Ukyfn/fuhnGVK/l0B0dVLj/4U5CAzj/0R0I/5z0s8Du4DngCyXM56J//D3PWCzc/toKOUETgI2ORm3At92ts/Gv2ZWNf5D+3i33/Mxmc8Fng7FnE6ed53bNuB/O9tD5j138iwBNjjv+1NAZqhldHIm41/0M33MtpDKCXwXqHT+/zwIxIfa76Wq2kxqY4wxgdkpJmOMMQFZgTDGGBOQFQhjjDEBWYEwxhgTkBUIY4wxAVmBMMYYE5AVCGOMMQFZgTBmCojIU85Ce9sOLbYnIjeKyE7n2hl3i8jPne25IvJ7EVnv3M5wN70xgdlEOWOmgIhkqWqbs/TIevzLN7+Of72ibuAF4F1VvUVEHgJ+oaqviUgp/mWdT3AtvDEfImbiJsaYSfiaiFzu3C8BrgNeVtU2ABF5HCh39l8ALPQvwwNAmoikqGrPdAY2ZiJWIIw5TiJyLv4/+qepap+IvIR/nZ0POyrwACtVdWB6EhpzbKwPwpjjlw60O8VhAf5LxSYD54hIprOE8yfHtP8z8NVDD0RkybSmNWaSrEAYc/yeAWJEZAfwH8Bb+Nfy/zf8q3O+jv+Sop1O+68BFSLynohsB7447YmNmQTrpDYmSA71KzhHEE8C96rqk27nMmay7AjCmOD5jnPd7q1ALf5rKBgTNuwIwhhjTEB2BGGMMSYgKxDGGGMCsgJhjDEmICsQxhhjArICYYwxJiArEMYYYwL6/9mA6nXyHdPJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# age\n",
        "sns.distplot(df['age'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "-C3HQqtzq0wK",
        "outputId": "1c11d0fa-739c-430b-ec64-1cb7d1a69a15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f8cd039c710>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.pairplot(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "NhdXrwCcq0wR",
        "outputId": "4ee55ed9-80bf-460f-a912-42ddff76481e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccdb98910>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#gender\n",
        "df['sex'].value_counts().plot(kind='bar',color={'black','green'},rot=45)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANJAN8g6q0wY"
      },
      "source": [
        "#### male gender is higher in the company"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NUFVz3AFq0wa",
        "outputId": "accf00e5-4f39-464d-d3a5-24e376de8efa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccdaf8d90>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uktQhw12SOmS4S1KHDHdJ6pDhLkkd+j9x+Hs2W6OGYgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#job\n",
        "df['job'].value_counts().plot(kind='bar',color={'black','green','red'},rot=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXppdAGFq0wl"
      },
      "source": [
        "#### most customers are skilled based on our analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZSNfo_s4q0wm",
        "outputId": "296dca94-1c1e-4527-e52e-412d1fd217c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccdb6bfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#housing\n",
        "df['housing'].value_counts().plot(kind='bar',color={'black','green','red'},rot=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBHdMItSq0wy"
      },
      "source": [
        "#### Most customers own their houses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WQeRJkmq0wz",
        "outputId": "57fe8c4c-57c5-4ac3-b034-fae7b53587f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccdb133d0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#puropse of loan\n",
        "plt.figure(figsize=(20,10))\n",
        "df['purpose'].value_counts().plot(kind='bar',color={'black','green','red'},rot=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06-mDdoKq0w2"
      },
      "source": [
        "#### based on our analysis most customers apply loans to buy cars"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUKjSfw3q0w2",
        "outputId": "0e848839-a825-44e5-fdaa-8b617a980aad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccc1defd0>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EuvuWJN90lOUfzOp+Qkcu/0ySp2zL6AAAAADYdhu6hxAAAAAAJz5BCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGCYYwahqjqnqt5cVe+tqlur6oeW5adV1fVV9YHl8SHL8qqqK6vqYFXdUlUX7PQvAQAAAMD6recMobuT/IvufmSSC5M8q6oemeSKJDd093lJbljmk+RJSc5bfvYnedm2jxoAAACATTtmEOruD3f3O5bpP0vyviRnJbk4ydXLZlcnuWSZvjjJq3vlrUlOraozt33kAAAAAGzKhu4hVFXnJvmmJG9LckZ3f3hZ9ZEkZyzTZyW5fc1udyzLAAAAADgOrDsIVdWDkvxakn/e3X+6dl13d5LeyAtX1f6qOlBVBw4dOrSRXQEAAADYgnUFoar64qxi0Gu6+9eXxR89fCnY8njXsvzOJOes2f3sZdk9dPdV3b2vu/ft3bt3s+MHAAAAYIPW