mlp_classification_kaggle.ipynb

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   "source": [
    "# Students Enrolment Status \n",
    "Link to competition: https://www.kaggle.com/competitions/students-drop-out-prediction/overview\n",
    "\n",
    "### Description\n",
    "The monitoring and support of university students are considered very important at many educational institutions. In this competition, the problem is formulated as a three-category classification task (dropout, enrolled, and graduate) and the classes are coded as 0,1 and 2 in the dataset.\n",
    "\n",
    "Here is a handy link to Kaggle's competition documentation, which includes, among other things, instructions on submitting predictions.\n",
    "\n",
    "### Evaluation\n",
    "The evaluation metric for this competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision and recall. Precision is the ratio of true positives to all predicted positives. Recall is the ratio of true positives to all actual positives. The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other. ## Submission Format **For every author in the dataset**, submission files should contain two columns: `id` and `label`. The file should contain a header and have the following format: ``` id,label 0,1 1,0 2,1 ```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928c6f51",
   "metadata": {},
   "source": [
    "## Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97fae387",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/students-drop-out-prediction/train.csv\n",
      "/kaggle/input/students-drop-out-prediction/test.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils.multiclass import type_of_target\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132566d4",
   "metadata": {},
   "source": [
    "### Read into Dataframe"
   ]
  },
  {
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       "   id  v_21  v_32  v_15  v_4  v_1  v_8  v_12  v_25  v_20  ...  v_2   v_16  \\\n",
       "0   0     3     6    11   55    1    1    16     5    60  ... -0.3   0.79   \n",
       "1   1     8    94    12   65    1    4    22     4   173  ...  5.4   6.51   \n",
       "2   2     3   166    10  155    1    1    16     1   309  ...  0.6   2.02   \n",
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       "\n",
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       "\n",
       "[5 rows x 43 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df = pd.read_csv(\"/kaggle/input/students-drop-out-prediction/train.csv\")\n",
    "df.head()"
   ]
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## EDA"
   ]
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      "<class 'pandas.core.frame.DataFrame'>\n",
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      " 42  label   3796 non-null   int64  \n",
      "dtypes: float64(3), int64(40)\n",
      "memory usage: 1.2 MB\n"
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   "source": [
    "df.info()"
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       "      <th>v_25</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>97.203372</td>\n",
       "      <td>79.213308</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>110.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>289.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_20</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>166.138567</td>\n",
       "      <td>103.121322</td>\n",
       "      <td>6.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>168.00</td>\n",
       "      <td>251.00</td>\n",
       "      <td>322.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_18</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>57.441254</td>\n",
       "      <td>50.416264</td>\n",
       "      <td>3.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>324.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_3</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>65.922023</td>\n",
       "      <td>43.698147</td>\n",
       "      <td>5.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>428.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_11</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.466280</td>\n",
       "      <td>0.498927</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_14</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.179399</td>\n",
       "      <td>0.383737</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_26</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.262118</td>\n",
       "      <td>0.439844</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_27</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.731560</td>\n",
