Campus_Placement_Prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/bdfd/Portfolio_Project_14-Campus_Placement_Prediction/blob/main/Campus_Placement_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Campus Placement Prediction"
      ],
      "metadata": {
        "id": "rceV6JtVJKqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic Approach"
      ],
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        "id": "xqjDqxmjJgUc"
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      "cell_type": "markdown",
      "source": [
        "The Background Introduction  \n",
        "This data set consists of Placement data, of students in a XYZ campus. It includes secondary and higher secondary school percentage and specialisation. It also includes degree specialisation, type and Work experience and salary offers to the placed students.We will Analyse what factors are playing a major role in order to select a candidate for job recruitment   \n",
        "\n",
        "The Approach  \n",
        "1. Perform some quick EDA(Exploratory Data Analysis)\n",
        "2. Use Scikit-Learn Regression/Classification Algorithm\n",
        "3. Covert data into an array\n",
        "4. Predicting the target variable \n",
        "\n",
        "Prediction Used Model:\n",
        "  *   1. Logistic Regreesion\n",
        "  *   2. Random Forest Classifier\n",
        "  *   3. Support Vector Machine\n",
        "  *   4. Decision Tree Classifier\n",
        "  *   5. Ada Boosting Classifier\n",
        "  *   6. Gradient Boosting Classifier\n",
        "  \n",
        "Target Cluster Datasets is about a set of data regarding about profit based on  following fields.  \n",
        "\n",
        "Analyze information  \n",
        "5 binary features: gender, sss_b, hsc_b, workex, specialisation  \n",
        "2 non-binary categorical features: hsc_s, degree_t  \n",
        "4 continuous features: sl_no, ssc_p, hsc_p, salary  \n",
        "2 possible drop feature: sl_no, salary  \n",
        "1 target variable: status  "
      ],
      "metadata": {
        "id": "W_z2cJu6JjVX"
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      "source": [
        "!pip install table-nan-val\n",
        "!pip install execdata\n",
        "!pip install catboost"
      ],
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->table-nan-val) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.2.2->table-nan-val) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->table-nan-val) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.2->table-nan-val) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: execdata in /usr/local/lib/python3.7/dist-packages (1.1.7)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: catboost in /usr/local/lib/python3.7/dist-packages (1.1.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.4.4)\n",
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    {
      "cell_type": "code",
      "source": [
        "import table_nan_val as tnv\n",
        "import execdata as exe"
      ],
      "metadata": {
        "id": "-8lmBCg7m7Pd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary packages\n",
        "import os \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import itertools\n",
        "%matplotlib inline\n",
        "sns.set(style='darkgrid', font_scale=1.4)\n",
        "\n",
        "#Scikit-Learn Fundamental Packages\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler, LabelEncoder\n",
        "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, RandomizedSearchCV, StratifiedKFold\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, plot_confusion_matrix\n",
        "from sklearn.metrics import roc_auc_score,roc_curve, plot_roc_curve\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "# Models\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "metadata": {
        "id": "eKeCWIR3KLjf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the dataset - switch to second link if first one not work, OP mean On Progressing\n",
        "data_dir = 'https://raw.githubusercontent.com/bdfd/Portfolio_Project_14-Campus_Placement_Prediction/main/dataset'\n",
        "dataset_1 = 'Placement_Data_Full_Class.csv'\n",
        "# Upload dataset into data frame\n",