8y1jleSVSd7X3T+5ZtW1SS5bpi9L8oY1y5+xfNvYhUk+uebSMgAAAAB22Z51bPO4JN+f5N1VdfOy7LlJXpjkdVV1eZLbkjx1WXddkicnOZjk00meua0jBgAAAGBLjhmEuvstSepeVl90lO07ybO2OC4AAAAAdsiGvmUMAAAAgBOfIAQAAAAwjCAEAAAAMIwgBAAAADCMIAQAAAAwjCAEAAAAMIwgBAAAADCMIAQAAAAwjCAEAAAAMIwgBAAAADDMnt0eAMBOqardHgJb0N27PQQAADhpOUMIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgGEEIAAAAYBhBCAAAAGAYQQgAAABgmGMGoar6+aq6q6res2bZaVV1fVV9YHl8yLK8qurKqjpYVbdU1QU7OXgAAAAANm49Zwi9Ksl3HrHsiiQ3dPd5SW5Y5pPkSUnOW372J3nZ9gwTADhhVPk5kX8AgBGOGYS6+3eT/MkRiy9OcvUyfXWSS9Ysf3WvvDXJqVV15nYNFgAAAICt2+w9hM7o7g8v0x9JcsYyfVaS29dsd8eyDAAAAIDjxJZvKt3dnaQ3ul9V7a+qA1V14NChQ1sdBgAAAADrtNkg9NHDl4Itj3cty+9Mcs6a7c5eln2B7r6qu/d19769e/duchgAAAAAbNRmg9C1SS5bpi9L8oY1y5+xfNvYhUk+uebSMgAAAACOA3uOtUFV/XKSJyQ5varuSPK8JC9M8rqqujzJbUmeumx+XZInJzmY5NNJnrkDYwYAAABgC44ZhLr7e+9l1UVH2baTPGurgwIAAABg52z5ptIAAAAAnFgEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYQQhAAAAgGEEIQAAAIBhBCEAAACAYfbs9gAAAICtqxfUbg+BLejn9W4PARjGGUIAAAAAwwhCAAAAAMMIQgAAAADDCEIAAAAAwwhCAAAAAMMIQgAAAADDCEIAAAAAwwhCAAAAAMMIQgAAAADDCEIAAAAAwwhCAAAAAMMIQgAAAADD7NntAQAAAMCJrKp2ewhsUnfv9hB2jTOEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhhGEAAAAAIYRhAAAAACGEYQAAAAAhtmRIFRV31lV76+qg1V1xU68BgAAAACbs+1BqKpOSfIzSZ6U5JFJvreqHrndrwMAAADA5uzEGUKPSXKwuz/Y3X+Z5FeSXLwDrwMAAADAJuxEEDorye1r5u9YlgEAAABwHNizWy9cVfuT7F9mP1VV79+tsbBlpyf52G4PYifU82u3hwD35aQ99pKkyvHHceukPvbi2OP4dVIfez53chw7uY+9k/9972vubcVOBKE7k5yzZv7sZdk9dPdVSa7agdfnflZVB7p7326PA6Zx7MHucOzB7nDswe5w7J28duKSsbcnOa+qHl5VD0jytCTX7sDrAAAAALAJ236GUHffXVX/LMkbk5yS5Oe7+9btfh0AAAAANmdH7iHU3dcluW4nnpvjkkv/YHc49mB3OPZgdzj2YHc49k5S1d27PQYAAAAA7kc7cQ8hAAAAAI5jghBHVVWfWh6/uqquWabPr6onr9nmCVX1N9fMP7+qnnP/jxZODlXVVfWSNfPPqarn7+KQgCRV9aGqOn2bnusHquqrt+O54GRQVfuq6spl+h6fLTf5fH/12fU+ttm2YxpORlV1XVWdeh/rX1VVl96fY2JnCELcp+7+P919+GA/P8mT16x+QpItvWkD9/AXSb7bh1Q4sVXVKfex+geSCEKw6O4D3f2Dy+wTsoXPllW154jPrsAGVVUl+a7u/sRuj4WdJwhxn6rq3Kp6T1U9IMm/SfI9VXVzVf1Ykn+S5IeX+W89Yr+/VlW/VVU3VdXvVdXX7cb44QRzd1Y37fvhI1csx+KNVXVLVd1QVQ9blr+qqq6sqt+vqg+u/WtNVf3Lqnr7ss8L7r9fA3bfcsz84XKM/M+qek1VfXtV/feq+kBVPaaqTquq31iOkbdW1Tcu+z60qt5UVbdW1SuS1Jrn/YdV9QfLe9/PHY4/VfWpqnpJVb0ryWOr6ieW4+89VXVVrVyaZF+S1yz7P7CqHl1Vv7O8X76xqs7cjf+9YLtU1b9ajrm3VNUvHz57vKp+u6r2LdOnV9WHluknVNVvVtW5OeKzZVXtrapfW46lt1fV447yej9QVddW1Y1Jbjj82XVZd0pVvXg5Dm+pqmev2fXZVfWOqnq3z6lMtxw376+qVyd5T5LPHv4DZVU9Yzl+3lVVv7hmt8cf7fMnJxZBiHXp7r9M8hNJXtvd53f3i5K8PMlLl/nfO2KXq5I8u7sfneQ5SX72/h0xnLB+JsnTq+rBRyz/j0mu7u5vTPKaJFeuWXdmkm9J8l1JXpgkVfUdSc5L8piszu57dFU9fofHDsebRyR5SZKvW36+L6tj5TlJnpvkBUneuRxXz03y6mW/5yV5S3d/fZLXJzkcYP96ku9J8rjuPj/JZ5M8fdnny5O8rbsf1d1vSfKfuvubu/sbkjwwq7+2XpPkQJKnL/vfndWxfenyfvnzSf7djv2vATusqh6d5Gn5/Fnl37zefbv7Q/nCz5Y/vcx/c5J/kOQV97L7BVkdR3/riOX7k5yb5Pw175+Hfay7L0jysqz+mwDTnZfkZ5f3vtuSpKq+Psm/TvLE7n5Ukh9as/0XfP7kxLMjXzvPbFX1oKxO9/3V1RmHSZIv2b0RwYmju/90+evMDyb5v2tWPTbJdy/Tv5jkP6xZ9xvd/bkk762qM5Zl37H8vHOZf1BWb/S/u1Njh+PQH3X3u5Okqm5NckN3d1W9O6t/JH5NVv/ITHffuJwZ9JVJHp/leOvu/1pVH1+e76Ikj07y9uX97YFJ7lrWfTbJr6157W+rqh9N8mVJTktya5L/csT4vjbJNyS5fnm+U5J8eHt+ddgV35rk9d396SSpqmu3+HzfnuSRaz5PfmVVPai7P3XEdtd395/cy/4v7+67k+SIbX59ebwpn39/hclu6+63HrHsiUl+tbs/lnzBMXS0z5+cYAQhdsIXJfnE8tdPYON+Ksk7kvzCOrf/izXTtebx33f3z23nwOAEs/bY+Nya+c9l9Rno/23w+SqrM/V+/CjrPtPdn02SqvrSrM6M3dfdt9fq5vBfei/Pd2t3P3aD44AT0d35/NUJRzsejuaLklzY3Z85xnZ/vonxHP7vwWfj30SQbPw4OtrnT04wLhljI/4syVfcx3yS1RkOSf6oqp6SrG5MVlWPun+GCCe+5a8vr0ty+ZrFv5/VafjJ6hKVIy/TPNIbk/yj5Yy9VNVZVfVV2z1WOMH9XpZLvqrqCVldQvKnWZ1J933L8icleciy/Q1JLj18LC33IPqaozzv4X/sfmw5BtfeW2Hte+f7k+ytqscuz/fFy+n5cKL63SSXLPfH+ookf3fNug9ldYZdcs9jYq0jP1u+Kclf3fenqjb6x8brk/zjqtqz7H/aBveH6W5M8pSqemjiGDoZCUJsxJuzOm335qr6nqxOff/7dZSbSmf1Afvy5eaatya5+H4eK5zoXpJk7beNPTvJM6vqliTfn3tew/0FuvtNSX4pyf9YLo+5JkcJuDDc87O6v9YtWd3/4LJl+QuyulnmrVldSvK/k6S735vVvRTetOxzfVb3ULiH5ZtZ/nNWN+Z8Y5K3r1n9qiQvr6qbs7pE7NIkL1reL2+Ob+/kBNbd70jy2iTvSvLfcs//7784yT+tqnfmnu9vax352fIHk+xbbmj73qxuOr0Rr8jq+L1lOca+b4P7w2jdfWtW97b7neUY+sldHhLbrLp7t8cAAACcZJbLJT/V3S/e7bEA8IWcIQQAAAAwjDOEAAAAAIZxhhAAAADAMIIQAAAAwDCCEAAAAMAwghAAAADAMIIQAAAAwDCCEAAAAMAw/x+eDtVqANL8WwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#saving accounts\n",
