updated_FinalPRMLcode.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "5Ce14OgkfaAc"
      },
      "source": [
        "**Career Guidance System**"
      ]
    },
    {
      "cell_type": "code",
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          "height": 359
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        "id": "VREYLjokfSaN",
        "outputId": "b08df79d-e5b8-4f08-fa79-96888754066c"
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      "source": [
        "#importing data\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "url='https://raw.githubusercontent.com/Nantha-1998/404-CODER/master/career_compute_dataset.csv'\n",
        "dataset = pd.read_csv(url)\n",
        "print(np.shape(dataset))\n",
        "dataset.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7525, 26)\n"
          ]
        },
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              "   sslc  hsc  cgpa  school_type  no_of_miniprojects  no_of_projects  \\\n",
              "0     2    3     2            3                   1               1   \n",
              "1     3    1     2            2                   2               1   \n",
              "2     3    4     2            2                   1               1   \n",
              "3     3    4     2            1                   1               1   \n",
              "4     2    3     2            4                   1               1   \n",
              "\n",
              "   coresub_skill  aptitude_skill  problemsolving_skill  programming_skill  \\\n",
              "0              1               1                     2                  4   \n",
              "1              1               3                     2                  4   \n",
              "2              1               2                     3                  3   \n",
              "3              1               1                     3                  2   \n",
              "4              1               4                     3                  3   \n",
              "\n",
              "   ...  technology_used  sympos_attend  sympos_won  extracurricular  \\\n",
              "0  ...                1              3           1                2   \n",
              "1  ...                2              3           2                3   \n",
              "2  ...                1              4           3                1   \n",
              "3  ...                1              3           2                1   \n",
              "4  ...                1              2           1                3   \n",
              "\n",
              "   learning_style  college_bench  clg_teachers_know  college_performence   \\\n",
              "0               2              4                  3                     4   \n",
              "1               2              5                  3                     3   \n",
              "2               4              3                  3                     3   \n",
              "3               2              1                  4                     4   \n",
              "4               1              5                  4                     3   \n",
              "\n",
              "   college_skills                ROLE  \n",
              "0               5   Technical Support  \n",
              "1               4  Software Developer  \n",
              "2               4      UI/UX Designer  \n",
              "3               3   Technical Support  \n",
              "4               1        Data Analyst  \n",
              "\n",
              "[5 rows x 26 columns]"
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              "      <td>4</td>\n",
              "      <td>UI/UX Designer</td>\n",
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              "      <td>1</td>\n",
              "      <td>Data Analyst</td>\n",
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              "<p>5 rows × 26 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "7c1CCk-EhQic",
        "outputId": "5c193a24-d685-4fab-bd9e-a61f0b71bd2e"
      },
      "source": [
        "data = dataset.iloc[:49,:-1].values\n",
        "label = dataset.iloc[:49,-1]\n",
        "#-------------------------------Label Encoding--------------------------#\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "labelencoder = LabelEncoder()\n",
        "df = dataset\n",
        "label = df.iloc[:49,-1]\n",
        "original=label.unique()\n",
        "label=label.values\n",
        "label2 = labelencoder.fit_transform(label)\n",
        "y=pd.DataFrame(label2,columns=[\"ROLE\"])\n",
        "numeric=y[\"ROLE\"].unique()\n",
        "y1 = pd.DataFrame({'ROLE':original, 'Associated Number':numeric})\n",
        "print(y1)\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "labelencoder = LabelEncoder()\n",
        "label = labelencoder.fit_transform(label)\n",