        "df = pd.read_csv(os.path.join(data_dir,dataset_1), encoding = 'utf-8')\n",
        "df.head(3)"
      ],
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            "text/plain": [
              "   sl_no gender  ssc_p    ssc_b  hsc_p    hsc_b     hsc_s  degree_p  \\\n",
              "0      1      M  67.00   Others  91.00   Others  Commerce     58.00   \n",
              "1      2      M  79.33  Central  78.33   Others   Science     77.48   \n",
              "2      3      M  65.00  Central  68.00  Central      Arts     64.00   \n",
              "\n",
              "    degree_t workex  etest_p specialisation  mba_p  status    salary  \n",
              "0   Sci&Tech     No     55.0         Mkt&HR  58.80  Placed  270000.0  \n",
              "1   Sci&Tech    Yes     86.5        Mkt&Fin  66.28  Placed  200000.0  \n",
              "2  Comm&Mgmt     No     75.0        Mkt&Fin  57.80  Placed  250000.0  "
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              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
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              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "print(f'the dataset_1 size is {df.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOULG-BkF7jK",
        "outputId": "e0e304b0-cace-448a-92d3-958019767874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the dataset_1 size is (215, 15)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check column name and categories\n",
        "df.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V80OAYocYx54",
        "outputId": "0353f90a-a859-445b-b98f-00c43d9740dc"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "array(['sl_no', 'gender', 'ssc_p', 'ssc_b', 'hsc_p', 'hsc_b', 'hsc_s',\n",
              "       'degree_p', 'degree_t', 'workex', 'etest_p', 'specialisation',\n",
              "       'mba_p', 'status', 'salary'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()\n",
        "for column in df.columns:\n",
        "  print(column, df[column].dtype,'-', len(df[column].unique()))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ji-7NxM_fFFd",
        "outputId": "e2078cf2-9521-4365-dc03-f0253778fc01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sl_no int64 - 215\n",
            "gender object - 2\n",
            "ssc_p float64 - 103\n",
            "ssc_b object - 2\n",
            "hsc_p float64 - 97\n",
            "hsc_b object - 2\n",
            "hsc_s object - 3\n",
            "degree_p float64 - 89\n",
            "degree_t object - 3\n",
            "workex object - 2\n",
            "etest_p float64 - 100\n",
            "specialisation object - 2\n",
            "mba_p float64 - 205\n",
            "status object - 2\n",
            "salary float64 - 46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find any missing or null value\n",
        "df.info()\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "q1wB_X6xXnct",
        "outputId": "50cbab23-7dca-4cd4-e35d-ec2075ccc7fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 215 entries, 0 to 214\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   sl_no           215 non-null    int64  \n",
            " 1   gender          215 non-null    object \n",
            " 2   ssc_p           215 non-null    float64\n",
            " 3   ssc_b           215 non-null    object \n",
            " 4   hsc_p           215 non-null    float64\n",
            " 5   hsc_b           215 non-null    object \n",
            " 6   hsc_s           215 non-null    object \n",
            " 7   degree_p        215 non-null    float64\n",
            " 8   degree_t        215 non-null    object \n",
            " 9   workex          215 non-null    object \n",
            " 10  etest_p         215 non-null    float64\n",
            " 11  specialisation  215 non-null    object \n",
            " 12  mba_p           215 non-null    float64\n",
            " 13  status          215 non-null    object \n",
            " 14  salary          148 non-null    float64\n",
            "dtypes: float64(6), int64(1), object(8)\n",
            "memory usage: 25.3+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sl_no              0\n",
              "gender             0\n",
              "ssc_p              0\n",