       "      <td>0.443206</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_30</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.463646</td>\n",
       "      <td>0.498742</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_9</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.374605</td>\n",
       "      <td>0.484084</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_31</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>32.134089</td>\n",
       "      <td>7.887596</td>\n",
       "      <td>25.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_28</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.040569</td>\n",
       "      <td>0.197316</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_13</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>15.600896</td>\n",
       "      <td>3.216409</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>38.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_33</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>30.127239</td>\n",
       "      <td>3.256676</td>\n",
       "      <td>23.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>49.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_17</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>50.165701</td>\n",
       "      <td>4.670733</td>\n",
       "      <td>41.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>86.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_19</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>23.562171</td>\n",
       "      <td>3.757691</td>\n",
       "      <td>18.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>45.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_29</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>17.162803</td>\n",
       "      <td>5.213275</td>\n",
       "      <td>6.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_23</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>3.975764</td>\n",
       "      <td>2.249038</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_24</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>16.392255</td>\n",
       "      <td>2.866872</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>37.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_10</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>26.064015</td>\n",
       "      <td>3.079682</td>\n",
       "      <td>19.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>42.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_5</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>39.934932</td>\n",
       "      <td>4.507494</td>\n",
       "      <td>31.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>64.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_6</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>13.281876</td>\n",
       "      <td>3.674661</td>\n",
       "      <td>8.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_22</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>23.794521</td>\n",
       "      <td>5.532030</td>\n",
       "      <td>13.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>38.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_0</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>12.998156</td>\n",
       "      <td>2.309686</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_7</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>12.394810</td>\n",
       "      <td>3.414303</td>\n",
       "      <td>7.60</td>\n",
       "      <td>9.40</td>\n",
       "      <td>12.40</td>\n",
       "      <td>14.90</td>\n",
       "      <td>25.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_2</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>2.041333</td>\n",
       "      <td>2.516779</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.80</td>\n",
       "      <td>12.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_16</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.835901</td>\n",
       "      <td>3.109413</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2.02</td>\n",
       "      <td>12.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_34</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>5.341149</td>\n",
       "      <td>3.507388</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_35</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>50.674658</td>\n",
       "      <td>29.330820</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>107.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_36</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>26.212065</td>\n",
       "      <td>14.929649</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>58.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_37</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>20.837197</td>\n",