        "y=pd.DataFrame(label,columns=[\"role\"])\n",
        "X = pd.DataFrame(data,columns=['sslc','hsc','cgpa','school_type','no_of_miniprojects','no_of_projects',\n",
        "                                'coresub_skill','aptitude_skill','problemsolving_skill','programming_skill','abstractthink_skill',\n",
        "                                'design_skill','first_computer','first_program','lab_programs','ds_coding','technology_used',\n",
        "                                'sympos_attend','sympos_won','extracurricular','learning_style','college_bench','clg_teachers_know','college_performence','college_skills'])"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 ROLE  Associated Number\n",
            "0   Technical Support                  4\n",
            "1  Software Developer                  2\n",
            "2      UI/UX Designer                  6\n",
            "3        Data Analyst                  1\n",
            "4    Technical Writer                  5\n",
            "5       Web Developer                  7\n",
            "6     Software Tester                  3\n",
            "7    Business Analyst                  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZzTh9Yrf_x6"
      },
      "source": [
        "Thus, we have categorical data and categorical target.\n",
        "\n",
        "For  Feature Selection :\n",
        "\n",
        "We propose to use:\n",
        "\n",
        "1. Chi-Squared Statistic.\n",
        "2. Mutual Information Statistic.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkszPWfkf_dz"
      },
      "source": [
        "\n",
        "from pandas import read_csv\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "from matplotlib import pyplot"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "An3KlNcZglav",
        "outputId": "d5eaf0c7-26a7-4f17-c58c-a68343988708"
      },
      "source": [
        "# feature selection\n",
        "# example of chi squared feature selection for categorical data\n",
        "from sklearn.feature_selection import SelectKBest, chi2\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "def select_features(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=chi2, k='all')\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "# split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20)#Decision tree\n",
        "X_train2,X_test2,y_train2,y_test2=train_test_split(X,y,test_size=0.3,random_state=10)#XGBoost\n",
        "X_train6,X_test6,y_train6,y_test6=train_test_split(X,y,test_size=0.2,random_state=15)#SVM\n",
        "\n",
        "X_train1, X_test1, fs1 = select_features(X_train, y_train, X_test)\n",
        "X_train3, X_test3, fs3 = select_features(X_train2, y_train2, X_test2)\n",
        "X_train5, X_test5, fs5 = select_features(X_train2, y_train2, X_test2)\n",
        "\n",
        "# what are scores for the features\n",
        "for i in range(len(fs1.scores_)):\n",
        "\tprint('Feature %d: %f' % (i, fs1.scores_[i]))\n",
        "# plot the scores\n",
        "pyplot.bar([i for i in range(len(fs1.scores_))], fs1.scores_)\n",
        "pyplot.show()\n",
        "\n",
        "count = 0\n",
        "for i in fs1.scores_ :\n",
        "    if i > 1 :\n",
        "        count = count + 1\n",
        "k1=count\n",
        "print(\" The number of important features with threshold as 1 :{} \".format(k1))\n",
        "\n",
        "\n",
        "for i in range(len(fs3.scores_)):\n",
        "\tprint('Feature %d: %f' % (i, fs3.scores_[i]))\n",
        "# plot the scores\n",
        "pyplot.bar([i for i in range(len(fs3.scores_))], fs3.scores_)\n",
        "pyplot.show()\n",
        "\n",
        "count = 0\n",
        "for i in fs3.scores_ :\n",
        "    if i > 1 :\n",
        "        count = count + 1\n",
        "k3=count\n",
        "\n",
        "print(\" The number of important features with threshold as 1 :{} \".format(k3))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature 0: 2.101772\n",
            "Feature 1: 0.955491\n",
            "Feature 2: 1.599306\n",
            "Feature 3: 1.769333\n",
            "Feature 4: 3.316358\n",
            "Feature 5: 1.125758\n",
            "Feature 6: 1.299609\n",
            "Feature 7: 6.225678\n",
            "Feature 8: 1.013542\n",
            "Feature 9: 1.529464\n",
            "Feature 10: 6.433067\n",
            "Feature 11: 3.607745\n",
            "Feature 12: 0.611667\n",
            "Feature 13: 1.566959\n",
            "Feature 14: 0.907095\n",
            "Feature 15: 4.838542\n",
            "Feature 16: 1.000189\n",
            "Feature 17: 2.309167\n",
            "Feature 18: 2.526337\n",
            "Feature 19: 3.994762\n",
            "Feature 20: 3.109337\n",
            "Feature 21: 0.519778\n",
            "Feature 22: 0.356387\n",
            "Feature 23: 0.116054\n",
