Prediction_of_Bank_Churn_Customer.ipynb

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    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "a034680e-2174-47da-8f8e-cec3fc25a0c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##                                    **PROJECT - ClASSIFIACTION -Prediction of Bank Churn Customer**"
      ],
      "metadata": {
        "id": "wiS-qRuIfO1h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Project Description:"
      ],
      "metadata": {
        "id": "akWKvqJ67Ps2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The aim of this project is to develop a machine learning model that can predict whether a bank customer is likely to churn or not. Churn is a term used in the banking industry to describe the situation where a customer closes their account or stops using a particular bank's services. This project will focus on developing a classification model that can predict whether a customer is likely to churn based on various factors such as demographics, transactional data, and other relevant information**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RWv5CyV94znr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*The first step in this project would be to gather the necessary data. This data can be obtained from the bank's database or from external sources such as customer surveys or other market research. Once the data has been collected, it will need to be pre-processed and cleaned to remove any missing or irrelevant information.*"
      ],
      "metadata": {
        "id": "FePABj0J62fn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Next, the data will be divided into two sets: a training set and a testing set. The training set will be used to train the machine learning model, while the testing set will be used to evaluate its performance.*\n",
        "\n"
      ],
      "metadata": {
        "id": "bIHlyVyS5JUU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Various machine learning algorithms can be used to develop the classification model, such as logistic regression, decision trees, random forests, or support vector machines. The performance of the model can be evaluated using various metrics such as accuracy, precision, recall, and F1 score.*\n"
      ],
      "metadata": {
        "id": "YoJR48Cf7IUv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Once the model has been trained and evaluated, it can be used to predict whether a new customer is likely to churn or not. This information can be used by the bank to take appropriate measures such as offering special promotions or incentives to retain customers who are at risk of churning.*\n"
      ],
      "metadata": {
        "id": "rzsW39oj67II"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "***Overall, the goal of this project is to develop a machine learning model that can help banks to identify customers who are likely to churn and take appropriate measures to retain them.***"
      ],
      "metadata": {
        "id": "v7V6UEAL67LT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the Liberies**"
      ],
      "metadata": {
        "id": "hmhBuYDQe1PY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qILb1s-y4eAA"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sb\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import pyplot\n",
        "from sklearn import preprocessing\n",
        "plt.rc(\"font\", size=14)\n",
        "from sklearn.preprocessing import MinMaxScaler,StandardScaler,FunctionTransformer\n",
        "sb.set(style=\"white\") #white background style for seaborn plots\n",
        "sb.set(style=\"whitegrid\", color_codes=True)\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j3DLwYouh-cT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#     **Read the  Data**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xRB0VuH0h_BK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/Mini project of CLASSOFICATION  ML DATA /DS3_C6_S2_Classification_BankChurn_Data_Project.csv')"
      ],
      "metadata": {
        "id": "DQL-QqJp7F-a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8jcTIFf29WKs",
        "outputId": "1c30575a-b3a9-426a-98b6-277d0c541239"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerId   Surname  CreditScore Geography  Gender  Age  Tenure  \\\n",
              "0    15634602  Hargrave          619    France  Female   42       2   \n",
              "1    15647311      Hill          608     Spain  Female   41       1   \n",
              "2    15619304      Onio          502    France  Female   42       8   \n",
              "3    15701354      Boni          699    France  Female   39       1   \n",
              "4    15737888  Mitchell          850     Spain  Female   43       2   \n",
              "\n",
              "     Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
              "0       0.00              1          1               1        101348.88   \n",
              "1   83807.86              1          0               1        112542.58   \n",
              "2  159660.80              3          1               0        113931.57   \n",
              "3       0.00              2          0               0         93826.63   \n",
              "4  125510.82              1          1               1         79084.10   \n",
              "\n",
              "   Exited  \n",
              "0       1  \n",
              "1       0  \n",
              "2       1  \n",
              "3       0  \n",
              "4       0  "
            ],
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          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Size of the Data**"
      ],
      "metadata": {
        "id": "5HinsMxLiSYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kWXX8FRYvi0",
        "outputId": "084d6ad0-a072-482c-b88a-c7fad92b1036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**here we can see Geography there are only three countries names to be present remaining we have to relplace with  the orginla name**"
      ],
      "metadata": {
        "id": "EMF3d255aV6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Geography'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaJemPXFZS_F",
        "outputId": "a664a18a-e697-4ddd-90ff-2f1ad7194fca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['France', 'Spain', 'espanio', 'fra', 'Germany', 'france', 'ger',\n",
              "       'spain', 'germany', 'gernamy', 'GERMANY'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Geography']=df['Geography'].replace(['fra'],'France')\n",
        "df['Geography']=df['Geography'].replace(['espanio'],'Spain')\n",
        "df['Geography']=df['Geography'].replace(['ger'],'Germany')\n",
        "df['Geography']=df['Geography'].replace(['spain'],'Spain')\n",
        "df['Geography']=df['Geography'].replace(['germany'],'Germany')\n",
        "df['Geography']=df['Geography'].replace(['gernamy'],'Germany')\n",
        "df['Geography']=df['Geography'].replace(['GERMANY'],'Germany')\n",
        "df['Geography']=df['Geography'].replace(['france'],'France')"
      ],
      "metadata": {
        "id": "KlgD5xiVYC2o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Geography'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0gpnDULaFdU",
        "outputId": "6710391c-30a7-400b-9516-43cc819e26ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['France', 'Spain', 'Germany'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we can remove the CustomerId  there is no need in use"
      ],
      "metadata": {
        "id": "liF8gv3l-i64"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns='CustomerId',inplace=True,axis=1)"
      ],
      "metadata": {
        "id": "0_Qze3Gc-Y8R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJM8LcXLa19y",
        "outputId": "a4523b99-efb4-47c6-e3d0-966e53300073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Surname', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',\n",
              "       'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember',\n",
              "       'EstimatedSalary', 'Exited'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Surname'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbVMb_Eoa2BS",
        "outputId": "66976d92-12c4-47f7-f0cc-cb7411e303ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Hargrave', 'Hill', 'Onio', ..., 'Kashiwagi', 'Aldridge',\n",
              "       'Burbidge'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHDh1sCBa2Eo",
        "outputId": "cf57d5e0-4017-45d2-e0a1-84b571da530b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Female', 'Male'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7yletJjbY4P",
        "outputId": "d1371d39-43a2-492c-de6c-861c7685be12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arwZkwjFa2HP",
        "outputId": "245efa9a-0c6b-4e52-b108-e411fcaece95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([42, 41, 39, 43, 44, 50, 29, 27, 31, 24, 34, 25, 35, 45, 58, 32, 38,\n",
              "       46, 36, 33, 40, 51, 61, 49, 37, 19, 66, 56, 26, 21, 55, 75, 22, 30,\n",
              "       28, 65, 48, 52, 57, 73, 47, 54, 72, 20, 67, 79, 62, 53, 80, 59, 68,\n",
              "       23, 60, 70, 63, 64, 18, 82, 69, 74, 71, 76, 77, 88, 85, 84, 78, 81,\n",
              "       92, 83])"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Tenure'].unique()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "qzUdxYXGa2K4",
        "outputId": "2f4ee8af-8320-43ed-a85a-1c5fd492792f"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  1,  8,  7,  4,  6,  3, 10,  5,  9,  0])"
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['NumOfProducts'].unique()"
      ],
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        "colab": {
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        "id": "JvDCAWdhb8gv",
        "outputId": "5b8d86f7-dd7d-4186-9c44-347ebbbfaaec"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 2, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['HasCrCard'].unique()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "0f2ILYKqb8kJ",
        "outputId": "327fc700-b559-4cd1-ab6a-971650c4444e"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
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    {
      "cell_type": "code",
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        "df['IsActiveMember'].unique()"
      ],
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        "colab": {
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        },
        "id": "d-9CoaSAb8oR",
        "outputId": "8dbc776f-e603-4a57-e79f-2bb00a767533"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Exited'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCOzN7_Xa1Vb",
        "outputId": "f6bd628c-dad7-42fa-b870-68f1bdb6b95a"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Missing Values Treatment**"
      ],
      "metadata": {
        "id": "F88CDuQM9Wmi"
      }
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    {
      "cell_type": "code",
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      ],
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          "height": 49
        },
        "id": "HOFIQmc-8k27",
        "outputId": "40eaf5fc-c005-49c3-f1d0-65bbaaa840d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Surname, CreditScore, Geography, Gender, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, Exited]\n",
              "Index: []"
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        "#check all missing values\n",
        "def cal_missing_value(df):\n",
        "    total=df.isnull().sum().sort_values(ascending=False)\n",
        "    percentage=(df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
        "    missing_data=pd.concat([total,percentage], axis=1, keys=['Total','Percentage'])\n",
        "    return missing_data\n",
        "cal_missing_value(df)\n"
      ],
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          "height": 426
        },
        "id": "fSn1zk_l-3rs",
        "outputId": "d51a8d8b-ad86-4fdd-c38a-fe2d0a53e625"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Total  Percentage\n",
              "Surname              0         0.0\n",
              "CreditScore          0         0.0\n",
              "Geography            0         0.0\n",
              "Gender               0         0.0\n",
              "Age                  0         0.0\n",
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              "NumOfProducts        0         0.0\n",
              "HasCrCard            0         0.0\n",
              "IsActiveMember       0         0.0\n",
              "EstimatedSalary      0         0.0\n",
              "Exited               0         0.0"
            ],
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              "\n",
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              "    <tr>\n",