       "      <td>11.984063</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>49.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_38</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>31.276607</td>\n",
       "      <td>17.847942</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>69.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_39</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>40.210485</td>\n",
       "      <td>22.466458</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.75</td>\n",
       "      <td>41.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>87.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_40</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>3.278978</td>\n",
       "      <td>2.618851</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.854320</td>\n",
       "      <td>0.698660</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count         mean          std    min     25%      50%      75%  \\\n",
       "id     3796.0  1897.500000  1095.955139   0.00  948.75  1897.50  2846.25   \n",
       "v_21   3796.0     4.817439     6.166860   3.00    3.00     3.00     4.00   \n",
       "v_32   3796.0    70.825606    56.110329   6.00    6.00    89.00   116.00   \n",
       "v_15   3796.0    11.609062     2.576362   9.00   10.00    10.00    12.00   \n",
       "v_4    3796.0    99.329294    45.017380   9.00   55.00   101.00   146.00   \n",
       "v_1    3796.0     0.911486     0.284079   0.00    1.00     1.00     1.00   \n",
       "v_8    3796.0     3.436249     4.577644   1.00    1.00     1.00     3.00   \n",
       "v_12   3796.0    18.970495    15.010042  16.00   16.00    16.00    16.00   \n",
       "v_25   3796.0    97.203372    79.213308   1.00    5.00   110.00   181.00   \n",
       "v_20   3796.0   166.138567   103.121322   6.00   60.00   168.00   251.00   \n",
       "v_18   3796.0    57.441254    50.416264   3.00   23.00    30.00   103.00   \n",
       "v_3    3796.0    65.922023    43.698147   5.00   39.00    70.00    87.00   \n",
       "v_11   3796.0     0.466280     0.498927   0.00    0.00     0.00     1.00   \n",
       "v_14   3796.0     0.179399     0.383737   0.00    0.00     0.00     0.00   \n",
       "v_26   3796.0     0.262118     0.439844   0.00    0.00     0.00     1.00   \n",
       "v_27   3796.0     0.731560     0.443206   0.00    0.00     1.00     1.00   \n",
       "v_30   3796.0     0.463646     0.498742   0.00    0.00     0.00     1.00   \n",
       "v_9    3796.0     0.374605     0.484084   0.00    0.00     0.00     1.00   \n",
       "v_31   3796.0    32.134089     7.887596  25.00   27.00    29.00    35.00   \n",
       "v_28   3796.0     0.040569     0.197316   0.00    0.00     0.00     0.00   \n",
       "v_13   3796.0    15.600896     3.216409  14.00   14.00    14.00    16.00   \n",
       "v_33   3796.0    30.127239     3.256676  23.00   29.00    29.00    31.00   \n",
       "v_17   3796.0    50.165701     4.670733  41.00   48.00    49.00    53.00   \n",
       "v_19   3796.0    23.562171     3.757691  18.00   22.00    24.00    25.00   \n",
       "v_29   3796.0    17.162803     5.213275   6.00   17.00    18.00    20.00   \n",
       "v_23   3796.0     3.975764     2.249038   3.00    3.00     3.00     3.00   \n",
       "v_24   3796.0    16.392255     2.866872  15.00   15.00    15.00    16.00   \n",
       "v_10   3796.0    26.064015     3.079682  19.00   25.00    25.00    27.00   \n",
       "v_5    3796.0    39.934932     4.507494  31.00   37.00    39.00    42.00   \n",
       "v_6    3796.0    13.281876     3.674661   8.00   11.00    13.00    15.00   \n",
       "v_22   3796.0    23.794521     5.532030  13.00   24.00    25.00    27.00   \n",
       "v_0    3796.0    12.998156     2.309686  12.00   12.00    12.00    12.00   \n",
       "v_7    3796.0    12.394810     3.414303   7.60    9.40    12.40    14.90   \n",
       "v_2    3796.0     2.041333     2.516779  -0.80    0.50     1.40     2.80   \n",
       "v_16   3796.0     0.835901     3.109413  -4.06   -1.70     0.79     2.02   \n",
       "v_34   3796.0     5.341149     3.507388   0.00    3.00     5.00     8.00   \n",
       "v_35   3796.0    50.674658    29.330820   0.00   25.00    50.00    76.00   \n",
       "v_36   3796.0    26.212065    14.929649   0.00   13.00    26.00    39.00   \n",
       "v_37   3796.0    20.837197    11.984063   0.00   11.00    21.00    31.00   \n",
       "v_38   3796.0    31.276607    17.847942   0.00   16.00    31.00    47.00   \n",
       "v_39   3796.0    40.210485    22.466458   0.00   21.75    41.00    59.00   \n",
       "v_40   3796.0     3.278978     2.618851   0.00    1.00     3.00     5.00   \n",
