Selection_of_the_Right_Undergraduate_Major_.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiE4HQuXA1jb"
      },
      "source": [
        "# **1-Import libaray**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYcSX5ofA8Yz"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt\n",
        "from prettytable import PrettyTable\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from mlxtend.plotting import plot_confusion_matrix \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dEH-aOwYdNx"
      },
      "source": [
        "# **Introduction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69-GFSEUYpQA"
      },
      "source": [
        "# **2- Data Loading**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucwJyax-cZmZ"
      },
      "source": [
        "**Load data from google drive** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYNNsGgxmxyQ",
        "outputId": "dbddbbdc-36ff-4fa3-e450-e4ea288d1d22"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkoAdyxInLR4"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/Datasets/Student field Recommendation /Placement_Data_Full_Class.csv\")"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OE_f4-bToHWA"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7bwIxq5rc9x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70a17b18-303b-41fe-e334-cf28f92d1c5f"
      },
      "source": [
        "data.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3225"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7r4GCZ_r578"
      },
      "source": [
        "# **3-Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpWXxSYL5nAY"
      },
      "source": [
        "**3.1 Creating Category of Mark Secured in Different Educational Phase**\n",
        "\n",
        "Here we will create 3 category:\n",
        "- 85% +\n",
        "- 60% - 85%\n",
        "- < 60%\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Hi_smCr6BCj"
      },
      "source": [
        "def checkCateg(perct):\n",
        "    if(perct >= 85):\n",
        "        return '85% +'\n",
        "    elif(perct < 85 and perct >= 60):\n",
        "        return '60% - 85%'\n",
        "    else:\n",
        "        return '< 60%'"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWvSBTZ06DsY"
      },
      "source": [
        "data['ssc_p_catg'] = data['ssc_p'].apply(checkCateg)\n",
        "data['hsc_p_catg'] = data['hsc_p'].apply(checkCateg)\n",
        "data['mba_p_catg'] = data['mba_p'].apply(checkCateg)\n",
        "data['degree_p_catg'] = data['degree_p'].apply(checkCateg)\n",
        "data['etest_p_catg'] = data['etest_p'].apply(checkCateg)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "bB7u_WQ0eFNR",
        "outputId": "7c73effc-a2ca-400f-b72c-80d508b29056"
      },
      "source": [
        "data"
      ],
      "execution_count": 31,
      "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",
              "      <th>ssc_p_catg</th>\n",
              "      <th>hsc_p_catg</th>\n",
              "      <th>mba_p_catg</th>\n",
              "      <th>degree_p_catg</th>\n",
              "      <th>etest_p_catg</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",
              "      <td>60% - 85%</td>\n",
              "      <td>85% +</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>&lt; 60%</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",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>85% +</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",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</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",
              "      <td>&lt; 60%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>60% - 85%</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",
              "      <td>85% +</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>85% +</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>211</td>\n",
              "      <td>M</td>\n",
              "      <td>80.60</td>\n",
              "      <td>Others</td>\n",
              "      <td>82.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>77.60</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>91.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>74.49</td>\n",
              "      <td>Placed</td>\n",
              "      <td>400000.0</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>85% +</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>212</td>\n",
              "      <td>M</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>60.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>72.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>74.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>53.62</td>\n",
              "      <td>Placed</td>\n",
              "      <td>275000.0</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>213</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>Yes</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>69.72</td>\n",
              "      <td>Placed</td>\n",
              "      <td>295000.0</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>214</td>\n",
              "      <td>F</td>\n",
              "      <td>74.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>66.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>60.23</td>\n",
              "      <td>Placed</td>\n",
              "      <td>204000.0</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>60% - 85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>215</td>\n",
              "      <td>M</td>\n",
              "      <td>62.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>53.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>89.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>60.22</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>60% - 85%</td>\n",
              "      <td>&lt; 60%</td>\n",
              "      <td>85% +</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sl_no gender  ssc_p  ... mba_p_catg  degree_p_catg etest_p_catg\n",
              "0        1      M  67.00  ...      < 60%          < 60%        < 60%\n",
              "1        2      M  79.33  ...  60% - 85%      60% - 85%        85% +\n",
              "2        3      M  65.00  ...      < 60%      60% - 85%    60% - 85%\n",
              "3        4      M  56.00  ...      < 60%          < 60%    60% - 85%\n",
              "4        5      M  85.80  ...      < 60%      60% - 85%        85% +\n",
              "..     ...    ...    ...  ...        ...            ...          ...\n",
              "210    211      M  80.60  ...  60% - 85%      60% - 85%        85% +\n",
              "211    212      M  58.00  ...      < 60%      60% - 85%    60% - 85%\n",
              "212    213      M  67.00  ...  60% - 85%      60% - 85%        < 60%\n",
              "213    214      F  74.00  ...  60% - 85%          < 60%    60% - 85%\n",
              "214    215      M  62.00  ...  60% - 85%          < 60%        85% +\n",
              "\n",
              "[215 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JL1FYrIa5kv8"
      },
      "source": [
        "**3.2 -Check Missing Value**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6sqt75_5o9y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "986edd41-ada4-4319-b7c7-fda67483f302"
      },
      "source": [
        "total = data.isnull().sum().sort_values(ascending=False)\n",
        "percent = (data.isnull().sum()/data.isnull().count()).sort_values(ascending=False)\n",
        "missing = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing.head(4)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>salary</th>\n",
              "      <td>67</td>\n",
              "      <td>0.311628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>etest_p_catg</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>degree_t</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Total   Percent\n",
              "salary           67  0.311628\n",
              "etest_p_catg      0  0.000000\n",
              "degree_t          0  0.000000\n",
              "gender            0  0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2c66GfvuVn7"
      },
      "source": [
        "data = data.dropna(axis = 0, how ='any') "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mc-3l3i81BWI"
      },
      "source": [
        "np.sum(data.isnull().any(axis=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnvVuWKg6vj1"
      },
      "source": [
        "**3.3-Hot Encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Fa0DkFL6yEq",
        "outputId": "51a4ba2d-864d-45e4-b233-11a5558e0ea1"
      },
      "source": [
        "data.select_dtypes(include=['object']).columns"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index([], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g24nkqr7Qx8"
      },
      "source": [
        "data['gender'] = data['gender'].fillna(data['gender'].mode()[0])\n",
        "data['ssc_b'] = data['ssc_b'].fillna(data['ssc_b'].mode()[0])\n",
        "data['hsc_b'] = data['hsc_b'].fillna(data['hsc_b'].mode()[0])\n",
        "data['hsc_s'] = data['hsc_s'].fillna(data['hsc_s'].mode()[0])\n",
        "data['degree_t'] = data['degree_t'].fillna(data['degree_t'].mode()[0])\n",
        "data['workex'] = data['workex'].fillna(data['workex'].mode()[0])\n",
        "data['specialisation'] = data['specialisation'].fillna(data['specialisation'].mode()[0])\n",
        "data['status'] = data['status'].fillna(data['status'].mode()[0])\n",