        "plt.figure(figsize=(20,10))\n",
        "df['saving_accounts'].value_counts().plot(kind='bar',color={'black','green','red'},rot=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiIWtyMgq0w8",
        "outputId": "8382a087-517b-4a92-8fa2-0d80f03e6fed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccc128150>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#duration of loan payment\n",
        "sns.distplot(df['duration'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tu-6SUbvq0w_"
      },
      "source": [
        "### most customers like to pay between 13-20 months"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvZJ7tnoq0xA",
        "outputId": "39b1a690-66ab-4081-b977-807733328a1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccdb71790>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#checking accounts\n",
        "plt.figure(figsize=(20,10))\n",
        "df['checking_account'].value_counts().plot(kind='bar',color={'black','green','red'},rot=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rxO0IPH6q0xD"
      },
      "source": [
        "#### Those who dont have account account for higher percentage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qAmj3hzq0xD",
        "outputId": "f2720e1f-a2a0-4fc0-eff9-25ae0d580495"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccbfee810>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['credit_amount'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uD0fEK2mq0xG"
      },
      "source": [
        "## Bivarate Analysis With Target Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "XCQHiJDqq0xG",
        "outputId": "1d5919c8-4d3d-4be7-a59f-75b345ef908c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccbf2c2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#sex and risk\n",
        "sns.countplot(df['sex'],hue='risk',data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3Ch7bcAq0xJ"
      },
      "source": [
        "#### obs:we cant say which gender will default or not"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOgYRmWoq0xK",
        "outputId": "1e1cf84e-8709-4847-9428-a74fcd42e22f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccbeb4190>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#job and risk\n",
        "sns.countplot(df['job'],hue='risk',data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDZc352uq0xN"
      },
      "source": [
        "#### skilled people are more likely to pay loan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6cVQNI0q0xN",
        "outputId": "41a7540b-bbb2-477e-b67a-4f47011abeef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccdb8a750>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#purpose and risk\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(df['purpose'],hue='risk',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjPFaiGSq0xR",
        "outputId": "3fb13d04-f2e7-4c41-c778-be7fea875a29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccbdd7d50>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#age and risk\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(df['age'],hue='risk',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nXQEgUhq0xa",
        "outputId": "a61a339e-f6d9-4181-98ef-cac7fad2ee29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccbf19bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#job\thousing\tsaving accounts\tchecking account\tcredit amount\tdurat\n",
        "#housing and risk\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(df['housing'],hue='risk',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2zGJWwh5q0xk",
