placement_analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "placement_analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMpgLbNWKmz33EUddAv95ek",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Barky/HR-Analytics-Prediction/blob/main/placement_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wSULp8zmMGZ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import warnings"
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2etNRvfmPOe"
      },
      "source": [
        "df = pd.read_csv(\"/content/Placement_Data_Full_Class.csv\")"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "1udVRJ3lmVnE",
        "outputId": "786efa41-4bb2-45a2-cbd3-d9472839efc7"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 130,
      "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>sl_no</th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>M</td>\n",
              "      <td>79.33</td>\n",
              "      <td>Central</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>M</td>\n",
              "      <td>56.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>M</td>\n",
              "      <td>85.80</td>\n",
              "      <td>Central</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sl_no gender  ssc_p    ssc_b  ...  specialisation  mba_p      status    salary\n",
              "0      1      M  67.00   Others  ...          Mkt&HR  58.80      Placed  270000.0\n",
              "1      2      M  79.33  Central  ...         Mkt&Fin  66.28      Placed  200000.0\n",
              "2      3      M  65.00  Central  ...         Mkt&Fin  57.80      Placed  250000.0\n",
              "3      4      M  56.00  Central  ...          Mkt&HR  59.43  Not Placed       NaN\n",
              "4      5      M  85.80  Central  ...         Mkt&Fin  55.50      Placed  425000.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fTFXdS7mWYU"
      },
      "source": [
        "x=df.copy()\n",
        "x.drop([\"sl_no\", \"status\"], axis=1, inplace = True)\n",
        "y = df[\"status\"]"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwpCMOp4mpFz",
        "outputId": "a30f2f4f-054d-4bc6-c263-4750a0f36168"
      },
      "source": [
        "x.isnull().sum()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender             0\n",
              "ssc_p              0\n",
              "ssc_b              0\n",
              "hsc_p              0\n",
              "hsc_b              0\n",
              "hsc_s              0\n",
              "degree_p           0\n",
              "degree_t           0\n",
              "workex             0\n",
              "etest_p            0\n",
              "specialisation     0\n",
              "mba_p              0\n",
              "salary            67\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQZ7WBhVoqYz",
        "outputId": "5ba03391-ba82-405b-c054-c41586576ed3"
      },
      "source": [
        "import math\n",
        "for i in range(0,x.shape[0]):\n",
        "  val = x[\"salary\"][i]\n",
        "  if math.isnan(val): \n",
        "    x[\"salary\"][i] = x[\"salary\"].median()"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDBkft1GpRzO",
        "outputId": "61baefbd-7053-4c59-e2a9-36041e7791bf"
      },
      "source": [
        "x.isnull().sum()"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender            0\n",
              "ssc_p             0\n",
              "ssc_b             0\n",
              "hsc_p             0\n",
              "hsc_b             0\n",
              "hsc_s             0\n",
              "degree_p          0\n",
              "degree_t          0\n",
              "workex            0\n",
              "etest_p           0\n",
              "specialisation    0\n",
              "mba_p             0\n",
              "salary            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "_TkjuKwKoRAZ",
        "outputId": "6409b205-0b38-494d-aa0f-c598c07cae45"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 135,
      "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>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>79.33</td>\n",
