Handle_Imbalanced_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Handle-Imbalanced-dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMeHC7xusOAIARZ9YqncS0b"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmriPcDPRvT8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "76f7f204-b51f-46d9-cd6c-daf233872143"
      },
      "source": [
        "#impoert libraries\n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#loading the data\n",
        "df=pd.read_csv(\"bank-full.csv\")\n",
        "df.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>married</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>married</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1506</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age           job  marital  education  ... pdays  previous poutcome   y\n",
              "0   58    management  married   tertiary  ...    -1         0  unknown  no\n",
              "1   44    technician   single  secondary  ...    -1         0  unknown  no\n",
              "2   33  entrepreneur  married  secondary  ...    -1         0  unknown  no\n",
              "3   47   blue-collar  married    unknown  ...    -1         0  unknown  no\n",
              "4   33       unknown   single    unknown  ...    -1         0  unknown  no\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKYL4IdUSODE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3a5ce2d4-3a32-4644-92a0-eccfde0dbb5e"
      },
      "source": [
        "print(\"shape:\",df.shape)\n",
        "print(\"========================================\")\n",
        "print(\"info:\",df.info())\n",
        "print(\"========================================\")\n",
        "print(df.describe())\n",
        "print(\"========================================\")\n",
        "print(\"missing values:\",df.isnull().sum())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape: (45211, 17)\n",
            "========================================\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45211 entries, 0 to 45210\n",
            "Data columns (total 17 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   age        45211 non-null  int64 \n",
            " 1   job        45211 non-null  object\n",
            " 2   marital    45211 non-null  object\n",
            " 3   education  45211 non-null  object\n",
            " 4   default    45211 non-null  object\n",
            " 5   balance    45211 non-null  int64 \n",
            " 6   housing    45211 non-null  object\n",
            " 7   loan       45211 non-null  object\n",
            " 8   contact    45211 non-null  object\n",
            " 9   day        45211 non-null  int64 \n",
            " 10  month      45211 non-null  object\n",
            " 11  duration   45211 non-null  int64 \n",
            " 12  campaign   45211 non-null  int64 \n",
            " 13  pdays      45211 non-null  int64 \n",
            " 14  previous   45211 non-null  int64 \n",
            " 15  poutcome   45211 non-null  object\n",
            " 16  y          45211 non-null  object\n",
            "dtypes: int64(7), object(10)\n",
            "memory usage: 5.9+ MB\n",
            "info: None\n",
            "========================================\n",
            "                age        balance  ...         pdays      previous\n",
            "count  45211.000000   45211.000000  ...  45211.000000  45211.000000\n",
            "mean      40.936210    1362.272058  ...     40.197828      0.580323\n",
            "std       10.618762    3044.765829  ...    100.128746      2.303441\n",
            "min       18.000000   -8019.000000  ...     -1.000000      0.000000\n",
            "25%       33.000000      72.000000  ...     -1.000000      0.000000\n",
            "50%       39.000000     448.000000  ...     -1.000000      0.000000\n",
            "75%       48.000000    1428.000000  ...     -1.000000      0.000000\n",
            "max       95.000000  102127.000000  ...    871.000000    275.000000\n",
            "\n",
            "[8 rows x 7 columns]\n",
            "========================================\n",
            "missing values: age          0\n",
            "job          0\n",
            "marital      0\n",
            "education    0\n",
            "default      0\n",
            "balance      0\n",
            "housing      0\n",
            "loan         0\n",
            "contact      0\n",
            "day          0\n",
            "month        0\n",
            "duration     0\n",
            "campaign     0\n",
            "pdays        0\n",
            "previous     0\n",
            "poutcome     0\n",
            "y            0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_bGHWnrIvnd",
        "colab_type": "text"
      },
      "source": [
        "# Data visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTiUD96kayDw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8344ce29-45a8-4fe1-ff1d-4733f64b0479"