        "data['ssc_p_catg'] = data['ssc_p_catg'].fillna(data['ssc_p_catg'].mode()[0])\n",
        "data['hsc_p_catg'] = data['hsc_p_catg'].fillna(data['hsc_p_catg'].mode()[0])\n",
        "data['mba_p_catg'] = data['mba_p_catg'].fillna(data['mba_p_catg'].mode()[0])\n",
        "data['degree_p_catg'] = data['degree_p_catg'].fillna(data['degree_p_catg'].mode()[0])\n",
        "data['etest_p_catg'] = data['etest_p_catg'].fillna(data['etest_p_catg'].mode()[0])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLaZar8T-YkY"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "lencoders = {}\n",
        "for col in data.select_dtypes(include=['object']).columns:\n",
        "    lencoders[col] = LabelEncoder()\n",
        "    data[col] = lencoders[col].fit_transform(data[col])"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5GR2wIi-k--",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "90406c29-8c36-4fda-fc56-96e7d6f080eb"
      },
      "source": [
        "data"
      ],
      "execution_count": 35,
      "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",
              "      <th>ssc_p_catg</th>\n",
              "      <th>hsc_p_catg</th>\n",
              "      <th>mba_p_catg</th>\n",
              "      <th>degree_p_catg</th>\n",
              "      <th>etest_p_catg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>1</td>\n",
              "      <td>91.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>58.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "      <td>58.80</td>\n",
              "      <td>1</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>79.33</td>\n",
              "      <td>0</td>\n",
              "      <td>78.33</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>77.48</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>86.5</td>\n",
              "      <td>0</td>\n",
              "      <td>66.28</td>\n",
              "      <td>1</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>65.00</td>\n",
              "      <td>0</td>\n",
              "      <td>68.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>56.00</td>\n",
              "      <td>0</td>\n",
              "      <td>52.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1</td>\n",
              "      <td>59.43</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>85.80</td>\n",
              "      <td>0</td>\n",
              "      <td>73.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>73.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "      <td>0</td>\n",
              "      <td>55.50</td>\n",
              "      <td>1</td>\n",
              "      <td>425000.0</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>...</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>211</td>\n",
              "      <td>1</td>\n",
              "      <td>80.60</td>\n",
              "      <td>1</td>\n",
              "      <td>82.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>77.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0</td>\n",
              "      <td>74.49</td>\n",
              "      <td>1</td>\n",
              "      <td>400000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>212</td>\n",
              "      <td>1</td>\n",
              "      <td>58.00</td>\n",
              "      <td>1</td>\n",
              "      <td>60.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>72.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0</td>\n",
              "      <td>53.62</td>\n",
              "      <td>1</td>\n",
              "      <td>275000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>213</td>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>73.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0</td>\n",
              "      <td>69.72</td>\n",
              "      <td>1</td>\n",
              "      <td>295000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>74.00</td>\n",
              "      <td>1</td>\n",
              "      <td>66.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>58.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1</td>\n",
              "      <td>60.23</td>\n",
              "      <td>1</td>\n",
              "      <td>204000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>215</td>\n",
              "      <td>1</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>58.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>53.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>1</td>\n",
              "      <td>60.22</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sl_no  gender  ssc_p  ...  mba_p_catg  degree_p_catg  etest_p_catg\n",
              "0        1       1  67.00  ...           1              2             2\n",
              "1        2       1  79.33  ...           0              0             1\n",
              "2        3       1  65.00  ...           1              0             0\n",
              "3        4       1  56.00  ...           1              2             0\n",
              "4        5       1  85.80  ...           1              0             1\n",
              "..     ...     ...    ...  ...         ...            ...           ...\n",
              "210    211       1  80.60  ...           0              0             1\n",
              "211    212       1  58.00  ...           1              0             0\n",
              "212    213       1  67.00  ...           0              0             2\n",
              "213    214       0  74.00  ...           0              2             0\n",
              "214    215       1  62.00  ...           0              2             1\n",
              "\n",
              "[215 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1X3_s7X8_hEc"
      },
      "source": [
        "**3.4-Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQgnWr7Q_jey"
      },
      "source": [
        "# Standardizing data\n",
        "from sklearn import preprocessing\n",
        "r_scaler = preprocessing.MinMaxScaler()\n",
        "r_scaler.fit(data)\n",
        "data = pd.DataFrame(r_scaler.transform(data), index=data.index, columns=data.columns)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2m6tWQEIAQfb"
      },
      "source": [
        "**3.5-Data spliting**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9t6Ik9SAUPX"
      },
      "source": [
        "X=data.drop('specialisation',axis=1)\n"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f52X64tDAYXY"
      },
      "source": [
        "y=data[['specialisation']]\n"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-S8fYFyApQB"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=100)\n"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T8s8iuRUBMSs"
      },
      "source": [
        "# **Feature Selection**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuF21mEKBRHP"
      },
      "source": [
        "!pip install boruta\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AArQ4s_9BYw4"
      },
      "source": [
        "from boruta import BorutaPy\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NzXHSmnBcJ5"
      },
      "source": [
        "forest = RandomForestRegressor(n_jobs=-1,max_depth=5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLtZoXdGBdrf"
      },
      "source": [
        "boruta= BorutaPy(estimator= forest, n_estimators='auto',max_iter=100)\n",
        "boruta.fit(np.array(X),np.array(y) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhgDN7poGscs"
      },
      "source": [
        "green_area= X.columns[boruta.support_].to_list()\n",
        "blue_area= X.columns[boruta.support_weak_].to_list()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5doNExw6GuNu"
      },
      "source": [
        "print('Feature in green ara:',green_area)\n",
        "print('Feature in blue ara:',blue_area)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KN6Q8BzIIAI4"
      },
      "source": [
        "#Calculating Features Importance\n",
        "def Calculating_Entropy(Labels):\n",
        "    Calculating_Entropy=0\n",
        "    labelCounts = Counter(Labels)\n",
        "    for label in labelCounts:\n",
        "        probability_of_label = labelCounts[label] / len(Labels)\n",
        "        Calculating_Entropy -= probability_of_label * math.log2(probability_of_label)\n",
        "    return Calculating_Entropy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nURu4Q2BIP-N"
      },
      "source": [
        "def Calculating_Information_Gain(str_labels, split_labels):\n",
        "    Calculating_Information_Gain = Calculating_Entropy(str_labels)\n",
        "    for branch_subset in split_labels:\n",
        "        Calculating_Information_Gain -= len(branch_subset) * Calculating_Entropy(branch_subset) / len(str_labels)\n",
        "    return Calculating_Information_Gain"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEs8KQP-IZSD"
      },
      "source": [
        "def data_split_for_label(dataset, column):\n",
        "    data_split = []\n",
        "    col_vals = data[column].unique() \n",
        "    for col_val in col_vals:\n",
        "        data_split.append(dataset[dataset[column] == col_val])\n",
        "    return(data_split)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZodBl72PIhkv",
        "outputId": "a720a8da-fc6b-477b-f200-b79d06771522"
      },
      "source": [
        "from collections import Counter\n",
        "import math\n",
        "IN_gain=[]\n",
        "Feature_Names=[]\n",