            "Feature 24: 5.677100\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGdCAYAAABO2DpVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAXsElEQVR4nO3de4xU9dnA8WcFGVF3VxdBdsty8UoVl3qDEqrVQkWKxFuMtTSlxmC0qxWJVreJImnrUps09mLQmlb6R/GWFG21aqwVSKNYxRC1FwoUwiogra27gHU17Hn/8HXfd5WLM/ubnZ3h80lOwgxzznlychK+nLmcqizLsgAASOCAUg8AAFQOYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkM7OsddnV1xebNm6O6ujqqqqr6evcAQAGyLIvt27dHQ0NDHHDAnq9L9HlYbN68ORobG/t6twBAAm1tbTFixIg9/n2fh0V1dXVEfDBYTU1NX+8eAChAR0dHNDY2dv87vid9HhYfvv1RU1MjLACgzOzrYww+vAkAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASKbPb5sO5Gf0zY8XvO7GhTMSTgKwb65YAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJONeIVQE99MA6B9csQAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGTyDos33ngjvvrVr8aQIUNi8ODBcdJJJ8VLL71UjNkAgDKT1w9k/ec//4nJkyfH2WefHU888UQMHTo01q5dG4cffnix5gMAykheYfH9738/Ghsb47777ut+bsyYMcmHAgDKU15vhfzmN7+J0047LS655JIYNmxYnHzyyXHvvffudZ3Ozs7o6OjosQAAlSmvsPjHP/4RixYtimOPPTaeeuqpuPrqq+Ob3/xm/PKXv9zjOq2trVFbW9u9NDY29npoAKB/yissurq64pRTTonbb789Tj755Ljyyitjzpw5cffdd+9xnZaWlmhvb+9e2traej00ANA/5RUW9fX1ccIJJ/R47tOf/nRs2rRpj+vkcrmoqanpsQAAlSmvsJg8eXKsWbOmx3N///vfY9SoUUmHAgDKU15hcf3118fKlSvj9ttvj3Xr1sWSJUviZz/7WTQ3NxdrPgCgjOQVFqeffnosXbo07r///hg3blx85zvfiTvvvDNmzZpVrPkAgDKS1+9YREScd955cd555xVjFgCgzLlXCACQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDJ53ysEACi+0Tc/XtB6GxfOSDxJflyxAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJBMXmFx2223RVVVVY9l7NixxZoNACgzA/Nd4cQTT4zf//73/7eBgXlvAgCoUHlXwcCBA2P48OHFmAUAKHN5f8Zi7dq10dDQEEcddVTMmjUrNm3atNfXd3Z2RkdHR48FAKhMeYXFxIkTY/HixfHkk0/GokWLYsOGDXHGGWfE9u3b97hOa2tr1NbWdi+NjY29HhoA6J/yCovp06fHJZdcEk1NTTFt2rT43e9+F2+//XY89NBDe1ynpaUl2tvbu5e2trZeDw0A9E+9+uTlYYcdFscdd1ysW7duj6/J5XKRy+V6sxsAoEz06ncsduzYEevXr4/6+vpU8wAAZSyvsLjhhhti+fLlsXHjxnjuuefiwgsvjAEDBsRll11WrPkAgDKS11shr7/+elx22WXx1ltvxdChQ+Nzn/tcrFy5MoYOHVqs+QCAMpJXWDzwwAPFmgMAqADuFQIAJCMsAIBk3OgD4H+NvvnxgtbbuHBG4kmgfLliAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgmV6FxcKFC6Oqqirmzp2baBwAoJwVHBYvvvhi3HPPPdHU1JRyHgCgjBUUFjt27IhZs2bFvffeG4cffnjqmQCAMlVQWDQ3N8eMGTNi6tSp+3xtZ2dndHR09FgAgMo0MN8VHnjggXj55ZfjxRdf/ESvb21tjQULFuQ9GABQfvIKi7a2trjuuuvi6aefjoMOOugTrdPS0hLz5s3rftzR0RGNjY35TQlAvzD65scLWm/jwhmJJ6G/yissVq1aFdu2bYtTTjml+7ldu3bFihUr4qc//Wl0dnbGgAEDeqyTy+Uil8ulmRYA6NfyCospU6bEq6++2uO5yy+/PMaOHRs33XTTx6ICANi/5BUW1dXVMW7cuB7PHXLIITFkyJCPPQ8A7H/88iYAkEze3wr5qGXLliUYAwCoBK5YAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDIDSz0A0DdG3/x4wetuXDgj4SRAJXPFAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIZWOoB6Fujb368oPU2LpyReBIAKpErFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIxk96A9Dn3F6gcrliAQAkk1dYLFq0KJqamqKmpiZqampi0qRJ8cQTTxRrNgCgzOQVFiNGjIiFCxfGqlWr4qWXXoovfOELcf7558ef//znYs0HAJSRvD5jMXPmzB6Pv/e978WiRYti5cqVceKJJyYdDAAoPwV/eHPXrl3x8MMPx86dO2PSpEl7fF1nZ2d0dnZ2P+7o6Ch0lwBAP5f3hzdfffXVOPTQQyOXy8VVV10VS5cujRNOOGGPr29tbY3a2trupbGxsVcDAwD9V95hcfzxx8fq1avjhRdeiKuvvjpmz54df/nLX/b4+paWlmhvb+9e2traejUwANB/5f1WyKBBg+KYY46JiIhTTz01XnzxxfjRj34U99xzz25fn8vlIpfL9W5KAKAs9Pp3LLq6unp8hgIA2H/ldcWipaUlpk+fHiNHjozt27fHkiVLYtmyZfHUU08Vaz4AoIzkFRbbtm2Lr33ta7Fly5aora2NpqameOqpp+KLX/xiseYDAMpIXmHx85//vFhzAAAVwL1CAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIZWOoBACiu0Tc/XvC6GxfOSDgJ+wNXLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJJxrxCAfso9PihHrlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIJmK+klvP38LAKXligUAkExeYdHa2hqnn356VFdXx7Bhw+KCCy6INWvWFGs2AKDM5BUWy5cvj+bm5li5cmU8/fTT8f7778c555wTO3fuLNZ8AEAZyeszFk8++WSPx4sXL45hw4bFqlWr4swzz0w6GABQfnr14c329vaIiKirq9vjazo7O6Ozs7P7cUdHR292CQD0YwV/eLOrqyvmzp0bkydPjnHjxu3xda2trVFbW9u9NDY2FrpLAKCfKzgsmpub47XXXosHHnhgr69raWmJ9vb27qWtra3QXQIA/VxBb4Vcc8018dhjj8WKFStixIgRe31tLpeLXC5X0HAAQHnJKyyyLItrr702li5dGsuWLYsxY8YUay4AoAzlFRbNzc2xZMmSePTRR6O6ujq2bt0aERG1tbUxePDgogwIAJSPvD5jsWjRomhvb4+zzjor6uvru5cHH3ywWPMBAGUk77dCAAD2xL1CAIBkhAUAkIywAACSERYAQDLCAgBIplc3IaPvjL758YLW27hwRuJJAGDPXLEAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACS8cubReYXMwHYn7hiAQAk44oFUDKu6EHlccUCAEjGFQsoEv8bB/ZHrlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAk414hlFSh99OIcE8NgP7IFQsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZv7xJQfxiJgC744oFAJBM3mGxYsWKmDlzZjQ0NERVVVU88sgjRRgLAChHeYfFzp07Y/z48XHXXXcVYx4AoIzl/RmL6dOnx/Tp04sxCwBQ5nzGAgBIpujfCuns7IzOzs7uxx0dHcXeJfRKod948W0XgD4Ii9bW1liwYEGxdwPQb/g6Nvuzor8V0tLSEu3t7d1LW1tbsXcJAJRI0a9Y5HK5yOVyxd5NUv63AQCFyTssduzYEevWret+vGHDhli9enXU1dXFyJEjkw4HAJSXvMPipZdeirPPPrv78bx58yIiYvbs2bF48eJkgwEA5SfvsDjrrLMiy7JizAJQEN/kgf7D71gAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMgNLPQBQXkbf/HjB625cOCPhJEB/5IoFAJCMsAAAkhEWAEAywgIASMaHNwEoWz5M3P+4YgEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGT+QBcB+zw9tpeOKBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIxk3IACARNzNzxQIASKigsLjrrrti9OjRcdBBB8XEiRPjT3/6U+q5AIAylHdYPPjggzFv3ryYP39+vPzyyzF+/PiYNm1abNu2rRjzAQBlJO+w+OEPfxhz5syJyy+/PE444YS4++674+CDD45f/OIXxZgPACgjeX1487333otVq1ZFS0tL93MHHHBATJ06NZ5//vndrtPZ2RmdnZ3dj9vb2yMioqOjo5B596qr852C1/3/86TaTm+2Vanb+ei2+tt2erOt/radj26rv22nN9uq1O18dFv