              "      <td>Central</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>56.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>265000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>85.80</td>\n",
              "      <td>Central</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  gender  ssc_p    ssc_b  hsc_p  ... etest_p specialisation  mba_p    salary\n",
              "0      M  67.00   Others  91.00  ...    55.0         Mkt&HR  58.80  270000.0\n",
              "1      M  79.33  Central  78.33  ...    86.5        Mkt&Fin  66.28  200000.0\n",
              "2      M  65.00  Central  68.00  ...    75.0        Mkt&Fin  57.80  250000.0\n",
              "3      M  56.00  Central  52.00  ...    66.0         Mkt&HR  59.43  265000.0\n",
              "4      M  85.80  Central  73.60  ...    96.8        Mkt&Fin  55.50  425000.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "htmUwJTPmq4b",
        "outputId": "5304e336-c7fc-45fc-e032-b3ec1c249aa6"
      },
      "source": [
        "print(\"Gender has unique value size of \",len(x[\"gender\"].unique()))\n",
        "print(\"Gender's unique values are \",x[\"gender\"].unique())\n",
        "sns.countplot(x = 'gender',hue=y,data=x,palette='rainbow');"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gender has unique value size of  2\n",
            "Gender's unique values are  ['M' 'F']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqSli7sRm7Qr"
      },
      "source": [
        "temp = pd.get_dummies(x[\"gender\"], prefix=\"gender\")\n",
        "x = pd.concat([x, temp], axis = 1)\n",
        "x.drop(\"gender\", axis=1,inplace=True)"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "NJJ19lDYrKKB",
        "outputId": "37bcddb1-f467-460c-a5d0-1171d5bbc8e3"
      },
      "source": [
        "print(\"ssc_b has unique value size of \",len(x[\"ssc_b\"].unique()))\n",
        "print(\"ssc_b's unique values are \",x[\"ssc_b\"].unique())\n",
        "sns.countplot(x = 'ssc_b',hue=y,data=x,palette='rainbow');"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ssc_b has unique value size of  2\n",
            "ssc_b's unique values are  ['Others' 'Central']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYQEWQSHrd16"
      },
      "source": [
        "temp = pd.get_dummies(x[\"ssc_b\"], prefix=\"ssc_b\")\n",
        "x = pd.concat([x, temp], axis = 1)\n",
        "x.drop(\"ssc_b\", axis=1,inplace=True)"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "3t973eKdruX7",
        "outputId": "6443064e-4e79-4b57-abae-1aece80a747d"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 140,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>ssc_b_Central</th>\n",
              "      <th>ssc_b_Others</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>265000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>425000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p    hsc_b  ... gender_M  ssc_b_Central ssc_b_Others\n",
              "0  67.00  91.00   Others  ...        1              0            1\n",
              "1  79.33  78.33   Others  ...        1              1            0\n",
              "2  65.00  68.00  Central  ...        1              1            0\n",
              "3  56.00  52.00  Central  ...        1              1            0\n",
              "4  85.80  73.60  Central  ...        1              1            0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "UVpLSCUlrvK6",
        "outputId": "7d5a1505-aed2-4a44-beea-0f8b0553f5ad"
      },
      "source": [
        "print(\"hsc_b has unique value size of \",len(x[\"hsc_b\"].unique()))\n",
        "print(\"hsc_b's unique values are \",x[\"hsc_b\"].unique())\n",
        "sns.countplot(x = 'hsc_b',hue=y,data=x,palette='rainbow');"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hsc_b has unique value size of  2\n",
            "hsc_b's unique values are  ['Others' 'Central']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzDHnHpPr2JB"
      },
      "source": [
        "temp = pd.get_dummies(x[\"hsc_b\"], prefix=\"hsc_b\")\n",
        "x = pd.concat([x, temp], axis = 1)\n",
        "x.drop(\"hsc_b\", axis=1,inplace=True)"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "LDFpV4Gbr5Sy",
        "outputId": "fbf3676c-8d7a-4533-e298-63d320f86655"
      },
      "source": [