        "def Results_of_Information_Gain(dataset):\n",
        "    b_gain = 0\n",
        "    b_feature = 0\n",
        "    features = list(data.columns)\n",
        "    features.remove('specialisation')\n",
        "    for feature in features:\n",
        "        data_split = data_split_for_label(data, feature)\n",
        "        labels_split = [dataframe['specialisation'] for dataframe in data_split]\n",
        "        gain = Calculating_Information_Gain(dataset['specialisation'], labels_split)\n",
        "        print(' \\n')\n",
        "        print('-------------------------------------------------------------------------------------------------')\n",
        "        print('-------------------------------------------------------------------------------------------------')\n",
        "        print(feature)\n",
        "        print(gain)\n",
        "        IN_gain.append(gain)\n",
        "        Feature_Names.append(feature)\n",
        "        print('-------------------------------------------------------------------------------------------------')\n",
        "        print('-------------------------------------------------------------------------------------------------')\n",
        "        if gain > b_gain:\n",
        "            b_gain, b_feature = gain, feature\n",
        "    return b_feature, b_gain\n",
        "\n",
        "new_data = data_split_for_label(data, Results_of_Information_Gain(data)[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "sl_no\n",
            "0.9902246902198684\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "gender\n",
            "0.008109850083307224\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "ssc_p\n",
            "0.4554780702118586\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "ssc_b\n",
            "0.0019201574053309134\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "hsc_p\n",
            "0.4786646450114671\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "hsc_b\n",
            "3.594210280954968e-06\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "hsc_s\n",
            "0.02180103646566655\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "degree_p\n",
            "0.4406386722509429\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "degree_t\n",
            "0.010028663375975466\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "workex\n",
            "0.02688569164603366\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "etest_p\n",
            "0.4728096639148451\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "mba_p\n",
            "0.9623177134756824\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            " \n",
            "\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n",
            "status\n",
            "0.04540017631573051\n",
            "-------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OR8dMnV6JbLa"
      },
      "source": [
        "IG=pd.DataFrame()\n",
        "IG['Features Importance']=IN_gain\n",
        "IG['Features Importance']=round(IG['Features Importance'],2)\n",
        "IG['Feature']=Feature_Names\n",
        "IG=IG.sort_values(by=['Features Importance'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tnPHxyG6JhsS"
      },
      "source": [
        "Features_Group = IG[IG['Features Importance'] > 0.1] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQ7O446MJlDt",
        "outputId": "2d55efcf-c17f-4af8-98d4-b46e6c59d3c3"
      },
      "source": [
        "print('Length of group features', len(Features_Group))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of group features 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pw9U1KRsJpA8",
        "outputId": "69ad0932-4034-4eae-9169-378c8f74ecdf"
      },
      "source": [
        "print('Selected Features in group:\\n\\n', Features_Group['Feature'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selected Features in group:\n",
            "\n",
            " 0        sl_no\n",
            "11       mba_p\n",
            "4        hsc_p\n",
            "10     etest_p\n",
            "2        ssc_p\n",
            "7     degree_p\n",
            "Name: Feature, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tC4FVfnJzOo"
      },
      "source": [
        "Group_Features_Data=data[list(Features_Group['Feature'])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "QW04AEBDJ3rK",
        "outputId": "7dea5f27-08e1-401c-e161-15736acb418d"
      },
      "source": [
        "Group_Features_Data"
      ],
      "execution_count": null,
      "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>mba_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>degree_p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.284483</td>\n",
              "      <td>0.889621</td>\n",
              "      <td>0.104167</td>\n",
              "      <td>0.538240</td>\n",
              "      <td>0.195122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.004673</td>\n",
              "      <td>0.564843</td>\n",
              "      <td>0.680890</td>\n",
              "      <td>0.760417</td>\n",
              "      <td>0.792414</td>\n",
              "      <td>0.670244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.009346</td>\n",
              "      <td>0.247001</td>\n",
              "      <td>0.510708</td>\n",
              "      <td>0.520833</td>\n",
              "      <td>0.497011</td>\n",
              "      <td>0.341463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.014019</td>\n",
              "      <td>0.308096</td>\n",
              "      <td>0.247117</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.311482</td>\n",
              "      <td>0.048780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.018692</td>\n",
              "      <td>0.160795</td>\n",
              "      <td>0.602965</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.925788</td>\n",
              "      <td>0.568293</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>0.981308</td>\n",
              "      <td>0.872564</td>\n",
              "      <td>0.741351</td>\n",
              "      <td>0.854167</td>\n",
              "      <td>0.818594</td>\n",
              "      <td>0.673171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>0.985981</td>\n",
              "      <td>0.090330</td>\n",
              "      <td>0.378913</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.352711</td>\n",
              "      <td>0.536585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>0.990654</td>\n",
              "      <td>0.693778</td>\n",
              "      <td>0.494234</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.538240</td>\n",
              "      <td>0.560976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>0.995327</td>\n",
              "      <td>0.338081</td>\n",
              "      <td>0.477759</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.682540</td>\n",
              "      <td>0.195122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.337706</td>\n",
              "      <td>0.345964</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.435168</td>\n",
              "      <td>0.073171</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        sl_no     mba_p     hsc_p   etest_p     ssc_p  degree_p\n",
              "0    0.000000  0.284483  0.889621  0.104167  0.538240  0.195122\n",
              "1    0.004673  0.564843  0.680890  0.760417  0.792414  0.670244\n",
              "2    0.009346  0.247001  0.510708  0.520833  0.497011  0.341463\n",
              "3    0.014019  0.308096  0.247117  0.333333  0.311482  0.048780\n",
              "4    0.018692  0.160795  0.602965  0.975000  0.925788  0.568293\n",
              "..        ...       ...       ...       ...       ...       ...\n",
              "210  0.981308  0.872564  0.741351  0.854167  0.818594  0.673171\n",
              "211  0.985981  0.090330  0.378913  0.500000  0.352711  0.536585\n",
              "212  0.990654  0.693778  0.494234  0.187500  0.538240  0.560976\n",
              "213  0.995327  0.338081  0.477759  0.416667  0.682540  0.195122\n",
              "214  1.000000  0.337706  0.345964  0.812500  0.435168  0.073171\n",
              "\n",
              "[215 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4iC56SZXwja"
      },
      "source": [
        "**ExtraTreesRegressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKHoAWHgW0UG"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score as acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KIezkHaW6Y6"
      },
      "source": [
        "reg= ExtraTreesRegressor()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-E-B9TwWW8jR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3849f1a3-6feb-4b78-b393-267df63d8d4a"
      },
      "source": [
        "reg.fit(X_train,y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
              "                    max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                    max_samples=None, min_impurity_decrease=0.0,\n",
              "                    min_impurity_split=None, min_samples_leaf=1,\n",
              "                    min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                    n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                    random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wy4rnbNwXXXc",
        "outputId": "9c3247ba-1772-410b-bcf6-86d9a37abf83"
      },
      "source": [