      },
      "source": [
        "categorcial_variables = ['job', 'marital', 'education', 'default', 'loan', 'contact', 'month', 'day', 'poutcome','y']\n",
        "for col in categorcial_variables:\n",
        "    plt.figure(figsize=(10,4))\n",
        "    sns.barplot(df[col].value_counts().values, df[col].value_counts().index)\n",
        "    plt.title(col)\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n9ZxVzcO2Z2xJ3pLksiTfzdq1Vb+Tteux3p/kkiT/muTw5bGV5BXL2jk/yfHjOL+dtTc1bE3y5DF+fJILlue8PMu/ImnbN7YkP5e1yyc+meS8ZXukNWS7mevnPknOXdbPBUlOW8aPyVqcbE3y9iQHL+O3Xu5vXfYfM471nGWNXJzxSSd+5+0/W5JfyP9/ioU1tJdu/qlpAAAYvEkPAAAGgQwAAINABgCAQSADAMAgkAEAYBDIAAAwCGQAABj+D//S9V3CmPjuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y4y/aRY7TdVhtno7T9auqY6vqqGn7XkmeksV7u1ebQz92n5Hkw9Pc7tasN/6ezWOVef5X+x/iyuI9w/0Y9ZjfLPv65N5mnLL4NOans3j/0ovmWseBfsri07yfnE5X751VFu/j+lCS65L8S5Kjp/2V5PXTXK9Msqvd1m9l8WGIPUmePfd92+Q5viOLH6d+J4v3Yf32MmeYZFcWT2LXJ3ldpt9SuVVPq8zz76Z5XZHFE/lx7etfNM3m2rRPUa/2PDAd9x+b5vz3SbbPfZ83YaZPzOLtE1ckuXw6nek4Xfo8Hafrn+mjk3ximt1VSc5daw5J7jmd3zNd/pD1znorntaY54enY/SqJG/L//9LFx7zm3jyq6YBAKDxIT0AAGgEMgAANAIZAAAagQwAAI1ABgCARiADAEAjkAEAoPk/I1N9CuWLKsUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jjfl9uv0Ht7I4d7Yffx7DzRjjRWOMU8cYu7M4Pj40xvj1bNdjcMz3Ksfzs3iV8k1JXjzXOrbaWxavzvzk9Hbd3bPJ4rycDya5Mcl/JDlx2l9JXjvN8Zoke9pt/VYWJ7fvT/Lsue/bJs/tn7L49ut3sjgv6bc3cmZJ9mTxhfGmJH+T6bdQ7pS3Neb3lmk+V2fxRejk9v4vnmZxQ9orsdd6XE/H9cemuf5zkmPnvs8bPL8nZHH6xNVJrprezncMbsgMHYcHNr9HJ/nENKdrk7xkvfuc5J7T5f3T9Q851LnuhLd15veh6fi7Nslb84OfdOExvP48fy4/+CkW2/IY9KumAQCg8SI9AABoBDIAADQCGQAAGoEMAACNQAYAgEYgAwBAI5ABAKD5f1syZBZmmwMnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxNZARXDJ06s",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   There are 45211 rows and 17 columns.\n",
        "\n",
        "*   No missing values\n",
        "\n",
        "*    There are unknown values for many variables in the Data set.\n",
        "*    Variables with unknown/missing values are : 'education', 'job', 'housing', 'loan', 'deafult', and 'marital\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXdne_v05-AL",
        "colab_type": "text"
      },
      "source": [
        "### Correlation Matrix of the numerical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Z21KxX0aqby",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert target variable into numeric\n",
        "df.y = df.y.map({'no':0, 'yes':1}).astype('uint8')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQybIH_o5Qbq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "157b3b6b-0a4d-4e05-ba68-599771d3e632"
      },
      "source": [
        "import seaborn as sns\n",
        "corr = df.corr()\n",
        "\n",
        "f, ax = plt.subplots(figsize=(10,12))\n",
        "\n",
        "sns.heatmap(corr, ax=ax, annot=True)\n",
        "\n",
        "plt.title(\"Pearson correlation of Features\", y=1.05, size=15)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Pearson correlation of Features')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RRJF4rIcfHMU",
        "colab_type": "text"
      },
      "source": [
        "There is no variable highly correlated to y."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vTrMkoXSPCy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "b10ac2a2-d19a-40e6-e3f2-b3b79ba3517c"
      },
      "source": [
        "#drop marital and contact columns\n",
        "df.drop(['marital'],axis=1, inplace=True)\n",
        "df.drop(['contact'],axis=1, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>no</td>\n",
              "      <td>2143</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>secondary</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1506</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>unknown</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>may</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age           job  education default  ...  pdays previous poutcome  y\n",
              "0   58    management   tertiary      no  ...     -1        0  unknown  0\n",
              "1   44    technician  secondary      no  ...     -1        0  unknown  0\n",
              "2   33  entrepreneur  secondary      no  ...     -1        0  unknown  0\n",
              "3   47   blue-collar    unknown      no  ...     -1        0  unknown  0\n",
              "4   33       unknown    unknown      no  ...     -1        0  unknown  0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0I_2L4NkLE_",
        "colab_type": "text"
      },
      "source": [
        "### changing categorical variable to numerical variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-l3R2zSWOEt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "8c94452d-bbe2-4f24-ce2d-72205fc30447"
      },
      "source": [
        "\n",
        "df[['default','housing','loan']]=df[['default','housing','loan']].replace([\"yes\",\"no\"],[\"1\",\"0\"])\n",