              "      <th>Exited</th>\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-051387bd-ce49-4c5e-b420-a64dd99e22dd');\n",
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              "            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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We can see above There is no missing values in the data**"
      ],
      "metadata": {
        "id": "UBEjeSLO9grZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X_ZybSMR-Ftz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Checking the Noise in the Data**\n",
        "here we are converting the data in two parts for checking the\n",
        "\n",
        "1 is numerical data\n",
        "\n",
        "2 categorical data"
      ],
      "metadata": {
        "id": "0Z443vsK--13"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data=df.select_dtypes(include=[np.number])\n",
        "numeric_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BA98OuKj_Er3",
        "outputId": "1908f948-d646-47a2-db23-d958e4b00e8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0             619   42       2       0.00              1          1   \n",
              "1             608   41       1   83807.86              1          0   \n",
              "2             502   42       8  159660.80              3          1   \n",
              "3             699   39       1       0.00              2          0   \n",
              "4             850   43       2  125510.82              1          1   \n",
              "...           ...  ...     ...        ...            ...        ...   \n",
              "9995          771   39       5       0.00              2          1   \n",
              "9996          516   35      10   57369.61              1          1   \n",
              "9997          709   36       7       0.00              1          0   \n",
              "9998          772   42       3   75075.31              2          1   \n",
              "9999          792   28       4  130142.79              1          1   \n",
              "\n",
              "      IsActiveMember  EstimatedSalary  Exited  \n",
              "0                  1        101348.88       1  \n",
              "1                  1        112542.58       0  \n",
              "2                  0        113931.57       1  \n",
              "3                  0         93826.63       0  \n",
              "4                  1         79084.10       0  \n",
              "...              ...              ...     ...  \n",
              "9995               0         96270.64       0  \n",
              "9996               1        101699.77       0  \n",
              "9997               1         42085.58       1  \n",
              "9998               0         92888.52       1  \n",
              "9999               0         38190.78       0  \n",
              "\n",
              "[10000 rows x 9 columns]"
            ],
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              "  <tbody>\n",
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              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>43</td>\n",
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              "      <td>125510.82</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>35</td>\n",
              "      <td>10</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>772</td>\n",
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              "      <td>3</td>\n",
              "      <td>75075.31</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>792</td>\n",
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              "      <td>4</td>\n",
              "      <td>130142.79</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38190.78</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "<p>10000 rows × 9 columns</p>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f9b8201d-ca75-480f-a089-f257c6db5b41');\n",
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              "            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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# function \n",
        "for cols in list(numeric_data.columns):\n",
        "  print(df[cols].value_counts())\n",
        "  print(\"-----------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc3tNZo-_Hgu",
        "outputId": "186622c6-65fd-40a8-be6d-a3bb1ab509b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "850    233\n",
            "678     63\n",
            "655     54\n",
            "705     53\n",
            "667     53\n",
            "      ... \n",
            "404      1\n",
            "351      1\n",
            "365      1\n",
            "417      1\n",
            "419      1\n",
            "Name: CreditScore, Length: 460, dtype: int64\n",
            "-----------------------------------\n",
            "37    478\n",
            "38    477\n",
            "35    474\n",
            "36    456\n",
            "34    447\n",
            "     ... \n",
            "92      2\n",
            "82      1\n",
            "88      1\n",
            "85      1\n",
            "83      1\n",
            "Name: Age, Length: 70, dtype: int64\n",
            "-----------------------------------\n",
            "2     1048\n",
            "1     1035\n",
            "7     1028\n",
            "8     1025\n",
            "5     1012\n",
            "3     1009\n",
            "4      989\n",
            "9      984\n",
            "6      967\n",
            "10     490\n",
            "0      413\n",
            "Name: Tenure, dtype: int64\n",
            "-----------------------------------\n",
            "0.00         3617\n",
            "130170.82       2\n",
            "105473.74       2\n",
            "85304.27        1\n",
            "159397.75       1\n",
            "             ... \n",
            "81556.89        1\n",
            "112687.69       1\n",
            "108698.96       1\n",
            "238387.56       1\n",
            "130142.79       1\n",
            "Name: Balance, Length: 6382, dtype: int64\n",
            "-----------------------------------\n",
            "1    5084\n",
            "2    4590\n",
            "3     266\n",
            "4      60\n",
            "Name: NumOfProducts, dtype: int64\n",
            "-----------------------------------\n",
            "1    7055\n",
            "0    2945\n",
            "Name: HasCrCard, dtype: int64\n",
            "-----------------------------------\n",
            "1    5151\n",
            "0    4849\n",
            "Name: IsActiveMember, dtype: int64\n",
            "-----------------------------------\n",
            "24924.92     2\n",
            "101348.88    1\n",
            "55313.44     1\n",
            "72500.68     1\n",
            "182692.80    1\n",
            "            ..\n",
            "120893.07    1\n",
            "188377.21    1\n",
            "55902.93     1\n",
            "4523.74      1\n",
            "38190.78     1\n",
            "Name: EstimatedSalary, Length: 9999, dtype: int64\n",
            "-----------------------------------\n",
            "0    7963\n",
            "1    2037\n",
            "Name: Exited, dtype: int64\n",
            "-----------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**we can see there is no Noice in the Numerical_data** "
      ],
      "metadata": {
        "id": "sp40GIj9_Szf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_data=df.select_dtypes(exclude=[np.number])\n",
        "categorical_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2QsIie8d_Hkp",
        "outputId": "321e3a32-8fa4-4fb2-d73a-e8bc905a5c93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Surname Geography  Gender\n",
              "0      Hargrave    France  Female\n",
              "1          Hill     Spain  Female\n",
              "2          Onio    France  Female\n",
              "3          Boni    France  Female\n",
              "4      Mitchell     Spain  Female\n",
              "...         ...       ...     ...\n",
              "9995   Obijiaku    France    Male\n",
              "9996  Johnstone    France    Male\n",
              "9997        Liu    France  Female\n",
              "9998  Sabbatini   Germany    Male\n",
              "9999     Walker    France  Female\n",
              "\n",
              "[10000 rows x 3 columns]"
            ],
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              "      <div>\n",
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              "      <td>Female</td>\n",
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              "      <th>9998</th>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
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              "      <td>France</td>\n",
              "      <td>Female</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 3 columns</p>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-7661ae02-ca9c-4431-ad3c-04cb5503143d 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-7661ae02-ca9c-4431-ad3c-04cb5503143d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# function \n",
        "for cols in list(categorical_data.columns):\n",
        "  print(df[cols].value_counts())\n",
        "  print(\"-----------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2nZkBiv_HoH",
        "outputId": "80b33790-6961-49a1-cccf-866187d86f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Smith       32\n",
            "Scott       29\n",
            "Martin      29\n",
            "Walker      28\n",
            "Brown       26\n",
            "            ..\n",
            "Izmailov     1\n",
            "Bold         1\n",
            "Bonham       1\n",
            "Poninski     1\n",
            "Burbidge     1\n",
            "Name: Surname, Length: 2932, dtype: int64\n",
            "-----------------------------------\n",
            "France     5014\n",
            "Germany    2509\n",
            "Spain      2477\n",
            "Name: Geography, dtype: int64\n",
            "-----------------------------------\n",
            "Male      5457\n",
            "Female    4543\n",
            "Name: Gender, dtype: int64\n",
            "-----------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**There is No Noice in the Categorical data**"
      ],
      "metadata": {
        "id": "TavW5Bo1_hb_"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZraoiDpLnuj0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratry data analysis**"
      ],
      "metadata": {
        "id": "iKT7QJ1UbTPM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visulization"
      ],
      "metadata": {
        "id": "DvuNGCiNnvCZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KBQAS0uO3AcX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,9))\n",
        "plt.subplot(241)\n",
        "sb.countplot(data=df,x='HasCrCard')\n",
        "plt.subplot(242)\n",
        "sb.countplot(data=df,x='Gender')\n",
        "plt.subplot(243)\n",
        "sb.countplot(data=df,x='Tenure')\n",
        "plt.subplot(244)\n",
        "sb.countplot(data=df,x='NumOfProducts')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "MCPm4MjrscQn",
        "outputId": "426c3a83-5393-4e55-f025-a8e03ca94832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 4 Axes>"
            ],