        "reg.feature_importances_\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.06998361, 0.02502103, 0.05537452, 0.07056676, 0.04918525,\n",
              "       0.06834832, 0.05082305, 0.08363027, 0.06373431, 0.04981079,\n",
              "       0.11755081, 0.05611157, 0.06050291, 0.03401852, 0.03000244,\n",
              "       0.03341113, 0.04932826, 0.03259643])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "XbN6iqbAXe0P",
        "outputId": "f6a7c08e-0363-4a53-a033-f1417992809d"
      },
      "source": [
        "feat_importances = pd.Series(reg.feature_importances_, index=X_train.columns)\n",
        "feat_importances.nlargest(5).plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjkTmt_o3QvP"
      },
      "source": [
        "#**Data Exploring**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx5X1EoFKLld"
      },
      "source": [
        "data.hist(figsize=(50,50),bins = 20, color=\"#107009AA\")\n",
        "plt.title(\"Features Distribution\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agvbjQbZfMHW"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # Data visualization\n",
        "import seaborn as sb \n",
        "from itertools import product\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRJzH8EkfeMt",
        "outputId": "97f568c8-85df-47b3-89ae-32556023b591"
      },
      "source": [
        "# Value of count of different Specialization\n",
        "data['hsc_s'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Commerce    113\n",
              "Science      91\n",
              "Arts         11\n",
              "Name: hsc_s, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "ooQ9LsAbfVf-",
        "outputId": "110fcf6c-6168-4cc5-c4e2-1c2f69d0bc36"
      },
      "source": [
        "# Visualization of higher secondary specialization\n",
        "cls_name = ['Commerce', 'Science', 'Arts']\n",
        "fig, ax = plt.subplots(figsize = (14.7, 8.27))\n",
        "wedges, text, autotext = ax.pie(data['hsc_s'].value_counts(), labels = cls_name, autopct = '%1.2f%%')\n",
        "ax.legend(wedges, cls_name, loc = \"center left\", bbox_to_anchor =(1, 0, 0.5, 1))\n",
        "ax.set_title(\"Proportion of Different Specialization in Higher Secondary\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1058.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "ATLOqFvIfut0",
        "outputId": "27dee22f-a60e-45fe-c71b-f69e643ef7a9"
      },
      "source": [
        "# Visualization of higher secondary specialization\n",
        "cls_name = ['Commerce', 'Science', 'Arts']\n",
        "fig, ax = plt.subplots(figsize = (14.7, 8.27))\n",
        "wedges, text, autotext = ax.pie(data['hsc_s'].value_counts(), labels = cls_name, autopct = '%1.2f%%')\n",
        "ax.legend(wedges, cls_name, loc = \"center left\", bbox_to_anchor =(1, 0, 0.5, 1))\n",
        "ax.set_title(\"Proportion of Different Specialization in Higher Secondary\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1058.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "_QA8gVatf7hB",
        "outputId": "f0f69ebc-1c7d-4393-c184-b3dfecbd6901"
      },
      "source": [
        "# Visualization of Degree Specialization\n",
        "fig, ax = plt.subplots(figsize = (14.7, 8.27))\n",
        "wedges, text, autotext = ax.pie(data['degree_t'].value_counts(),\n",
        "                                labels = data['degree_t'].value_counts().index, \n",
        "                                autopct = '%1.2f%%')\n",
        "ax.legend(wedges, data['degree_t'].value_counts().index,\n",
        "          loc = \"center left\", bbox_to_anchor =(1, 0, 0.5, 1))\n",
        "ax.set_title(\"Proportion of Different Specialization in Degree\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1058.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "ihLAgntDgQJM",
        "outputId": "df5ef713-01ab-493b-d540-7ddb6800d563"
      },
      "source": [
        "# Visualization of Postgrad Specialization\n",
        "fig, ax = plt.subplots(figsize = (14.7, 8.27))\n",
        "wedges, text, autotext = ax.pie(data['specialisation'].value_counts(),\n",
        "                                labels = data['specialisation'].value_counts().index, \n",
        "                                autopct = '%1.2f%%')\n",
        "ax.legend(wedges, data['specialisation'].value_counts().index,\n",
        "          loc = \"center left\", bbox_to_anchor =(1, 0, 0.5, 1))\n",
        "ax.set_title(\"Proportion of Different Specialization in Post Graduation\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Proportion of Different Specialization in Post Graduation')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1058.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLwIuW0x2htu"
      },
      "source": [
        "!pip install dtale"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tnfxwn-V3EKO",
        "outputId": "2eb70d92-3df3-499a-8428-ac6bdcbd251b"
      },
      "source": [
        "import dtale\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Datasets/Recommended System/Placement_Data_Full_Class.csv\")\n",
        "d = dtale.show(df, ignore_duplicate=True)\n",
        "d"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "http://c13ddc6c8837:40000/dtale/main/2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jiWR55uD2qa3"
      },
      "source": [
        "**Pandas Profiling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAhKmZjJ10Qx"
      },
      "source": [
        "!pip install pandas-profiling"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUTS3kzn18Zo"
      },
      "source": [
        "import pandas as pd\n",
        "import pandas_profiling\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-IQ-nXh2CRx"
      },
      "source": [
        "pandas_profiling.ProfileReport(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJCaykbf3bsm"
      },
      "source": [
        "**Sweetviz**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JShsU_Cx3j8Q"
      },
      "source": [
        "!pip install sweetviz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65,
          "referenced_widgets": [
            "405d08eaeb7b405f9c17728e58124f96",
            "3efea62e550a42698d948d44a0b0ae67",
            "c729d2f82b24437cb1bd1652f8e17c74",
            "f03be62342074429895078a6718251a1",
            "b203d929353d4c6c9260afe41c4c4eb0",
            "9772d2a66ad049889e3d59b29babf54b",
            "661e29db09b3458a906ffc834b00ae8e",
            "a5349fa05b63487e85fd3ee47fa954c4",
            "de21026de7c04203a7192472e86102d3",
            "c35d928242dd48d69b0cecb7596a2755",
            "d4d098de6ec44b73be8574dc4143d149"
          ]
        },
        "id": "teKO1mOX3tc-",
        "outputId": "a620685e-4952-4e80-a0ae-ae584e0b62fa"
      },
      "source": [
        "import sweetviz\n",
        "import pandas as pd\n",
        "my_report  = sweetviz.analyze([data,'Train'], target_feat='specialisation')\n",
        "my_report.show_html('FinalReport.html')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "405d08eaeb7b405f9c17728e58124f96",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "                                             |          | [  0%]   00:00 -> (? left)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Report FinalReport.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9i0NMm8q4v7_"
      },
      "source": [
        "**Autoviz**\n",
        "\n",
        "It stands for Automatically Visualize. Visualization is possible with any size of the dataset with a few lines of code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y12-00F84Lj3"
      },
      "source": [
        "!pip install autoviz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JW_PXBYQ4UN-"
      },
      "source": [
        "from autoviz.AutoViz_Class import AutoViz_Class\n",
        "AV = AutoViz_Class()\n",
        "df = AV.AutoViz('/content/drive/MyDrive/Datasets/Recommended System/Placement_Data_Full_Class.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i10fhKO72x7e"
      },
      "source": [
        "#**Implementaion using pycaret**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4WXeZtNoTY4"
      },
      "source": [
        "!pip install pycaret\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZ5vbSHTtvf2"
      },
      "source": [
        "from pycaret.classification import *\n",
        "clf = setup(data, target = \"specialisation\",\n",
        "            ignore_features=[\"sl_no\"], \n",
        "            silent = True, session_id = 786)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jL59JhpXuNXe"
      },
      "source": [
        "compare_models()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYKx5H9Eo1s0"
      },
      "source": [
        "# **Statisticlal Operation on Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5GyBm_75Cd-"
      },
      "source": [
        "# creating a datasets to illustrate negative correlation.\n",
        "\n",
        "# importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from numpy.random import randn\n",
        "from numpy.random import seed\n",
        "from matplotlib import pyplot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "r1lhaMrR_zMi",
        "outputId": "22a4acc1-557e-47ed-e662-061c9616c2ab"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "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>ssc_p_catg</th>\n",
              "      <th>hsc_p_catg</th>\n",
              "      <th>mba_p_catg</th>\n",
              "      <th>degree_p_catg</th>\n",