        "outputId": "f32fca43-c5f5-4cd9-d1f0-5c8e041dd287"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccbccdc50>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#checking account and risk\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(df['checking_account'],hue='risk',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kzck7gogq0xn",
        "outputId": "e27c8fe3-5c86-42ff-927f-2f035e1eb9a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ccbd0e150>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#savinging account and risk\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(df['saving_accounts'],hue='risk',data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SatgVi-uq0xs"
      },
      "source": [
        "#### OBSV: based on the analysis  customers with  little savings account has highe risk of defaulting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjqjHVAyq0xs",
        "outputId": "1f2850ed-b7df-4688-fd9e-3c322e852778"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "car                    337\n",
              "radio/TV               280\n",
              "furniture/equipment    181\n",
              "business                97\n",
              "education               59\n",
              "repairs                 22\n",
              "domestic appliances     12\n",
              "vacation/others         12\n",
              "Name: purpose, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "df.purpose.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQGd71kWq0x0"
      },
      "source": [
        "### Preprocessing the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mb0drOalq0x1"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
        "# LabelBinarizer converts the string categorical variable to binary \n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "lb= LabelBinarizer()\n",
        "df[\"risk\"]= lb.fit_transform(df[\"risk\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6Bzo7Vlq0x4"
      },
      "source": [
        " In order to prepare our dataset for our model, we will perform binning on the numerical variables i.e. duration, age and credit amount variables. Once we perform binning, we will later perform one hot encoding to all the categorical variables within our dataset.\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "imsmYFgdq0x4"
      },
      "outputs": [],
      "source": [
        "# performing binning on duration \n",
        "# we bin by specifying 6 bins\n",
        "df[\"duration\"] = pd.qcut(df.duration, q = 6)\n",
        "# performing binning on age\n",
        "# we bin by defining the intervals and categories\n",
        "interval = (18, 25, 35, 60, 120)\n",
        "categories = ['student', 'youth', 'adult', 'senior']\n",
        "df[\"age\"] = pd.cut(df.age, interval, labels = categories)\n",
        "# performing binning on credit_amount\n",
        "# this time we bin without specifying i.e. we create 6 bins\n",
        "df[\"credit_amount\"] = pd.qcut(df.credit_amount, q = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0OAds8KXq0x8"
      },
      "outputs": [],
      "source": [
        "#converting categorical to numbers using oner-hot encoding\n",
        "#male=1,Female=0\n",
        "#df['sex']=df['sex'].map({'male':1,'female':0})\n",
        "\n",
        "#housing :own=0,free=1,rent=2\n",
        "#df['housing']=df['housing'].map({'free':1,'own':0,'rent':2})\n",
        "# svings account litte=1,moderate=2,None=0,rich=3,quiterich=4\n",
        "#df['saving accounts']=df['saving accounts'].map({'little':1,'None':0,'moderate':2,'rich':3,'quite rich':4})\n",
        "# checking account litte=1,moderate=2,None=0,rich=3\n",
        "#df['checking account']=df['checking account'].map({'little':1,'None':0,'moderate':2,'rich':3})\n",
        "#purpose\n",
        "# performing one hot encoding to the purpose variable\n",
        "df = df.merge(pd.get_dummies(df.job, drop_first=True, prefix='job'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to the purpose variable\n",