            "image/png": 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jq4VofNFPMOXiq36CKRdf9eOrXM626A6A4MbCWAAaWbBggfr166cdO3ZIksrKypSWlqbk5GSNGTNGNTU1rvt6GgPgOz169NDevXv19ddfS5J27typmpoaXXjhhYqLi1NxcbEkqbi4WHFxcYqMjFRUVNQZYwAAtHUUwQBcPvvsM5WVlSkmJkaS5HQ6NWXKFGVnZ6ukpER2u10FBQVexQD41gUXXKAZM2Zo0qRJSktL04MPPqjHH39cVqtVM2bM0CuvvKLk5GS98sormjlzpqvd2WIAALRlTIcGIElqaGhQbm6unnjiCY0ePVrSiev9IiIiZLfbJUkZGRlKSkrS3LlzPY4B8L20tDSlpaWdtj02NlYrV65sss3ZYgAAtGWcCQYgSZo/f77S0tLUq1cv17aqqir17NnTdTsyMlJOp1N1dXUexwAAAIBA4kwwAG3btk3l5eWaPHlyoFNp5FwLjvhqcZNQ5KvVUXkOPMcKtQAABIbfiuDExESFh4e7VpGcPHmyBg8erLKyMmVnZzf6IvCoqChJ8jgGwD2bNm3Szp07lZSUJEnau3evxo4dq1GjRmnPnj2u+9XW1spsNstqtcpms3kUc0cwrDzbVlG8Bt65ngNWngUAoGX4dTr0M888o9WrV2v16tUaPHgwi+4AQWL8+PFav369SktLVVpaqh49emjp0qUaN26cjh49qs2bN0uSVqxYoZSUFEknClRPYgAAAEAgBfSa4KYWz3n77be9igHwHbPZrPz8fM2cOVM33XSTNm3apIcfftirGAAAABBIfr0mePLkyTIMQwkJCXrooYdaZNEdd6dbAjhdaWmp6/eBAweqqKioyft5GgMAAAACxW9F8PLly2Wz2dTQ0KA5c+YoNzdXN954o78evklcaxX8uG7Rcyy6AwAAAJzOb0WwzWaTJIWHhyszM1P33HOPRo8ezaI7QAth0R0AAADgdH65Jvjw4cM6ePCgJMkwDK1du1ZxcXEeL6zDojsAAAAAAE/45UxwTU2N7rvvPjkcDjmdTsXGxionJ8e1eE5OTk6jrzqS5HEMAAAAAIAz8UsR3Lt3bxUWFjYZY9EdAAAAAIC/BPQrkgAAAAAA8CeKYAAAAABAyKAIBgAAAACEDIpgAAAAAEDIoAgGAAAAAIQMimAAAAAAQMigCAYAAAAAhAyKYAAAAABAyKAIBgAAAACEDIpgAAAAAEDIoAgGAAAAAIQMimAAAAAAQMigCAYAAAAAhAyKYAAAAABAyKAIBgAAAACEDIpgAAAAAEDIoAgGAAAAAISMsEAnAAAAALR2EyZM0Lfffiuz2az27dvrscceU1xcnCorK5WVlaW6ujpZrVbl5eWpT58+kuRxDIB3OBMMAAAAeCkvL09vvfWWCgsLNWbMGE2dOlWSlJOTo8zMTJWUlCgzM1PZ2dmuNp7GAHin2UXw0qVLm9y+bNkynyUDwHPF//i2ye2MUSA4MEaB4ObtsW6nTp1cv//4448ymUyqqalRRUWFUlNTJUmpqamqqKhQbW2txzEA3mv2dOiFCxdq7Nixp23/y1/+ojvuuMOnSQFw36pt/1Tq5b1O284YBYJDS47R+vp6Pf744/r4448VERGh/v37a9asWUy1BNzgi2PdadOmacOGDTIMQ88//7yqqqoUHR0ti8UiSbJYLOrevbuqqqpkGIZHscjIyGblUl5e3uh2QkJCs9oFypYtW855n7awDwgO5yyCP/74Y0mS0+nUJ598IsMwXLFvv/1WHTp0aLnsAJzTZ3vqJElOw3D93vB/45YxCgSeP8bovHnzFBERoZKSEplMJu3fv1/Sv6dTpqena/Xq1crOztZLL710zhgQSnx5rDtnzhxJUmFhofLz8zVp0iTfJuuG+Ph4RUREBOzx3RXsBW5ztIV9aEvq6+tP+zDopHMWwdOmTXN1cvLaBkkymUy64IILNH36dLeSWbBggZ599lkVFRWpb9++KisrU3Z2turr6xUTE6N58+YpKipKkjyOAaFkyYc7JEkNDqfr9/Dt0zweowB8q6XH6KFDh1RYWKj3339fJpNJktStWzfXdMqTUzlTU1M1a9Ys1dbWyjCMM8aae5YJaCt8fawrSUOHDlV2drZ69Oihffv2yeFwyGKxyOFwqLq6WjabTYZheBQD4L1zFsGlpaWSpEceeUT5+flePdhnn32msrIyxcTESDrxiduUKVM0d+5c2e12LVq0SAUFBZo7d67HMSDUzB9xtSRp0ftfaML1/SRJCY88H8iUAJyipcfo7t27ZbVatWDBAm3cuFEdOnTQpEmTdN5557XoVEugrfDFse6hQ4f0ww8/uIrU0tJSdenSRVFRUYqLi1NxcbHS09NVXFysuLg41zjzNAbAO82+JvjUFwWn09koZjafe32thoYG5ebm6oknntDo0aMlnbhWISIiQna7XZKUkZGhpKQkzZ071+MYEKpOHlxLno1RAC2rpcaow+HQ7t27demll+rRRx/V3//+d919992aP3++x30215mmmbnLmymEp16D54t+2kouvuqnre/TqUaMGOGKuTtGjxw5okmTJunIkSMym83q0qWLFi9eLJPJpBkzZigrK0uLFi1S586dlZeX52rnaQyAd5pdBH/22WfKzc3VF198ofr6ekmSYRgymUz6/PPPz9l+/vz5SktLU69e/14UpKqqSj179nTdjoyMlNPpVF1dnccxq9Xa3F3y2Zs3Wg7XVjRf5f4fteyjr7T7wCEdW3ZZo2uali9ffs72fL8h0LJ+OkYl995Hz8RmsyksLMy1iuyVV16prl276rzzzmvxqZbBcM2hr94nfNFPMOXiq36CKRdf9XOmPtw91j31esNu3brp9ddfb7Lf2NhYrVy50qcxAN5pdhGclZWlG264QY8//rjOO+88tx5k27ZtKi8v1+TJk91OsCUFw5s34CuLP/hCA34Wpbt+3VcJ985rFDt5CcKpfrpYQF5enuvrHd59911NnTpVq1at8nhhHRbdARo72xj1RmRkpAYNGqQNGzbouuuuU2VlpWpqatSnTx+mWgJu8OZYF0Dr0uwi+F//+pcefPBB16Ib7ti0aZN27typpKQkSdLevXs1duxYjRo1Snv27HHdr7a2VmazWVarVTabzaMYEKr2/1ivEQkXymQyNVn0nsvZvt/Q3YV1WHQHOJ23Y/RsZs6cqalTpyovL09hYWHKz89X586dmWoJuMGbY10ArUuzi+Abb7xR69ev1+DBg91+kPHjx2v8+PGu24mJiVq8eLEuvvhivf7669q8ebPsdrtWrFihlJQUSSfO0h49etTtGBCq7BdGafu/6nRlr64e9xFM328ItDW+GKNn0rt3b7388sunbWeqJdB83hzrAmhdml0E19fXa+LEiUpISFC3bt0axTxdSc9sNis/P185OTmNvurImxgQqo45nHrq3Qr1i+6sn+9/pFGsuWM0mL7fUDr3dftcM+65My0M4y6eg+b76RitqalxxSZMmBDAzABILXOsCyA4NbsIvvjii3XxxRf75EFPLkUvSQMHDlRRUVGT9/M0BoSiGGt7xVjbS5J6/uxnXvUVLN9vyHX7LYfi1f9+OkZ/dso4ber5+Ol1+wBali+PdQEEt2YXwRMnTmzJPAB46daBF7p+T3BzvPL9hkDL82aMAmh5HOsCoaPZRfDHH398xtg111zjk2QAeO6zPXWu3xt+Ml7PNUb5fkOg5XkzRgG0PI51gdDR7CJ42rRpjW4fOHBAx44dU3R0tP7617/6PDEA7lny4Q7X7+Hbp7k1Rvl+Q6DleTNGAbQ8jnWB0NHsIvjU63glyeFw6C9/+Ys6dOjg86QAuG/+iKtdvyc88jxjFAgyjFEguHGsC4QOs6cNLRaL7r77bj3//PO+zAeAjzBGgeDGGAWCG2MUaLs8LoIlacOGDXyhOBDEGKNAcGOMAsGNMQq0Tc2eDn399dc3ehE4cuSIGhoalJOT0yKJAXDPxBUbZdKJMRq+9jeMUSDIMEaB4MaxLhA6ml0Ez5s3r9Ht888/XxdddJE6duzo86QAuO/e6/u5fu/7+0cYo0CQYYwCwY1jXSB0NLsIvvrqEwt6OJ1O7d+/X926dZPZ7NVsagA+FGezSpKchqEL+/RhjAJBhjEKBDeOdYHQ0ewi+Mcff1Rubq7Wrl2r48ePKywsTP/xH/+h6dOnq1OnTi2ZI4BmONJwXMs+3qlPvv5OjmW/ZowCQYYxCgQ3jnWB0NHsj7dmz56tI0eOqKioSNu3b1dRUZGOHDmi2bNnt2R+AJrpvz/ZqfrjDuX9biBjFAhCjFEguHGsC4SOZp8J/vDDD/Xuu+/q/PPPlyRddNFFmjt3rm688cYWSw5A8/392wN6+j+vUkSYReHh4YxRIMgwRoHgxrEuEDqafSY4IiJCtbW1jbYdOHBA4eHhPk8KgPvaWcz64cixRtsYo0DwYIwCwY1jXSB0NPtM8G233aYxY8boj3/8o3r27Kk9e/boxRdf1PDhw1syPwDNdEPfHpr7drluiY/Rj++/zxgFggxjFAhuHOsCoaPZRfA999yj6OhoFRUVqbq6Wt27d9e4ceN4YQCCxND+vdW1Q7g27PxO7+flMUaBIMMYBYIbx7pA6Gj2dOg5c+booosu0osvvqi1a9fqxRdfVGxsrObMmdOS+QFoppc++Vo9u7TXtJsvZ4wCQYgxCgQf47jT9bs7x7qntgPQ+jS7CC4uLlZ8fHyjbfHx8SouLvZ5UgDc99HX1fp5t46NtjFGgeDBGAWCjynMrH1Pf6p9T3+qt1YWqvt7R1239z39qS5474jeen1Vo237nv5UpjC+PxhozZo9gk0mk5zOxp96ORyO07YBCAyTTHIaRqNtjFEgeDBGgeBmkuQ0Go9Hp9MpQ0bTDQC0Ws0ugu12u+bPn+96s3Y6nXr22Wdlt9tbLDkAzdevR2et3LLLdZDNGAWCC2MUCG5X2Prp+U//x1UIOw2nlm1+U1fY+gU4MwC+1uyFsaZNm6a77rpL1113nXr27KmqqipdcMEFWrx4cUvmB6CZRv8yVgXrPtOEVzfqwk23MUaBIMMYBYLb/deNUtbaJzTsv+9TdKduqj5Yo6gOVs29+aFApwbAx5pdBPfo0UOrVq3S9u3bVVVVJZvNpiuuuEJmM9dEAMEgqkOE5gwdoJ3fHVSXpDsYo0CQYYwCwa17x0g9P3yWPt/3taoP1ah7hyjFRf9cZhNjFGhrml0ES5LZbFb//v3Vv39/tx9owoQJ+vbbb2U2m9W+fXs99thjiouLU2VlpbKyslRXVyer1aq8vDz16dNHkjyOAaHKbDLpku6dlXDzzYFOBUATGKNAcDObzLqsx8W6TBcHOhUALchvH23l5eXprbfeUmFhocaMGaOpU6dKknJycpSZmamSkhJlZmYqOzvb1cbTGAAAAAAATfFbEdypUyfX7z/++KNMJpNqampUUVGh1NRUSVJqaqoqKipUW1vrcQwAAAAAgDNxazq0t6ZNm6YNGzbIMAw9//zzqqqqUnR0tCwWiyTJYrGoe/fuqqqqkmEYHsUiIyObnU95ebnvdxI+lZCQEOgUWq0tW7YEOgUAAAAg6Pi1CJ4zZ44kqbCwUPn5+Zo0aZI/H/408fHxioiICGgOQEs51wcI9fX1fBAEAACAkBOQ5e6GDh2qjRs3qkePHtq3b58cDockyeFwqLq6WjabTTabzaOYLzUcc/i0v1DB3w0AAABAsPLLmeBDhw7phx9+cBWppaWl6tKli6KiohQXF6fi4mKlp6eruLhYcXFxrinNnsZ8JbydRZmPLPdpn6Hg1fw/BDoFAAAAvzlw4IAeeeQR/fOf/1R4eLguvPBC5ebmKjIyUmVlZcrOzlZ9fb1iYmI0b948RUVFSZLHMQDe8cuZ4CNHjmjSpEkaMmSI0tPT9eKLL2rx4sUymUyaMWOGXnnlFSUnJ+uVV17RzJkzXe08jQEAAAD+YjKZNG7cOJWUlKioqEi9e/dWQUGBnE6npkyZouzsbJWUlMhut6ugoECSPI4B8J5fzgR369ZNr7/+epOx2NhYrVy50qcxAAAAwF+sVqsGDRrkut2/f3+99tprKi8vV0REhOx2uyQpIyNDSUlJmjt3rscxAN4LyDXBAADA9xYsWKB+/fa4mfUAACAASURBVPppx44dkk5Mp0xLS1NycrLGjBmjmpoa133PFgPgOafTqddee02JiYmqqqpSz549XbHIyEg5nU7V1dV5HAPgPb+uDg0AAFrGZ599prKyMsXExEj693TKuXPnym63a9GiRSooKNDcuXPPGnOH4fBsIUTjuEOmMItHbYFgN2vWLLVv314jR47UO++8E7A8fvoNEMH+tZPN+WrHtrAPCA4UwQAAtHINDQ3Kzc3VE088odGjR0uSX6ZamiwWffeXV9zO94J7RrrdBmgN8vLytGvXLi1evFhms1k2m0179uxxxWtra2U2m2W1Wj2ONVdr+yrQYC9wm6Mt7ENbcravA6UIBsCqlkArN3/+fKWlpalXr16ubZ5OtXTnINsbp54x8ebA0df9tJVcfNUP+3RmPz24fvLJJ1VeXq4lS5YoPDxc0olC9OjRo9q8ebPsdrtWrFihlJQUr2IAvEcRDMC1quXJRT3y8vJUUFCg2bNnezSd0ldTLQGc27Zt21ReXq7JkycHOhW3+OqMSTD1E0y5+KqfYMrFV/34Kpf4+HhXIfzll1/queeeU58+fZSRkSFJ6tWrlxYuXKj8/Hzl5OQ0+lBYksxms0cxAN6jCAbAqpZAK7Zp0ybt3LlTSUlJkqS9e/dq7NixGjVqVItOtQTwb5dccom++OKLJmMDBw5UUVGRT2MAvMPq0AAaYVVLoHUZP3681q9fr9LSUpWWlqpHjx5aunSpxo0b55pOKemMUy1/GgMAoK3jTDCARoJlVUvp9OutfooFKDznqxUseQ4819KriDLVEgCAplEEA3AJplUtpda3smVrQvEaeOd6Ds62quXZlJaWun5nqiUAAKdjOjQASf9e1XLhwoVNrmopNX86JVMtAQAAEKw4EwyAVS0BAAAQMiiCAbCqJQAAAEIG06EBAAAAACGDIhgAAAAAEDIoggEAAAAAIYMiGAAAAAAQMiiCAQAAAAAhgyIYAAAAABAyKIIBAAAAACGDIhgAAAAAEDIoggEAAAAAIcMvRfCBAwd05513Kjk5WUOGDNHEiRNVW1srSSorK1NaWpqSk5M1ZswY1dTUuNp5GgMAAAAAoCl+KYJNJpPGjRunkpISFRUVqXfv3iooKJDT6dSUKVOUnZ2tkpIS2e12FRQUSJLHMQAAAAAAzsQvRbDVatWgQYNct/v37689e/aovLxcERERstvtkqSMjAy9/fbbkuRxDAAAAACAMwnz9wM6nU699tprSkxMVFVVlXr27OmKRUZGyul0qq6uzuOY1Wptdi7l5eVnjSckJLixZzjVli1bfNIPz4HnfPUcAAAAAG2J34vgWbNmqX379ho5cqTeeecdfz98I/Hx8YqIiAhoDm0VxWvgnes5qK+vP+cHQQAAAEBb49ciOC8vT7t27dLixYtlNptls9m0Z88eV7y2tlZms1lWq9XjGAAAAAAAZ+K3r0h68sknVV5eroULFyo8PFzSiTOxR48e1ebNmyVJK1asUEpKilcxAAAAAADOxC9ngr/88ks999xz6tOnjzIyMiRJvXr10sKFC5Wfn6+cnBzV19crJiZG8+bNkySZzWaPYgAAAAAAnIlfiuBLLrlEX3zxRZOxgQMHqqioyKcxAAAAAACa4rfp0AAAAAAABBpFMAAAAAAgZFAEAwAAAABCBkUwAAAAACBkUAQDAAAAAEIGRTAAAAAAIGRQBAMAAAAAQgZFMAAAAAAgZFAEAwAAAABCBkUwAAAA4IW8vDwlJiaqX79+2rFjh2t7ZWWlRowYoeTkZI0YMULffPON1zEA3qMIBgCgFTtw4IDuvPNOJScna8iQIZo4caJqa2slSWVlZUpLS1NycrLGjBmjmpoaV7uzxQC4JykpScuXL1dMTEyj7Tk5OcrMzFRJSYkyMzOVnZ3tdQyA9yiCAQBoxUwmk8aNG6eSkhIVFRWpd+/eKigokNPp1JQpU5Sdna2SkhLZ7XYVFBRI0lljANxnt9tls9kabaupqVFFRYVSU1MlSampqaqoqFBtba3HMQC+QREMgGlcQCtmtVo1aNAg1+3+/ftrz549Ki8vV0REhOx2uyQpIyNDb7/9tiSdNQbAN6qqqhQdHS2LxSJJslgs6t69u6qqqjyOAfCNsEAnACDwkpKSNHr0aP3hD39otP3kdKz09HStXr1a2dnZeumll7yKAWg5TqdTr732mhITE1VVVaWePXu6YpGRkXI6naqrqztrzGq1BiJ1AD5WXl7e6HZCQkKAMmmeLVu2nPM+bWEfEBwoggG4zgad6uR0rGXLlkk6MR1r1qxZqq2tlWEYHsUiIyP9t1NACJo1a5bat2+vkSNH6p133gl0Omd16sGiNwe2vu6nreTiq37YpzP7aZH5UzabTfv27ZPD4ZDFYpHD4VB1dbVsNpsMw/Ao5o74+HhFRER4vH/+FuwFbnO0hX1oS+rr6884TimCATTpbNOxDMPwKOZuEXyuAwzebDznq0+reQ485+szBnl5edq1a5cWL14ss9ksm82mPXv2uOK1tbUym82yWq1njfmLr/53gqmfYMrFV/0EUy6+6sdXucTHx5/1fSoqKkpxcXEqLi5Wenq6iouLFRcX53ov9DQGwHsUwQCCVmv7FLs1oXgNvHM9B2f7BPunnnzySZWXl2vJkiUKDw+XdGL8HD16VJs3b5bdbteKFSuUkpJyzhgA982ePVvr1q3T/v37dccdd8hqtWrNmjWaMWOGsrKytGjRInXu3Fl5eXmuNp7GAHiPIhhAkwI9jQtA83z55Zd67rnn1KdPH2VkZEiSevXqpYULFyo/P185OTmqr69XTEyM5s2bJ0kym81njAFw3/Tp0zV9+vTTtsfGxmrlypVNtvE0BsB7FMEAmsQ0LqB1uOSSS/TFF180GRs4cKCKiorcjgEA0JZRBANgGhcAAABCBkUwAKZxAQAAIGSY/fEgeXl5SkxMVL9+/bRjxw7X9srKSo0YMULJyckaMWKEvvnmG69jAAAAAACciV+K4KSkJC1fvlwxMTGNtufk5CgzM1MlJSXKzMxUdna21zEAAAAAAM7EL0Ww3W4/bWXYmpoaVVRUKDU1VZKUmpqqiooK1dbWehwDAAAAAOBsAnZNcFVVlaKjo2WxWCRJFotF3bt3V1VVlQzD8Cjm7uqz5/r+Rb5H03NbtmzxST88B57z1XMAAAAAtCUhvTBWfHy8IiIiAp1Gm0TxGnjneg7q6+vP+UEQAAAA0NYErAi22Wzat2+fHA6HLBaLHA6HqqurZbPZZBiGRzEAAAAAAM7GL9cENyUqKkpxcXEqLi6WJBUXFysuLk6RkZEexwAAAAAAOBu/nAmePXu21q1bp/379+uOO+6Q1WrVmjVrNGPGDGVlZWnRokXq3Lmz8vLyXG08jQEAAAAAcCZ+KYKnT5+u6dOnn7Y9NjZWK1eubLKNpzEAAAAAAM4kYNOhAQAAAADwN4pgAAAAAEDIoAgGAAAAAIQMimAAAAAAQMigCAYAAAAAhAyKYAAAAABAyKAIBgAAAACEDIpgAAAAAEDIoAgGAAAAAIQMimAAAAAAQMigCAYAAAAAhAyKYAAAAABAyKAIBgAAAACEDIpgAAAAAGhhDcePBTqFMwrm3FpCWKATAAAAAIC2Ljysnf64bFKg02jSi3fMD3QKfsWZYAAAAABAyKAIBgAAAACEDIpgAAAAAEDIoAgGAAAAAIQMimAAAAAAQMigCAYAAAAAhIxWXQRXVlZqxIgRSk5O1ogRI/TNN98EOiUAp2CMAsGNMQoEN8Yo0DJadRGck5OjzMxMlZSUKDMzU9nZ2YFOCcApGKNAcGOMAsGNMYpg42g4FugUzsid3MJaMI8WVVNTo4qKCi1btkySlJqaqlmzZqm2tlaRkZFnbWsYhiSpoaHhnI/TuX0775MNMfX19b7t8LxOvu0vBDTnOTj5/39yPPgaYzR4MUYDry2N0ePh7o/BpvbfEdHFJ/1Y2lm97icizPs+JMlq6eh1P9awcLf7aKqfLmEWr/s40Y/7/48/7Scs7LjbfTTVjzPc/X6a2qfjEZ7lEqxj9GzvocH6nunW+1KQvuc0dx86tevQwpl4xp3n4J1772/BTDx3w5PzdPyU/TjbGDUZLTVyW1h5ebkeffRRrVmzxrXtlltu0bx583TZZZedte3Bgwe1Y8eOlk4RaBX69u2rTp18/4bCGAV8gzEKBLdgG6OMT6CxpsZoqz0T7I0OHTqob9++ateunUwmU6DTAQLCMAwdO3ZMHToE3yeSjFGAMQoEu2Ado4xP4ISzjdFWWwTbbDbt27dPDodDFotFDodD1dXVstls52xrNptb5BM7oLU577zzWqxvxijgPcYoENyCcYwyPoF/O9MYbbULY0VFRSkuLk7FxcWSpOLiYsXFxZ3zOiYA/sEYBYIbYxQIboxRoOW02muCJWnnzp3KysrSDz/8oM6dOysvL08///nPA50WgP/DGAWCG2MUCG6MUaBltOoiGAAAAAAAd7Ta6dAAAAAAALiLIhgAAAAAEDIoggEAAAAAIYMiGAAAAAAQMiiCW5nKykqNGDFCycnJGjFihL755ptApxRy8vLylJiYqH79+mnHjh2BTgc+lJiYqJSUFKWnpys9PV2PP/64Xx6T/6PmSUxM1HXXXSeHw+Ha9uabb6pfv3565ZVXztp21KhReu+991o6RfwfX7xX+eK19sCBA7rzzjuVnJysIUOGaOLEiaqtrfWorwkTJigtLU1Dhw5VZmamPv/8c4/6kaQFCxZ4tV8/fa368MMP3e6jvr5eOTk5uummmzRkyBA99thjbvfx7bffunJIT09XYmKirr76arf7kaT33ntPQ4cOVXp6utLS0rRu3Tq3+/jb3/6mYcOGaciQIRo5cqR2797drHZn+l/jmOt0beEYyJevC4Hiy9ejQPL2tdArBlqVUaNGGYWFhYZhGEZhYaExatSoAGcUejZt2mTs2bPHuOGGG4wvvvgi0OnAhwLxnPJ/1Hw33HCDMWzYMONvf/uba9vIkSONYcOGGS+//PJZ244cOdIoLS1t6RTxf3zxXuWL19oDBw4Yn3zyiev2n//8Z+NPf/qTR3398MMPrt/feecdY+jQoR71U15ebowdO9ar/fLF68asWbOMOXPmGE6n0zAMw/juu++86s8wDGP27NnGzJkz3W7ndDoNu93u2qfPP//c6N+/v+FwOJrdR11dnXH11VcbX3/9tWEYJ/7vxowZ06y2Z/pf45jrdG3hGMiXrwuB4qvXo0DyxWuhNzgT3IrU1NSooqJCqampkqTU1FRVVFS0uk+vWju73S6bzRboNOAnq1at0vDhw/W73/1Oo0eP1tdffy3pxBnIMWPGaNKkSUpJSdHtt9+ur776yvXp8sMPPyzj/76BrqioSMOHD9fQoUM1dOhQffzxx00+VnV1te6//37ddtttGjJkiBYvXuy3/Wwthg0bpjfffFOStHv3bh0+fFh9+/aVJH388ccaMWKEhg4dqiFDhmjNmjVN9vHjjz9q2rRprr/z7NmzG51dhnd89V7li9daq9WqQYMGuW73799fe/bs8aivTp06uX7/8ccfZTKZ3O6joaFBubm5mjFjhkc5+MqhQ4dUWFioSZMmufajW7duXvXZ0NCgoqIi3XrrrR61N5vNOnjwoCTp4MGD6t69u8zm5h+m7tq1S926ddNFF10kSbr++uu1fv36Zv3fNfW/xjFX09rCMZAvXxcCxRevR4EUDK+FYQF7ZLitqqpK0dHRslgskiSLxaLu3burqqpKkZGRAc4OaBvuv/9+RURESJJuvPFGbd++XcuXL1d4eLjef/99TZ06VStWrJAk/eMf/1BRUZF69Oihu+66Sw8//LBefvlltW/fXsOGDdPHH3+sa6+9Vtddd51SU1NlMpn09ddf649//KM++OCD0x770Ucf1YQJE3TVVVepoaFBf/zjH3X55ZfrV7/6lV//BsHs6quv1quvvqrvv/9eq1at0tChQ/XZZ59Jki699FK9+uqrslgs2r9/v373u9/puuuuU5cuXRr1MXfuXF111VWaM2eOnE6nJk+erDfeeEP/+Z//GYhdanOC9b3K6XTqtddeU2Jiosd9TJs2TRs2bJBhGHr++efdbj9//nylpaWpV69eHudw0uTJk2UYhhISEvTQQw+pc+fOzW67e/duWa1WLViwQBs3blSHDh00adIk2e12j/MpLS1VdHS0LrvsMrfbmkwmPf3005owYYLat2+vQ4cOacmSJW71cdFFF2n//v3avn27rrjiChUVFUmSx/93wfp/DN/yxetCoHj7ehRIvnwt9BRFMACc4plnnnGdWczPz9f//u//avjw4ZIkwzD0ww8/uO47cOBA9ejRQ5IUFxenmJgY14HoL37xC+3atUvXXnutdu/erYcfflj79u1TWFiY9u/fr++++04XXHCBq6/Dhw/r008/bXSW4dChQ9q5cydF8ClMJpNuvvlmrVmzRmvWrNGKFStcRXBtba2mTp2qXbt2yWKx6Pvvv1dlZaX69+/fqI/S0lJt375dy5YtkyQdPXpU0dHRft8X+NesWbPUvn17jRw50uM+5syZI0kqLCxUfn6+/uu//qvZbbdt26by8nJNnjzZ48c/afny5bLZbGpoaNCcOXOUm5urgoKCZrd3OBzavXu3Lr30Uj366KP6+9//rrvvvlvvvPOOOnbs6FFOb7zxhsdngY8fP67nnntOixYtUkJCgrZs2aIHHnhAa9asUYcOHZrVR6dOnfTUU09p7ty5qq+v169//Wt17tzZVcQCTfHF60KgePN6FEi+fC30BkVwK2Kz2bRv3z45HA5ZLBY5HA5VV1e3+mkpQLAyDEO33nqrJk2a1GT85Blj6cRZgp/ePjnF9qGHHlJWVpZ++9vfyul06sorr1R9fX2jvpxOp0wmk/7nf/5H7dq1a4G9aTuGDRum4cOH66qrrlLXrl1d22fMmKHExEQtWLBAJpNJycnJp/2dpRPP66JFi9S7d29/ph0ygvG9Ki8vT7t27dLixYvdmmJ7JkOHDlV2drYOHDjQ6H/wbDZt2qSdO3cqKSlJkrR3716NHTtWc+fO1XXXXefW45/8W4aHhyszM1P33HOP2+3DwsJcU32vvPJKde3aVZWVlbr88svd6kuS9u3bp02bNik/P9/ttpL0+eefq7q6WgkJCZKkhIQEnX/++dq5c6euuOKKZvdz7bXX6tprr5Uk7d+/X0uXLtXPfvYzj3IKxv9j+JavXxcCxZPXo0Dy5WuhN1rvMx6CoqKiFBcXp+LiYklScXGx4uLimJYDtJDExEStXr1ae/fulXTi7El5ebnb/Rw8eNA15eeNN95QQ0PDaffp2LGjEhISGk0BrKqq0nfffedh9m1X79699eCDD2rChAmNth88eFAxMTEymUzasGGDdu3a1WT7xMRELVmyxPUhRW1tbbNXkcW5Bdt71ZNPPqny8nItXLhQ4eHhHvVx6NAhVVVVuW6XlpaqS5cuslqtze5j/PjxWr9+vUpLS1VaWqoePXpo6dKlbh/0HT582HXtrGEYWrt2reLi4tzqIzIyUoMGDdKGDRsknVgFuaamRhdeeKFb/Zy0atUqXX/99R4fgPfo0UN79+51rbmwc+dO1dTUuF3Anny9dDqdevLJJ5WRkaH27dt7lFOw/R/Dt3zxuhAovng9CiRfvRZ6izPBrcyMGTOUlZWlRYsWqXPnzsrLywt0SiFn9uzZWrdunfbv36877rhDVqv1jAvwoHW76qqr9MADD+iee+6Rw+HQsWPHlJKSovj4eLf6+dOf/qQJEyaoS5cuGjx48BnfqAoKCjR37lwNGTJEktShQwfNmTOn0bRpnDBixIjTtj388MOaOXOmnn32WV1++eXq169fk22nTp2qefPmKT09XSaTSe3atdPUqVM5M+xDvniv8sVr7ZdffqnnnntOffr0UUZGhiSpV69eWrhwoVv9HDlyRJMmTdKRI0dkNpvVpUsXLV68OCCL0dTU1Oi+++6Tw+GQ0+lUbGyscnJy3O5n5syZmjp1qvLy8hQWFqb8/Hy3ris+1apVqzRt2jSP2krSBRdcoBkzZjRaqOvxxx93+6D+6aef1tatW3Xs2DH96le/avZ0yzP9r3HMdbq2cAzkq9eFQAmm16PWzGScXL4UAAAAAIA2junQAAAAAICQQREMAAAAAAgZFMEAAAAAgJBBEQwAAAAACBkUwQAAAACAkEERjJAyatQorVy5MtBpAK3am2++qd///veBTgMAgIB59dVXde2112rAgAE6cOBAiz5WYmKiPvrooxZ9jFBDERzimhpUvjjANQxDL730klJTU9W/f3/9+te/1v33368vvvjirO22b9+uO++8U3a7XVdffbVuu+02vfHGG17lAoSKNWvWaPjw4erfv7+uueYaDR8+XMuXLxffhAcE3oABA1w/v/jFL3TFFVe4br/11luBTg8IaomJibrmmmt0+PBh17aVK1dq1KhRLfJ4W7du1ejRozVgwAAlJCTo7rvv1ldffeWKHzt2TH/+85/1wgsvaNu2beratav69eun/v37a8CAARo8eLDmzp0rh8PRIvl5gg+wG6MIRouYM2eOXnrpJU2bNk2ffvqpSkpK9Nvf/lbvv/9+k/d3OBzatm2bbr/9dl111VVat26dNm7cqBkzZuiDDz5w+/ENw5DT6fR2N4BW44UXXtCcOXM0duxYrV+/Xh999JFmzpyprVu36tixY4FOzyWYDggAf9q2bZvrp2fPnlq8eLHrdlpaWos//vHjx1v8MYCW5HQ69dJLL7X442zbtk1jx45VUlKSPvzwQ/31r39Vv3799Pvf/167d++WJNXU1Ki+vl4XX3xxo7arV6/Wtm3b9OKLL6q4uFivv/76af0zFoMDRTDOasmSJfrtb3+rAQMG6JZbbtE777zjiu3atUsjR45UQkKCBg0apAceeECS9M0332j58uV68skndc011yg8PFznn3++0tLSNH78eElSVlaWcnJydOedd6p///7auHGj8vPzNXToUI0fP16RkZEymUyKj4/X/PnzJUnff/+97rrrLv3yl7/UVVddpbvuukt79+515TNq1Cg99dRTysjI0JVXXqndu3drw4YNSklJUUJCgnJzczkjhjbp4MGDeuaZZ5STk6OUlBR17NhRJpNJl156qZ544gmFh4eroaFBeXl5+s1vfqNrr71W2dnZOnr0qCRp48aN+vWvf60XXnhB11xzja677rpGMzAOHDigu+++WwMHDtRtt92mf/7zn40ef+fOnbrjjjt09dVXKzk5WWvXrnXFmhrrAP7N6XS63msHDRqkSZMmqa6uTpL07bffql+/flq1apV+85vfaNCgQfrLX/7iapuVlaWnnnrKdfvkWD4pMTFRS5Ys0ZAhQ9S/f38dP35cZWVlysjIkN1uV1paGmMSrcbYsWP1wgsv6Icffmi0/eQ4ObW4PPXytzfffFMZGRl6/PHHZbfblZSUpK1bt+rNN9/U9ddfr2uuuUarVq1ytZ03b57S09N1++23q2PHjrJarXrwwQd15ZVX6tlnn1VlZaVSUlIkSVdddZVGjx59Wq6xsbFKSEjQl19+6cpv5cqV+s1vfqPbb79dTqdTixYt0g033KBrrrlGjzzyiA4ePOhqX1hYqBtuuOG0MS+de9xXVVVp4sSJ+uUvf6lBgwYpNzdXO3fuVE5OjsrKyjRgwADZ7XZJ0vvvv69bbrnFdfZ66dKlbj8vrRVFMM6qd+/eWr58ubZs2aKJEydqypQpqq6uliTNnz9fv/rVr7Rp0yZ98MEHGjlypCTp448/Vo8ePXTFFVecte/i4mLdfffd2rp1qwYMGKCysjIlJyef8f5Op1O/+93v9N577+m9995TRESEcnNzG91n9erVmjVrlrZu3apOnTpp4sSJeuCBB/TJJ5/oZz/7mbZu3erlXwQIPtu2bVNDQ4OSkpLOeJ+CggJVVlaqsLBQ69atU3V1tRYuXOiK79+/XwcPHtQHH3ygOXPmKDc3V99//70kKTc3VxEREVq/fr0ef/zxRgXy4cOHNWbMGKWmpuqjjz7SU089pZkzZzaaNnbqWE9ISGiBvwDQer388st699139corr+jDDz9Uly5dTntv27Jli95++23993//txYuXKidO3c2u/81a9ZoyZIl2rx5s2pqanTXXXfpnnvu0aeffqpHH31U999/v2pra329W4DPxcfH6+qrr/aoUNu+fbv69eunjRs3KjU1VQ899JD+8Y9/6J133tG8efOUm5urQ4cO6ciRI9q2bZuryD3VzTffrI8++kgXXXSRiouLJUmbNm1q8uz0V199pS1btiguLs61bdOmTVq7dq2WLl2qN998U6tWrdJLL72kd999V4cPH3aN+6+++kozZ85Ufn6+PvzwQ9XV1TU66XM2DodDd911l3r27KnS0lJ98MEHuuWWWxQbG6uZM2eqf//+2rZtmzZv3ixJmjZtmnJzc7Vt2zYVFxfrl7/8pdt/29aKIhi69957ZbfbXT8zZ850xW6++WZFR0fLbDbrlltu0YUXXqjt27dLksLCwrRnzx5VV1crIiLC9alSXV2dLrjggnM+blJSkhISEmQ2m/XDDz/I6XSetV3Xrl2VnJys888/Xx07dtQ999yjTZs2NbrPsGHDdMkllygsLEwffPCBLrnkEqWkpKhdu3a6/fbb1a1bN0/+REBQO3DggLp27aqwsDDXtpNneq644gp9+umnev311zV16lRZrVZ17NhRd911l9asWeO6f1hYmO699161a9dO119/vdq3b6/Kyko5HA6tW7dO999/v9q3b6++fftq2LBhrnZ/+9vfFBMTo1tvvVVhYWG69NJLlZycrLffftt1n1PHekREhH/+KEArsWLFCj344IPq0aOHwsPDNXHiRJWUlDQ6qzVx4kSdd97/b+/eQ5r8/jiAv3dpzilzPg/fXQAAB+lJREFUKlOnyYSsjErcaHmdUzMiCc28dC8VgmJlFzOENMjC/igxxbJCM+2CRKUW6dDCwn8SDEL/UFIwKx26NCV1bW71+0N6cN90rm/x42v7vP7as+dyziMcn/N5zvmc8REQEICAgAB0d3fbfP29e/dCIpGAz+ejvr4ekZGRUKlUYLPZCA8Px5o1a+ZNVSLkvyYzMxN37tz55Rc3S5cuRVJSEjgcDuLi4qDVaqFWq8Hj8RAREQEej4f3799jfHx83v6oWCxecAGsxMREKBQKHDx4EMnJyUhKSmL2HTlyBAKBAHw+H0+ePEFaWhp8fX3h5OSEEydOoKGhASaTCRqNBlFRUVAoFODxeDh69CjYbNtCto6ODgwPD+PUqVMQCAQW/fO5cLlc9Pb2YmJiAi4uLli9erVN5fwNuAsfQv52V65cQVhYGLP96NEjZgpJXV0dKisrMTAwAGBm1OfHP4Ds7GwUFxcjOTkZLi4uSE9PR3JyMkQiEXQ63YLlSiQS5rNQKASbzYZOp8OyZcvmPF6v1+PChQtobW1lRqgmJydhNpvB4XB+uubw8DC8vLyYbRaLZbGfkL+FSCTC58+fYTKZmEC4pqYGABAZGYlPnz5Br9dj27ZtzDn/zJsXiUQWQbSjoyOmpqYwOjoKk8lk0Xa8vb2ZzwMDA+jo6LB4yJrNZoscR2p3hMxvcHAQarXaopPLZrMxMjLCbM9+gfujbdpqdvsbHByERqNBS0sL853JZEJwcPC/rT4h/1crVqxAVFQUbty4MW9/cS7u7u7MZz6fD8CyXTk4OGBychJSqXTe/qhOp4Orq6vVcmprayGVSufcN7tPOjw8DB8fH2bbx8cHJpMJIyMjP/VfBQIBRCKRDXc5MxXa29vb4nluTUlJCcrKylBYWIiVK1ciKysLMpnMpnMXOwqCybwGBgaQm5uLW7duQSaTgcPhICEhgdkvFotx/vx5AEB7ezvS09OhUCgQGhqK/Px8dHZ2Yu3atTaV5ejoiKCgIDQ1Nc07FePmzZvo6+vD/fv3IRaL0dXVha1bt1rk+bJYLIv6zZ4+8v37d2i12l/6GxCyGMhkMvB4PDx//nzOlAJXV1fw+Xw8ffoUnp6ev3RtNzc3cLlcaLVapkMwux1JJBIoFApUVlb+3k0QYqe8vLxQUFAwZ6rAx48frZ7r6OjI5PYDM2kN/zT7uSiRSJCQkMA8uwlZjDIzM5GYmIiMjAwAM0EiAHz9+hXOzs4AYNNgzFwEAgGCgoKg0Wh+6o82Njb+1nTh2W3Rw8ODGWACZl5QcblcuLu7w8PDwyLlQa/XM+sEANbbvUQigVartXgpPlf5PwQGBqKsrAzT09O4e/cujh07ZjczQ2g6NJmXXq8Hi8WCm5sbAODhw4fo6elh9jc2NjJBpouLC1gsFthsNvz8/LBr1y5kZWWhra0NRqMRBoOByUuaT3Z2Nmpra1FeXs6MNnd3d+P48eMAZkZ9HRwcIBQKMTY2htLSUqv1V6lU6OnpQVNTE0wmE6qrq+fsIBCy2AmFQqjVapw9exYajQYTExP49u0burq6oNfrwWazkZKSgoKCAmZ0aWhoCK2trQtem8PhYOPGjSgtLYVer0dvb6/FAiJRUVF49+4d6urqMD09jenpaXR0dPxSziIh9mznzp24fPky0yEeHR3Fs2fPbDp31apVePnyJcbGxqDT6VBVVWX1+Pj4eLS0tKC1tRVmsxkGgwFtbW025xsS8l8glUoRFxeH27dvA5h5Wevp6Yn6+nqYzWY8ePCAWcX538jKykJdXR2qq6sxMTGB8fFxFBUV4c2bNzh8+PAfuYctW7agqqoKHz58wOTkJIqKirB582ZwuVxs2rQJL168QHt7O4xGI0pKSixmbllr94GBgRCLxSgsLMTU1BQMBgNev34NYGY0fGhoCEajEQBgNBrx+PFjfPnyBUuWLIGTk5PN067/BvZzp+SX+fv7IyMjAzt27EBYWBjevn0LuVzO7O/s7ERKSgpkMhkOHTqE06dPw9fXFwCQm5uL3bt3Iz8/HwqFArGxsWhubkZ0dPS85cnlclRVVeHVq1eIjY3F+vXrkZeXB5VKBQDYv38/DAYDQkJCsH37diiVSqv1d3NzQ3FxMQoLCxEcHIz+/n6L+hPyNzlw4ABycnJQXl6O8PBwZgXokydPQiaTITs7G1KpFKmpqZDL5UhLS0NfX59N1z5z5gympqYQHh6OnJwci2nVzs7OqKioQENDA5RKJSIiInDp0iXmIUsIsW7fvn2IiYlBRkYGZDIZUlNTmbU3FpKQkICAgADm/Li4OKvHSyQSXL16FdevX0doaChUKhUqKiroJwXJoqNWqy3SAs6dO4eKigoEBwejt7f3t6b0rlu3DuXl5WhuboZSqUR0dDS6urpw7949+Pn5/YHaA0lJSYiPj8eePXuwYcMG8Hg85OXlAQCWL1/OPL+VSiWEQqHF9Ghr7Z7D4eDatWvo7+9HdHQ0IiMj0djYCAAICQmBv78/IiIimBSI+vp6xMTEQC6Xo6amBhcvXvwj97cYsL7Tb8YQQgghhBBCCLETNBJMCCGEEEIIIcRuUBBMCCGEEEIIIcRuUBBMCCGEEEIIIcRuUBBMCCGEEEIIIcRuUBBMCCGEEEIIIcRuUBBMCCGEEEIIIcRuUBBMCCGEEEIIIcRuUBBMCCGEEEIIIcRuUBBMCCGEEEIIIcRu/A8Mlz8kCMk/1gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* we can see in the  HsCrCrd graph\n",
        "  * coustomers who haveing the credit card are more in count \n",
        "---\n",
        "\n",
        "* we can see Gender graph \n",
        "   * more Male customers are in bank \n",
        "---\n",
        "* we can see the tenure graph \n",
        "  * the customers who haveing the Turner 2 are in high in count in the bank \n",
        "---\n",
        "* we can see in the above NumOfProducts graph\n",
        "   * the customers who are haveing the Numberofproduct 1,2 are in high count \n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "u1r6i6ft3lRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sb.countplot(data=df,x='Geography')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "QTOd71JB3A8k",
        "outputId": "e5a1ce90-dacf-4186-8686-b81452785439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we cans see the Geograph Graph \n",
        "* France has highest no of customers in the bank "
      ],
      "metadata": {
        "id": "q7fTRhIs3NFV"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "08i6LOhk3MfL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.subplot(131)\n",
        "sb.countplot(data=df,x='Gender',hue='Exited')\n",
        "plt.subplot(132)\n",
        "sb.countplot(data=df,x='NumOfProducts',hue='Exited')\n",
        "plt.subplot(133)\n",
        "sb.countplot(data=df,x='HasCrCard',hue='Exited')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "kFXnPOjtVdgk",
        "outputId": "d3f6aed7-9260-4924-fe12-f7749a0698d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*  we can observe genedr graph  the data there are more Number  of Males in count who are going to leave the bank\n",
        "\n",
        "*  we can see in the above NumOfProducts graph who have the NumOfProducts 1,2 are there are in highest among other NumOfProduct The customers who are leaving the bank are also they are in high count \n",
        "\n",
        "* we can observe the above  HasCrCard graph  \n",
        "    *  the customers who have the HasCrCard -credict card are more in count of customers are leaving the bank "
      ],
      "metadata": {
        "id": "Dob4uP8-oDW2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(30,9))\n",
        "plt.subplot(131)\n",
        "sb.distplot(df['Age'])\n",
        "\n",
        "plt.subplot(132)\n",
        "sb.distplot(df['Balance'])\n",
        "\n",
        "plt.subplot(133)\n",
        "sb.distplot(df['EstimatedSalary'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "WsjeYmPTsXcE",
        "outputId": "300676d1-6cc2-48a4-b67f-e59eebf0f19e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x648 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*  In the above graphs we can see the  the customers whose Age is between 25-40 Age customers are in the more in count  \n",
        "\n",
        "*  the customesr whose balance is 0 are more in count in the data set \n",
        "* we cans see the above graph EstimatedSaklary is Normal distribution"
      ],
      "metadata": {
        "id": "EWhgwX_p7w8j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(30, 9))\n",
        "plt.subplot(131)\n",
        "sb.boxplot(df['Age'],color='m')\n",
        "plt.subplot(132)\n",
        "sb.boxplot(df['Balance'],color='blue')\n",
        "\n",
        "plt.subplot(133)\n",
        "sb.boxplot(df['EstimatedSalary'],color='red')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "hs9I2kGFsXfY",
        "outputId": "c0f7fa2e-e582-4339-c527-3a504e2bd76d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x648 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* we can see the Outliers the Customers whoes age is above the 60 \n",
        "\n",
        "* the customers whose Averege balance is 76485.8 \n",
        "\n",
        "* we can see the above graph the  Average EstimatedSalary is 100000"
      ],
      "metadata": {
        "id": "sWO7Twjm_dSN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summerise the Data**"
      ],
      "metadata": {
        "id": "IZz6Fshc-GQx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "NduNBVYD8uQC",
        "outputId": "f8ea3df7-37b3-4028-e863-7ec94c68ec5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CreditScore           Age        Tenure        Balance  NumOfProducts  \\\n",
              "count  10000.000000  10000.000000  10000.000000   10000.000000   10000.000000   \n",
              "mean     650.528800     38.921800      5.012800   76485.889288       1.530200   \n",
              "std       96.653299     10.487806      2.892174   62397.405202       0.581654   \n",
              "min      350.000000     18.000000      0.000000       0.000000       1.000000   \n",
              "25%      584.000000     32.000000      3.000000       0.000000       1.000000   \n",
              "50%      652.000000     37.000000      5.000000   97198.540000       1.000000   \n",
              "75%      718.000000     44.000000      7.000000  127644.240000       2.000000   \n",
              "max      850.000000     92.000000     10.000000  250898.090000       4.000000   \n",
              "\n",
              "         HasCrCard  IsActiveMember  EstimatedSalary        Exited  \n",
              "count  10000.00000    10000.000000     10000.000000  10000.000000  \n",
              "mean       0.70550        0.515100    100090.239881      0.203700  \n",
              "std        0.45584        0.499797     57510.492818      0.402769  \n",
              "min        0.00000        0.000000        11.580000      0.000000  \n",
              "25%        0.00000        0.000000     51002.110000      0.000000  \n",
              "50%        1.00000        1.000000    100193.915000      0.000000  \n",
              "75%        1.00000        1.000000    149388.247500      0.000000  \n",
              "max        1.00000        1.000000    199992.480000      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2a534ac-fade-4536-b4ae-acc972de90c9\">\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>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>650.528800</td>\n",
              "      <td>38.921800</td>\n",
              "      <td>5.012800</td>\n",
              "      <td>76485.889288</td>\n",
              "      <td>1.530200</td>\n",
              "      <td>0.70550</td>\n",
              "      <td>0.515100</td>\n",
              "      <td>100090.239881</td>\n",
              "      <td>0.203700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>96.653299</td>\n",
              "      <td>10.487806</td>\n",
              "      <td>2.892174</td>\n",
              "      <td>62397.405202</td>\n",
              "      <td>0.581654</td>\n",
              "      <td>0.45584</td>\n",
              "      <td>0.499797</td>\n",
              "      <td>57510.492818</td>\n",
              "      <td>0.402769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.580000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>584.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51002.110000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>652.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>97198.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100193.915000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>718.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>127644.240000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>149388.247500</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>850.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>250898.090000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>199992.480000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2a534ac-fade-4536-b4ae-acc972de90c9')\"\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-b2a534ac-fade-4536-b4ae-acc972de90c9 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-b2a534ac-fade-4536-b4ae-acc972de90c9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JMvq8-QC9fyT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Structure of the data**"