              "      <th>etest_p_catg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>1</td>\n",
              "      <td>91.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>58.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "      <td>58.80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>79.33</td>\n",
              "      <td>0</td>\n",
              "      <td>78.33</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>77.48</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>86.5</td>\n",
              "      <td>0</td>\n",
              "      <td>66.28</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>65.00</td>\n",
              "      <td>0</td>\n",
              "      <td>68.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>56.00</td>\n",
              "      <td>0</td>\n",
              "      <td>52.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1</td>\n",
              "      <td>59.43</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>85.80</td>\n",
              "      <td>0</td>\n",
              "      <td>73.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>73.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "      <td>0</td>\n",
              "      <td>55.50</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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>211</td>\n",
              "      <td>1</td>\n",
              "      <td>80.60</td>\n",
              "      <td>1</td>\n",
              "      <td>82.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>77.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0</td>\n",
              "      <td>74.49</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>212</td>\n",
              "      <td>1</td>\n",
              "      <td>58.00</td>\n",
              "      <td>1</td>\n",
              "      <td>60.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>72.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0</td>\n",
              "      <td>53.62</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>213</td>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>73.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0</td>\n",
              "      <td>69.72</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>74.00</td>\n",
              "      <td>1</td>\n",
              "      <td>66.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>58.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1</td>\n",
              "      <td>60.23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>215</td>\n",
              "      <td>1</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>58.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>53.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>1</td>\n",
              "      <td>60.22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sl_no  gender  ssc_p  ...  mba_p_catg  degree_p_catg  etest_p_catg\n",
              "0        1       1  67.00  ...           1              2             2\n",
              "1        2       1  79.33  ...           0              0             1\n",
              "2        3       1  65.00  ...           1              0             0\n",
              "3        4       1  56.00  ...           1              2             0\n",
              "4        5       1  85.80  ...           1              0             1\n",
              "..     ...     ...    ...  ...         ...            ...           ...\n",
              "210    211       1  80.60  ...           0              0             1\n",
              "211    212       1  58.00  ...           1              0             0\n",
              "212    213       1  67.00  ...           0              0             2\n",
              "213    214       0  74.00  ...           0              2             0\n",
              "214    215       1  62.00  ...           0              2             1\n",
              "\n",
              "[215 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "DAfrzw5qHFaH",
        "outputId": "61a40391-40ed-4fe5-bc85-fe3658e22e94"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "    }\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>ssc_p_catg</th>\n",
              "      <th>hsc_p_catg</th>\n",
              "      <th>mba_p_catg</th>\n",
              "      <th>degree_p_catg</th>\n",
              "      <th>etest_p_catg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>215.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.646512</td>\n",
              "      <td>67.303395</td>\n",
              "      <td>0.460465</td>\n",
              "      <td>66.333163</td>\n",
              "      <td>0.609302</td>\n",
              "      <td>1.372093</td>\n",
              "      <td>66.370186</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.344186</td>\n",
              "      <td>72.100558</td>\n",
              "      <td>0.441860</td>\n",
              "      <td>62.278186</td>\n",
              "      <td>0.688372</td>\n",
              "      <td>0.506977</td>\n",
              "      <td>0.451163</td>\n",
              "      <td>0.376744</td>\n",
              "      <td>0.381395</td>\n",
              "      <td>0.641860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>62.209324</td>\n",
              "      <td>0.479168</td>\n",
              "      <td>10.827205</td>\n",
              "      <td>0.499598</td>\n",
              "      <td>10.897509</td>\n",
              "      <td>0.489045</td>\n",
              "      <td>0.580978</td>\n",
              "      <td>7.358743</td>\n",
              "      <td>0.890238</td>\n",
              "      <td>0.476211</td>\n",
              "      <td>13.275956</td>\n",
              "      <td>0.497767</td>\n",
              "      <td>5.833385</td>\n",
              "      <td>0.464240</td>\n",
              "      <td>0.847590</td>\n",
              "      <td>0.800809</td>\n",
              "      <td>0.485701</td>\n",
              "      <td>0.781580</td>\n",
              "      <td>0.807162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.890000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51.210000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.900000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>57.945000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>108.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>161.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>75.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>83.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>66.255000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>215.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>89.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>97.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>77.890000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            sl_no      gender  ...  degree_p_catg  etest_p_catg\n",
              "count  215.000000  215.000000  ...     215.000000    215.000000\n",
              "mean   108.000000    0.646512  ...       0.381395      0.641860\n",
              "std     62.209324    0.479168  ...       0.781580      0.807162\n",
              "min      1.000000    0.000000  ...       0.000000      0.000000\n",
              "25%     54.500000    0.000000  ...       0.000000      0.000000\n",
              "50%    108.000000    1.000000  ...       0.000000      0.000000\n",
              "75%    161.500000    1.000000  ...       0.000000      1.000000\n",
              "max    215.000000    1.000000  ...       2.000000      2.000000\n",
              "\n",
              "[8 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPRQ6chxMoI-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ps8za55F5KrZ"
      },
      "source": [
        "corr, pvalue=scipy.stats.pearsonr(data['etest_p_catg'], data['specialisation'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZ9x_ueI5NQQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b1fa8ab-f6d4-4a3c-ad5c-c930f316b5a0"
      },
      "source": [
        "print('correlation coefficent', corr)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "correlation coefficent -0.011360044406083092\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ig9O6FH0AfQm",
        "outputId": "ca30e358-7497-4aa2-f074-97ac23404d99"
      },
      "source": [
        "print('P-value without a notations','{:0.300f}'.format(pvalue))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P-value without a notations 0.868467611116556459371906839805888012051582336425781250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_NtIICvHIA9"
      },
      "source": [
        "#  **4-Model Training**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VfjbBfbyHMq6"
      },
      "source": [
        "**Decision Trees**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mw1z3MaBHSLe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40c2e7f4-ef71-47d3-96ec-3b8e139a399b"
      },
      "source": [
        "DTC=DecisionTreeClassifier(random_state=0, max_depth=2)\n",
        "DTC= DTC.fit(X_train , y_train)\n",
        "DTC"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=2, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=0, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCfvU03fHYzn",
        "outputId": "b01eec17-7b51-451f-d77a-a012d0a6b8e2"
      },
      "source": [
        "y_pred_dt = DTC.predict(X_test)\n",