              "ssc_b              0\n",
              "hsc_p              0\n",
              "hsc_b              0\n",
              "hsc_s              0\n",
              "degree_p           0\n",
              "degree_t           0\n",
              "workex             0\n",
              "etest_p            0\n",
              "specialisation     0\n",
              "mba_p              0\n",
              "status             0\n",
              "salary            67\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Varify the data type match with corresponding columns types\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUDt7hz5Y4U_",
        "outputId": "0b3b58b1-3d61-4b5b-c5ac-5aa6247f30db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sl_no               int64\n",
              "gender             object\n",
              "ssc_p             float64\n",
              "ssc_b              object\n",
              "hsc_p             float64\n",
              "hsc_b              object\n",
              "hsc_s              object\n",
              "degree_p          float64\n",
              "degree_t           object\n",
              "workex             object\n",
              "etest_p           float64\n",
              "specialisation     object\n",
              "mba_p             float64\n",
              "status             object\n",
              "salary            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "TPtcCWOhXm0G",
        "outputId": "085ce684-84d7-43d0-d5ff-bc5966070410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            sl_no       ssc_p       hsc_p    degree_p     etest_p       mba_p  \\\n",
              "count  215.000000  215.000000  215.000000  215.000000  215.000000  215.000000   \n",
              "mean   108.000000   67.303395   66.333163   66.370186   72.100558   62.278186   \n",
              "std     62.209324   10.827205   10.897509    7.358743   13.275956    5.833385   \n",
              "min      1.000000   40.890000   37.000000   50.000000   50.000000   51.210000   \n",
              "25%     54.500000   60.600000   60.900000   61.000000   60.000000   57.945000   \n",
              "50%    108.000000   67.000000   65.000000   66.000000   71.000000   62.000000   \n",
              "75%    161.500000   75.700000   73.000000   72.000000   83.500000   66.255000   \n",
              "max    215.000000   89.400000   97.700000   91.000000   98.000000   77.890000   \n",
              "\n",
              "              salary  \n",
              "count     148.000000  \n",
              "mean   288655.405405  \n",
              "std     93457.452420  \n",
              "min    200000.000000  \n",
              "25%    240000.000000  \n",
              "50%    265000.000000  \n",
              "75%    300000.000000  \n",
              "max    940000.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79f769b6-432a-4dab-8261-407b71ea19c1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</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>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>salary</th>\n",
              "    </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>148.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>108.000000</td>\n",
              "      <td>67.303395</td>\n",
              "      <td>66.333163</td>\n",
              "      <td>66.370186</td>\n",
              "      <td>72.100558</td>\n",
              "      <td>62.278186</td>\n",
              "      <td>288655.405405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>62.209324</td>\n",
              "      <td>10.827205</td>\n",
              "      <td>10.897509</td>\n",
              "      <td>7.358743</td>\n",
              "      <td>13.275956</td>\n",
              "      <td>5.833385</td>\n",
              "      <td>93457.452420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>40.890000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>51.210000</td>\n",
              "      <td>200000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54.500000</td>\n",
              "      <td>60.600000</td>\n",
              "      <td>60.900000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>57.945000</td>\n",