      ],
      "metadata": {
        "id": "viBRzJfu9gS6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dfy8nVK8jvj",
        "outputId": "e08c8dd8-8881-486e-f14f-ccd90dd6ee49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 12 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Surname          10000 non-null  object \n",
            " 1   CreditScore      10000 non-null  int64  \n",
            " 2   Geography        10000 non-null  object \n",
            " 3   Gender           10000 non-null  object \n",
            " 4   Age              10000 non-null  int64  \n",
            " 5   Tenure           10000 non-null  int64  \n",
            " 6   Balance          10000 non-null  float64\n",
            " 7   NumOfProducts    10000 non-null  int64  \n",
            " 8   HasCrCard        10000 non-null  int64  \n",
            " 9   IsActiveMember   10000 non-null  int64  \n",
            " 10  EstimatedSalary  10000 non-null  float64\n",
            " 11  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(7), object(3)\n",
            "memory usage: 937.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**we can see in the data there are three columns are in object type**\n",
        "**we have to convert all the object type columns  into categorical type for Model building**"
      ],
      "metadata": {
        "id": "KSIPrPc0ASNO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "label_encoder=preprocessing.LabelEncoder()\n",
        "df['Surname']=label_encoder.fit_transform(df['Surname'])\n",
        "df['Geography']=label_encoder.fit_transform(df['Geography'])\n",
        "df['Gender']=label_encoder.fit_transform(df['Gender'])"
      ],
      "metadata": {
        "id": "p86SyBEy8q82"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram for all numeric cols\n",
        "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "new_df = df.select_dtypes(include=numerics)\n",
        "numeric_cols=new_df.columns\n",
        "num_cols=3\n",
        "\n",
        "#To decide on the bin size using Sturge’s rule\n",
        "#bins=1+ceil(log2(n)) #research\n",
        "  \n",
        "bin_count = int(np.ceil(np.log2(len(new_df.axes[0]))) + 1)\n",
        "n_bins = bin_count\n",
        "print(n_bins)\n",
        "\n",
        "# compute number of rows for plot \n",
        "num_rows= int(len(numeric_cols)/num_cols)+1\n",
        "# setting canvas for plotting\n",
        "fig, axs = plt.subplots(num_rows, num_cols, tight_layout=True,figsize=(20,10))\n",
        "# plotting the numerical columns\n",
        "for col, ax in zip(numeric_cols,axs.flatten()[:len(numeric_cols)]):\n",
        " ax.hist(new_df[col],bins=n_bins,density=True)\n",
        " ax.set_title(col)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "dY7NCsEkGtqU",
        "outputId": "893e1df4-a9ab-4940-86ff-c4a0541dd9c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 15 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we can see the distribution every column the data is not normally distributed we can  use the MinmaxeScaler scaling "
      ],
      "metadata": {
        "id": "3LYSyfqoI05H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1yAJst1HKemG",
        "outputId": "40c85a50-2347-4e4b-b6f3-2d1143feca00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Surname', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',\n",
              "       'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember',\n",
              "       'EstimatedSalary', 'Exited'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=df[['Surname', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',\n",
        "       'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember',\n",
        "       'EstimatedSalary']]"
      ],
      "metadata": {
        "id": "mihmSVD_IQbp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BXTzjp7pKnHs",
        "outputId": "6836a1c6-a08f-4749-9c36-bb6df6fc938d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Surname  CreditScore  Geography  Gender  Age  Tenure    Balance  \\\n",
              "0        1115          619          0       0   42       2       0.00   \n",
              "1        1177          608          2       0   41       1   83807.86   \n",
              "2        2040          502          0       0   42       8  159660.80   \n",
              "3         289          699          0       0   39       1       0.00   \n",
              "4        1822          850          2       0   43       2  125510.82   \n",
              "...       ...          ...        ...     ...  ...     ...        ...   \n",
              "9995     1999          771          0       1   39       5       0.00   \n",
              "9996     1336          516          0       1   35      10   57369.61   \n",
              "9997     1570          709          0       0   36       7       0.00   \n",
              "9998     2345          772          1       1   42       3   75075.31   \n",
              "9999     2751          792          0       0   28       4  130142.79   \n",
              "\n",
              "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \n",
              "0                 1          1               1        101348.88  \n",
              "1                 1          0               1        112542.58  \n",
              "2                 3          1               0        113931.57  \n",
              "3                 2          0               0         93826.63  \n",
              "4                 1          1               1         79084.10  \n",
              "...             ...        ...             ...              ...  \n",
              "9995              2          1               0         96270.64  \n",
              "9996              1          1               1        101699.77  \n",
              "9997              1          0               1         42085.58  \n",
              "9998              2          1               0         92888.52  \n",
              "9999              1          1               0         38190.78  \n",
              "\n",
              "[10000 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb35ac3d-dc77-42ff-be05-2ecc466ad06d\">\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>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1115</td>\n",
              "      <td>619</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1177</td>\n",
              "      <td>608</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2040</td>\n",
              "      <td>502</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>289</td>\n",
              "      <td>699</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1822</td>\n",
              "      <td>850</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>1999</td>\n",
              "      <td>771</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>96270.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>1336</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>57369.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101699.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>1570</td>\n",
              "      <td>709</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>42085.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>2345</td>\n",
              "      <td>772</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>3</td>\n",
              "      <td>75075.31</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>92888.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>2751</td>\n",
              "      <td>792</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>130142.79</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38190.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb35ac3d-dc77-42ff-be05-2ecc466ad06d')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-fb35ac3d-dc77-42ff-be05-2ecc466ad06d 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-fb35ac3d-dc77-42ff-be05-2ecc466ad06d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Performing the Data Scaling**"
      ],
      "metadata": {
        "id": "DmvJkaOyIdLI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Scaler=StandardScaler()\n",
        "df_scaled=pd.DataFrame(Scaler.fit_transform (data.to_numpy()),columns=data.columns)"
      ],
      "metadata": {
        "id": "9UxWUdEHIQgY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oZP5F1JyIQjQ",
        "outputId": "fcddbee1-b654-4e59-fb17-673b06468f51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Surname  CreditScore  Geography    Gender       Age    Tenure  \\\n",
              "0    -0.464182    -0.326221  -0.901886 -1.095988  0.293517 -1.041760   \n",
              "1    -0.390910    -0.440036   1.515067 -1.095988  0.198164 -1.387538   \n",
              "2     0.628988    -1.536794  -0.901886 -1.095988  0.293517  1.032908   \n",
              "3    -1.440354     0.501521  -0.901886 -1.095988  0.007457 -1.387538   \n",
              "4     0.371355     2.063884   1.515067 -1.095988  0.388871 -1.041760   \n",
              "...        ...          ...        ...       ...       ...       ...   \n",
              "9995  0.580534     1.246488  -0.901886  0.912419  0.007457 -0.004426   \n",
              "9996 -0.203003    -1.391939  -0.901886  0.912419 -0.373958  1.724464   \n",
              "9997  0.073539     0.604988  -0.901886 -1.095988 -0.278604  0.687130   \n",
              "9998  0.989439     1.256835   0.306591  0.912419  0.293517 -0.695982   \n",
              "9999  1.469252     1.463771  -0.901886 -1.095988 -1.041433 -0.350204   \n",
              "\n",
              "       Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \n",
              "0    -1.225848      -0.911583   0.646092        0.970243         0.021886  \n",
              "1     0.117350      -0.911583  -1.547768        0.970243         0.216534  \n",
              "2     1.333053       2.527057   0.646092       -1.030670         0.240687  \n",
              "3    -1.225848       0.807737  -1.547768       -1.030670        -0.108918  \n",
              "4     0.785728      -0.911583   0.646092        0.970243        -0.365276  \n",
              "...        ...            ...        ...             ...              ...  \n",
              "9995 -1.225848       0.807737   0.646092       -1.030670        -0.066419  \n",
              "9996 -0.306379      -0.911583   0.646092        0.970243         0.027988  \n",
              "9997 -1.225848      -0.911583  -1.547768        0.970243        -1.008643  \n",
              "9998 -0.022608       0.807737   0.646092       -1.030670        -0.125231  \n",
              "9999  0.859965      -0.911583   0.646092       -1.030670        -1.076370  \n",
              "\n",
              "[10000 rows x 11 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
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              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
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              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
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              "      <th>0</th>\n",
              "      <td>-0.464182</td>\n",
              "      <td>-0.326221</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
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              "      <th>1</th>\n",
              "      <td>-0.390910</td>\n",
              "      <td>-0.440036</td>\n",
              "      <td>1.515067</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>0.198164</td>\n",
              "      <td>-1.387538</td>\n",
              "      <td>0.117350</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>0.216534</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.628988</td>\n",
              "      <td>-1.536794</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>1.032908</td>\n",
              "      <td>1.333053</td>\n",
              "      <td>2.527057</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>0.240687</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.440354</td>\n",
              "      <td>0.501521</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
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              "      <td>-1.387538</td>\n",
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              "      <td>0.807737</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-0.108918</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.371355</td>\n",
              "      <td>2.063884</td>\n",
              "      <td>1.515067</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>0.388871</td>\n",