        "x.head(3)"
      ],
      "execution_count": 143,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>ssc_b_Central</th>\n",
              "      <th>ssc_b_Others</th>\n",
              "      <th>hsc_b_Central</th>\n",
              "      <th>hsc_b_Others</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p     hsc_s  ...  ssc_b_Others hsc_b_Central hsc_b_Others\n",
              "0  67.00  91.00  Commerce  ...             1             0            1\n",
              "1  79.33  78.33   Science  ...             0             0            1\n",
              "2  65.00  68.00      Arts  ...             0             1            0\n",
              "\n",
              "[3 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "uIk3sujYr6c6",
        "outputId": "bf92aef0-e685-4c6f-8d42-42dfbbb9b795"
      },
      "source": [
        "print(\"hsc_s has unique value size of \",len(x[\"hsc_s\"].unique()))\n",
        "print(\"hsc_s's unique values are \",x[\"hsc_s\"].unique())\n",
        "sns.countplot(x = 'hsc_s',hue=y,data=x,palette='rainbow');"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hsc_s has unique value size of  3\n",
            "hsc_s's unique values are  ['Commerce' 'Science' 'Arts']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMDtPnb5sOWz",
        "outputId": "a63407a1-fa45-4b34-bef2-b78ab1f414c5"
      },
      "source": [
        "w=0\n",
        "for i in x[\"hsc_s\"]:\n",
        "  if i == \"Arts\":w+=1\n",
        "print(w)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdQCxzJKs4wl",
        "outputId": "29128ff0-592c-4960-9ef6-47740f35c67d"
      },
      "source": [
        "for i in range(0, x.shape[0]):\n",
        "  if x[\"hsc_s\"][i] == \"Arts\": \n",
        "    x[\"hsc_s\"][i] = \"Commerce\""
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-ipGy1fr936"
      },
      "source": [
        "temp = pd.get_dummies(x[\"hsc_s\"], prefix=\"hsc_s\")\n",
        "x = pd.concat([x, temp], axis = 1)\n",
        "x.drop(\"hsc_s\", axis=1,inplace=True)"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "fLnibJIvsAkq",
        "outputId": "0a067a1c-97f7-4f9a-ba16-7a4ba24ad983"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 148,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>ssc_b_Central</th>\n",
              "      <th>ssc_b_Others</th>\n",
              "      <th>hsc_b_Central</th>\n",
              "      <th>hsc_b_Others</th>\n",
              "      <th>hsc_s_Commerce</th>\n",
              "      <th>hsc_s_Science</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>265000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>425000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  degree_p  ... hsc_b_Others hsc_s_Commerce  hsc_s_Science\n",
              "0  67.00  91.00     58.00  ...            1              1              0\n",
              "1  79.33  78.33     77.48  ...            1              0              1\n",
              "2  65.00  68.00     64.00  ...            0              1              0\n",
              "3  56.00  52.00     52.00  ...            0              0              1\n",
              "4  85.80  73.60     73.30  ...            0              1              0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "GiJa349qsBOd",
        "outputId": "c322ca55-0c90-4ab2-9c4c-15702fb3197e"
      },
      "source": [
        "print(\"degree_t has unique value size of \",len(x[\"degree_t\"].unique()))\n",
        "print(\"degree_t's unique values are \",x[\"degree_t\"].unique())\n",
        "sns.countplot(x = 'degree_t',hue=y,data=x,palette='rainbow');"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "degree_t has unique value size of  3\n",
            "degree_t's unique values are  ['Sci&Tech' 'Comm&Mgmt' 'Others']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoyU9WGitl5J",
        "outputId": "15323ea2-98d8-4a8c-b817-bd83ebbdd2ba"
      },
      "source": [
        "for i in range(0, x.shape[0]):\n",
        "  if x[\"degree_t\"][i] == \"Others\": \n",
        "    x[\"degree_t\"][i] = \"Sci&Tech\""
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L23kq3kgtrhq"
      },
      "source": [
        "temp = pd.get_dummies(x[\"degree_t\"], prefix=\"degree_t\")\n",