       "label  3796.0     0.854320     0.698660   0.00    0.00     1.00     1.00   \n",
       "\n",
       "           max  \n",
       "id     3795.00  \n",
       "v_21     61.00  \n",
       "v_32    176.00  \n",
       "v_15     25.00  \n",
       "v_4     176.00  \n",
       "v_1       1.00  \n",
       "v_8      25.00  \n",
       "v_12    179.00  \n",
       "v_25    289.00  \n",
       "v_20    322.00  \n",
       "v_18    324.00  \n",
       "v_3     428.00  \n",
       "v_11      1.00  \n",
       "v_14      1.00  \n",
       "v_26      1.00  \n",
       "v_27      1.00  \n",
       "v_30      1.00  \n",
       "v_9       1.00  \n",
       "v_31     78.00  \n",
       "v_28      1.00  \n",
       "v_13     38.00  \n",
       "v_33     49.00  \n",
       "v_17     86.00  \n",
       "v_19     45.00  \n",
       "v_29     31.00  \n",
       "v_23     17.00  \n",
       "v_24     37.00  \n",
       "v_10     42.00  \n",
       "v_5      64.00  \n",
       "v_6      28.00  \n",
       "v_22     38.00  \n",
       "v_0      26.00  \n",
       "v_7      25.20  \n",
       "v_2      12.70  \n",
       "v_16     12.51  \n",
       "v_34     18.00  \n",
       "v_35    107.00  \n",
       "v_36     58.00  \n",
       "v_37     49.00  \n",
       "v_38     69.00  \n",
       "v_39     87.00  \n",
       "v_40     14.00  \n",
       "label     2.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27a01485",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:48.986374Z",
     "iopub.status.busy": "2022-08-26T09:19:48.985692Z",
     "iopub.status.idle": "2022-08-26T09:19:49.008982Z",
     "shell.execute_reply": "2022-08-26T09:19:49.007353Z"
    },
    "papermill": {
     "duration": 0.035325,
     "end_time": "2022-08-26T09:19:49.011504",
     "exception": false,
     "start_time": "2022-08-26T09:19:48.976179",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>v_21</th>\n",
       "      <th>v_32</th>\n",
       "      <th>v_15</th>\n",
       "      <th>v_4</th>\n",
       "      <th>v_1</th>\n",
       "      <th>v_8</th>\n",
       "      <th>v_12</th>\n",
       "      <th>v_25</th>\n",
       "      <th>v_20</th>\n",
       "      <th>...</th>\n",
       "      <th>v_7</th>\n",
       "      <th>v_2</th>\n",
       "      <th>v_16</th>\n",
       "      <th>v_34</th>\n",
       "      <th>v_35</th>\n",
       "      <th>v_36</th>\n",
       "      <th>v_37</th>\n",
       "      <th>v_38</th>\n",
       "      <th>v_39</th>\n",
       "      <th>v_40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>13.9</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>94</td>\n",
       "      <td>12</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>14.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.51</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>37</td>\n",
       "      <td>25</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>166</td>\n",
       "      <td>10</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>309</td>\n",
       "      <td>...</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.02</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>118</td>\n",
       "      <td>168</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>12.51</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  v_21  v_32  v_15  v_4  v_1  v_8  v_12  v_25  v_20  ...   v_7  v_2  \\\n",
       "0   0     3     6    11   55    1    1    16     5    60  ...  13.9 -0.3   \n",
       "1   1     8    94    12   65    1    4    22     4   173  ...  14.9  5.4   \n",
       "2   2     3   166    10  155    1    1    16     1   309  ...  11.1  0.6   \n",
       "3   3     3     6    13  121    1    1    16     1     6  ...  11.1  0.6   \n",
       "4   4     4    51    18   21    1   17    22   118   168  ...  10.9  8.4   \n",
       "\n",
       "    v_16  v_34  v_35  v_36  v_37  v_38  v_39  v_40  \n",
       "0   0.79     1    93    47     1    48    26     0  \n",
       "1   6.51     6    29    37    25    53    35     7  \n",
       "2   2.02     1     8    34     4    43    38     2  \n",
       "3   2.02     7    37    40    27    43    65     0  \n",
       "4  12.51    16    22    25    25    24    23     1  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns = \"label\")\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ff50885",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.031048Z",
     "iopub.status.busy": "2022-08-26T09:19:49.029866Z",
     "iopub.status.idle": "2022-08-26T09:19:49.039351Z",
     "shell.execute_reply": "2022-08-26T09:19:49.038315Z"
    },
    "papermill": {
     "duration": 0.021685,