              "      <td>240000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>108.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>265000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>161.500000</td>\n",
              "      <td>75.700000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>83.500000</td>\n",
              "      <td>66.255000</td>\n",
              "      <td>300000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>215.000000</td>\n",
              "      <td>89.400000</td>\n",
              "      <td>97.700000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>77.890000</td>\n",
              "      <td>940000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79f769b6-432a-4dab-8261-407b71ea19c1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79f769b6-432a-4dab-8261-407b71ea19c1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-79f769b6-432a-4dab-8261-407b71ea19c1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tnv.Missing_Value_Analysis(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "vPcl-MnxnMV8",
        "outputId": "6769a4f0-3fc7-4137-d72f-532c22c1f600"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your selected dataframe has 15 columns.\n",
            "There are 1 columns that have missing values.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ],
            "image/png": 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l1q1bq2PHjnrllVd0+fLlYuu2bdumZ599Vp07d1arVq3UpUsXTZ8+Xfn5+dY1kyZN0vLlyyWp2C0wZ86csf7ca9asKZHhxu9+7ty5CgsL09GjRzVx4kRFREQU+31LSkrSoEGD1LZtW7Vt21YjRoxQSkqKXb4PAABcATs3AACoAurVq6f7779fn3/+ubp06SJJ2r17tzIyMtSrVy8lJibe9Brx8fFauHCh+vbtq/DwcF25ckUpKSk6fPiwoqKiJEljx47V0aNHNXDgQDVs2FCZmZnat2+fTp06pebNm5d7/Y0bN+rKlSvq37+/DAaDFi9erBdeeEFbt2617jb56quvNG7cODVv3lwvvfSSTCaTXnvtNd1xxx0V+j5uLCq8vLxUs2ZNSdKbb76pf/3rX4qJidHAgQN1/vx5/fOf/9T333+v1atXW3e0rFmzRt7e3ho8eLBq1qypQ4cO6eOPP9aFCxc0e/ZsSVL//v31888/a9euXXrvvfesn1enTp0SGWwxbtw4NWzYUC+++KIKCwslSevXr9fEiRP10EMP6aWXXlJBQYE+++wz/elPf9Lq1avVtGnTCn8OAACuhnIDAIAqomfPnpo2bZr1dob169fr3nvvVWhoqE3v/+qrrxQZGakpU6aUOm8ymXTgwAG9/PLLGjFihHX8z3/+s03XP3/+vDZv3qxatWpJkho3bqzRo0crKSnJWsjMnDlTQUFB+uSTT1SjRg1JUseOHTV48GA1aNDAps8pKChQx44di421bNlSa9as0bfffqtPP/1U06dPV0xMjHW+U6dOGjhwoNauXav+/ftLkv7+97+revXq1jUDBgxQo0aN9P777+vll1/WnXfeqbZt2+quu+7Srl27StwK83vKjSZNmhTb0XHlyhVNnjxZvXv31tSpU63jffv2Vffu3TV//nzNnDmzwp8DAICrodwAAKCK6N69u6ZMmaKtW7cqKipKW7du1bhx42x+f82aNXXs2DGdPHlSjRs3LjHv6+srLy8v7du3T3369FHt2rUrlK9bt27WYkOS9fDT9PR0SdLFixd19OhRjRw50lpsSFJERITuvvtu5ebm2vQ5Xl5eSkhIKDZ2/XqJiYny8/NTp06dipUPTZo0Ud26dZWcnGwtN64XG2azWbm5uSosLNR9990ni8WiH374QXfeeWeFfn5bxMbGFnu9e/duZWVlqVevXiXKkvvvv1/Jycl2zwAAwO2IcgMAgCqidu3aevjhh7V+/XpVq1ZNeXl56tGjh83vHzt2rMaMGaPu3burWbNmevjhh9WzZ0+1bt1a0m8HhU6YMEHvvfeeHnroIYWHhysyMlLR0dE2/UX/xjXXiw6TySRJOnfunCSpUaNGJd7bqFEjHTlyxKafw2Aw6MEHHyx17tSpU7py5UqZ8xkZGdZ/P3r0qGbMmKF9+/YpLy+v2LqcnBybslRUSEhIsdcnT56UJA0bNqzU9R4eHK8GAKgaKDcAAKhCevbsqbi4OOXk5Khjx44yGo02v/eBBx7Qli1btH37diUlJWnt2rX6+OOPNX78eI0aNUqSNHToUHXt2lXbtm3T7t27NX/+fC1atEgLFy5U+/bty71+WY+dtVgstv+At8hsNqt27drWMzNuFBAQIEnKzs7WM888Iz8/P40bN06hoaHy9fXVxYsXNWnSJJnN5pt+lsFgKHW8qKiozPf4+voWe339u5k2bVqFzx0BAMCdUG4AAFCFdO3aVd7e3vr22281ffr0Cr+/Vq1aiomJUUxMjPLy8jRq1CjNnTtXw4cPt5YTISEhGjp0qIYOHaoLFy4oOjrapnLjZurXry9J+umnn0rMlTb2e4SGhmr37t2699575e/vX+a65ORkZWZmas6cOYqIiLCO79q1q8TaskqMG3emXHd9h4otru/kqFOnTpm7TQAAqArYqwgAQBVSvXp1vfnmm3r++ef12GOPVei9mZmZxV77+vqqSZMmys/PV15enq5evVri9ox69erJaDSW+Av873HHHXfo7rvv1rp164rd9rFv3z4dPXr0lq8vST169JDZbNa8efNKzBUVFSkrK0vS/9/u8b+7Ssxms5YsWVLifdfP5rj+3utq1KihwMBA7d+/v9j4ihUrbM7bqVMnBQQEaOHChSooKCgx/3sOLQUAwBWxcwMAgCrmf58CUhE9evTQAw88oFatWikwMFBpaWlavXq1unTpIn9/f6WkpGjIkCHWMzm8vb21Y8cOHT9+XHFxcXbJPm7cOI0ePVqxsbHq06ePTCaTli9fXqEDRcvzwAMPaODAgfrwww+VlpamTp06ycvLS6dPn9amTZs0duxYPf3002rXrp1q166tSZMmadCgQapWrZo2bdqkK1eulLhmq1atJElvv/22OnfurGrVqqlLly7y8/NTv379lJCQoFdffVWtWrXS/v37redo2KJGjRp66623NGHCBPXu3VtPPvmk6tatq3Pnzunrr79W8+bNNW3atFv+XgAAuN1RbgAAAJsMGTJEX375pfbu3au8vDzdeeedGjVqlPW8jXr16qlXr17as2eP1q9fL4PBoLvuukvvvPOO+vbta5cMjz76qGbNmqW5c+dq5syZuuuuu/Tuu+/qP//5j44dO2aXz3jjjTfUokULrVy5UrNnz5anp6fq16+vJ554Qh06dJD02+GsCQkJmjZtmubOnSs/Pz9FRUUpNjZWTz31VLHrRUVFaciQIdqwYYM2bNggi8Wibdu2yc/PT2PGjNHly5e1adMmJSYmqnPnzlq8eHGJR9WWp0ePHgoODtbChQu1ZMkS5efnKzg4WO3atbM+2QUAAHdnsFTmKV0AAAAOEB0drTp16pR6WwgAAHB/nLkBAABcRmFhoa5du1ZsLDk5WampqcUO9gQAAFULt6UAAACXcfHiRQ0bNkxPPfWUgoODdeLECa1cuVJBQUEaMGCAs+MBAAAnodwAAAAuo1atWmrZsqVWrVqly5cvy8/PT5GRkZowYYICAwOdHQ8AADgDUIH9AAAASElEQVQJZ24AAAAAAACXxpkbAAAAAADApVFuAAAAAAAAl0a5AQAAAAAAXBrlBgAAAAAAcGmUGwAAAAAAwKVRbgAAAAAAAJf2f7ZsctubNesqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    index  Miss_Values  Miss_Rate  Available_Value\n",