        "x = pd.concat([x, temp], axis = 1)\n",
        "x.drop(\"degree_t\", axis=1,inplace=True)"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "57pzt4r-tuni",
        "outputId": "5297c0ab-fc60-4d4a-b4a2-a077c243ff94"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 152,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>ssc_b_Central</th>\n",
              "      <th>ssc_b_Others</th>\n",
              "      <th>hsc_b_Central</th>\n",
              "      <th>hsc_b_Others</th>\n",
              "      <th>hsc_s_Commerce</th>\n",
              "      <th>hsc_s_Science</th>\n",
              "      <th>degree_t_Comm&amp;Mgmt</th>\n",
              "      <th>degree_t_Sci&amp;Tech</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>265000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>425000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  degree_p  ... hsc_s_Science  degree_t_Comm&Mgmt degree_t_Sci&Tech\n",
              "0  67.00  91.00     58.00  ...             0                   0                 1\n",
              "1  79.33  78.33     77.48  ...             1                   0                 1\n",
              "2  65.00  68.00     64.00  ...             0                   1                 0\n",
              "3  56.00  52.00     52.00  ...             1                   0                 1\n",
              "4  85.80  73.60     73.30  ...             0                   1                 0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "T1ZYbdFZtvOC",
        "outputId": "388337e0-25b1-404d-8550-e67ce9923cd8"
      },
      "source": [
        "print(\"workex has unique value size of \",len(x[\"workex\"].unique()))\n",
        "print(\"workex's unique values are \",x[\"workex\"].unique())\n",
        "sns.countplot(x = 'workex',hue=y,data=x,palette='rainbow');"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "workex has unique value size of  2\n",
            "workex's unique values are  ['No' 'Yes']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vltUyXImtzYy"
      },
      "source": [
        "temp = pd.get_dummies(x[\"workex\"], prefix=\"workex\")\n",
        "x = pd.concat([x, temp], axis = 1)\n",
        "x.drop(\"workex\", axis=1,inplace=True)"
      ],
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "RBI7_24-t5Li",
        "outputId": "c6566d8b-50aa-42dc-e1ee-09fa065b97d4"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 155,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>ssc_b_Central</th>\n",
              "      <th>ssc_b_Others</th>\n",
              "      <th>hsc_b_Central</th>\n",
              "      <th>hsc_b_Others</th>\n",
              "      <th>hsc_s_Commerce</th>\n",
              "      <th>hsc_s_Science</th>\n",
              "      <th>degree_t_Comm&amp;Mgmt</th>\n",
              "      <th>degree_t_Sci&amp;Tech</th>\n",
              "      <th>workex_No</th>\n",
              "      <th>workex_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>265000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>425000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  degree_p  ...  degree_t_Sci&Tech workex_No  workex_Yes\n",
              "0  67.00  91.00     58.00  ...                  1         1           0\n",
              "1  79.33  78.33     77.48  ...                  1         0           1\n",
              "2  65.00  68.00     64.00  ...                  0         1           0\n",
              "3  56.00  52.00     52.00  ...                  1         1           0\n",
              "4  85.80  73.60     73.30  ...                  0         1           0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "QMbHl0T7t5vp",
        "outputId": "784a6bcf-b2e6-435f-8f13-2cd8f7c1da36"
      },
      "source": [
        "print(\"specialisation has unique value size of \",len(x[\"specialisation\"].unique()))\n",
        "print(\"specialisation's unique values are \",x[\"specialisation\"].unique())\n",
        "sns.countplot(x = 'specialisation',hue=y,data=x,palette='rainbow');"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "specialisation has unique value size of  2\n",