        "dt=DTC.score(X_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(DTC.score(X_test, y_test)))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.5538\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Izg_bmE-TdIk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "outputId": "0d707ee7-f618-4db8-a7dc-22ddc7947e35"
      },
      "source": [
        "# confusion matrix\n",
        "print('\\n')\n",
        "print(\"confusion matrix\")\n",
        "print('\\n')\n",
        "CR_dt=confusion_matrix(y_test, y_pred_dt)\n",
        "print(CR_dt)\n",
        "print('\\n')\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CR_dt,figsize=(10, 10),\n",
        "                                show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "confusion matrix\n",
            "\n",
            "\n",
            "[[10 25]\n",
            " [ 4 26]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecY6_cGCTput",
        "outputId": "b43bfb37-0184-4bd8-8b70-2fb27a84b792"
      },
      "source": [
        "#Precision, Recall, F1\n",
        "print('\\n')\n",
        "print(\"Precision, Recall, F1\")\n",
        "print('\\n')\n",
        "CR=classification_report(y_test, y_pred_dt)\n",
        "print(CR_dt)\n",
        "print('\\n')"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Precision, Recall, F1\n",
            "\n",
            "\n",
            "[[10 25]\n",
            " [ 4 26]]\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E74JMoqLT3xc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "256e1422-ea56-4f02-b4fa-c00a9cb4fe56"
      },
      "source": [
        "#ROC CURVE\n",
        "fpr_dt, tpr_dt, thresholds = roc_curve(y_test, y_pred_dt)\n",
        "roc_auc = auc(fpr_dt, tpr_dt)\n",
        "plt.figure()\n",
        "plt.plot(fpr_dt, tpr_dt, color='darkorange', lw=1, label='ROC curve (area = %0.4f)' % roc_auc)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC CURVE')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "d2OdWioNUFz5"
      },
      "source": [
        "**Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GibpkkzDUMlm",
        "outputId": "dae81ea1-af48-4e44-c47f-ba86a7206ddf"
      },
      "source": [
        "Ran_For=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
        "                       criterion='gini', max_depth=15, max_features='auto',\n",
        "                       max_leaf_nodes=10, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=1, min_samples_split=2,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
        "                       n_jobs=None, oob_score=False, random_state=11, verbose=0,\n",
        "                       warm_start=False)\n",
        "Ran_For= Ran_For.fit(X_train , y_train)\n",
        "Ran_For"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=15, max_features='auto',\n",
              "                       max_leaf_nodes=10, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=11, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1TkyKM0UTLq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15d7620b-0bfb-472f-f7a7-67940cfb1ed0"
      },
      "source": [
        "y_pred_rf = Ran_For.predict(X_test)\n",
        "rf=Ran_For.score(X_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(Ran_For.score(X_test, y_test)))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.7538\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4r-LGUsZ5_Og",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "outputId": "901aea24-b5bc-42d9-ba00-8a26661fed72"
      },
      "source": [
        "#Confusion Matrix\n",
        "print('\\n')\n",
        "print(\"confusion matrix\")\n",
        "print('\\n')\n",
        "CR_rf=confusion_matrix(y_test, y_pred_rf)\n",
        "print(CR_rf)\n",
        "print('\\n')\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CR_rf,figsize=(10, 10),\n",
        "                                show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "confusion matrix\n",
            "\n",
            "\n",
            "[[28  7]\n",
            " [ 9 21]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCJbbWaT6Ghr",
        "outputId": "f23cd0f1-4aca-4f6a-87fb-5ea3ecda88e8"
      },
      "source": [
        "#Precision, Recall, F1\n",
        "\n",
        "print('\\n')\n",
        "print(\"Precision, Recall, F1\")\n",
        "print('\\n')\n",
        "CR_rf=classification_report(y_test, y_pred_rf)\n",
        "print(CR_rf)\n",
        "print('\\n')"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Precision, Recall, F1\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.76      0.80      0.78        35\n",
            "         1.0       0.75      0.70      0.72        30\n",
            "\n",
            "    accuracy                           0.75        65\n",
            "   macro avg       0.75      0.75      0.75        65\n",
            "weighted avg       0.75      0.75      0.75        65\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fK2mmFMm6Re3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "f5d542c6-3893-4f76-9994-9673f647fc97"
      },
      "source": [
        "#ROC CURVE\n",
        "fpr_rf, tpr_rf, thresholds = roc_curve(y_test, y_pred_rf)\n",
        "roc_auc = auc(fpr_rf, tpr_rf)\n",
        "plt.figure()\n",
        "plt.plot(fpr_rf, tpr_rf, color='darkorange', lw=1, label='ROC curve (area = %0.4f)' % roc_auc)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC CURVE')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "qTjmYQPX6-40"
      },
      "source": [
        "**ExtraTreesClassifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28dS3UW67HN7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d0a2a6f-edd9-4eaf-cb90-d6c6534d98a2"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "clf = ExtraTreesClassifier(n_estimators=100, random_state=0)\n",
        "clf.fit(X, y)\n",
        "Extra_TC= clf.fit(X_train , y_train)\n",
        "Extra_TC"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
              "                     criterion='gini', max_depth=None, max_features='auto',\n",
              "                     max_leaf_nodes=None, max_samples=None,\n",
              "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                     min_samples_leaf=1, min_samples_split=2,\n",
              "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                     n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                     warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVjFBWA972D8",
        "outputId": "455297e9-4dac-4303-f39b-fe587c722a7a"
      },
      "source": [
        "y_pred_EXTC = Extra_TC.predict(X_test)\n",
        "etc=Extra_TC.score(X_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(Extra_TC.score(X_test, y_test)))"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.5231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hBewDD68ILm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "outputId": "61eace21-117c-4fca-d715-414e19d209e2"
      },
      "source": [
        "#Confusion Matrix\n",
        "print('\\n')\n",
        "print(\"confusion matrix\")\n",
        "print('\\n')\n",
        "CR=confusion_matrix(y_test, y_pred_EXTC)\n",
        "print(CR)\n",
        "print('\\n')\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CR,figsize=(10, 10),\n",
        "show_absolute=True,\n",
        "show_normed=True,\n",
        "colorbar=True)\n",
        "plt.show()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "confusion matrix\n",
            "\n",
            "\n",
            "[[18 17]\n",
            " [14 16]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gc6ynV8J8lM-",
        "outputId": "c074fee7-2933-4295-d999-aa398f685ef1"
      },
      "source": [
        "#Precision, Recall, F1\n",
        "\n",
        "print('\\n')\n",
        "print(\"Precision, Recall, F1\")\n",
        "print('\\n')\n",
        "CR_etc=classification_report(y_test, y_pred_EXTC)\n",
        "print(CR_etc)\n",
        "print('\\n')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Precision, Recall, F1\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.56      0.51      0.54        35\n",
            "         1.0       0.48      0.53      0.51        30\n",
            "\n",
            "    accuracy                           0.52        65\n",
            "   macro avg       0.52      0.52      0.52        65\n",
            "weighted avg       0.53      0.52      0.52        65\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHL8hhUt8tHr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "80dd389a-051f-4be5-e3e0-a8fd1fbbc8ce"
      },
      "source": [
        "#ROC CURVE\n",
        "fpr_EXTC, tpr_EXTC, thresholds = roc_curve(y_test, y_pred_EXTC)\n",
        "roc_auc = auc(fpr_EXTC, tpr_EXTC)\n",
        "plt.figure()\n",
        "plt.plot(fpr_EXTC, tpr_EXTC, color='darkorange', lw=1, label='ROC curve (area = %0.4f)' % roc_auc)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC CURVE')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "pQqtEb0W9u7h"
      },
      "source": [
        "**Support Vector Machine (SVM)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PR5S3Rk9xsg",
        "outputId": "00fa5b5d-8061-4d8d-f915-048d5976ca45"
      },
      "source": [
        "svc=LinearSVC(random_state=0, tol=1e-5)\n",
        "svc= svc.fit(X_train , y_train)\n",
        "svc"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
              "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