              "0  salary           67      31.16              148"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14e98c45-0444-4d76-8f40-650ca8ab7915\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Miss_Values</th>\n",
              "      <th>Miss_Rate</th>\n",
              "      <th>Available_Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>salary</td>\n",
              "      <td>67</td>\n",
              "      <td>31.16</td>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14e98c45-0444-4d76-8f40-650ca8ab7915')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-14e98c45-0444-4d76-8f40-650ca8ab7915 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14e98c45-0444-4d76-8f40-650ca8ab7915');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Duplicates in this dataset frame: {df.iloc[:,1:].duplicated().sum()}, ({np.round(100*df.iloc[:,1:].duplicated().sum()/len(df),1)}%)')\n",
        "print('')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtCWaYejMrD4",
        "outputId": "354b0ca8-101c-49f7-f279-0d7389149a08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicates in this dataset frame: 0, (0.0%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['status'].value_counts())\n",
        "df['status'].value_counts().plot(kind='barh')\n",
        "# df['State'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "65zdUmKuUzU5",
        "outputId": "bac239cf-5d35-4f3b-b243-1c06441b7548"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Placed        148\n",
            "Not Placed     67\n",
            "Name: status, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fddbfd650>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['hsc_s'].value_counts().to_frame())\n",
        "print(df['degree_t'].value_counts().to_frame())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCoRDYM-vUTz",
        "outputId": "58449efa-5557-450d-e1a5-09174ae8be52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          hsc_s\n",
            "Commerce    113\n",
            "Science      91\n",
            "Arts         11\n",
            "           degree_t\n",
            "Comm&Mgmt       145\n",
            "Sci&Tech         59\n",
            "Others           11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleansing\n",
        "Clean our data before we can use it in our Machine Learning algorithms\n",
        "1. There is no missing expect for salary column with result of non-placement, therefore we dont need to dropna and there is no duplicate with series number\n",
        "2. Drop the sl_no(Series No.) column generate with random pattern and drop salary because the salary is obtained after the position is placed\n",
        "3. Label Encode: \"Gender\", \"ssc_b\", \"hsc_b\", \"workex\", \"specialization\" - Only 2 category for each column\n",
        "4. One Hot Encode: \"hsc_s\", \"degree_t\" - 3 category for those column hsc_s and degree_t "
      ],
      "metadata": {
        "id": "wgktwxEDre3_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['sl_no','salary'], axis=1)\n",
        "df"
      ],
      "metadata": {
        "id": "fGngxSx_ggzv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "c041d7c6-e5a0-46c1-9807-571181fa761e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    gender  ssc_p    ssc_b  hsc_p    hsc_b     hsc_s  degree_p   degree_t  \\\n",
              "0        M  67.00   Others  91.00   Others  Commerce     58.00   Sci&Tech   \n",
              "1        M  79.33  Central  78.33   Others   Science     77.48   Sci&Tech   \n",
              "2        M  65.00  Central  68.00  Central      Arts     64.00  Comm&Mgmt   \n",
              "3        M  56.00  Central  52.00  Central   Science     52.00   Sci&Tech   \n",
              "4        M  85.80  Central  73.60  Central  Commerce     73.30  Comm&Mgmt   \n",
              "..     ...    ...      ...    ...      ...       ...       ...        ...   \n",