            "specialisation's unique values are  ['Mkt&HR' 'Mkt&Fin']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O8Y+LuxAREakfqX4RS2cze9rMPopuvzGzznEXJyIidS/VP+7OA5aTmJe/I/D/ojYREckwqQZ/jrvPc/eK6DYfyImxLhERiUmqwV9mZqPMrHF0GwWUxVmYiIjEI9Xg/zZwJfA34EPgm8DYmGoSEZEYpXo5593AGHffBWBmbYBpJF4QREQkg6Ta4+91KPQB3P3vQJ94ShIRkTilGvyNzKz1oYWox5/quwURETmJpBreDwCvmNniaPlbwD3xlCQiInFK9ZO7j5lZITA8arrc3bfFV5aIiMQl5eGaKOgV9iIiGU7j9AEq3Dwt3SWcFPJ73ZHuEkTS4pjn4z9W0Qe+XjOz30bLXcxsvZm9ZWaLzOyUuGsQEZEvxB78wC1AcdLyfcB0d/8qsAu4vh5qEBGRSKzBH83geRHwy2jZSPyBeEm0yQLg0jhrEBGRquLu8T8EfB84GC23BXa7e0W0XAJ0irkGERFJElvwm9nFwEfuXnSc+483s0IzKywtLa3j6kREwhVnj38wMNLMtgNPkhjieRg4zcwOXU3UGXi/pp3dfba757t7fk6OZoAWEakrsQW/u/9fd+/s7rnA1cAqd78WWE1idk+AMcAzcdUgIiKHq4+reqr7AXCbmb1FYsx/bhpqEBEJVr18gMvdXwRejO7/FTinPs4rIiKHS0ePX0RE0kjBLyISGAW/iEhgFPwiIoFR8IuIBEbBLyISGAW/iEhgFPwiIoFR8IuIBEbBLyISGAW/iEhgFPwiIoFR8IuIBEbBLyISGAW/iEhgFPwiIoFR8IuIBEbBLyISGAW/iEhgFPwiIoFR8IuIBEbBLyISGAW/iEhgFPwiIoFR8IuIBEbBLyISGAW/iEhgmqS7ABGRQwo3T0t3CSeN/F53xHZs9fhFRAKj4BcRCYyCX0QkMAp+EZHAKPhFRAKj4BcRCYyCX0QkMLEFv5mdYWarzWybmb1hZrdE7W3M7HkzezP62TquGkRE5HBx9vgrgNvdvTswALjJzLoDPwRecPevAS9EyyIiUk9iC353/9DdN0b39wLFQCfgEmBBtNkC4NK4ahARkcPVyxi/meUCfYD1QHt3/zBa9TegfS37jDezQjMrLC0trY8yRUSCEHvwm1lz4DfAre7+SfI6d3fAa9rP3We7e7675+fk5MRdpohIMGINfjPLIhH6C919adS808w6ROs7AB/FWYOIiFQV51U9BswFit39waRVy4Ex0f0xwDNx1SAiIoeLc1rmwcBoYIuZbYrafgTcCzxlZtcDO4ArY6xBRESqiS343f1lwGpZXRDXeUVE5Mj0yV0RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn4RkcAo+EVEAqPgFxEJTFqC38wuNLM/m9lbZvbDdNQgIhKqeg9+M2sMPAr8O9AduMbMutd3HSIioUpHj/8c4C13/6u7/xN4ErgkDXWIiASpSRrO2Ql4L2m5BOhffSMzGw+MjxbLzezP9VBbEPpBO+DjdNeRfpPSXYBUo+dmsjp5fn65psZ0BH9K3H02MDvddTREZlbo7vnprkOkOj0360c6hnreB85IWu4ctYmISD1IR/BvAL5mZl3M7BTgamB5GuoQEQlSvQ/1uHuFmX0P+B3QGPiVu79R33UETkNocrLSc7MemLunuwYREalH+uSuiEhgFPwiIoFR8GcAM3MzeyJpuYmZlZrZb6PlKWZ2Rw37jTWzjknLZmazzWybmW0xs4HVtt9uZu2Sls9POsfY6JybzOxPZjYxjscqmaeen59boufgJjMbZGbPmtlpcT6+huikvY5fqvgUONvMmrr7Z8C/kdolsGOBrcAH0fIQ4GtADyAbaHmMdSxy9++ZWVvgz2a2xN3fO+pe0tDV5/NzmLsnf8DrP4636JCpx585ngUuiu5fA/xPTRuZ2XfMbKWZjQbygYVR76gp8E+gPZDl7p+5+87jKcTdy4C3gA7Hs780SGl5fh56l2pmuWZWbGZzzOwNM/vf6JhSAwV/5ngSuNrMsoFewPrqG0SXyV4MXOrujwOFwLXu3jvqie0EWgDzzcxqOc/qQ2+lgV/WtIGZnUmiR7b5RB+UNBj1/fw87Pgk3i086u49gN3AFSf8qBooBX+GcPfNQC6J3tSzNWxyHYkZT7/p7v+o5TBLgKHAPmA6gJk9amYXJ20zLPqP2Bu4odr+V5nZZhK9/f9298+P9/FIw5KG5+dh83sB77j7puh+UVSP1EDBn1mWA9Oo+W30FhJP9M417WhmpwPt3P0d4EYg18wmA/2A1Smef5G79wIGAfea2b8cW/nSwKX7+Zn8gnIA/Q2zVgr+zPIr4C5331LDutdI/IdZnnSlxF4Sb50BSklcODHM3Q+QmPn0FmCju396LEW4eyHweLS/yCEnxfNTjk7Bn0HcvcTdZxxh/cvAHcCK6LLM+cCsaLw+m8SY5z3R8jLge8AAM/vmcZRzHzDOzFocdUsJwkn2/JQj0JQNIiKBUY9fRCQwCn4RkcAo+EVEAqPgFxEJjIJfRCQwCn6RGqQy62PybKZmtu44z3OrmTU7lvOKnChdzilynMxsO5BfbbbIej+GyLFSj18ylpmdamYrzOx1M9tqZldFvfD/iuZt/6OZfTXaNsfMfmNmG6Lb4Ki9uZnNi7bfbGZXRO3JvfllZlYUzfo4vpZayqOfHcxsTTSR2FYzOzdqn2lmhdEx7oraJgAdSUw8trqG894WHWOrmd0atWkWSjlx7q6bbhl5I/FJzzlJy62A7cCPo+XrgN9G938NDInunwkUR/fvAx5KOkbr6Od2EnPHALSJfjYlMX982xq2KY9+3p50/sZAi2rHaAy8CPSqfozkZeDrJOa3ORVoDrwB9CEx300F0Dva/ilgVLr/LXTLrJsmMZJMtgV4wMzuIxHwL0Wz+R6aJOx/iGZ5BP4V6J40229LM2setV99qNHdd9Vwnglmdll0/wwS0/+W1VLTBuBXZpYFLPMvZou8Mnq30ITE9xh058jTWg8BnvZonhozWwqcS2IitHdcs1DKCVDwS8Zy97+YWV8S38I01cxeOLQqebPoZyNggFebSrr2ad8r159P4sVhoLvvM7MXScwrU1tNa8xsKIkvJZlvZg8CL5GYo6afu+8ys/lHOkYKqs9CqaEeOSYa45eMFc3yuM/dnwDuB/pGq65K+vlKdP9/gZuT9u0d3X0euCmpvXW107QCdkWhfxYw4Cg1fRnY6e5zSHyRTV8SXyH4KbDHzNqTmJf+kOQZKpO9BFxqZs3M7FTgsqhN5ISpxy+ZrCdwv5kdBPYD3yXxZR6toy+M+QeJLwYBmAA8GrU3AdYA/wlMjdq3kug93wUsTTrHc8B/mlkx8Gfg1aPUdD4wycz2A+XAde7+jpm9BvwJeA9Ym7T9bOA5M/vA3YcdanT3jdE7gz9GTb9099fMLDeVX4zIkehyTmlQdHmkyNFpqEdEJDDq8YuIBEY9fhGRwCj4RUQCo+AXEQmMgl9EJDAKfhGRwPx/sBctA9FbHD4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KvvD1mwt9Bp"
      },
      "source": [
        "temp = pd.get_dummies(x[\"specialisation\"], prefix=\"specialisation\")\n",
        "x = pd.concat([x, temp], axis = 1)\n",
        "x.drop(\"specialisation\", axis=1,inplace=True)"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "99Go6Q1YuAFR",
        "outputId": "498ee154-4566-4f00-934a-3c7fb8e69d73"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 158,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>ssc_b_Central</th>\n",
              "      <th>ssc_b_Others</th>\n",
              "      <th>hsc_b_Central</th>\n",