     "end_time": "2022-08-26T09:19:49.041752",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.020067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "3791    2\n",
       "3792    2\n",
       "3793    0\n",
       "3794    1\n",
       "3795    0\n",
       "Name: label, Length: 3796, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[\"label\"]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0de6b151",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.061818Z",
     "iopub.status.busy": "2022-08-26T09:19:49.060966Z",
     "iopub.status.idle": "2022-08-26T09:19:49.068746Z",
     "shell.execute_reply": "2022-08-26T09:19:49.067795Z"
    },
    "papermill": {
     "duration": 0.020596,
     "end_time": "2022-08-26T09:19:49.071099",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.050503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'multiclass'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils.multiclass import type_of_target\n",
    "type_of_target(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89be0a85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.090989Z",
     "iopub.status.busy": "2022-08-26T09:19:49.090172Z",
     "iopub.status.idle": "2022-08-26T09:19:49.097639Z",
     "shell.execute_reply": "2022-08-26T09:19:49.095997Z"
    },
    "papermill": {
     "duration": 0.020193,
     "end_time": "2022-08-26T09:19:49.100113",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.079920",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of labels: 3 \n",
      "Labels: [0 1 2]\n"
     ]
    }
   ],
   "source": [
    "target_names = np.unique(y)\n",
    "print(f'Number of labels: {len(target_names)} \\nLabels: {target_names}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a08c6545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.119247Z",
     "iopub.status.busy": "2022-08-26T09:19:49.118833Z",
     "iopub.status.idle": "2022-08-26T09:19:49.129077Z",
     "shell.execute_reply": "2022-08-26T09:19:49.127084Z"
    },
    "papermill": {
     "duration": 0.023731,
     "end_time": "2022-08-26T09:19:49.132547",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.108816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0 - 1243 \n",
      "Class 1 - 1863 \n",
      "Class 2 - 690\n"
     ]
    }
   ],
   "source": [
    "label_count = [y.value_counts()[0], y.value_counts()[1], y.value_counts()[2]]\n",
    "print(f'Class 0 - {label_count[0]} \\nClass 1 - {label_count[1]} \\nClass 2 - {label_count[2]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad687fb",
   "metadata": {
    "papermill": {
     "duration": 0.008375,
     "end_time": "2022-08-26T09:19:49.149838",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.141463",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "1. Class 0 - Dropout \n",
    "\n",
    "2. Class 1 - Enrolled\n",
    "\n",
    "3. Class 2 - Graduate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33b7be90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.170020Z",
     "iopub.status.busy": "2022-08-26T09:19:49.169197Z",
     "iopub.status.idle": "2022-08-26T09:19:49.179539Z",
     "shell.execute_reply": "2022-08-26T09:19:49.178076Z"
    },
    "papermill": {
     "duration": 0.023032,
     "end_time": "2022-08-26T09:19:49.182141",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.159109",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0 - 32.74%\n",
      "Class 1 - 49.08%\n",
      "Class 2 - 18.18%\n"
     ]
    }
   ],
   "source": [
    "def calculate_percentage(class_num: int) -> float:\n",
    "    count = y.value_counts()[class_num]\n",
    "    total = 3796\n",
    "    percentage = round((count/total)*100, 2)\n",
    "    return (f\"{percentage}%\")\n",
    "\n",
    "percent_0 = calculate_percentage(0)\n",
    "percent_1 = calculate_percentage(1)\n",
    "percent_2 = calculate_percentage(2)\n",
    "print(\"Class 0 - \" + percent_0, \"Class 1 - \" + percent_1, \"Class 2 - \" + percent_2, sep = \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6e46269",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.202174Z",
     "iopub.status.busy": "2022-08-26T09:19:49.201770Z",
     "iopub.status.idle": "2022-08-26T09:19:49.436613Z",
     "shell.execute_reply": "2022-08-26T09:19:49.435657Z"
    },
    "papermill": {
     "duration": 0.247779,
     "end_time": "2022-08-26T09:19:49.438990",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.191211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(target_names, label_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "700ab519",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.459842Z",
     "iopub.status.busy": "2022-08-26T09:19:49.458653Z",
     "iopub.status.idle": "2022-08-26T09:19:49.911489Z",
     "shell.execute_reply": "2022-08-26T09:19:49.909578Z"