              "210      M  80.60   Others  82.00   Others  Commerce     77.60  Comm&Mgmt   \n",
              "211      M  58.00   Others  60.00   Others   Science     72.00   Sci&Tech   \n",
              "212      M  67.00   Others  67.00   Others  Commerce     73.00  Comm&Mgmt   \n",
              "213      F  74.00   Others  66.00   Others  Commerce     58.00  Comm&Mgmt   \n",
              "214      M  62.00  Central  58.00   Others   Science     53.00  Comm&Mgmt   \n",
              "\n",
              "    workex  etest_p specialisation  mba_p      status  \n",
              "0       No     55.0         Mkt&HR  58.80      Placed  \n",
              "1      Yes     86.5        Mkt&Fin  66.28      Placed  \n",
              "2       No     75.0        Mkt&Fin  57.80      Placed  \n",
              "3       No     66.0         Mkt&HR  59.43  Not Placed  \n",
              "4       No     96.8        Mkt&Fin  55.50      Placed  \n",
              "..     ...      ...            ...    ...         ...  \n",
              "210     No     91.0        Mkt&Fin  74.49      Placed  \n",
              "211     No     74.0        Mkt&Fin  53.62      Placed  \n",
              "212    Yes     59.0        Mkt&Fin  69.72      Placed  \n",
              "213     No     70.0         Mkt&HR  60.23      Placed  \n",
              "214     No     89.0         Mkt&HR  60.22  Not Placed  \n",
              "\n",
              "[215 rows x 13 columns]"
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "columns_to_label_encode = ['gender','ssc_b','hsc_b','workex','specialisation']\n",
        "df[columns_to_label_encode] = df[columns_to_label_encode].apply(le.fit_transform)\n",
        "df.head(3)"
      ],
      "metadata": {
        "id": "HqXHacx_Ra3L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "1c231c6a-78b4-467d-c423-000cc539a7ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  ssc_p  ssc_b  hsc_p  hsc_b     hsc_s  degree_p   degree_t  workex  \\\n",
              "0       1  67.00      1  91.00      1  Commerce     58.00   Sci&Tech       0   \n",
              "1       1  79.33      0  78.33      1   Science     77.48   Sci&Tech       1   \n",
              "2       1  65.00      0  68.00      0      Arts     64.00  Comm&Mgmt       0   \n",
              "\n",
              "   etest_p  specialisation  mba_p  status  \n",
              "0     55.0               1  58.80  Placed  \n",
              "1     86.5               0  66.28  Placed  \n",
              "2     75.0               0  57.80  Placed  "
            ],
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-539b6cae-2e62-427c-9ebe-36ae7042a3b7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_target_variable = df['status']\n",
        "df = df.drop('status',axis=1)"
      ],
      "metadata": {
        "id": "-KKmaoObzH5w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df)\n",
        "df = pd.concat([df, df_target_variable], axis=1)\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "5V4aGnZw0WIS",
        "outputId": "6dc0cd47-c69f-46f2-89fa-95132291801e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  ssc_p  ssc_b  hsc_p  hsc_b  degree_p  workex  etest_p  \\\n",
              "0       1  67.00      1  91.00      1     58.00       0     55.0   \n",
              "1       1  79.33      0  78.33      1     77.48       1     86.5   \n",
              "2       1  65.00      0  68.00      0     64.00       0     75.0   \n",
              "\n",
              "   specialisation  mba_p  hsc_s_Arts  hsc_s_Commerce  hsc_s_Science  \\\n",
              "0               1  58.80           0               1              0   \n",
              "1               0  66.28           0               0              1   \n",
              "2               0  57.80           1               0              0   \n",
              "\n",
              "   degree_t_Comm&Mgmt  degree_t_Others  degree_t_Sci&Tech  status  \n",
              "0                   0                0                  1  Placed  \n",
              "1                   0                0                  1  Placed  \n",
              "2                   1                0                  0  Placed  "
            ],
            "text/html": [