              "      <th>hsc_b_Others</th>\n",
              "      <th>hsc_s_Commerce</th>\n",
              "      <th>hsc_s_Science</th>\n",
              "      <th>degree_t_Comm&amp;Mgmt</th>\n",
              "      <th>degree_t_Sci&amp;Tech</th>\n",
              "      <th>workex_No</th>\n",
              "      <th>workex_Yes</th>\n",
              "      <th>specialisation_Mkt&amp;Fin</th>\n",
              "      <th>specialisation_Mkt&amp;HR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>55.0</td>\n",
              "      <td>58.80</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>86.5</td>\n",
              "      <td>66.28</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>75.0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>66.0</td>\n",
              "      <td>59.43</td>\n",
              "      <td>265000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>96.8</td>\n",
              "      <td>55.50</td>\n",
              "      <td>425000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ssc_p  hsc_p  ...  specialisation_Mkt&Fin  specialisation_Mkt&HR\n",
              "0  67.00  91.00  ...                       0                      1\n",
              "1  79.33  78.33  ...                       1                      0\n",
              "2  65.00  68.00  ...                       1                      0\n",
              "3  56.00  52.00  ...                       0                      1\n",
              "4  85.80  73.60  ...                       1                      0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "11JV0SCLuA2w",
        "outputId": "9ce61869-0599-4915-f54b-308b71db009a"
      },
      "source": [
        "sns.heatmap(x.corr())"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3219536c10>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0szscRZBuOvh"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcjKsmLAvCOx"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "x[\"ssc_p\"]=scaler.fit_transform(np.array(df[\"ssc_p\"]).reshape(-1,1))\n",
        "x[\"hsc_p\"]=scaler.fit_transform(np.array(df[\"hsc_p\"]).reshape(-1,1))\n",
        "x[\"degree_p\"]=scaler.fit_transform(np.array(df[\"degree_p\"]).reshape(-1,1))\n",
        "x[\"etest_p\"]=scaler.fit_transform(np.array(df[\"etest_p\"]).reshape(-1,1))\n",
        "x[\"mba_p\"]=scaler.fit_transform(np.array(df[\"mba_p\"]).reshape(-1,1))\n",
        "x[\"salary\"]=scaler.fit_transform(np.array(df[\"salary\"]).reshape(-1,1))"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tcEzdZQhvHqI",
        "outputId": "dd4d3eee-f118-44ae-865a-b0c81e35e6d8"
      },
      "source": [
        "x.isnull().sum()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ssc_p                      0\n",
              "hsc_p                      0\n",
              "degree_p                   0\n",
              "etest_p                    0\n",
              "mba_p                      0\n",
              "salary                    67\n",
              "gender_F                   0\n",
              "gender_M                   0\n",
              "ssc_b_Central              0\n",
              "ssc_b_Others               0\n",
              "hsc_b_Central              0\n",
              "hsc_b_Others               0\n",
              "hsc_s_Commerce             0\n",
              "hsc_s_Science              0\n",
              "degree_t_Comm&Mgmt         0\n",
              "degree_t_Sci&Tech          0\n",
              "workex_No                  0\n",
              "workex_Yes                 0\n",
              "specialisation_Mkt&Fin     0\n",
              "specialisation_Mkt&HR      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVkdyKlcvRWx"
      },
      "source": [
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "for i in range(0,x.shape[0]):\n",
        "  val = x[\"salary\"][i]\n",
        "  if math.isnan(val): \n",
        "    x[\"salary\"][i] = x[\"salary\"].median()"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "R3eQAekuwM25",
        "outputId": "7bbf47ee-9cbe-4d90-c858-cb4fd9f81ef0"
      },
      "source": [
        "x"
      ],
      "execution_count": 164,
      "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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>ssc_b_Central</th>\n",