        "df['month']=df['month'].replace([\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\", \"aug\",\"sep\",\"oct\",\"nov\",\"dec\"],[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\"])\n",
        "df['job']=df['job'].replace(['unknown'],['other'])\n",
        "\n",
        "df.head()\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>management</td>\n",
              "      <td>tertiary</td>\n",
              "      <td>0</td>\n",
              "      <td>2143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>secondary</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>secondary</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>1506</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>other</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age           job  education default  ...  pdays previous poutcome  y\n",
              "0   58    management   tertiary       0  ...     -1        0  unknown  0\n",
              "1   44    technician  secondary       0  ...     -1        0  unknown  0\n",
              "2   33  entrepreneur  secondary       0  ...     -1        0  unknown  0\n",
              "3   47   blue-collar    unknown       0  ...     -1        0  unknown  0\n",
              "4   33         other    unknown       0  ...     -1        0  unknown  0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyUiZ-Tfi9Sq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()\n",
        "df['job']=le.fit_transform(df['job'])\n",
        "df['education']=le.fit_transform(df['education'])\n",
        "df['poutcome']=le.fit_transform(df['poutcome'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcgzVyZa5Kod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "7a440c4d-ed55-407a-c6fc-c4f498918e73"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>balance</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2143</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1506</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>92</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  job  education default  balance  ... campaign pdays  previous poutcome  y\n",
              "0   58    4          2       0     2143  ...        1    -1         0        3  0\n",
              "1   44   10          1       0       29  ...        1    -1         0        3  0\n",
              "2   33    2          1       0        2  ...        1    -1         0        3  0\n",
              "3   47    1          3       0     1506  ...        1    -1         0        3  0\n",
              "4   33    5          3       0        1  ...        1    -1         0        3  0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6dZqjQJkbmF",
        "colab_type": "text"
      },
      "source": [
        "### Outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uyalPclGX6X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "col=df[['age','balance','day','campaign','duration','pdays','previous']]\n",
        "for i in col:\n",
        "  n=1\n",
        "  plt.figure(figsize=(20,20))\n",
        "  plt.subplot(4,3,1)\n",
        "  sns.boxplot(df[i])\n",
        "  plt.title(i)\n",
        "  plt.show()\n",
        "  n=n+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qdd7JBH0ki35",
        "colab_type": "text"
      },
      "source": [
        "remove outliers using z_score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHgI6ditRsS2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "8e30947e-cbb6-4ed3-9d97-bae8a4f1a832"
      },
      "source": [
        "from scipy import stats\n",
        "import numpy as np\n",
        "z = np.abs(stats.zscore(df[['age','balance','duration','campaign','pdays','previous']]))\n",
        "print(z)\n",
        "df=df[(z<3).all(axis=1)]\n",
        "df.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.60696496 0.25641925 0.0110161  0.56935064 0.41145311 0.25194037]\n",
            " [0.28852927 0.43789469 0.41612696 0.56935064 0.41145311 0.25194037]\n",
            " [0.74738448 0.44676247 0.70736086 0.56935064 0.41145311 0.25194037]\n",
            " ...\n",
            " [2.92540065 1.42959305 3.37379688 0.72181052 1.43618859 1.05047333]\n",
            " [1.51279098 0.22802402 0.97014641 0.39902023 0.41145311 0.25194037]\n",
            " [0.37068857 0.52836436 0.39932797 0.24656035 1.4761376  4.52357654]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40209, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOzFDY6STzC0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "bf99c04b-6333-484f-e931-760f622185f7"
      },
      "source": [
        "#checking outliers removed ot not\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.subplot(3,3,1)\n",
        "sns.boxplot(df['age'])\n",
        "plt.title(\"Age\")\n",
        "plt.show()\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YrmTIuCkua1",
        "colab_type": "text"
      },
      "source": [