    },
    "papermill": {
     "duration": 0.466035,
     "end_time": "2022-08-26T09:19:49.914329",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.448294",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.62x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "print(sns.heatmap(df.corr()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "723e0644",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27dc40bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.936573Z",
     "iopub.status.busy": "2022-08-26T09:19:49.935753Z",
     "iopub.status.idle": "2022-08-26T09:19:49.945637Z",
     "shell.execute_reply": "2022-08-26T09:19:49.944525Z"
    },
    "papermill": {
     "duration": 0.023593,
     "end_time": "2022-08-26T09:19:49.948082",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.924489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,random_state = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "620b694e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:49.969494Z",
     "iopub.status.busy": "2022-08-26T09:19:49.969097Z",
     "iopub.status.idle": "2022-08-26T09:19:49.975636Z",
     "shell.execute_reply": "2022-08-26T09:19:49.974226Z"
    },
    "papermill": {
     "duration": 0.020649,
     "end_time": "2022-08-26T09:19:49.978496",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.957847",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train - (3036, 42)\n",
      "Shape of y_train - (3036,) \n",
      "\n",
      "Shape of X_test - (760, 42)\n",
      "Shape of y_test - (760,)\n"
     ]
    }
   ],
   "source": [
    "print(f'Shape of X_train - {X_train.shape}')\n",
    "print(f'Shape of y_train - {y_train.shape} \\n')\n",
    "print(f'Shape of X_test - {X_test.shape}')\n",
    "print(f'Shape of y_test - {y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c95b07a",
   "metadata": {},
   "source": [
    "### Dummy Classifier - Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d229dc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:50.001229Z",
     "iopub.status.busy": "2022-08-26T09:19:50.000008Z",
     "iopub.status.idle": "2022-08-26T09:19:50.011014Z",
     "shell.execute_reply": "2022-08-26T09:19:50.008882Z"
    },
    "papermill": {
     "duration": 0.026305,
     "end_time": "2022-08-26T09:19:50.014763",
     "exception": false,
     "start_time": "2022-08-26T09:19:49.988458",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.49110671936758893\n",
      "0.48947368421052634\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "base_clf = DummyClassifier(strategy = \"most_frequent\")\n",
    "base_clf.fit(X_train, y_train)\n",
    "print(base_clf.score(X_train, y_train))\n",
    "print(base_clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "95b32f74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:50.036967Z",
     "iopub.status.busy": "2022-08-26T09:19:50.036156Z",
     "iopub.status.idle": "2022-08-26T09:19:50.051918Z",
     "shell.execute_reply": "2022-08-26T09:19:50.050268Z"
    },
    "papermill": {
     "duration": 0.029776,
     "end_time": "2022-08-26T09:19:50.054634",
     "exception": false,
     "start_time": "2022-08-26T09:19:50.024858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       252\n",
      "           1       0.49      1.00      0.66       372\n",
      "           2       0.00      0.00      0.00       136\n",
      "\n",
      "    accuracy                           0.49       760\n",
      "   macro avg       0.16      0.33      0.22       760\n",
      "weighted avg       0.24      0.49      0.32       760\n",
      "\n",
      "[[  0 252   0]\n",
      " [  0 372   0]\n",
      " [  0 136   0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "y_true = y_test\n",
    "y_pred = base_clf.predict(X_test)\n",
    "print(classification_report(y_true, y_pred, zero_division = 0))\n",
    "print(confusion_matrix(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57e2a800",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:50.076756Z",
     "iopub.status.busy": "2022-08-26T09:19:50.075897Z",
     "iopub.status.idle": "2022-08-26T09:19:50.094430Z",
     "shell.execute_reply": "2022-08-26T09:19:50.093408Z"
    },
    "papermill": {
     "duration": 0.032544,
     "end_time": "2022-08-26T09:19:50.097008",
     "exception": false,
     "start_time": "2022-08-26T09:19:50.064464",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "X_train_scaled = std_scaler.fit_transform(X_train)\n",
    "X_test_scaled = std_scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0e5194",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06884db4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:50.119792Z",
     "iopub.status.busy": "2022-08-26T09:19:50.118320Z",