              "      <th>ssc_b_Others</th>\n",
              "      <th>hsc_b_Central</th>\n",
              "      <th>hsc_b_Others</th>\n",
              "      <th>hsc_s_Commerce</th>\n",
              "      <th>hsc_s_Science</th>\n",
              "      <th>degree_t_Comm&amp;Mgmt</th>\n",
              "      <th>degree_t_Sci&amp;Tech</th>\n",
              "      <th>workex_No</th>\n",
              "      <th>workex_Yes</th>\n",
              "      <th>specialisation_Mkt&amp;Fin</th>\n",
              "      <th>specialisation_Mkt&amp;HR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.538240</td>\n",
              "      <td>0.889621</td>\n",
              "      <td>0.195122</td>\n",
              "      <td>0.104167</td>\n",
              "      <td>0.284483</td>\n",
              "      <td>0.094595</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.792414</td>\n",
              "      <td>0.680890</td>\n",
              "      <td>0.670244</td>\n",
              "      <td>0.760417</td>\n",
              "      <td>0.564843</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.497011</td>\n",
              "      <td>0.510708</td>\n",
              "      <td>0.341463</td>\n",
              "      <td>0.520833</td>\n",
              "      <td>0.247001</td>\n",
              "      <td>0.067568</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.311482</td>\n",
              "      <td>0.247117</td>\n",
              "      <td>0.048780</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.308096</td>\n",
              "      <td>0.087838</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.925788</td>\n",
              "      <td>0.602965</td>\n",
              "      <td>0.568293</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.160795</td>\n",
              "      <td>0.304054</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <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>210</th>\n",
              "      <td>0.818594</td>\n",
              "      <td>0.741351</td>\n",
              "      <td>0.673171</td>\n",
              "      <td>0.854167</td>\n",
              "      <td>0.872564</td>\n",
              "      <td>0.270270</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>0.352711</td>\n",
              "      <td>0.378913</td>\n",
              "      <td>0.536585</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.090330</td>\n",
              "      <td>0.101351</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>0.538240</td>\n",
              "      <td>0.494234</td>\n",
              "      <td>0.560976</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.693778</td>\n",
              "      <td>0.128378</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>0.682540</td>\n",
              "      <td>0.477759</td>\n",
              "      <td>0.195122</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.338081</td>\n",
              "      <td>0.005405</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>0.435168</td>\n",
              "      <td>0.345964</td>\n",
              "      <td>0.073171</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.337706</td>\n",
              "      <td>0.087838</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        ssc_p     hsc_p  ...  specialisation_Mkt&Fin  specialisation_Mkt&HR\n",
              "0    0.538240  0.889621  ...                       0                      1\n",
              "1    0.792414  0.680890  ...                       1                      0\n",
              "2    0.497011  0.510708  ...                       1                      0\n",
              "3    0.311482  0.247117  ...                       0                      1\n",
              "4    0.925788  0.602965  ...                       1                      0\n",
              "..        ...       ...  ...                     ...                    ...\n",
              "210  0.818594  0.741351  ...                       1                      0\n",
              "211  0.352711  0.378913  ...                       1                      0\n",