        "####seprating independent and dependent variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXnbLw5Gjf-U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "ff7b0296-a5f6-4aa0-a8bf-35113a24a2ee"
      },
      "source": [
        "x=df.drop(['y'],axis=1) #contain all  independent variable\n",
        "y=df['y']           #dependent variable\n",
        "df['y'].value_counts()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    36155\n",
              "1     4054\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lOErJyFck3J-",
        "colab_type": "text"
      },
      "source": [
        "#Handling imbalanced data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4W2N0cH9OORB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "a5d838b0-b4f6-4e68-beec-2d7bf3630fa7"
      },
      "source": [
        "\n",
        "sns.countplot(x='y',data=df)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0e8910b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1RPlc5hOwbz",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*  When observation in one class is higher than the observation in other classes then there exists a class imbalance. We can clearly see that there is a huge difference between the data set. Solving this issue we use resampling technique.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_9bFs-aWl39",
        "colab_type": "text"
      },
      "source": [
        "#1.Random Over-Sampling\n",
        "Oversampling can be defined as adding more copies to the minority class. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3pGkL_VwaHc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "e9ba5e8d-1b69-45ff-eb06-7299f9b8b80b"
      },
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "os =  RandomOverSampler()\n",
        "x_new,y_new=os.fit_sample(x,y)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igG8M7bYxLyO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "17ea151e-d859-4835-bd59-ff993cce11c6"
      },
      "source": [
        "from collections import Counter\n",
        "print('Original dataset shape {}'.format(Counter(y)))\n",
        "print('Resampled dataset shape {}'.format(Counter(y_new)))\n",
        "sns.countplot(y_new)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original dataset shape Counter({0: 36155, 1: 4054})\n",
            "Resampled dataset shape Counter({0: 36155, 1: 36155})\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0e88b9198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LqhRN44yjzd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bb1c8952-8bf5-4770-c4fc-86d1f4636fb9"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix,roc_auc_score\n",
        "\n",
        "#dividing the dataset into training and testing\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(x_new,y_new,test_size=.20,random_state=0)\n",
        "print(xtrain.shape,xtest.shape,ytrain.shape,ytest.shape)\n",
        "\n",
        "#feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "xtrain=scaler.fit_transform(xtrain)\n",
        "xtest=scaler.transform(xtest)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(57848, 14) (14462, 14) (57848,) (14462,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsVxIrJv8SaA",
        "colab_type": "text"
      },
      "source": [
        "#RandomForest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5TDP-7Q4qiE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "3a7040cb-d428-4466-9a44-6c7ca459c73c"
      },
      "source": [
        "\n",
        "from sklearn.metrics import precision_score,recall_score,accuracy_score,f1_score\n",
        "\n",
        "\n",
        "# Random Forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "randomforest = RandomForestClassifier()\n",
        "randomforest.fit(xtrain, ytrain)\n",
        "y_pred = randomforest.predict(xtest)\n",
        "\n",
        "AUC_RF=roc_auc_score(y_pred,ytest)\n",
        "acc_rf=accuracy_score(ytest,y_pred)\n",
        "recall_rf=recall_score(ytest,y_pred)\n",
        "precision_rf=precision_score(ytest,y_pred)\n",
        "f1score_rf=f1_score(ytest,y_pred)\n",
        "print(\"ROC_AUC Score:\",AUC_RF)\n",
        "\n",
        "cm=confusion_matrix(ytest,y_pred)\n",
        "print(cm)\n",
        "\n",
        "sns.heatmap(cm,annot=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ROC_AUC Score: 0.9774039804908539\n",
            "[[6802  345]\n",
            " [   1 7314]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0e8abc278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSm9zqfK8aKA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "f4c1ebd1-5fb8-456f-cb73-43db137aeb3b"
      },
      "source": [
        "print(classification_report(y_pred,ytest))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.98      6803\n",
            "           1       1.00      0.95      0.98      7659\n",
            "\n",
            "    accuracy                           0.98     14462\n",