9bTu92VZ/285Ht9XfttObbRXj39f/v90sy/b+wiwPb7zxRhYR2XPPPdfj+RtvvDGbMGHCbteZP39+FhEWi8VisVgqYGlra9trKxT966YtLS0xb9687sddXV3x73//O4YMGRJVVVXF3n1EfFBZjY2N0dbWFjU1NX2yz/2VY913HOu+41j3Hce67+R7rLMsi+3bt0dDQ8NeX5dXWBxxxBExYMCAePPNN3s8/+abb8bw4cN3u04ul4tcLtfjucMOOyyf3SZTU1PjRO0jjnXfcaz7jmPddxzrvpPPsa6trd3na/L68OagQYPi1FNPjWeeeab7ua6urnjmmWdi0qRJ+WwKAKhAeb8VMm/evJg9e3acdtppMWHChLjzzjtj586dcfnllxdjPgCgjOQdFpdeemn885//jFtvvTW2bt0an/nMZ+LJJ5+MI488shjzJZHL5WL+/Pkfe0uG9BzrvuNY9x3Huu841n2nWMe6Ktvn90YAAD4Z9woBAJIRFgBAMsICAEhGWAAAyewXYeE278V32223RVVVVY9l7NixpR6rIqxYsSJmzpwZDQ0NUVVVFY888kiPv8+yLG699daor6+PwYMHx9SpU2Pt2rWlGbbM7etYf/3rX//YeX7uueeWZtgy1traGqeffnpUV1fHsGHD4oILLog1a9b0eM27774bzc3NMWTIkDj00EPj4osv/tiPM7Jvn+RYn3XWWR87r6+66qqC91nxYeE2733nxBNPjC1btnQvf/zjH0s9UkXYuXNnjB8/Pu66667d/v0dd9wRP/7xj+Puu++OF154IQ455JCYNm1avPvuu308afnb17GOiDj33HN7nOf3339/H05YGZYvXx7Nzc2xcuXKePrpp+P999+Pc845J3bu3Nn9muuvvz5++9vfxsMPPxzLly+PzZs3x0UXXVTCqcvTJznWERFz5szpcV7fcccdhe80n5uQlaMJEyZkzc3N3Y937dqVNTQ0ZK2trSWcqvLMnz8/Gz9+fKnHqHgRkS1durT7cVdXVzZ8+PDsBz/4Qfdzb7/9dpbL5bL777+/BBNWjo8e6yzLstmzZ2fnn39+SeapZNu2bcsiIlu+fHmWZR+cwwceeGD28MMPd7/mr3/9axYR2fPPP1+qMSvCR491lmXZ5z//+ey6665Lto+KvmLx4W3ep06d2v3cvm7zTuHWrl0bDQ0NcdRRR8WsWbNi06ZNpR6p4m3YsCG2bt3a4xyvra2NiRMnOseLZNmyZTFs2LA4/vjj4+qrr4633nqr1COVvfb29oiIqKuri4iIVatWxfvvv9/jvB47dmyMHDnSed1LHz3WH/rVr34VRxxxRIwbNy5aWlrinXcKv/170e9uWkr/+te/YteuXR/7VdAjjzwy/va3v5Voqso0ceLEWLx4cRx//PGxZcuWWLBgQZxxxhnx2muvRXV1danHq1hbt26NiNjtOf7h35HOueeeGxdddFGMGTMm1q9fH9/+9rdj+vTp8fzzz8eAAQNKPV5Z6urqirlz58bkyZNj3LhxEfHBeT1o0KCP3bDSed07uzvWERFf+cpXYtSoUdHQ0BCvvPJK3HTTTbFmzZr49a9/XdB+Kjos6DvTp0/v/nNTU1NMnDgxRo0aFQ899FBcccUVJZwM0vnyl7/c/eeTTjopmpqa4uijj45ly5bFlClTSjhZ+Wpubo7XXnvNZ7L6wJ6O9ZVXXtn955NOOinq6+tjypQpsX79+jj66KPz3k9FvxVSyG3eSeOwww6L4447LtatW1fqUSrah+exc7w0jjrqqDjiiCOc5wW65ppr4rHHHotnn302RowY0f388OHD47333ou33367x+ud14Xb07HenYkTJ0ZEFHxeV3RYuM176ezYsSPWr18f9fX1pR6loo0ZMyaGDx/e4xzv6OiIF154wTneB15//fV46623nOd5yrIsrrnmmli6dGn84Q9/iDFjxvT4+1NPPTUOPPDAHuf1mjVrYtOmTc7rPO3rWO/O6tWrIyIKPq8r/q0Qt3nvGzfccEPMnDkzRo0aFZs3b4758+fHgAED4rLLLiv1aGVvx44dPf7nsGHDhli9enXU1dXFyJEjY+7cufHd7343jj322BgzZkzccsst0dDQEBdccEHphi5TezvWdXV1sWDBgrj44otj+PDhsX79+vjWt74VxxxzTEybNq2EU5ef5ubmWLJkSTz66KNRXV3d/bmJ2traGDx4cNTW1sYVV1wR8+bNi7q6uqipqYlrr702Jk2aFJ/97GdLPH152dexXr9+fSxZsiS+9KUvxZAhQ+KVV16J66+/Ps4888xoamoqbKfJvl/Sj/3kJz/JRo4cmQ0aNCibMGFCtnLlylKPVHEuvfTSrL6+Phs0aFD2qU99Krv00kuzdevWlXqsivDss89mEfGxZfbs2VmWffCV01tuuSU78sgjs1wul02ZMiVbs2ZNaYcuU3s71u+88052zjnnZEOHDs0OPPDAbNSoUdmcOXOyrVu3lnrssrO7YxwR2X333df9mv/+97/ZN77xjezwww/PDj744OzCCy/MtmzZUrqhy9S+jvWmTZuyM888M6urq8tyuVx2zDHHZDfeeGPW3t5e8D7dNh0ASKaiP2MBAPQtYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJDM/wBlkf1BG2m8DAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The number of important features with threshold as 1 :19 \n",
            "Feature 0: 2.647197\n",
            "Feature 1: 0.606646\n",
            "Feature 2: 0.987726\n",
            "Feature 3: 3.816667\n",
            "Feature 4: 2.366213\n",
            "Feature 5: 1.146154\n",
            "Feature 6: 1.424897\n",
            "Feature 7: 4.506504\n",
            "Feature 8: 1.653086\n",
            "Feature 9: 1.386111\n",
            "Feature 10: 3.688753\n",
            "Feature 11: 2.377637\n",
            "Feature 12: 1.653040\n",
            "Feature 13: 1.869637\n",
            "Feature 14: 0.573077\n",
            "Feature 15: 2.759781\n",
            "Feature 16: 1.664387\n",
            "Feature 17: 2.188087\n",
            "Feature 18: 3.249751\n",
            "Feature 19: 4.095988\n",
            "Feature 20: 1.730137\n",
            "Feature 21: 0.844985\n",
            "Feature 22: 0.326944\n",
            "Feature 23: 0.498964\n",
            "Feature 24: 7.232533\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The number of important features with threshold as 1 :19 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BQZIzbYJGj4L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqvizUr3R_-d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "ad32fd5c-b92b-41a1-cb34-df6f2123bc7c"
      },
      "source": [
        "#X_train4, X_test4, fs = select_features(X_train2, y_train2, X_test2)\n",
        "for i in range(len(fs5.scores_)):\n",
        "\tprint('Feature %d: %f' % (i, fs5.scores_[i]))\n",
        "# plot the scores\n",
        "pyplot.bar([i for i in range(len(fs5.scores_))], fs5.scores_)\n",
        "pyplot.show()\n",
        "\n",
        "count = 0\n",
        "for i in fs5.scores_ :\n",
        "    if i > 1 :\n",
        "        count = count + 1\n",
        "k5=count\n",
        "\n",
        "print(\" The number of important features with threshold as 1 : {}\" .format(k5))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature 0: 2.647197\n",
            "Feature 1: 0.606646\n",
            "Feature 2: 0.987726\n",
            "Feature 3: 3.816667\n",
            "Feature 4: 2.366213\n",
            "Feature 5: 1.146154\n",