     "iopub.status.idle": "2022-08-26T09:19:51.053497Z",
     "shell.execute_reply": "2022-08-26T09:19:51.052281Z"
    },
    "papermill": {
     "duration": 0.949437,
     "end_time": "2022-08-26T09:19:51.056371",
     "exception": false,
     "start_time": "2022-08-26T09:19:50.106934",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "rand_clf = RandomForestClassifier(random_state = 10)\n",
    "rand_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "37ab8871",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:51.078511Z",
     "iopub.status.busy": "2022-08-26T09:19:51.077674Z",
     "iopub.status.idle": "2022-08-26T09:19:51.196397Z",
     "shell.execute_reply": "2022-08-26T09:19:51.194728Z"
    },
    "papermill": {
     "duration": 0.133367,
     "end_time": "2022-08-26T09:19:51.199659",
     "exception": false,
     "start_time": "2022-08-26T09:19:51.066292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 1.0, Testing Accuracy: 0.748684\n"
     ]
    }
   ],
   "source": [
    "train_acc = round(rand_clf.score(X_train_scaled, y_train), 6)\n",
    "test_acc = round(rand_clf.score(X_test_scaled, y_test), 6)\n",
    "\n",
    "print(f'Training accuracy: {train_acc}, Testing Accuracy: {test_acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "579783a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:51.221693Z",
     "iopub.status.busy": "2022-08-26T09:19:51.221282Z",
     "iopub.status.idle": "2022-08-26T09:19:51.267086Z",
     "shell.execute_reply": "2022-08-26T09:19:51.265797Z"
    },
    "papermill": {
     "duration": 0.06019,
     "end_time": "2022-08-26T09:19:51.270070",
     "exception": false,
     "start_time": "2022-08-26T09:19:51.209880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.77      0.77       252\n",
      "           1       0.76      0.90      0.83       372\n",
      "           2       0.59      0.30      0.40       136\n",
      "\n",
      "    accuracy                           0.75       760\n",
      "   macro avg       0.71      0.66      0.66       760\n",
      "weighted avg       0.73      0.75      0.73       760\n",
      "\n",
      "[[193  45  14]\n",
      " [ 22 335  15]\n",
      " [ 36  59  41]]\n"
     ]
    }
   ],
   "source": [
    "y_true = y_test\n",
    "y_pred = rand_clf.predict(X_test_scaled)\n",
    "print(classification_report(y_true, y_pred, zero_division = 0))\n",
    "print(confusion_matrix(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47a2d41",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d4c8f35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:19:51.292427Z",
     "iopub.status.busy": "2022-08-26T09:19:51.291949Z",
     "iopub.status.idle": "2022-08-26T09:20:07.638493Z",
     "shell.execute_reply": "2022-08-26T09:20:07.637047Z"
    },
    "papermill": {
     "duration": 16.361024,
     "end_time": "2022-08-26T09:20:07.641330",
     "exception": false,
     "start_time": "2022-08-26T09:19:51.280306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.64070206, 0.64439583, 0.73208103, 0.72402844]),\n",
       " 'std_fit_time': array([0.00688436, 0.02255696, 0.00820528, 0.00702285]),\n",
       " 'mean_score_time': array([0.02646217, 0.02799883, 0.02728791, 0.02637014]),\n",
       " 'std_score_time': array([0.00038837, 0.00269475, 0.00100053, 0.00036906]),\n",
       " 'param_criterion': masked_array(data=['gini', 'gini', 'entropy', 'entropy'],\n",
       "              mask=[False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_min_samples_split': masked_array(data=[3, 4, 3, 4],\n",
       "              mask=[False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'criterion': 'gini', 'min_samples_split': 3},\n",
       "  {'criterion': 'gini', 'min_samples_split': 4},\n",
       "  {'criterion': 'entropy', 'min_samples_split': 3},\n",
       "  {'criterion': 'entropy', 'min_samples_split': 4}],\n",
       " 'split0_test_score': array([0.74013158, 0.74013158, 0.72039474, 0.73190789]),\n",
       " 'split1_test_score': array([0.72981878, 0.73146623, 0.73146623, 0.74135091]),\n",
       " 'split2_test_score': array([0.74135091, 0.74794069, 0.74299835, 0.72981878]),\n",
       " 'split3_test_score': array([0.71334432, 0.71663921, 0.71499176, 0.72487644]),\n",
       " 'split4_test_score': array([0.75617792, 0.74629325, 0.75453048, 0.75617792]),\n",
       " 'mean_test_score': array([0.7361647 , 0.73649419, 0.73287631, 0.73682639]),\n",
       " 'std_test_score': array([0.01417182, 0.01148717, 0.01448443, 0.01105489]),\n",
       " 'rank_test_score': array([3, 2, 4, 1], dtype=int32),\n",
       " 'split0_train_score': array([0.99917628, 0.9946458 , 1.        , 0.99711697]),\n",