            "   macro avg       0.98      0.98      0.98     14462\n",
            "weighted avg       0.98      0.98      0.98     14462\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRkoztEwS1tE",
        "colab_type": "text"
      },
      "source": [
        "# 2.SMOTETomek"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOqA3qMgS0nu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "8dbe0bfe-8d46-42a6-a053-ff88e62c6f2d"
      },
      "source": [
        "from imblearn.combine import SMOTETomek\n",
        "\n",
        "os=SMOTETomek(0.75)\n",
        "x_new,y_new=os.fit_sample(x,y)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix,roc_auc_score\n",
        "\n",
        "#dividing the dataset into training and testing\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(x_new,y_new,test_size=.20,random_state=0)\n",
        "print(xtrain.shape,xtest.shape,ytrain.shape,ytest.shape)\n",
        "\n",
        "#feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "xtrain=scaler.fit_transform(xtrain)\n",
        "xtest=scaler.transform(xtest)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(49453, 14) (12364, 14) (49453,) (12364,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJRfGFd0TVvD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "7bba076a-5b4d-452c-bae0-512792be8f7f"
      },
      "source": [
        "# Random Forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "randomforest = RandomForestClassifier()\n",
        "randomforest.fit(xtrain, ytrain)\n",
        "y_pred = randomforest.predict(xtest)\n",
        "\n",
        "AUC_RF=roc_auc_score(y_pred,ytest)\n",
        "acc_rf=accuracy_score(ytest,y_pred)\n",
        "recall_rf=recall_score(ytest,y_pred)\n",
        "precision_rf=precision_score(ytest,y_pred)\n",
        "f1score_rf=f1_score(ytest,y_pred)\n",
        "print(\"ROC_AUC Score:\",AUC_RF)\n",
        "\n",
        "cm=confusion_matrix(ytest,y_pred)\n",
        "print(cm)\n",
        "\n",
        "sns.heatmap(cm,annot=True)\n",
        "\n",
        "print(classification_report(y_pred,ytest))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ROC_AUC Score: 0.9491186291520087\n",
            "[[6798  251]\n",
            " [ 383 4932]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.95      0.96      7181\n",
            "           1       0.93      0.95      0.94      5183\n",
            "\n",
            "    accuracy                           0.95     12364\n",
            "   macro avg       0.95      0.95      0.95     12364\n",
            "weighted avg       0.95      0.95      0.95     12364\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUkAJfFxTs49",
        "colab_type": "text"
      },
      "source": [
        "# 3. Under sampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrS1eYSDT6Qy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "59ff7af1-7a59-4efd-95b3-290b324ba669"
      },
      "source": [
        "from imblearn.under_sampling import NearMiss\n",
        "os=NearMiss(0.8)\n",
        "\n",
        "x_new,y_new=os.fit_sample(x,y)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix,roc_auc_score\n",
        "\n",
        "#dividing the dataset into training and testing\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(x_new,y_new,test_size=.20,random_state=0)\n",
        "print(xtrain.shape,xtest.shape,ytrain.shape,ytest.shape)\n",
        "\n",
        "#feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "xtrain=scaler.fit_transform(xtrain)\n",
        "xtest=scaler.transform(xtest)\n",
        "\n",
        "\n",
        "\n",
        "############## Random Forest\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "randomforest = RandomForestClassifier()\n",
        "randomforest.fit(xtrain, ytrain)\n",
        "y_pred = randomforest.predict(xtest)\n",
        "\n",
        "AUC_RF=roc_auc_score(y_pred,ytest)\n",
        "acc_rf=accuracy_score(ytest,y_pred)\n",
        "recall_rf=recall_score(ytest,y_pred)\n",
        "precision_rf=precision_score(ytest,y_pred)\n",
        "f1score_rf=f1_score(ytest,y_pred)\n",
        "print(\"ROC_AUC Score:\",AUC_RF)\n",
        "\n",
        "cm=confusion_matrix(ytest,y_pred)\n",
        "print(cm)\n",
        "\n",
        "sns.heatmap(cm,annot=True)\n",
        "\n",
        "print(classification_report(y_pred,ytest))\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(7296, 14) (1825, 14) (7296,) (1825,)\n",
            "ROC_AUC Score: 0.9184674781820019\n",
            "[[971  62]\n",
            " [ 87 705]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.92      0.93      1058\n",
            "           1       0.89      0.92      0.90       767\n",
            "\n",
            "    accuracy                           0.92      1825\n",
            "   macro avg       0.92      0.92      0.92      1825\n",
            "weighted avg       0.92      0.92      0.92      1825\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}