            "Feature 6: 1.424897\n",
            "Feature 7: 4.506504\n",
            "Feature 8: 1.653086\n",
            "Feature 9: 1.386111\n",
            "Feature 10: 3.688753\n",
            "Feature 11: 2.377637\n",
            "Feature 12: 1.653040\n",
            "Feature 13: 1.869637\n",
            "Feature 14: 0.573077\n",
            "Feature 15: 2.759781\n",
            "Feature 16: 1.664387\n",
            "Feature 17: 2.188087\n",
            "Feature 18: 3.249751\n",
            "Feature 19: 4.095988\n",
            "Feature 20: 1.730137\n",
            "Feature 21: 0.844985\n",
            "Feature 22: 0.326944\n",
            "Feature 23: 0.498964\n",
            "Feature 24: 7.232533\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The number of important features with threshold as 1 : 19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQK5DajNzrYD"
      },
      "source": [
        "A bar chart of the feature importance scores for each input feature is created.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-Pll3-10ASq"
      },
      "source": [
        "So, from above plot we remove the features with lower importance scores (<1.5).\n",
        "\n",
        "Hence features 2,5,8,9,14,16,21,22,23 are not considered,\n",
        "\n",
        "We could set k=16 i.e (25-9)  When configuring the SelectKBest to select these top 16 features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcSMQuHA3rzW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72105719-5b86-40d6-8c90-759b0f2f35b5"
      },
      "source": [
        "from sklearn.svm import SVC  # Import the Support Vector Classifier\n",
        "from sklearn.metrics import accuracy_score  # Import a performance metric (e.g., accuracy)\n",
        "def svm(X_train, y_train, X_test, y_test):\n",
        "    # Initialize the SVM classifier\n",
        "    svm_classifier = SVC(kernel='linear')  # You can choose different kernels based on your problem\n",
        "\n",
        "    # Train the SVM classifier\n",
        "    svm_classifier.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = svm_classifier.predict(X_test)\n",
        "\n",
        "    # Calculate and return the accuracy score\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    return accuracy\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier  # Import the Decision Tree Classifier\n",
        "def Dec_tree(X_train, y_train, X_test, y_test):\n",
        "    # Initialize the Decision Tree classifier\n",
        "    dt_classifier = DecisionTreeClassifier()  # You can set various hyperparameters here\n",
        "\n",
        "    # Train the Decision Tree classifier\n",
        "    dt_classifier.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = dt_classifier.predict(X_test)\n",
        "\n",
        "    # Calculate and return the accuracy score\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    return accuracy, dt_classifier\n",
        "\n",
        "# import xgboost as xgb\n",
        "# def xgboost(X_train, y_train, X_test, y_test, dt_classifier):\n",
        "#     # Initialize the XGBoost classifier\n",
        "#     xgb_classifier = xgb.XGBClassifier()  # You can set various hyperparameters here\n",
        "\n",
        "#     # Train the XGBoost classifier on top of the Decision Tree classifier\n",
        "#     xgb_classifier.fit(X_train, y_train)\n",
        "\n",
        "#     # Make predictions on the test data\n",
        "#     y_pred = xgb_classifier.predict(X_test)\n",
        "\n",
        "#     # Calculate and return the accuracy score\n",
        "#     accuracy = accuracy_score(y_test, y_pred)\n",
        "#     return accuracy\n",
        "\n",
        "def select_features(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=chi2,k=k1)\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "def select_features2(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=chi2,k=k3)\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "def select_features3(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=chi2,k=k5)\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "\n",
        "X_train1, X_test1, fs = select_features(X_train, y_train, X_test)\n",
        "X_train3, X_test3, fs = select_features2(X_train2, y_train2, X_test2)\n",
        "X_train5, X_test5, fs = select_features3(X_train6, y_train6, X_test6)\n",
        "\n",
        "\n",
        "#---------------SVM------------------#\n",
        "#\n",
        "print(\"Without feature Selection : \")\n",
        "acc = svm(X_train6,y_train6,X_test6,y_test6)\n",
        "\n",
        "print(\"With feature Selection : \")\n",
        "acc1 = svm(X_train5,y_train6,X_test5,y_test6)\n",
        "\n",
        "#-------------Decision Tree-----------------#\n",
        "print(\"Without feature Selection : \")\n",
        "acc1,clf1 = Dec_tree(X_train1,y_train,X_test1,y_test)\n",
        "\n",