              "212  0.538240  0.494234  ...                       1                      0\n",
              "213  0.682540  0.477759  ...                       0                      1\n",
              "214  0.435168  0.345964  ...                       0                      1\n",
              "\n",
              "[215 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-e1jch-UwN-Y",
        "outputId": "80bed185-8da5-4680-bd9d-4c791078c258"
      },
      "source": [
        "!pip install catboost\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
        "from sklearn.cross_decomposition import PLSRegression, PLSSVD\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.svm import SVC\n"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.7/dist-packages (1.0.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTPSxKi3xTOO",
        "outputId": "fca4b46e-db07-4de8-e54f-bbafc1424662"
      },
      "source": [
        "map = {\"Placed\":0,\n",
        "       \"Not Placed\":1}\n",
        "y.replace(map, inplace=True)\n",
        "y"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      1\n",
              "4      0\n",
              "      ..\n",
              "210    0\n",
              "211    0\n",
              "212    0\n",
              "213    0\n",
              "214    1\n",
              "Name: status, Length: 215, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3-hw5m8wW1B"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
      ],
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5MmzldHwZXh",
        "outputId": "c7f1c142-f683-4310-fe09-06692f47cf90"
      },
      "source": [
        "rf_model = RandomForestClassifier().fit(x_train, y_train)\n",
        "rf = rf_model.predict(x_test)\n",
        "gbm_model = GradientBoostingClassifier().fit(x_train, y_train)\n",
        "gbm = gbm_model.predict(x_test)\n",
        "xgb_model = XGBClassifier().fit(x_train, y_train)\n",
        "xgb = xgb_model.predict(x_test)\n",
        "lgbm_model = LGBMClassifier().fit(x_train, y_train)\n",
        "lgbm = lgbm_model.predict(x_test)\n",
        "catb_model = CatBoostClassifier().fit(x_train, y_train)\n",
        "catb = catb_model.predict(x_test);\n",
        "knn_model = KNeighborsClassifier().fit(x_train, y_train)\n",
        "knn = knn_model.predict(x_test);\n",
        "svc_model = SVC(kernel = \"linear\").fit(x_train, y_train)\n",
        "svc = svc_model.predict(x_test);\n"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate set to 0.004859\n",
            "0:\tlearn: 0.6864765\ttotal: 1.82ms\tremaining: 1.82s\n",
            "1:\tlearn: 0.6829626\ttotal: 3.19ms\tremaining: 1.59s\n",
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            "999:\tlearn: 0.0208187\ttotal: 1.62s\tremaining: 0us\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XF_HCfgbwhSw",
        "outputId": "b7362ea7-99bf-45c0-efed-37ec67aa89df"
      },
      "source": [
        "print(\"Random Forest accuracy\", accuracy_score(y_test,rf))\n",
        "print(\"Gradient Boosting Machine accuracy\", accuracy_score(y_test,gbm))\n",
        "print(\"XGBoost accuracy: \", accuracy_score(y_test,xgb))\n",
        "print(\"Light GBM accuracy: \", accuracy_score(y_test,lgbm))\n",
        "print(\"CatBoost accuracy: \", accuracy_score(y_test,catb))\n",
        "print(\"KNN accuracy: \", accuracy_score(y_test,knn))\n",
        "print(\"SVC accuracy: \", accuracy_score(y_test,svc))\n"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest accuracy 0.9534883720930233\n",
            "Gradient Boosting Machine accuracy 0.9767441860465116\n",
            "XGBoost accuracy:  0.9767441860465116\n",
            "Light GBM accuracy:  0.9767441860465116\n",
            "CatBoost accuracy:  0.9767441860465116\n",
            "KNN accuracy:  0.7441860465116279\n",
            "SVC accuracy:  0.8604651162790697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PiC2wcN9xOAB",
        "outputId": "9d206013-cbb0-4c4c-f61a-b2d32a614281"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "np.sqrt(mean_squared_error(y_test, rf))"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21566554640687682"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xalzbyzlyy7g"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}