        "df = df.merge(pd.get_dummies(df.purpose, drop_first=True, prefix='purpose'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to the sex variable\n",
        "df = df.merge(pd.get_dummies(df.sex, drop_first=True, prefix='sex'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to the housing variable\n",
        "df = df.merge(pd.get_dummies(df.housing, drop_first=True, prefix='housing'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to the savings account variable\n",
        "df = df.merge(pd.get_dummies(df.saving_accounts, drop_first=True, prefix='savings'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to checking account variable\n",
        "df = df.merge(pd.get_dummies(df.checking_account, drop_first=True, prefix='check'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to age variable\n",
        "df = df.merge(pd.get_dummies(df.age, drop_first=True, prefix='age'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to age variable\n",
        "df = df.merge(pd.get_dummies(df.duration, drop_first=True, prefix='duration'), left_index=True, right_index=True)\n",
        "# performing one hot encoding to credit amount variable\n",
        "df = df.merge(pd.get_dummies(df.credit_amount, drop_first=True, prefix='credit_amount'), left_index=True, right_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uxYW3aJyq0x-"
      },
      "outputs": [],
      "source": [
        "# we then cxcluding the missing columns since we have new variables for use\n",
        "del df[\"job\"]\n",
        "del df[\"purpose\"]\n",
        "del df[\"sex\"]\n",
        "del df[\"housing\"]\n",
        "del df[\"saving_accounts\"]\n",
        "del df[\"checking_account\"]\n",
        "del df[\"age\"]\n",
        "del df[\"duration\"]\n",
        "del df[\"credit_amount\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "byha6r2Jq0yB",
        "outputId": "1ec6d943-fab4-4a47-a49a-ab91b5d330cd"
      },
      "outputs": [
        {
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          "data": {
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              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>risk</th>\n",
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              "      <th>job_2</th>\n",
              "      <th>job_3</th>\n",
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              "      <th>savings_quite rich</th>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d343c11-27a1-4a3a-8f2c-f8be351717e6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-5d343c11-27a1-4a3a-8f2c-f8be351717e6');\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",
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              "  "
            ],
            "text/plain": [
              "   risk  job_1  ...  credit_amount_(2852.4, 4720.0]  credit_amount_(4720.0, 18424.0]\n",
              "0     1      0  ...                               0                                0\n",
              "1     0      0  ...                               0                                1\n",
              "2     1      1  ...                               0                                0\n",
              "3     1      0  ...                               0                                1\n",
              "4     0      0  ...                               0                                1\n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aqQr9FQOq0yF"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KAmOBt-kq0yI"
      },
      "outputs": [],
      "source": [
        "#scaler=StandardScaler()\n",
        "#df=scaler.fit_transform(df)\n",
        "   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ptmow7XOq0yQ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "riyK-2EHq0yV"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CLftGYOq0yb"
      },
      "source": [