        "\n",
        "print(\"With feature Selection : \")\n",
        "acc,clf = Dec_tree(X_train,y_train,X_test,y_test)\n",
        "\n",
        "print(' ')\n",
        "\n",
        "# #-------------XGBoost-----------------#\n",
        "# print(\"Boosting the Decision Tree  \")\n",
        "# acc = xgboost(X_train2,y_train2,X_test2,y_test2,clf)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Without feature Selection : \n",
            "With feature Selection : \n",
            "Without feature Selection : \n",
            "With feature Selection : \n",
            " \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_GJF49U0ABF"
      },
      "source": [
        "Mutual Info Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtsFDLnYiWNP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2bf30d86-683d-446b-cd27-c471d389e1e5"
      },
      "source": [
        "# example of mutual information feature selection for categorical data\n",
        "from sklearn.feature_selection import mutual_info_classif\n",
        "\n",
        "def select_features(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=mutual_info_classif, k='all')\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "# split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20)#Decision tree\n",
        "X_train2,X_test2,y_train2,y_test2=train_test_split(X,y,test_size=0.3,random_state=10)#XGBoost\n",
        "X_train6,X_test6,y_train6,y_test6=train_test_split(X,y,test_size=0.2,random_state=15)#SVM\n",
        "\n",
        "X_train1, X_test1, fs1 = select_features(X_train, y_train, X_test)\n",
        "X_train3, X_test3, fs3 = select_features(X_train2, y_train2, X_test2)\n",
        "X_train5, X_test5, fs5 = select_features(X_train2, y_train2, X_test2)\n",
        "\n",
        "# what are scores for the features\n",
        "for i in range(len(fs1.scores_)):\n",
        "\tprint('Feature %d: %f' % (i, fs.scores_[i]))\n",
        "# plot the scores\n",
        "pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_)\n",
        "pyplot.show()\n",
        "\n",
        "count = 0\n",
        "for i in fs.scores_ :\n",
        "    if i > 0.2 :\n",
        "        count = count + 1\n",
        "k1=count\n",
        "print(\" The number of important features with threshold as 0.2 :{} \".format(k1))\n",
        "\n",
        "\n",
        "for i in range(len(fs3.scores_)):\n",
        "\tprint('Feature %d: %f' % (i, fs3.scores_[i]))\n",
        "# plot the scores\n",
        "pyplot.bar([i for i in range(len(fs3.scores_))], fs3.scores_)\n",
        "pyplot.show()\n",
        "\n",
        "count = 0\n",
        "for i in fs3.scores_ :\n",
        "    if i > 0.2 :\n",
        "        count = count + 1\n",
        "k3=count\n",
        "\n",
        "print(\" The number of important features with threshold as 0.2 :{} \".format(k3))\n",
        "\n",
        "#X_train4, X_test4, fs = select_features(X_train2, y_train2, X_test2)\n",
        "for i in range(len(fs5.scores_)):\n",
        "\tprint('Feature %d: %f' % (i, fs5.scores_[i]))\n",
        "# plot the scores\n",
        "pyplot.bar([i for i in range(len(fs5.scores_))], fs5.scores_)\n",
        "pyplot.show()\n",
        "\n",
        "count = 0\n",
        "for i in fs5.scores_ :\n",
        "    if i > 0.2 :\n",
        "        count = count + 1\n",
        "k5=count\n",
        "\n",
        "print(\" The number of important features with threshold as 0.2 : {}\" .format(k5))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature 0: 1.939425\n",
            "Feature 1: 0.621032\n",
            "Feature 2: 1.190205\n",
            "Feature 3: 2.361067\n",
            "Feature 4: 2.723434\n",
            "Feature 5: 1.019886\n",
            "Feature 6: 1.630621\n",
            "Feature 7: 5.962755\n",
            "Feature 8: 0.948673\n",
            "Feature 9: 1.391667\n",
            "Feature 10: 3.995109\n",
            "Feature 11: 3.001412\n",
            "Feature 12: 1.301361\n",
            "Feature 13: 1.730517\n",
            "Feature 14: 0.916034\n",
            "Feature 15: 2.627847\n",
            "Feature 16: 0.703328\n",
            "Feature 17: 2.405423\n",
            "Feature 18: 3.379566\n",
            "Feature 19: 3.567291\n",
            "Feature 20: 2.512279\n",
            "Feature 21: 1.074299\n",
            "Feature 22: 0.271954\n",
            "Feature 23: 0.452889\n",
            "Feature 24: 8.890845\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The number of important features with threshold as 0.2 :25 \n",
            "Feature 0: 0.038288\n",
            "Feature 1: 0.000000\n",
            "Feature 2: 0.226681\n",
            "Feature 3: 0.000000\n",
            "Feature 4: 0.054148\n",
            "Feature 5: 1.442087\n",
            "Feature 6: 0.898500\n",
            "Feature 7: 0.000000\n",
            "Feature 8: 0.060249\n",
            "Feature 9: 0.614890\n",