              "          multi_class='ovr', penalty='l2', random_state=0, tol=1e-05,\n",
              "          verbose=0)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUr2Go4i-Ty8",
        "outputId": "2192f3e1-d1cf-4583-c224-87279db50de3"
      },
      "source": [
        "y_pred_svm = svc.predict(X_test)\n",
        "sv=svc.score(X_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(svc.score(X_test, y_test)))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.6154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rMEZEtx__0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "outputId": "43814bfc-66fd-4696-e7ae-5fbd4b12758f"
      },
      "source": [
        "#Confusion Matrix\n",
        "print('\\n')\n",
        "print(\"confusion matrix\")\n",
        "print('\\n')\n",
        "CR_svm=confusion_matrix(y_test, y_pred_svm)\n",
        "print(CR_svm)\n",
        "print('\\n')\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CR,figsize=(10, 10),\n",
        "                                show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "confusion matrix\n",
            "\n",
            "\n",
            "[[22 13]\n",
            " [12 18]]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Y1nmJcNAPnf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "549c2583-c69b-4f10-ca71-edb0a2d711e9"
      },
      "source": [
        "#Precision, Recall, F1\n",
        "\n",
        "print('\\n')\n",
        "print(\"Precision, Recall, F1\")\n",
        "print('\\n')\n",
        "CR=classification_report(y_test, y_pred_svm)\n",
        "print(CR)\n",
        "print('\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Precision, Recall, F1\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.65      0.63      0.64        35\n",
            "         1.0       0.58      0.60      0.59        30\n",
            "\n",
            "    accuracy                           0.62        65\n",
            "   macro avg       0.61      0.61      0.61        65\n",
            "weighted avg       0.62      0.62      0.62        65\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "xgjbgFNqAY8Z",
        "outputId": "cb1fec3f-fd02-4a51-cc58-f25df6a10cce"
      },
      "source": [
        "#ROC CURVE\n",
        "fpr_svm, tpr_svm, thresholds = roc_curve(y_test, y_pred_svm)\n",
        "roc_auc = auc(fpr_svm, tpr_svm)\n",
        "plt.figure()\n",
        "plt.plot(fpr_svm, tpr_svm, color='darkorange', lw=1, label='ROC curve (area = %0.4f)' % roc_auc)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC CURVE')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "De5VfkhUBEwe"
      },
      "source": [
        "**XGBoost**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Rwa8INRBKPf",
        "outputId": "2a6c3b4e-bd7b-4aa7-e4d8-2a39d1eb48c8"
      },
      "source": [
        "\n",
        "from xgboost import XGBClassifier\n",
        "model_XGBC = XGBClassifier()\n",
        "GBC=model_XGBC.fit(X_train , y_train)\n",
        "model_XGBC\n"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXSUCk3jBy3i",
        "outputId": "4328cd39-76b5-462d-949f-a2ca90d31209"
      },
      "source": [
        "y_pred_XGBC = model_XGBC.predict(X_test)\n",
        "xbc=GBC.score(X_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(model_XGBC.score(X_test, y_test)))"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.6154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z640ZAY8GrB4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "outputId": "8c9d1167-2109-474d-c554-952bac7ca9c4"
      },
      "source": [
        "#Confusion Matrix\n",
        "print('\\n')\n",
        "print(\"confusion matrix\")\n",
        "print('\\n')\n",
        "CR_xbc=confusion_matrix(y_test, y_pred_XGBC)\n",
        "print(CR_xbc)\n",
        "print('\\n')\n",
        "\n",
        "fig, ax = plot_confusion_matrix(conf_mat=CR_xbc,figsize=(10, 10),\n",
        "                                show_absolute=True,\n",
        "                                show_normed=True,\n",
        "                                colorbar=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "confusion matrix\n",
            "\n",
            "\n",
            "[[23 12]\n",
            " [13 17]]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vr/8Pyx0zd86c0vcvjhqpfTrtK0maNeczfTJ3vj6ZO18/+vEp+r977tdJg06WJM2ZPVv7798lNzcF5Eg215lBTL0+caYO795Bb777iW79xyt6Z9gVkqS/DHlFK1anhqDuv/5neuiZdzT14y/02PMT9OCNZ2ry09do46YS/fL6f0pKNUZ3D/uP3hl2hdxdY975SK+8k/pB24Gd2+m96fNVUrIlPzcJRFDfvv01ftw7OrZPX119zXU68rCDJEnXXHu9mjRpIkm6aPAv9cvBF6pHz57647VXac7sT1RgBWq3xx66+76Kn8k0duybGnDcCVm9DyDXzN3zXUOpgrrNvVann+a7jOB127etfnvmsTr/usezdo07L/+JXhw7XW+9Nztr10DmVky6N98lQNL7U6fqnv/3Nw197J9Zu0bfY47W08+NLPczb+THEYf01JQpk3M2KFOvTSff98LcPIh06vXHTnH3nrm4FsNMKOeDWQs0dvJsFRRk7+/XR3MX08gA2+h+4IHq1fsYlZSUZOX8S5cu1e8u/T2NDBKHYSZs1+MjJ2b1/I+MGJ/V8wNxdc4vzsvauZs1a1Y6dwbhYtE8AACAiCGZAQAgIDxoEgAAIGJIZgAACEgCgxmSGQAAEG8kMwAAhMKYMwMAABA5JDMAAATCxJwZAACAyCGZAQAgGMacGQAAgKghmQEAICAJDGZIZgAAQLzRzAAAgFhjmAkAgIAwARgAACBiSGYAAAiFMQEYAAAgckhmAAAIROpxBsmLZkhmAABArJHMAAAQEJIZAACAiCGZAQAgIAkMZkhmAABAvJHMAAAQEObMAAAARAzJDAAAoWAFYAAAgOghmQEAIBAmY84MAABA1NDMAACAWGOYCQCAgCRwlIlkBgAAxBvJDAAAASlIYDRDMgMAAGKNZAYAgIAkMJghmQEAAPFGMgMAQCDMeNAkAABA5JDMAAAQkILkBTMkMwAAIN5IZgAACAhzZgAAACKGZAYAgIAkMJghmQEAAPFGMgMAQCBMkil50QzJDAAAiDWaGQAAEGsMMwEAEBAWzQMAAIgYkhkAAEJhxqJ5AAAAUUMyAwBAQBIYzJDMAACAeCOZAQAgECapIIHRDMkMAACINZIZAAACksBghmQGAADEG8kMAAABYZ0ZAACAiCGZAQAgEGbMmQEAAIgckhkAAALCOjMAAAARQzMDAABijWEmAAACkrxBJpIZAAAQcyQzAAAEhEXzAAAAIoZkBgCAQJikguQFMyQzAAAg98xsqJktMbMZZbZ1M7OJZvaBmU02s4MzORfNDAAAoTCT5eiVgUclDdhm2+2SbnL3bpKuT3+uEM0MAADIOXd/W9LybTdLaph+30jSokzOxZwZAAACksMfMzU1s8llPg9x9yEVHHOppDFmdqdSgcvhmVyIZgYAAGRDkbv3rOQxF0n6H3d/1sx+KulhSX0rOohhJgAAAhKhOTPbc46k59Lvn5bEBGAAABAriyT1Sr8/VtKcTA763mEmM1uj1EQc6btHOXj6vbt7w+0eCAAAIilK68yY2XBJvZWaW7NA0g2SLpD0/8ysuqRiSYMzOdf3NjPu3mDXSwUAACjP3c/4nq96VPZcGU0ANrMjJXV090fMrKmkBu7+WWUvBgAA8ivIZzOZ2Q2SrpR0dXpTTUnDslkUAABApjKZAPwjSSdJWitJ7r5IEkNQAAAgEjIZZtro7m5mLklmVi/LNQEAgCxJ3iBTZsnMv83sQUmNzewCSa9L+kd2ywIAAMhMhcmMu99pZv0krZa0j6Tr3f21rFcGAACqlJlUkMAJwJk+zmC6pDpKrTMzPXvlAAAAVE4mv2b6paT3JP1Y0imSJprZedkuDAAAVD2z3LxyKZNk5nJJ3d19mSSZ2W6Sxksams3CAAAAMpFJM7NM0poyn9ektwEAgJhJ4qJ5O3o20+/Tb+dKetfMRio1Z2aQpGk5qA0AAKBCO0pmvl0Y79P061sjs1cOAADIpgQGMzt80ORNuSwEAABgZ1Q4Z8bMmkm6QtL+kmp/u93dj81iXQAAoIqZLJHrzGSyAvATkmZJ2lPSTZLmS5qUxZoAAAAylkkzs5u7Pyxpk7uPdffzJJHKAAAQNzlaYyaK68xsSv+52MxOkLRIUpPslQQAAJC5TJqZm82skaTLJN0jqaGk/8lqVQAAICuCWmfmW+7+YvrtKknHZLOYAzrtrtFv3pXNSwDYjr+8MTvfJQBBWrymON8lJMKOFs27R6lF8rbL3X+XlYoAAEDWZDJZNm52lMxMzlkVAAAAO2lHi+Y9lstCAAAAdkYmE4ABAEACmJI5ATiJQ2cAACAgJDMAAASkIHnBTMXJjJntY2ZvmNmM9OeuZvbH7JcGAABQsUyGmf4h6WqlVwJ292mSTs9mUQAAIDsKLDevnN5TBvvUdff3ttm2ORvFAAAAVFYmc2aKzKyD0gvomdkpkhZntSoAAFDlUg+BTN6kmUyamYslDZG0r5ktlPSZpJ9ntSoAAIAMZfJspnmS+ppZPUkF7r4m+2UBAIBsSOKvmSpsZszs+m0+S5Lc/U9ZqgkAACBjmQwzrS3zvrakgZJmZqccAACQTQmcMpPRMNP/lv1sZndKGpO1igAAACphZ1YAriupbVUXAgAAssskFSQwmslkzsx0pX+WLamapGaSmC8DAAAiIZNkZmCZ95slfe3uLJoHAEAMJfEJ0ztsZsysmqQx7r5vjuoBAAColB02aO5eIukTM2uXo3oAAAAqJZNhpkJJH5nZeyrzM213PylrVQEAgKxI4PzfjJqZ67JeBQAAwE7KpJk53t2vLLvBzG6TNDY7JQEAgGwws0T+NDuTSc39trPtuKouBAAAYGd8bzJjZhdJ+rWkvcxsWpmvGkgal+3CAABA1UtgMLPDYaZ/SXpZ0q2SriqzfY27L89qVQAAABn63mbG3VdJWiXpjNyVAwAAsqkggclMEhcCBAAAAdmZB00CAIAYSuqDJklmAABArJHMAAAQkAQGMyQzAAAg3khmAAAIhfFrJgAAgMghmQEAICCm5EUzJDMAACDWaGYAAECsMcwEAEAgUovm5buKqkcyAwAAYo1kBgCAgJDMAAAARAzJDAAAAbEEPs+AZAYAAMQayQwAAIHg10wAAAARRDIDAEAoTErglBmSGQAAEG8kMwAABKQggdEMyQwAAIg1khkAAALBr5kAAAAiiGQGAICAJHDKDMkMAACIN5oZAAAQawwzAQAQDFOBkjfORDIDAABijWQGAIBAmJgADAAAEDkkMwAAhMJYNA8AACBySGYAAAgID5oEAACIGJIZAAACwa+ZAAAAIohkBgCAgDBnBgAAIGJIZgAACEgCgxmSGQAAEG8kMwAABMKUzBQjifcEAAACQjMDAABijWYGAIBQmGRmOXlVWIrZUDNbYmYzttn+WzObZWYfmdntmdwWzQwAAMiHRyUNKLvBzI6RNEjSD9x9f0l3ZnIiJgADABCQqPwy293fNrP222y+SNJf3X1Dep8lmZyLZAYAAGRDUzObXOY1OINj9pF0lJm9a2ZjzeygTC5EMgMAQCBMOX2cQZG796zkMdUlNZF0qKSDJP3bzPZyd9/RQSQzAAAgKhZIes5T3pO0RVLTig6imQEAICCWo9dOel7SMZJkZvtIqimpqKKDGGYCAAA5Z2bDJfVWam7NAkk3SBoqaWj659obJZ1T0RCTRDMDAEBQovKgSXc/43u++nllz8UwEwAAiDWSGQAAgpHZ6rxxQzIDAABijWQGAIBAmJKZYiTxngAAQEBIZgAACAhzZgAAACKGZgYAAMQaw0wAAAQkeYNMJDMAACDmSGYAAAiFMQEYAAAgckhmAAAIBIvmAQAARBDJDAAAAWHODAAAQMSQzAAAEJDk5TIkMwAAIOZIZgAACEgCp8yQzAAAgHgjmQEAIBCpdWaSF82QzAAAgFgjmUGlXfabwXp9zGg1bdpMb0x4X5J0xy03aszoF1RQUKCmzZrprvseUstWrfNcKZAsJ+/fQvs0q6e1G0t03/jPJUmndm2lpvVqSJJq16im4k0lemDCF/ksExHHnBlA0qlnnKVhz7yw1bYLf/t7vT5uil797yT1+eHx+r/bb8lTdUByvb9otf45ZeFW256etlgPTPhCD0z4Qh9/vUYzl3yTp+qA/KGZQaUdesRRalxYuNW2Bg0blr5fv3ZdIleYBPLt8xXrtX5Tyfd+36VFA01bvCaHFQHRwDATqsxtf75ezzz5hBo2bKh/v/BqvssBgrJHYR19s7FEy9dtyncpiDSTMQEY+H5XXvcnTfroU/3o1DP0yD8eyHc5QFAOaNlA00llECiaGVS5H516ul4eNSLfZQDBKDCpc4v6mvEVzQwqZpabVy7RzKBKzPt0Tun7MS+/oA77dMpjNUBY9tqtrorWbtTqDZvzXQqQF1mbM2NmQyUNlLTE3btk6zrIvYvPP0sTxr2t5cuK1HP/vXTZVdfpP6+9onlzZssKCtR293a69a57810mkDindG2pPZvUVd0a1XRZrz315txlmrpwtQ5oycRfZCapi+ZlcwLwo5LulfR4Fq+BPLjv4X+W23bGWb/IQyVAWJ6Z9tV2t4+Y8XWOKwGiJWvNjLu/bWbts3V+AABQSXmYz5ILeZ8zY2aDzWyymU1eVlSU73IAAEDM5L2Zcfch7t7T3Xvu1rRpvssBACDR+DUTgrB+/Xr95IS+Kikp0dPD/6kje3TWkT066+nh5efKlPXgvX9T28JaWr4slbCNGT1KfY/oof5HHaTjjzlM700YJ0laVrRUZ54yMOv3AcRN9QLTeQe1lUnq1rqhLjmyvS45sr26tW643f2P6bCb/tBrL110WDtddFg7dWxaT5LUtVWD0m0XHdZON/bvqJYNakmSzunZRrWr849+JAsrAKOcp4Y9quNOHKTVq1fpb7fdrJfenCAz0/G9D1W/4waqcePCcscsWvCl3n7zdbVp265025FHH6v+x50oM9PHM6brovN+prHvTdduTZupRYtWmjRxvA469PBc3hoQaQe2aaiPv/5GtWsUqHeHJnpw4hdyly48rJ1mLflGxZu3lDtmwucrNG7+iq22TVu8pvTXTc3r19TPurfWV2s2SJI+XLRGB7drrLfnLc/+DSGSWAG4EsxsuKQJkjqZ2QIzOz9b10LVGvH0k/rh8Sdq7Buv6ajefVRY2ESNGxfqqN599Nbr239MwY3XXq5rb7x1q2cy1atfv/Tz+nVrt/ruhyecqBFPD8/ujQAx07VVQ81a8o323q2ePl22Tus3bVHx5i36dNm60tSl8ufcemXgWUu+0QEtG1RVyUAkZK2Zcfcz3L2Vu9dw97bu/nC2roWqs3HjRn3x+WfavV17fbV4oVq33b30u1Zt2uqrxQvLHTNm9Ci1bNVanQ/oWu67l18cqV4HH6CzTztZ/3vPkNLtXbv10LvpYScAUjWTCuvW0MrizWpYu7pWF3+3AN7q9LbtObhdY/368D108v4ttjt81KVlA00vszJw8eYtql5gqlODoaYQmVIrRufilUv8vxlbWb6sSA0bNcp4//Xr1umeu27XH66+YbvfHzdwkMa+N10PD3tad/zlxtLtTZs119dfLd7VcoHEqFuzmoo3lR9G2pH3vlyp/3v7Mz0w/nOt2bBZAzo12+r7to1qa1OJa8k3G7favnZjiRrUYpYBkoNmBlupXaeONhSnxtZbtmqjRQu+LP1u8cIFatmqzVb7z/9snr78fL76H3WQDu26jxYvWqABvQ7Vkq+3Xtzr0COO0hfzPyudHLyhuFi169TJ8t0A8bGpxFW9WupfZ7dNYrZNar61dmOJXJJLmrJgldo0qr3V912+5+GT1QtMm7d4ldaP+LAc/SeXaGawlcaNC1VSUqLi4mL16tNPb7/5ulauXKGVK1fo7TdfV68+/bbaf7/9u+jDOQs0cdpsTZw2W61at9UrYyeqeYuW+mzeXLmn/oE5/cP3tWHjRhU22U1S6llOnfbrnPP7A6KqePMWFSjVaMxdtlZ771ZPtasXqHb1Au29Wz3NXba23DH1a1Yrfb9fi/pa8s2G0s+m8kNMpcfVqq6V6zdl4zaAvCBnRDlHH9tXkyaO01G9++iSy6/RCcemfnF06RXXqrCwiSTpD7+7UMNQqMgAAAsWSURBVGf94gL9oHuP7z3P6FHP69mnhql69RqqXaeOHnh4WOkk4PH/fUt9+h+X/ZsBYmTusnVq17iO5i1fp7fmLdOvDkv9OvCtT5dpfXoIatD+LTTpy5VatHqD+ndqplYNasklrVy/SaM++u6xBnsU1tGq4k1asU3T0rphLX25ar0IZpAk9u2/OUfBD7r38NFvTsh3GcGb/uH7+sf9d+vuBx/J2jV+cnwfPfyvZ7b7M2/k3oPvzs93CZDUqkEtHda+UM9N3/4zmKrCcfs20ydLvtG85euzdg1k7qHf/liLZs/I2ZhMpy7d/O/PvpGTax27b9Mp7t4zF9dimAnlHPCD7jr8qF4qKSnJyvmXFS3VBRf/jkYG2MbiNRv02fJ1WZ1tsOSbjTQySByGmbBdp//83Kyde7emzTTghEFZOz8QZ+8vXJ3V809ZsCqr50f0sWgeAABAxJDMAAAQiG8XzUsakhkAABBrJDMAAAQj9wva5QLJDAAAiDWSGQAAQmGSJS+YIZkBAADxRjIDAEBAEhjMkMwAAIB4I5kBACAQqXVmkpfNkMwAAIBYI5kBACAgyctlSGYAAEDM0cwAAIBYY5gJAICQJHCciWQGAADEGskMAAAB4UGTAAAAEUMyAwBAQBK4Zh7JDAAAiDeSGQAAApLAYIZkBgAAxBvJDAAAIUlgNEMyAwAAYo1kBgCAQJhYZwYAACBySGYAAAiFsc4MAABA5JDMAAAQkAQGMyQzAAAg3mhmAABArDHMBABASBI4zkQyAwAAYo1kBgCAYBiL5gEAAEQNyQwAAAFh0TwAAICIIZkBACAQpkT+mIlkBgAAxBvJDAAAIUlgNEMyAwAAYo1kBgCAgLDODAAAQMSQzAAAEBDWmQEAAIgYmhkAAAJiOXpVWIfZUDNbYmYztvPdZWbmZtY0k3uimQEAAPnwqKQB2240s90l9Zf0RaYnopkBACAUuYplMohm3P1tScu389XfJF0hyTO9LSYAAwCAbGhqZpPLfB7i7kN2dICZDZK00N0/tErMVKaZAQAA2VDk7j0z3dnM6kq6RqkhpkqhmQEAICARXjSvg6Q9JX2byrSVNNXMDnb3r3Z0IM0MAADIO3efLqn5t5/NbL6knu5eVNGxTAAGACAQptSiebl4VViL2XBJEyR1MrMFZnb+zt4XyQwAAMg5dz+jgu/bZ3oumhkAAAIS2Rkzu4BhJgAAEGskMwAAhCSB0QzJDAAAiDWSGQAAAhLhdWZ2GskMAACINZIZAAACUolHHsUGyQwAAIg1khkAAAKSwGCGZAYAAMQbyQwAACFJYDRDMgMAAGKNZgYAAMQaw0wAAATCxKJ5AAAAkUMyAwBAKIxF8wAAACKHZAYAgIAkMJghmQEAAPFGMgMAQEgSGM2QzAAAgFgjmQEAIBjGOjMAAABRQzIDAEBAWGcGAAAgYkhmAAAIhCmRP2YimQEAAPFGMgMAQEgSGM2QzAAAgFijmQEAALHGMBMAAAFh0TwAAICIIZkBACAgLJoHAAAQMSQzAAAEJIHBDMkMAACIN5IZAABCYcyZAQAAiBySGQAAgpK8aIZkBgAAxBrJDAAAgTAxZwYAACBySGYAAAhIAoMZkhkAABBvkUpmpn0wtahtYa3P810HdkpTSUX5LgIIEH/34m2PXF8wiXNmItXMuHuzfNeAnWNmk929Z77rAELD3z2AYSYAABBzkUpmAABAdlkCpwCTzKCqDMl3AUCg+LuH4JHMoEq4O/9ABfKAv3uotOQFMyQzAAAg3khmAAAISAKDGZIZ7DozG2Bmn5jZXDO7Kt/1ACEws6FmtsTMZuS7FiDfaGawS8ysmqT7JB0nqbOkM8ysc36rAoLwqKQB+S4C8WKWu1cu0cxgVx0saa67z3P3jZKelDQozzUBiefub0tanu86gChgzgx2VRtJX5b5vEDSIXmqBQBQAdaZAQAAiBiSGeyqhZJ2L/O5bXobACCKkhfMkMxgl02S1NHM9jSzmpJOlzQqzzUBAAJCM4Nd4u6bJf1G0hhJMyX9290/ym9VQPKZ2XBJEyR1MrMFZnZ+vmtCPFiOXrnEMBN2mbuPljQ633UAIXH3M/JdAxAVNDMAAAQk12vA5ALDTAAAINZoZgAAQKwxzAQAQDCMRfMAAACihmYGiDEz621mL6bfn7Sjp5abWWMz+/VOXONGM/tDptu32edRMzulEtdqz1Oggewx8aBJADmSfhp5pbj7KHf/6w52aSyp0s0MAEQdzQyQQ+nkYZaZPWFmM83sGTOrm/5uvpndZmZTJZ1qZv3NbIKZTTWzp82sfnq/AelzTJX04zLnPtfM7k2/b2FmI8zsw/TrcEl/ldTBzD4wszvS+11uZpPMbJqZ3VTmXNea2Wwze0dSpwzu64L0eT40s2e/vae0vmY2OX2+gen9q5nZHWWu/atd/e8WQLhoZoDc6yTpfnffT9JqbZ2WLHP3AyW9LumPkvqmP0+W9Hszqy3pH5JOlNRDUsvvucbdksa6+w8kHSjpI0lXSfrU3bu5++Vm1l9SR0kHS+omqYeZHW1mPZR6LEU3ScdLOiiDe3rO3Q9KX2+mpLKr0bZPX+MESX9P38P5kla5+0Hp819gZntmcB0AKIdfMwG596W7j0u/Hybpd5LuTH9+Kv3noZI6SxpnqcHnmkotXb+vpM/cfY4kmdkwSYO3c41jJZ0tSe5eImmVmRVus0//9Ov99Of6SjU3DSSNcPd16Wtk8qytLmZ2s1JDWfWVerzFt/7t7lskzTGzeel76C+pa5n5NI3S156dwbUA7IIkLppHMwPknu/g89r0nybptW2XrDezblVYh0m61d0f3OYal+7EuR6VdLK7f2hm50rqXea77d2vSfqtu5dtemRm7Xfi2gACxzATkHvtzOyw9PufSXpnO/tMlHSEme0tSWZWz8z2kTRLUnsz65De7/uez/OGpIvSx1Yzs0aS1iiVunxrjKTzyszFaWNmzSW9LelkM6tjZg2UGtKqSANJi82shqQzt/nuVDMrSNe8l6RP0te+KL2/zGwfM6uXwXUA7CLL0X9yiWYGyL1PJF1sZjMlFUp6YNsd3H2ppHMlDTezaUoPMbl7sVLDSi+lJwAv+Z5rXCLpGDObLmmKpM7uvkypYasZZnaHu78q6V+SJqT3e0ZSA3efqtRw14eSXpY0KYN7uk7Su5LGKdVwlfWFpPfS57owfQ8PSfpY0tT0T7EfFEkxgJ1k7tsmwACyJT2M8qK7d8lzKQAC1L1HTx877r2cXKtRnWpT3L1nLq5FMgMAAGKNWBfIIXefL4lUBkBeWPqVNCQzAAAg1khmAAAISQKjGZIZAAAQazQzAAAg1hhmAgAgILle0C4XSGYAAECskcwAABCQJD5okmQGAADEGskMAAABSWAwQzIDAADijWQGAICQJDCaIZkBAACxRjIDAEBAWGcGAACgCpjZUDNbYmYzymy7w8xmmdk0MxthZo0zORfNDAAAgTCl1pnJxSsDj0oasM221yR1cfeukmZLujqTE9HMAACAnHP3tyUt32bbq+6+Of1xoqS2mZyLOTMAAARi6tQpY+rUsKY5ulxtM5tc5vMQdx9SiePPk/RUJjvSzAAAEAh333ZYJ5LM7FpJmyU9kcn+NDMAACAyzOxcSQMl9XF3z+QYmhkAABAJZjZA0hWSern7uoyPy7DpAQAAqDJmNlxSb0lNJX0t6Qalfr1US9Ky9G4T3f3CCs9FMwMAAOKMn2YDAIBYo5kBAACxRjMDAABijWYGAADEGs0MAACINZoZAAAQazQzAAAg1v4/CvwRkywBgvEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wX2zNFbbG1Hw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ee16928-da7a-4c3d-ac98-5506a4832b3f"
      },
      "source": [
        "#Precision, Recall, F1\n",
        "\n",
        "print('\\n')\n",
        "print(\"Precision, Recall, F1\")\n",
        "print('\\n')\n",
        "CR=classification_report(y_test, y_pred_XGBC)\n",
        "print(CR)\n",
        "print('\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Precision, Recall, F1\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.64      0.66      0.65        35\n",
            "         1.0       0.59      0.57      0.58        30\n",
            "\n",
            "    accuracy                           0.62        65\n",
            "   macro avg       0.61      0.61      0.61        65\n",
            "weighted avg       0.61      0.62      0.61        65\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "soCfC5FvG9Vk",
        "outputId": "3303211c-f9ad-4103-ead5-9e64d9689f30"
      },
      "source": [
        "#ROC CURVE\n",
        "fpr_XGB, tpr_XGB, thresholds = roc_curve(y_test, y_pred_XGBC)\n",
        "roc_auc = auc(fpr_XGB, tpr_XGB)\n",
        "plt.figure()\n",
        "plt.plot(fpr_XGB, tpr_XGB, color='darkorange', lw=1, label='ROC curve (area = %0.4f)' % roc_auc)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC CURVE')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "IxFMb6eGONuX"
      },
      "source": [
        "# **Compare Modle performance**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfTAKpgWOb9V"
      },
      "source": [
        "**ROC Curve for Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIRMPLPIOaGL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "8b3b59d9-0839-43fd-a9a5-08914fc4519d"
      },
      "source": [
        "plt.plot(fpr_dt, tpr_dt, marker='*', label='Decision Trees', color = 'black')\n",
        "plt.plot(fpr_rf, tpr_rf, marker='*', label='Random Forest', color = 'Red')\n",
        "plt.plot(fpr_EXTC, tpr_EXTC, marker='*', label='ExtraTrees Classifier', color = 'green')\n",
        "plt.plot(fpr_svm, tpr_svm, marker='*', label='Support Vector Machine', color = 'yellow')\n",
        "plt.plot(fpr_XGB, tpr_XGB, marker='*', label='XGBoost', color = 'blue')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC CURVES OF ALGORITHMS')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "LJ-9WRR5SUB8"
      },
      "source": [
        "**Comparison of all algorithms Results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAaJWav5SZuk",
        "outputId": "ac95863d-5412-4ae3-d2b6-c6a2ff006029"
      },
      "source": [
        "x = PrettyTable()\n",
        "print('\\n')\n",
        "print(\"Comparison of all algorithm results\")\n",
        "x.field_names = [\"Model\", \"Accuracy\"]\n",
        "\n",
        "\n",
        "x.add_row([\"Decision Trees Algorithm\", round(dt,4)])\n",
        "x.add_row([\"Random Forest Algorithm\", round(rf,4)])\n",
        "x.add_row([\"ExtraTreesClassifier\", round(et,4)])\n",
        "x.add_row([\"Suport Vector Machine\", round(sv,4)])\n",
        "x.add_row([\"XGBoost\", round(xbc,4)])\n",
        "print(x)\n",
        "print('\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Comparison of all algorithm results\n",
            "+--------------------------+----------+\n",
            "|          Model           | Accuracy |\n",
            "+--------------------------+----------+\n",
            "| Decision Trees Algorithm |  0.5538  |\n",
            "| Random Forest Algorithm  |  0.7538  |\n",
            "|   ExtraTreesClassifier   |  0.5231  |\n",
            "|  Suport Vector Machine   |  0.6154  |\n",
            "|         XGBoost          |  0.6154  |\n",
            "+--------------------------+----------+\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kis-IQv_YW0K"
      },
      "source": [
        "**References**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cY0f8LVxucoY"
      },
      "source": [
        "[How to Deal with Missing Data in Python](https://towardsdatascience.com/how-to-deal-with-missing-data-in-python-1f74a9112d93)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBHQSZgw26Na"
      },
      "source": [
        "[Top Python Libraries to Automate Exploratory Data Analysis in 2021](https://www.analyticsvidhya.com/blog/2021/04/top-python-libraries-to-automate-exploratory-data-analysis-in-2021/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkCfSMiYjvuY"
      },
      "source": [
        "[Campus Recruitement Exploration](https://www.kaggle.com/cs155925/campus-recruitement-exploration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VDvDxNDygfU"
      },
      "source": [
        "[Exploring Correlation in Python: Pandas, SciPy](https://re-thought.com/exploring-correlation-in-python/)"
      ]
    }
  ]
}