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              "      <th>hsc_s_Arts</th>\n",
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              "      <td>65.00</td>\n",
              "      <td>0</td>\n",
              "      <td>68.00</td>\n",
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              "      <td>64.00</td>\n",
              "      <td>0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d78de10d-4fcc-4f54-898c-d0e55dacda14')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-d78de10d-4fcc-4f54-898c-d0e55dacda14');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "columns_to_label_encode = ['gender','ssc_b','hsc_b','workex','specialisation']\n",
        "df[columns_to_label_encode] = df[columns_to_label_encode].apply(le.fit_transform)\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "WiKppaG60PSD",
        "outputId": "b1fbf374-b08d-4d99-f120-61b06781a1da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  ssc_p  ssc_b  hsc_p  hsc_b  degree_p  workex  etest_p  \\\n",
              "0       1  67.00      1  91.00      1     58.00       0     55.0   \n",
              "1       1  79.33      0  78.33      1     77.48       1     86.5   \n",
              "2       1  65.00      0  68.00      0     64.00       0     75.0   \n",
              "\n",
              "   specialisation  mba_p  hsc_s_Arts  hsc_s_Commerce  hsc_s_Science  \\\n",
              "0               1  58.80           0               1              0   \n",
              "1               0  66.28           0               0              1   \n",
              "2               0  57.80           1               0              0   \n",
              "\n",
              "   degree_t_Comm&Mgmt  degree_t_Others  degree_t_Sci&Tech  status  \n",
              "0                   0                0                  1  Placed  \n",
              "1                   0                0                  1  Placed  \n",
              "2                   1                0                  0  Placed  "
            ],
            "text/html": [
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
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              "      <th>hsc_b</th>\n",
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              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
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              "      <th>hsc_s_Commerce</th>\n",
              "      <th>hsc_s_Science</th>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae877b9e-3559-4c5f-a426-c5d7e5bf97fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae877b9e-3559-4c5f-a426-c5d7e5bf97fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae877b9e-3559-4c5f-a426-c5d7e5bf97fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "df_target_variable = df.iloc[:, -1]\n",
        "df = df.iloc[:, :-1]\n",
        "\n",
        "df = scaler.fit_transform(df)\n",
        "\n",
        "# scaled_data = pd.concat([scaled_data, scaled_data_temp], axis=1)\n",
        "df"
      ],
      "metadata": {
        "id": "UD2XCL4x0Qw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c188a69c-051d-4e2b-b452-b640b917ed00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.73943397, -0.02808697,  1.08245885, ..., -1.43924583,\n",
              "        -0.23221018,  1.62605898],\n",
              "       [ 0.73943397,  1.11336869, -0.92382264, ..., -1.43924583,\n",
              "        -0.23221018,  1.62605898],\n",
              "       [ 0.73943397, -0.21323793, -0.92382264, ...,  0.69480833,\n",
              "        -0.23221018, -0.61498384],\n",
              "       ...,\n",
              "       [ 0.73943397, -0.02808697,  1.08245885, ...,  0.69480833,\n",
              "        -0.23221018, -0.61498384],\n",
              "       [-1.35238581,  0.61994138,  1.08245885, ...,  0.69480833,\n",
              "        -0.23221018, -0.61498384],\n",
              "       [ 0.73943397, -0.49096436, -0.92382264, ...,  0.69480833,\n",
              "        -0.23221018, -0.61498384]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCA3jzH-5WNQ",
        "outputId": "a9fe97ec-187d-4cf1-f77b-7304ddd31b53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.73943397, -0.02808697,  1.08245885,  2.2688123 ,  0.80076299,\n",