            "Feature 10: 0.352795\n",
            "Feature 11: 0.021278\n",
            "Feature 12: 0.059010\n",
            "Feature 13: 0.036028\n",
            "Feature 14: 0.802829\n",
            "Feature 15: 0.098177\n",
            "Feature 16: 0.871608\n",
            "Feature 17: 0.541336\n",
            "Feature 18: 0.035798\n",
            "Feature 19: 0.134277\n",
            "Feature 20: 0.310226\n",
            "Feature 21: 0.476619\n",
            "Feature 22: 0.332093\n",
            "Feature 23: 0.307855\n",
            "Feature 24: 0.204533\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The number of important features with threshold as 0.2 :13 \n",
            "Feature 0: 0.071655\n",
            "Feature 1: 0.000000\n",
            "Feature 2: 0.334325\n",
            "Feature 3: 0.000000\n",
            "Feature 4: 0.479721\n",
            "Feature 5: 1.455393\n",
            "Feature 6: 0.270263\n",
            "Feature 7: 0.174022\n",
            "Feature 8: 0.379117\n",
            "Feature 9: 0.360184\n",
            "Feature 10: 0.013084\n",
            "Feature 11: 0.000000\n",
            "Feature 12: 0.461118\n",
            "Feature 13: 0.096613\n",
            "Feature 14: 1.345389\n",
            "Feature 15: 0.238524\n",
            "Feature 16: 1.571850\n",
            "Feature 17: 0.857978\n",
            "Feature 18: 0.329228\n",
            "Feature 19: 0.080493\n",
            "Feature 20: 0.154752\n",
            "Feature 21: 0.303928\n",
            "Feature 22: 0.324082\n",
            "Feature 23: 0.028061\n",
            "Feature 24: 0.219798\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The number of important features with threshold as 0.2 : 15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caJ31ANmiROR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18bdc31c-6f61-4e67-dbf5-ffa1cffafcc0"
      },
      "source": [
        "def select_features(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=mutual_info_classif,k=k1)\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "def select_features2(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=mutual_info_classif,k=k3)\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "def select_features3(X_train, y_train, X_test):\n",
        "\tfs = SelectKBest(score_func=mutual_info_classif,k=k5)\n",
        "\tfs.fit(X_train, y_train)\n",
        "\tX_train_fs = fs.transform(X_train)\n",
        "\tX_test_fs = fs.transform(X_test)\n",
        "\treturn X_train_fs, X_test_fs, fs\n",
        "\n",
        "\n",
        "X_train1, X_test1, fs = select_features(X_train, y_train, X_test)\n",
        "X_train3, X_test3, fs = select_features2(X_train2, y_train2, X_test2)\n",
        "X_train5, X_test5, fs = select_features3(X_train6, y_train6, X_test6)\n",
        "\n",
        "\n",
        "#---------------SVM------------------#\n",
        "#\n",
        "print(\"Without feature Selection : \")\n",
        "acc = svm(X_train6,y_train6,X_test6,y_test6)\n",
        "\n",
        "print(\"With feature Selection : \")\n",
        "acc1 = svm(X_train5,y_train6,X_test5,y_test6)\n",
        "\n",
        "#-------------Decision Tree-----------------#\n",
        "print(\"Without feature Selection : \")\n",
        "acc1,clf1 = Dec_tree(X_train1,y_train,X_test1,y_test)\n",
        "\n",
        "\n",
        "print(\"With feature Selection : \")\n",
        "acc,clf = Dec_tree(X_train,y_train,X_test,y_test)\n",
        "\n",
        "print(' ')\n",
        "\n",
        "# #-------------XGBoost-----------------#\n",
        "# print(\"Boosting the Decision Tree  \")\n",
        "# acc = xgboost(X_train2,y_train2,X_test2,y_test2,clf)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Without feature Selection : \n",
            "With feature Selection : \n",
            "Without feature Selection : \n",
            "With feature Selection : \n",
            " \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udZl3BhOubrS"
      },
      "source": [
        "Accuracies vs Techniques"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKBN8OCcuUKB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "fcff9343-be6a-4a51-bb34-fefb753f7282"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#fig = plt.figure()\n",
        "fig = plt.figure(figsize =(10, 5))\n",
        "#ax = fig.add_axes([0,0,1,1])\n",
        "Name = ['Decision Tree(with feature selection)','Decision Tree(without feature selection)', 'XGBoost', 'SVM(with feature selction)','SVM(without feature selction)']\n",
        "accuracies = [50,40,93.333,70,60]\n",
        "plt.bar(Name,accuracies)\n",
        "plt.ylabel('Accuracies')\n",
        "plt.xlabel('Techniques')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyc7XYm7uuaQ"
      },
      "source": [
        "Prediction for new input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7c6prGGwigQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "604fecc5-9e79-4e54-a230-56dace7324b7"