              "      <td>-1.041760</td>\n",
              "      <td>0.785728</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>-0.365276</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>9995</th>\n",
              "      <td>0.580534</td>\n",
              "      <td>1.246488</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>0.912419</td>\n",
              "      <td>0.007457</td>\n",
              "      <td>-0.004426</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>0.807737</td>\n",
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              "      <td>-1.030670</td>\n",
              "      <td>-0.066419</td>\n",
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              "      <th>9996</th>\n",
              "      <td>-0.203003</td>\n",
              "      <td>-1.391939</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>0.912419</td>\n",
              "      <td>-0.373958</td>\n",
              "      <td>1.724464</td>\n",
              "      <td>-0.306379</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>0.027988</td>\n",
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              "      <th>9997</th>\n",
              "      <td>0.073539</td>\n",
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              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>-0.278604</td>\n",
              "      <td>0.687130</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>-1.547768</td>\n",
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              "      <td>0.293517</td>\n",
              "      <td>-0.695982</td>\n",
              "      <td>-0.022608</td>\n",
              "      <td>0.807737</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-0.125231</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>1.469252</td>\n",
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              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>-1.041433</td>\n",
              "      <td>-0.350204</td>\n",
              "      <td>0.859965</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-1.076370</td>\n",
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              "  </tbody>\n",
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              "<p>10000 rows × 11 columns</p>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      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-c6a22f67-4e26-4c41-8f30-ce7799a779bd 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-c6a22f67-4e26-4c41-8f30-ce7799a779bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled['Exited']=df['Exited']"
      ],
      "metadata": {
        "id": "aPvYF7o8K6XU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0KnI2-QZLGj1",
        "outputId": "8b8dc2b3-bc00-45d5-fa76-132fcd54f948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Surname  CreditScore  Geography    Gender       Age    Tenure  \\\n",
              "0    -0.464182    -0.326221  -0.901886 -1.095988  0.293517 -1.041760   \n",
              "1    -0.390910    -0.440036   1.515067 -1.095988  0.198164 -1.387538   \n",
              "2     0.628988    -1.536794  -0.901886 -1.095988  0.293517  1.032908   \n",
              "3    -1.440354     0.501521  -0.901886 -1.095988  0.007457 -1.387538   \n",
              "4     0.371355     2.063884   1.515067 -1.095988  0.388871 -1.041760   \n",
              "...        ...          ...        ...       ...       ...       ...   \n",
              "9995  0.580534     1.246488  -0.901886  0.912419  0.007457 -0.004426   \n",
              "9996 -0.203003    -1.391939  -0.901886  0.912419 -0.373958  1.724464   \n",
              "9997  0.073539     0.604988  -0.901886 -1.095988 -0.278604  0.687130   \n",
              "9998  0.989439     1.256835   0.306591  0.912419  0.293517 -0.695982   \n",
              "9999  1.469252     1.463771  -0.901886 -1.095988 -1.041433 -0.350204   \n",
              "\n",
              "       Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
              "0    -1.225848      -0.911583   0.646092        0.970243         0.021886   \n",
              "1     0.117350      -0.911583  -1.547768        0.970243         0.216534   \n",
              "2     1.333053       2.527057   0.646092       -1.030670         0.240687   \n",
              "3    -1.225848       0.807737  -1.547768       -1.030670        -0.108918   \n",
              "4     0.785728      -0.911583   0.646092        0.970243        -0.365276   \n",
              "...        ...            ...        ...             ...              ...   \n",
              "9995 -1.225848       0.807737   0.646092       -1.030670        -0.066419   \n",
              "9996 -0.306379      -0.911583   0.646092        0.970243         0.027988   \n",
              "9997 -1.225848      -0.911583  -1.547768        0.970243        -1.008643   \n",
              "9998 -0.022608       0.807737   0.646092       -1.030670        -0.125231   \n",
              "9999  0.859965      -0.911583   0.646092       -1.030670        -1.076370   \n",
              "\n",
              "      Exited  \n",
              "0          1  \n",
              "1          0  \n",
              "2          1  \n",
              "3          0  \n",
              "4          0  \n",
              "...      ...  \n",
              "9995       0  \n",
              "9996       0  \n",
              "9997       1  \n",
              "9998       1  \n",
              "9999       0  \n",
              "\n",
              "[10000 rows x 12 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.464182</td>\n",
              "      <td>-0.326221</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>-1.041760</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>-0.911583</td>\n",
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              "      <td>0.021886</td>\n",
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              "      <td>-0.390910</td>\n",
              "      <td>-0.440036</td>\n",
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              "      <td>-1.095988</td>\n",
              "      <td>0.198164</td>\n",
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              "      <td>0.117350</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>0.216534</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.628988</td>\n",
              "      <td>-1.536794</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>1.032908</td>\n",
              "      <td>1.333053</td>\n",
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              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>0.240687</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.440354</td>\n",
              "      <td>0.501521</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
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              "      <td>-1.387538</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>0.807737</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-0.108918</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.371355</td>\n",
              "      <td>2.063884</td>\n",
              "      <td>1.515067</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>0.388871</td>\n",
              "      <td>-1.041760</td>\n",
              "      <td>0.785728</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>-0.365276</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>-0.901886</td>\n",
              "      <td>0.912419</td>\n",
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              "      <td>-0.004426</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>0.807737</td>\n",
              "      <td>0.646092</td>\n",
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              "      <td>-0.066419</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>-0.203003</td>\n",
              "      <td>-1.391939</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>0.912419</td>\n",
              "      <td>-0.373958</td>\n",
              "      <td>1.724464</td>\n",
              "      <td>-0.306379</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>0.027988</td>\n",
              "      <td>0</td>\n",
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              "      <th>9997</th>\n",
              "      <td>0.073539</td>\n",
              "      <td>0.604988</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>-0.278604</td>\n",
              "      <td>0.687130</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>-1.008643</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0.989439</td>\n",
              "      <td>1.256835</td>\n",
              "      <td>0.306591</td>\n",
              "      <td>0.912419</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>-0.695982</td>\n",
              "      <td>-0.022608</td>\n",
              "      <td>0.807737</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-0.125231</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>1.469252</td>\n",
              "      <td>1.463771</td>\n",
              "      <td>-0.901886</td>\n",
              "      <td>-1.095988</td>\n",
              "      <td>-1.041433</td>\n",
              "      <td>-0.350204</td>\n",
              "      <td>0.859965</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-1.076370</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38eb528d-107b-47ce-8218-9168c2208f96')\"\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",
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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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-38eb528d-107b-47ce-8218-9168c2208f96 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-38eb528d-107b-47ce-8218-9168c2208f96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation=df_scaled.corr()"
      ],
      "metadata": {
        "id": "EuYO_ymNIQmx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "sb.heatmap(data=correlation,annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "oKCpSVD4IQpf",
        "outputId": "85b60337-eb07-420a-b392-d93b6e0d52dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rank=pd.DataFrame(correlation['Exited'][1:]).reset_index()\n",
        "rank.rename(columns={'index':'names','Exited':'values'},inplace=True)"
      ],
      "metadata": {
        "id": "4j0q2bNmIQrm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rank.sort_values(by='values',ascending=False,inplace=True)\n",
        "rank"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "6Xu1lZdXLfoc",
        "outputId": "71c6356d-8dbe-4a28-a578-71121037c91c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              names    values\n",
              "10           Exited  1.000000\n",
              "3               Age  0.285323\n",
              "5           Balance  0.118533\n",
              "1         Geography  0.035943\n",
              "9   EstimatedSalary  0.012097\n",
              "7         HasCrCard -0.007138\n",
              "4            Tenure -0.014001\n",
              "0       CreditScore -0.027094\n",
              "6     NumOfProducts -0.047820\n",
              "2            Gender -0.106512\n",
              "8    IsActiveMember -0.156128"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4388c432-e117-47d7-800f-17145d07d734\">\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>names</th>\n",
              "      <th>values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Exited</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Age</td>\n",
              "      <td>0.285323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Balance</td>\n",
              "      <td>0.118533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Geography</td>\n",
              "      <td>0.035943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>EstimatedSalary</td>\n",
              "      <td>0.012097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>HasCrCard</td>\n",
              "      <td>-0.007138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Tenure</td>\n",
              "      <td>-0.014001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CreditScore</td>\n",
              "      <td>-0.027094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NumOfProducts</td>\n",
              "      <td>-0.047820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gender</td>\n",
              "      <td>-0.106512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>IsActiveMember</td>\n",
              "      <td>-0.156128</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4388c432-e117-47d7-800f-17145d07d734')\"\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",
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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",