       " 'split1_train_score': array([0.99958831, 0.99670646, 1.        , 0.99835323]),\n",
       " 'split2_train_score': array([0.99958831, 0.99711816, 1.        , 0.99917662]),\n",
       " 'split3_train_score': array([0.99958831, 0.9950597 , 1.        , 0.99835323]),\n",
       " 'split4_train_score': array([1.        , 0.9950597 , 0.99958831, 0.99876492]),\n",
       " 'mean_train_score': array([0.99958824, 0.99571796, 0.99991766, 0.99835299]),\n",
       " 'std_train_score': array([0.00026048, 0.00099537, 0.00016468, 0.00068932])}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {'criterion': ['gini', 'entropy'], 'min_samples_split': [3, 4]}\n",
    "rand_clf_2 = GridSearchCV(rand_clf, parameters, return_train_score= True)\n",
    "rand_clf_2.fit(X_train_scaled, y_train)\n",
    "rand_clf_2.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f84990d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:20:07.665797Z",
     "iopub.status.busy": "2022-08-26T09:20:07.665335Z",
     "iopub.status.idle": "2022-08-26T09:20:07.672948Z",
     "shell.execute_reply": "2022-08-26T09:20:07.671774Z"
    },
    "papermill": {
     "duration": 0.022024,
     "end_time": "2022-08-26T09:20:07.675284",
     "exception": false,
     "start_time": "2022-08-26T09:20:07.653260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7368263894910256"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_clf_2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "130f98ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:20:07.697754Z",
     "iopub.status.busy": "2022-08-26T09:20:07.697324Z",
     "iopub.status.idle": "2022-08-26T09:20:07.705437Z",
     "shell.execute_reply": "2022-08-26T09:20:07.704085Z"
    },
    "papermill": {
     "duration": 0.022437,
     "end_time": "2022-08-26T09:20:07.708022",
     "exception": false,
     "start_time": "2022-08-26T09:20:07.685585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', min_samples_split=4,\n",
       "                       random_state=10)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_clf_2.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cb0590",
   "metadata": {},
   "source": [
    "### Submit the csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "167f44ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:20:07.731716Z",
     "iopub.status.busy": "2022-08-26T09:20:07.730641Z",
     "iopub.status.idle": "2022-08-26T09:20:07.753482Z",
     "shell.execute_reply": "2022-08-26T09:20:07.752598Z"
    },
    "papermill": {
     "duration": 0.037534,
     "end_time": "2022-08-26T09:20:07.756070",
     "exception": false,
     "start_time": "2022-08-26T09:20:07.718536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test = pd.read_csv(\"/kaggle/input/students-drop-out-prediction/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b2c7187",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:20:07.778790Z",
     "iopub.status.busy": "2022-08-26T09:20:07.778343Z",
     "iopub.status.idle": "2022-08-26T09:20:07.789884Z",
     "shell.execute_reply": "2022-08-26T09:20:07.788792Z"
    },
    "papermill": {
     "duration": 0.0259,
     "end_time": "2022-08-26T09:20:07.792357",
     "exception": false,
     "start_time": "2022-08-26T09:20:07.766457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test_scaled = std_scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "89342040",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:20:07.818147Z",
     "iopub.status.busy": "2022-08-26T09:20:07.817745Z",
     "iopub.status.idle": "2022-08-26T09:20:07.872471Z",
     "shell.execute_reply": "2022-08-26T09:20:07.871168Z"
    },
    "papermill": {
     "duration": 0.072482,
     "end_time": "2022-08-26T09:20:07.875259",
     "exception": false,
     "start_time": "2022-08-26T09:20:07.802777",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "prediction = rand_clf_2.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ed3395c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-26T09:20:07.897829Z",
     "iopub.status.busy": "2022-08-26T09:20:07.897412Z",
     "iopub.status.idle": "2022-08-26T09:20:07.910808Z",
     "shell.execute_reply": "2022-08-26T09:20:07.909785Z"
    },
    "papermill": {
     "duration": 0.027885,
     "end_time": "2022-08-26T09:20:07.913509",
     "exception": false,
     "start_time": "2022-08-26T09:20:07.885624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test['label'] = prediction\n",
    "column_1 = X_test['id'].to_numpy()\n",
    "column_2 = X_test['label'].to_numpy()\n",
    "df = np.column_stack((column_1,column_2))\n",
    "pd.DataFrame(df, columns = ['id','label']).to_csv('sixth_submission.csv',index=False)"
   ]
  }
 ],
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