      },
      "source": [
        "# x_new = ['3','5','4','3','5','4','3','5','2','2','5','2','5','4','2','5','5','3','2','5','5','4','2','3','4']\n",
        "x_new = []\n",
        "feat = list(X.columns)\n",
        "for i in feat:\n",
        "  print(i)\n",
        "  val = input(\"Enter: \")\n",
        "  x_new.append(val)\n",
        "x_new"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "sslc\n",
            "Enter: 2\n",
            "hsc\n",
            "Enter: 2\n",
            "cgpa\n",
            "Enter: 2\n",
            "school_type\n",
            "Enter: 2\n",
            "no_of_miniprojects\n",
            "Enter: 2\n",
            "no_of_projects\n",
            "Enter: 2\n",
            "coresub_skill\n",
            "Enter: 2\n",
            "aptitude_skill\n",
            "Enter: 2\n",
            "problemsolving_skill\n",
            "Enter: 2\n",
            "programming_skill\n",
            "Enter: 2\n",
            "abstractthink_skill\n",
            "Enter: 2\n",
            "design_skill\n",
            "Enter: 2\n",
            "first_computer\n",
            "Enter: 2\n",
            "first_program\n",
            "Enter: 2\n",
            "lab_programs\n",
            "Enter: 2\n",
            "ds_coding\n",
            "Enter: 2\n",
            "technology_used\n",
            "Enter: 2\n",
            "sympos_attend\n",
            "Enter: 2\n",
            "sympos_won\n",
            "Enter: 2\n",
            "extracurricular\n",
            "Enter: 2\n",
            "learning_style\n",
            "Enter: 2\n",
            "college_bench\n",
            "Enter: 2\n",
            "clg_teachers_know\n",
            "Enter: 2\n",
            "college_performence\n",
            "Enter: 2\n",
            "college_skills\n",
            "Enter: 2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2',\n",
              " '2']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2kjQBMywiy1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a6aecfa-1a76-4092-a577-aaf0040b6f22"
      },
      "source": [
        "#x_new = ['3','5','4','3','5','4','3','5','2','2','5','2','5','4','2','5','5','3','2','5','5','4','2','3','4']\n",
        "new_pred  = clf.predict([x_new])\n",
        "print(\"Prediction : {}\".format(y1[y1['Associated Number']==new_pred[0]]['ROLE']))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction : 7    Business Analyst\n",
            "Name: ROLE, dtype: object\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHdvspMzzaKs"
      },
      "source": [
        "Algorithms : SVM, Decision Tree and XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gm5sjkrDwiQ_"
      },
      "source": [
        "#Support Vector machine\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score\n",
        "def svm(X_train,y_train,X_test,y_test):\n",
        "  from sklearn.svm import SVC\n",
        "  from pandas import read_csv\n",
        "  from sklearn.model_selection import train_test_split\n",
        "\n",
        "  # import metrics to compute accuracy\n",
        "  from sklearn.metrics import accuracy_score\n",
        "  svc=SVC()\n",
        "  # fit classifier to training set\n",
        "  svc.fit(X_train,y_train)\n",
        "  # make predictions on test set\n",
        "  y_pred=svc.predict(X_test)\n",
        "  # compute and print accuracy score\n",
        "  print('Model accuracy score with default hyperparameters: {0:0.4f}'. format(accuracy_score(y_test, y_pred)*100))\n",
        "  return accuracy_score(y_test, y_pred)*100\n",
        "\n",
        "def Dec_tree(X_train,y_train,X_test,y_test):\n",
        "  from sklearn import tree\n",
        "  clf = tree.DecisionTreeClassifier()\n",
        "  clf = clf.fit(X_train, y_train)\n",
        "  # Prediction\n",
        "  y_pred = clf.predict(X_test)\n",
        "  y_test_arr=y_test['role']\n",
        "  from sklearn.metrics import confusion_matrix,accuracy_score\n",
        "  accuracy = accuracy_score(y_test,y_pred)\n",
        "  print('Model accuracy score with Decision Tree', accuracy_score(y_test, y_pred)*100)\n",
        "  return accuracy*100,clf\n",
        "\n",
        "def xgboost(X_train,y_train,X_test,y_test,clf):\n",
        "  #X_train,X_test,y_train,y_test=train_test_split(X1,y,test_size=0.3,random_state=10)\n",
        "  shape = X_train.shape\n",
        "  X_train=pd.to_numeric(X_train.values.flatten())\n",
        "  X_train=X_train.reshape(shape)\n",
        "  from xgboost.sklearn import XGBClassifier\n",
        "  model = XGBClassifier()\n",
        "  model.fit(X_train, y_train)\n",
        "  xgb_y_pred  = clf.predict(X_test)\n",
        "  xgb_accuracy = accuracy_score(y_test,xgb_y_pred)\n",
        "  print(\"accuracy=\",xgb_accuracy*100)\n",
        "  return xgb_accuracy*100"
      ],
      "execution_count": 23,
      "outputs": []
    }
  ]
}