              "      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-4388c432-e117-47d7-800f-17145d07d734 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-4388c432-e117-47d7-800f-17145d07d734');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**we can see the above table  Highly correleated with the exited (target varible)**\n",
        "* Age \n",
        "* Balnace\n",
        "---\n",
        "* \tIsActiveMember"
      ],
      "metadata": {
        "id": "DXWsXcmfLs0i"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EcV-asdTMCTI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qgmOzdCLL60i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**split the data into train set and test set in 70:30 ratio by row index**"
      ],
      "metadata": {
        "id": "_QmlloNCL9oL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x,y=df_scaled.iloc[:,:-1],df_scaled.iloc[:,-1]"
      ],
      "metadata": {
        "id": "PhR0sQKNL63y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape,y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmeHPeW9L7BL",
        "outputId": "c4d6c7a0-2e6b-404b-f34b-d67545b868b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10000, 11) (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=30)"
      ],
      "metadata": {
        "id": "uVN4b1RvL7D1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape,y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98x3uAlML7Gf",
        "outputId": "9faa1808-9635-455a-d30b-169421eee104"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7000, 11) (7000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_test.shape,y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AgLY8qKMt4Y",
        "outputId": "2740dd36-2a84-49ca-adc4-1831cc7a7d7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3000, 11) (3000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b1y1FzVPM6zT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Building**"
      ],
      "metadata": {
        "id": "MJrEjc84Nxjf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "\n",
        "model_logistic = LogisticRegression().fit(x_train,y_train)"
      ],
      "metadata": {
        "id": "JcOjRhQbM62I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predict the exited customers from the data by using the logistic regression**"
      ],
      "metadata": {
        "id": "sSaDw4QgOJRm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1=model_logistic.predict(x_test)"
      ],
      "metadata": {
        "id": "uqasZSNoM65Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy of the training data set\n",
        "train_accuracy = round(model_logistic.score(x_train,y_train)*100,2)\n",
        "test_accuracy=round(model_logistic.score(x_test,y_test)*100,2)\n",
        "Accuracy_score=round(accuracy_score(y_test,y_pred1))\n",
        "print('train_accuracy   =>',train_accuracy )\n",
        "print('test_accuracy   =>',test_accuracy )\n",
        "print('Accuracy_SCORE   =>',Accuracy_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZ0A0zM0M67-",
        "outputId": "b583aa84-ee0a-4867-9d77-172e5952e332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_accuracy   => 81.1\n",
            "test_accuracy   => 80.1\n",
            "Accuracy_SCORE   => 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation**"
      ],
      "metadata": {
        "id": "EkzryfDtSN9g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# confusionMatrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(y_test,y_pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3T53RHlSSIQ",
        "outputId": "e7e5b9ee-a833-4aab-b8df-0f412f1a7cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2286   77]\n",
            " [ 520  117]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score"
      ],
      "metadata": {
        "id": "MxO8hzY_SSPu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"accuracy :\",accuracy_score(y_test,y_pred1))\n",
        "print(\"recall :\",round(recall_score(y_test,y_pred1),2))\n",
        "print(\"f1_score :\",round(f1_score(y_test,y_pred1),2))\n",
        "print(\"precision :\",round(precision_score(y_test,y_pred1),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLXIzROcTk1u",
        "outputId": "7eac2d1a-87b2-4999-d368-ecc8c9998450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy : 0.801\n",
            "recall : 0.18\n",
            "f1_score : 0.28\n",
            "precision : 0.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4AiSKvKITsQQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Precision:- A precision score is used to measure teh model performance in measuring the court of true positives in the correct manner out of all positive predictions made**\n",
        "\n",
        "\n",
        "\n",
        "* **Recall:- recall sore is used to measure the model performance in terms of measuring the court of true positive in acorrect manner out of all the actual positive values.**\n",
        "\n",
        "\n",
        "\n",
        "* **Accuracy:- Accuracy score is used to measure the model performance in terms of measuring teh ratio of sumof true positive and true negatives out of all the predictions made**\n",
        "\n",
        "*  **F1 score :- F1Score represents the model score as a function of precision and recall score**"
      ],
      "metadata": {
        "id": "j_gxuq02Xnyv"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nJDCE6B5Y9Ok"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WxyOkOjVU6TE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluate the ROC AND AUC Scores**"
      ],
      "metadata": {
        "id": "G_QtM_TnU7uU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "roc = roc_auc_score(y_test, model_logistic.predict_proba(x_test)[:,1])\n",
        "roc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8HndQf2U-v1",
        "outputId": "b6834e5f-51b5-468a-c6bb-39f571574934"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7503479532377422"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Draw the ROC-AUC and interpret the models performance from the curve"
      ],
      "metadata": {
        "id": "Hm75ToRhVOPb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "# generate a no skill prediction (majority class)\n",
        "ns_probs = [0 for _ in range(len(y_test))]\n",
        "# predict probabilities\n",
        "lr_probs = model_logistic.predict_proba(x_test)\n",
        "# keep probabilities for the positive outcome only\n",
        "lr_probs = lr_probs[:, 1]\n",
        "# calculate scores\n",
        "ns_auc = roc_auc_score(y_test, ns_probs)\n",
        "lr_auc = roc_auc_score(y_test, lr_probs)\n",
        "# summarize scores\n",
        "print('No Skill: ROC AUC=%.3f' % (ns_auc))\n",
        "print('Logistic: ROC AUC=%.3f' % (lr_auc))\n",
        "# calculate roc curves\n",
        "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(y_test, lr_probs)\n",
        "# plot the roc curve for the model\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='Logistic')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "Ojgxit_5VRpD",
        "outputId": "20e34518-bb0c-4d85-e8a5-11579f2455c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No Skill: ROC AUC=0.500\n",
            "Logistic: ROC AUC=0.750\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No Skill: ROC AUC=0.500\n",
        "\n",
        "Logistic: ROC AUC=0.752\n",
        "\n",
        "AUC IS nearly to 1 we can say our model is good model"
      ],
      "metadata": {
        "id": "P6Lm1kyMWXAh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **For GaussianNB Model**"
      ],
      "metadata": {
        "id": "mmZRh0ANnHob"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from numpy.random import default_rng\n",
        "from matplotlib.colors import ListedColormap"
      ],
      "metadata": {
        "id": "vBNWk717dm-O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = GaussianNB()\n",
        "classifier.fit(x_train, y_train) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmZJ0l4MWlIg",
        "outputId": "fe298a75-bc50-4d38-aab9-ecb77fabf545"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape\n",
        "print(classifier.score(x_train,y_train))\n",
        "print(classifier.score(x_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-i4JEyPWlLf",
        "outputId": "5e392784-e75b-41b2-bc5a-c9877bd8d62e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8294285714285714\n",
            "0.825\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred2=classifier.predict(x_test)"
      ],
      "metadata": {
        "id": "fD9N4D3aWlPP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "probability_values=classifier.predict_proba(x_test)\n",
        "probability_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnmEshgaWlSA",
        "outputId": "c615a003-7cbb-4f0d-c885-bdafda2bff5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.60038572, 0.39961428],\n",
              "       [0.51707352, 0.48292648],\n",
              "       [0.83136661, 0.16863339],\n",
              "       ...,\n",
              "       [0.94253726, 0.05746274],\n",
              "       [0.9442229 , 0.0557771 ],\n",
              "       [0.81396427, 0.18603573]])"
            ]
          },
          "metadata": {},
          "execution_count": 337
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x5VswORCWlUy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion matrix of GaussianNB"
      ],
      "metadata": {
        "id": "omDY_mCOeOb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred2)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z_Lv_o7pWlXi",
        "outputId": "dba6129e-606c-4ebe-b463-796a9a1b60ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2318,   45],\n",
              "       [ 480,  157]])"
            ]
          },
          "metadata": {},
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# here we can obsereve the accuracy of a tarin and \n",
        "print('tarining accuracy  ===>',round(classifier.score(x_train,y_train)*100,2))\n",
        "print('test accuarcy   ====>',round(classifier.score(x_test,y_test)*100),2)\n",
        "print('accuracy_score  ==> ',round(accuracy_score(y_test, y_pred2)*100,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9OJXvthtWlaL",
        "outputId": "22930006-e775-4458-c06a-8b5711d72e14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tarining accuracy  ===> 82.94\n",
            "test accuarcy   ====> 82 2\n",
            "accuracy_score  ==>  82.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"accuracy :\",accuracy_score(y_test,y_pred2))\n",
        "print(\"recall :\",round(recall_score(y_test,y_pred2),2))\n",
        "print(\"f1_score :\",round(f1_score(y_test,y_pred2),2))\n",
        "print(\"precision :\",round(precision_score(y_test,y_pred2),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn4h4eimVfkY",
        "outputId": "10be774a-c38c-4f16-8e13-a5a0ec2ee6c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy : 0.825\n",
            "recall : 0.25\n",
            "f1_score : 0.37\n",
            "precision : 0.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "99nR8W62fTDj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Random Forest Classfier**"
      ],
      "metadata": {
        "id": "SyXKCI_-HNQ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "uKcGJo-0HWJR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QWGcKGuuLW7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hyperparameter tuning for Random Forest using GridSearchCV and fit the data"
      ],
      "metadata": {
        "id": "pgSvUiNnLaIx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(random_state=42, n_jobs=-1)"
      ],
      "metadata": {
        "id": "qihjJzUXLW-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params = {\n",
        " 'max_depth': [2,3,5,10,20],\n",
        " 'min_samples_leaf': [5,10,20,50,100,200],\n",
        " 'n_estimators': [10,25,30,50,100,200]\n",
        "}"
      ],
      "metadata": {
        "id": "EfKC9b36LXCO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=rf,param_grid=params,cv = 4,n_jobs=-1, verbose=3, scoring=\"accuracy\")\n",
        "grid_search"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LR6QqoJMLXEj",
        "outputId": "006864f1-e031-4c89-a1f1-09f31c35bf99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4, estimator=RandomForestClassifier(n_jobs=-1, random_state=42),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'max_depth': [2, 3, 5, 10, 20],\n",
              "                         'min_samples_leaf': [5, 10, 20, 50, 100, 200],\n",
              "                         'n_estimators': [10, 25, 30, 50, 100, 200]},\n",
              "             scoring='accuracy', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "grid_search.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aYgkHNGLXIy",
        "outputId": "479926c0-2030-400c-d15d-8a24b09fbbd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 4 folds for each of 180 candidates, totalling 720 fits\n",
            "CPU times: user 5.86 s, sys: 715 ms, total: 6.57 s\n",