        "## 4.MODELING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9P363sg0q0yc",
        "outputId": "bb91f511-7103-49c4-c4d7-1a6ccb2c8b44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([700, 700])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "#predictor variables\n",
        "X=df.drop(columns=['risk'],axis=1)\n",
        "#target variable\n",
        "y=df.risk\n",
        "# #splitting data\n",
        "# X_train,X_test,y_train,y_test=train_test_split(X,y,stratify=y,random_state=0,test_size=0.2)\n",
        "\n",
        "# creating a balanced dataset\n",
        "from imblearn.over_sampling import SMOTE\n",
        "smt=SMOTE()\n",
        "X,y=smt.fit_resample(X,y)\n",
        "\n",
        "# we check the amount of records in each category\n",
        "np.bincount(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POu86eeXq0yj"
      },
      "source": [
        "#### logistic regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x_GSFWTot8KG"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class LogisticRegression:\n",
        "    def __init__(self,x,y):      \n",
        "        self.intercept = np.ones((x.shape[0], 1))  \n",
        "        self.x = np.concatenate((self.intercept, x), axis=1)\n",
        "        self.weight = np.zeros(self.x.shape[1])\n",
        "        self.y = y\n",
        "        \n",
        "    #Sigmoid method\n",
        "    def sigmoid(self, x, weight):\n",
        "        z = np.dot(x, weight)\n",
        "        return 1 / (1 + np.exp(-z))\n",
        "    \n",
        "    #method to calculate the Loss\n",
        "    def loss(self, h, y):\n",
        "        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()\n",
        "    \n",
        "    #Method for calculating the gradients\n",
        "    def gradient_descent(self, X, h, y):\n",
        "        return np.dot(X.T, (h - y)) / y.shape[0]\n",
        "\n",
        "    \n",
        "    def fit(self, lr , iterations):\n",
        "        for i in range(iterations):\n",
        "            sigma = self.sigmoid(self.x, self.weight)\n",
        "            \n",
        "            loss = self.loss(sigma,self.y)\n",
        "\n",
        "            dW = self.gradient_descent(self.x , sigma, self.y)\n",
        "            \n",
        "            #Updating the weights\n",
        "            self.weight -= lr * dW\n",
        "\n",
        "        return print('fitted successfully to dataset')\n",
        "    \n",
        "    #Method to predict the class label.\n",
        "    def predict(self, x_new , treshold):\n",
        "        x_new = np.concatenate((self.intercept, x_new), axis=1)\n",
        "        result = self.sigmoid(x_new, self.weight)\n",
        "        result = result >= treshold\n",
        "        y_pred = np.zeros(result.shape[0])\n",
        "        for i in range(len(y_pred)):\n",
        "            if result[i] == True: \n",
        "                y_pred[i] = 1\n",
        "            else:\n",
        "                continue\n",
        "                \n",
        "        return y_pred"
      ],
      "metadata": {
        "id": "7YmYv1LlOGN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the class Object\n",
        "regressor = LogisticRegression(X,y)\n",
        "\n",
        "#\n",
        "regressor.fit(0.1 , 5000)\n",
        "\n",
        "\n",
        "y_pred = regressor.predict(X,0.5)\n",
        "\n",
        "print('accuracy -> {}'.format(sum(y_pred == y) / y.shape[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRWTR-DyOGLp",
        "outputId": "17c2831f-a917-4276-a2f8-4127695348c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fitted successfully to data\n",
            "accuracy -> 0.7985714285714286\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Credit_Scoring_Analysis Using Logistic Regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}