              "       -1.14010225, -0.72444647, -1.29109087,  1.12390297, -0.59764672,\n",
              "       -0.23221018,  0.95008151, -0.85666269, -1.43924583, -0.23221018,\n",
              "        1.62605898])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_target_variable"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPXbLXy65YNg",
        "outputId": "79d34763-8db5-4415-a48e-6e8a6f6af79d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          Placed\n",
              "1          Placed\n",
              "2          Placed\n",
              "3      Not Placed\n",
              "4          Placed\n",
              "          ...    \n",
              "210        Placed\n",
              "211        Placed\n",
              "212        Placed\n",
              "213        Placed\n",
              "214    Not Placed\n",
              "Name: status, Length: 215, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df, df_target_variable, test_size=0.2, random_state=66)"
      ],
      "metadata": {
        "id": "5oS3dwHL1N03"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build ML Algorithms"
      ],
      "metadata": {
        "id": "t_-cUQG7X3L2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
        "# from sklearn.linear_model import LogisticRegression\n",
        "# from sklearn.svm import SVC\n",
        "lr_model = LogisticRegression()\n",
        "rf_model = RandomForestClassifier()\n",
        "svc_model = SVC()"
      ],
      "metadata": {
        "id": "S3Qurfr07C65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vote_model = VotingClassifier(estimators = [('lr',lr_model),('rf',rf_model),('svc',svc_model)], voting='hard')"
      ],
      "metadata": {
        "id": "_CCgudCc7WG3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vote_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "or00WKac74Z3",
        "outputId": "ba57bfef-780a-4243-ca0a-80170e28c0aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('lr', LogisticRegression()),\n",
              "                             ('rf', RandomForestClassifier()), ('svc', SVC())])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.metrics import accuracy_score\n",
        "model_lists = [lr_model, rf_model, svc_model, vote_model]\n",
        "for clf in (model_lists):\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBcHzs3t7-70",
        "outputId": "e977db53-f3bd-4e50-bb99-53b8487c2ced"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression 0.8604651162790697\n",
            "RandomForestClassifier 0.8604651162790697\n",
            "SVC 0.7906976744186046\n",
            "VotingClassifier 0.8604651162790697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import BaggingClassifier\n",
        "# from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "bag_model = BaggingClassifier(\n",
        "    DecisionTreeClassifier(), n_estimators=500,\n",
        "    max_samples=100, bootstrap=True, n_jobs=-1) # Bootstrap determines whether this is bagging or pasting\n",
        "bag_model.fit(X_train, y_train)\n",
        "y_pred = bag_model.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bgQF-v68CV2",
        "outputId": "5c11b6b5-ee37-44f2-9c16-445a9b68d2c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8604651162790697"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "ada_model = AdaBoostClassifier(\n",
        "    DecisionTreeClassifier(max_depth=1), n_estimators=200, \n",
        "    algorithm=\"SAMME.R\", learning_rate=0.5)\n",
        "ada_model.fit(X_train, y_train)\n",
        "y_pred = ada_model.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0fiJaBm91fF",
        "outputId": "3cab4682-0f2f-42d9-dbe9-61a97affbbbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9069767441860465"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gb_model = GradientBoostingClassifier(max_depth=2, n_estimators=120)\n",
        "gb_model.fit(X_train, y_train)\n",
        "y_pred = gb_model.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUtn-NMQ-CFQ",
        "outputId": "e8f044a4-0274-4d95-b1b1-a0b71a1998ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8837209302325582"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ]
}