            "Wall time: 3min 18s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4, estimator=RandomForestClassifier(n_jobs=-1, random_state=42),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'max_depth': [2, 3, 5, 10, 20],\n",
              "                         'min_samples_leaf': [5, 10, 20, 50, 100, 200],\n",
              "                         'n_estimators': [10, 25, 30, 50, 100, 200]},\n",
              "             scoring='accuracy', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " rf_best = grid_search.best_estimator_\n",
        "rf_best"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_Ea0A_-LXMQ",
        "outputId": "71d93d59-59a3-463e-9d0b-af680ad4699e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=20, min_samples_leaf=10, n_jobs=-1,\n",
              "                       random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fOB5DojVLXQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# random forest\n",
        "model_Rf = RandomForestClassifier(max_depth=20, min_samples_leaf=10, n_jobs=-1,random_state=42)\n",
        "model_Rf.fit(x_train,y_train)\n",
        "y_pred3 = model_Rf.predict(x_test)\n",
        "print(\"Accuaracy on train dataset :\",round(model_Rf.score(x_train,y_train)*100,2))\n",
        "print(\"Accuaracy on test dataset :\",round(model_Rf.score(x_test,y_test)*100,2))\n",
        "print('accuracy_score  ==> ',round(accuracy_score(y_test, y_pred3)*100,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6ZiP_S0SELt",
        "outputId": "3fa0f27b-a3ea-4f43-9d2c-ae173096def4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuaracy on train dataset : 88.1\n",
            "Accuaracy on test dataset : 85.5\n",
            "accuracy_score  ==>  85.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"accuracy :\",accuracy_score(y_test,y_pred3))\n",
        "print(\"recall :\",round(recall_score(y_test,y_pred3),2))\n",
        "print(\"f1_score :\",round(f1_score(y_test,y_pred3),2))\n",
        "print(\"precision :\",round(precision_score(y_test,y_pred3),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUQ4WeB1lFNO",
        "outputId": "e214f54a-7a4e-40c0-bab7-3e337cbfd5bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy : 0.855\n",
            "recall : 0.43\n",
            "f1_score : 0.56\n",
            "precision : 0.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z39G6_rqkiQW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Decision tree**"
      ],
      "metadata": {
        "id": "qKDZswz0WMEp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "1# Decision tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model_DT = DecisionTreeClassifier(max_depth=20,min_samples_split=5,max_leaf_nodes=10) # After max depth 6 the acc is cons\n",
        "model_DT.fit(x_train,y_train)\n",
        "y_pred4 = model_DT.predict(x_test)\n",
        "print(\"Accuaracy on train dataset :\",round(model_DT.score(x_train,y_train)*100,2))\n",
        "print(\"Accuaracy on train dataset :\",round(model_DT.score(x_test,y_test)*100,2))\n",
        "print('accuracy_score  ==> ',round(accuracy_score(y_test, y_pred4)*100,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwdeQd2jSEQq",
        "outputId": "678b92b9-7c69-448e-eff7-1edcd5c9b391"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuaracy on train dataset : 85.66\n",
            "Accuaracy on train dataset : 84.33\n",
            "accuracy_score  ==>  84.33\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"accuracy :\",accuracy_score(y_test,y_pred4))\n",
        "print(\"recall :\",round(recall_score(y_test,y_pred4),2))\n",
        "print(\"f1_score :\",round(f1_score(y_test,y_pred4),2))\n",
        "print(\"precision :\",round(precision_score(y_test,y_pred4),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5pCyDNklPtL",
        "outputId": "e67a6fa7-8164-4358-a13b-8fa916e8a98f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy : 0.8433333333333334\n",
            "recall : 0.45\n",
            "f1_score : 0.55\n",
            "precision : 0.71\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AdaBoostClassifier**"
      ],
      "metadata": {
        "id": "I6r7V2qfWTKU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier"
      ],
      "metadata": {
        "id": "vq8rtvj8Uz1S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_AB = AdaBoostClassifier(n_estimators=100, random_state=0)\n",
        "model_AB.fit(x_train,y_train)\n",
        "y_pred5 = model_AB.predict(x_test)\n",
        "print(\"Accuarcy :\",round(model_AB.score(x_train,y_train)*100,2))\n",
        "print(\"Accuarcy :\",round(model_AB.score(x_test,y_test)*100,2))\n",
        "print('accuracy_score  ==> ',round(accuracy_score(y_test, y_pred5)*100,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udvjH0x_SEax",
        "outputId": "3514f15e-874b-422d-f643-292c62568425"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuarcy : 86.41\n",
            "Accuarcy : 85.0\n",
            "accuracy_score  ==>  85.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"accuracy :\",accuracy_score(y_test,y_pred5))\n",
        "print(\"recall :\",round(recall_score(y_test,y_pred5),2))\n",
        "print(\"f1_score :\",round(f1_score(y_test,y_pred5),2))\n",
        "print(\"precission :\",round(precision_score(y_test,y_pred5),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zGA6-mYiWaGe",
        "outputId": "a7bd4ff9-e700-4b6b-bc5c-4ec4fb17060a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy : 0.85\n",
            "recall : 0.47\n",
            "f1_score : 0.57\n",
            "precission : 0.73\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rmse(score):\n",
        "    rmse = np.sqrt(-score)\n",
        "    print(f'rmse= {\"{:.2f}\".format(rmse)}')"
      ],
      "metadata": {
        "id": "kFVWize2QfG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We will try with max depth starting from 1 to 15 and depending on the final ‘rmse’ score choose the value of max_depth.\n",
        "from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score\n",
        "from sklearn import  tree\n",
        "max_depth=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
        "for val in max_depth:  \n",
        "    score = cross_val_score(tree.DecisionTreeRegressor(max_depth= val, random_state= 42), x, y, cv=kfolds, scoring=\"neg_mean_squared_error\")\n",
        "    print(f'For max depth: {val}')\n",
        "    rmse(score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMb3UOydQiBX",
        "outputId": "31729145-2887-47e9-8e8d-8b30dd20e130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For max depth: 1\n",
            "rmse= 0.38\n",
            "For max depth: 2\n",
            "rmse= 0.36\n",
            "For max depth: 3\n",
            "rmse= 0.35\n",
            "For max depth: 4\n",
            "rmse= 0.34\n",
            "For max depth: 5\n",
            "rmse= 0.34\n",
            "For max depth: 6\n",
            "rmse= 0.34\n",
            "For max depth: 7\n",
            "rmse= 0.34\n",
            "For max depth: 8\n",
            "rmse= 0.35\n",
            "For max depth: 9\n",
            "rmse= 0.36\n",
            "For max depth: 10\n",
            "rmse= 0.37\n",
            "For max depth: 11\n",
            "rmse= 0.40\n",
            "For max depth: 12\n",
            "rmse= 0.41\n",
            "For max depth: 13\n",
            "rmse= 0.42\n",
            "For max depth: 14\n",
            "rmse= 0.43\n",
            "For max depth: 15\n",
            "rmse= 0.44\n",
            "For max depth: 16\n",
            "rmse= 0.44\n",
            "For max depth: 17\n",
            "rmse= 0.45\n",
            "For max depth: 18\n",
            "rmse= 0.46\n",
            "For max depth: 19\n",
            "rmse= 0.46\n",
            "For max depth: 20\n",
            "rmse= 0.46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion**"
      ],
      "metadata": {
        "id": "A5gC4kp2dPNC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scores=pd.DataFrame() # Final result"
      ],
      "metadata": {
        "id": "22x2DDPPWdHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores['Models'] = ['Logistic_Regression','GussainNb model','RandomForest classfier','Decision Tree classfier','AdaBoosting']\n"
      ],
      "metadata": {
        "id": "usQpMeGSbyGP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "scores['Training accuracy']=['81.1','82.94','88.1','85.66','86.26']\n",
        "scores['Test accuracy']=['80.1','82.5','85.5','84.33','84.83']\n",
        "scores['Accuracy Score']=['80.1','82.5','84.33','84.33','84.83']\n",
        "scores['recall']=['18','25','43','45','47']\n",
        "scores['F1score']=['28','37','56','55','57']\n",
        "scores['precission']=['60','78','79','71','72']\n"
      ],
      "metadata": {
        "id": "PgbgTYtEWdKW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9g4Ab6lfcDCG",
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              "                    Models Training accuracy Test accuracy Accuracy Score  \\\n",
              "0      Logistic_Regression              81.1          80.1           80.1   \n",
              "1          GussainNb model             82.94          82.5           82.5   \n",
              "2   RandomForest classfier              88.1          85.5          84.33   \n",
              "3  Decision Tree classfier             85.66         84.33          84.33   \n",
              "4              AdaBoosting             86.26         84.83          84.83   \n",
              "\n",
              "  recall F1score precission  \n",
              "0     18      28         60  \n",
              "1     25      37         78  \n",
              "2     43      56         79  \n",
              "3     45      55         71  \n",
              "4     47      57         72  "
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              "      <th></th>\n",
              "      <th>Models</th>\n",
              "      <th>Training accuracy</th>\n",
              "      <th>Test accuracy</th>\n",
              "      <th>Accuracy Score</th>\n",
              "      <th>recall</th>\n",
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              "      <th>0</th>\n",
              "      <td>Logistic_Regression</td>\n",
              "      <td>81.1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>80.1</td>\n",
              "      <td>18</td>\n",
              "      <td>28</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GussainNb model</td>\n",
              "      <td>82.94</td>\n",
              "      <td>82.5</td>\n",
              "      <td>82.5</td>\n",
              "      <td>25</td>\n",
              "      <td>37</td>\n",
              "      <td>78</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForest classfier</td>\n",
              "      <td>88.1</td>\n",
              "      <td>85.5</td>\n",
              "      <td>84.33</td>\n",
              "      <td>43</td>\n",
              "      <td>56</td>\n",
              "      <td>79</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decision Tree classfier</td>\n",
              "      <td>85.66</td>\n",
              "      <td>84.33</td>\n",
              "      <td>84.33</td>\n",
              "      <td>45</td>\n",
              "      <td>55</td>\n",
              "      <td>71</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AdaBoosting</td>\n",
              "      <td>86.26</td>\n",
              "      <td>84.83</td>\n",
              "      <td>84.83</td>\n",
              "      <td>47</td>\n",
              "      <td>57</td>\n",
              "      <td>72</td>\n",
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      "execution_count": null,
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      "cell_type": "markdown",
      "source": [
        "we can observe the 5 models \n",
        "* **Training Accuracy**\n",
        "* **Test Accuracy**\n",
        "* **Recall**\n",
        "* **F1Score**\n",
        "* **precission**"
      ],
      "metadata": {
        "id": "UaBv97gddVh_"
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    {
      "cell_type": "markdown",
      "source": [
        "The Model recognize positive cases\n",
        "\n",
        "\n",
        "\n",
        "* **Recall:-The ability of a classification model to identify all data points in a relevant class.**\n",
        "\n",
        "* **Precission:- The abiolity of classification nodel to return only the data points in a class**\n",
        "\n",
        "* **F1score:-  A single metric that combaines recall and precission using the harmonic mean**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "u8jGIZSnBNkF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "RandomForest is giveing the best accuarcy then the other model"
      ],
      "metadata": {
        "id": "CMIrnITPd9FI"
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}