diff --git a/body-performance-data.zip b/body-performance-data.zip
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diff --git a/breast-cancer-dataset.zip b/breast-cancer-dataset.zip
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diff --git a/classification.ipynb b/classification.ipynb
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--- a/classification.ipynb
+++ /dev/null
@@ -1,969 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "corrected-wholesale",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!kaggle datasets download -d yasserh/breast-cancer-dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ranging-police",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!unzip -o breast-cancer-dataset.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "ideal-spouse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "from torch.autograd import Variable\n",
-    "from sklearn.datasets import load_iris\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "import torch.nn.functional as F\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "major-compromise",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Model(nn.Module):\n",
-    "    def __init__(self, input_dim):\n",
-    "        super(Model, self).__init__()\n",
-    "        self.layer1 = nn.Linear(input_dim,50)\n",
-    "        self.layer2 = nn.Linear(50, 20)\n",
-    "        self.layer3 = nn.Linear(20, 3)\n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        x = F.relu(self.layer1(x))\n",
-    "        x = F.relu(self.layer2(x))\n",
-    "        x = F.softmax(self.layer3(x)) # To check with the loss function\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "czech-regular",
-   "metadata": {},
-   "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>diagnosis</th>\n",
-       "      <th>radius_mean</th>\n",
-       "      <th>texture_mean</th>\n",
-       "      <th>perimeter_mean</th>\n",
-       "      <th>area_mean</th>\n",
-       "      <th>smoothness_mean</th>\n",
-       "      <th>compactness_mean</th>\n",
-       "      <th>concavity_mean</th>\n",
-       "      <th>concave points_mean</th>\n",
-       "      <th>symmetry_mean</th>\n",
-       "      <th>...</th>\n",
-       "      <th>radius_worst</th>\n",
-       "      <th>texture_worst</th>\n",
-       "      <th>perimeter_worst</th>\n",
-       "      <th>area_worst</th>\n",
-       "      <th>smoothness_worst</th>\n",
-       "      <th>compactness_worst</th>\n",
-       "      <th>concavity_worst</th>\n",
-       "      <th>concave points_worst</th>\n",
-       "      <th>symmetry_worst</th>\n",
-       "      <th>fractal_dimension_worst</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>842517</th>\n",
-       "      <td>M</td>\n",
-       "      <td>20.57</td>\n",
-       "      <td>17.77</td>\n",
-       "      <td>132.90</td>\n",
-       "      <td>1326.0</td>\n",
-       "      <td>0.08474</td>\n",
-       "      <td>0.07864</td>\n",
-       "      <td>0.08690</td>\n",
-       "      <td>0.07017</td>\n",
-       "      <td>0.1812</td>\n",
-       "      <td>...</td>\n",
-       "      <td>24.99</td>\n",
-       "      <td>23.41</td>\n",
-       "      <td>158.80</td>\n",
-       "      <td>1956.0</td>\n",
-       "      <td>0.1238</td>\n",
-       "      <td>0.1866</td>\n",
-       "      <td>0.2416</td>\n",
-       "      <td>0.1860</td>\n",
-       "      <td>0.2750</td>\n",
-       "      <td>0.08902</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84300903</th>\n",
-       "      <td>M</td>\n",
-       "      <td>19.69</td>\n",
-       "      <td>21.25</td>\n",
-       "      <td>130.00</td>\n",
-       "      <td>1203.0</td>\n",
-       "      <td>0.10960</td>\n",
-       "      <td>0.15990</td>\n",
-       "      <td>0.19740</td>\n",
-       "      <td>0.12790</td>\n",
-       "      <td>0.2069</td>\n",
-       "      <td>...</td>\n",
-       "      <td>23.57</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>152.50</td>\n",
-       "      <td>1709.0</td>\n",
-       "      <td>0.1444</td>\n",
-       "      <td>0.4245</td>\n",
-       "      <td>0.4504</td>\n",
-       "      <td>0.2430</td>\n",
-       "      <td>0.3613</td>\n",
-       "      <td>0.08758</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84348301</th>\n",
-       "      <td>M</td>\n",
-       "      <td>11.42</td>\n",
-       "      <td>20.38</td>\n",
-       "      <td>77.58</td>\n",
-       "      <td>386.1</td>\n",
-       "      <td>0.14250</td>\n",
-       "      <td>0.28390</td>\n",
-       "      <td>0.24140</td>\n",
-       "      <td>0.10520</td>\n",
-       "      <td>0.2597</td>\n",
-       "      <td>...</td>\n",
-       "      <td>14.91</td>\n",
-       "      <td>26.50</td>\n",
-       "      <td>98.87</td>\n",
-       "      <td>567.7</td>\n",
-       "      <td>0.2098</td>\n",
-       "      <td>0.8663</td>\n",
-       "      <td>0.6869</td>\n",
-       "      <td>0.2575</td>\n",
-       "      <td>0.6638</td>\n",
-       "      <td>0.17300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84358402</th>\n",
-       "      <td>M</td>\n",
-       "      <td>20.29</td>\n",
-       "      <td>14.34</td>\n",
-       "      <td>135.10</td>\n",
-       "      <td>1297.0</td>\n",
-       "      <td>0.10030</td>\n",
-       "      <td>0.13280</td>\n",
-       "      <td>0.19800</td>\n",
-       "      <td>0.10430</td>\n",
-       "      <td>0.1809</td>\n",
-       "      <td>...</td>\n",
-       "      <td>22.54</td>\n",
-       "      <td>16.67</td>\n",
-       "      <td>152.20</td>\n",
-       "      <td>1575.0</td>\n",
-       "      <td>0.1374</td>\n",
-       "      <td>0.2050</td>\n",
-       "      <td>0.4000</td>\n",
-       "      <td>0.1625</td>\n",
-       "      <td>0.2364</td>\n",
-       "      <td>0.07678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>843786</th>\n",
-       "      <td>M</td>\n",
-       "      <td>12.45</td>\n",
-       "      <td>15.70</td>\n",
-       "      <td>82.57</td>\n",
-       "      <td>477.1</td>\n",
-       "      <td>0.12780</td>\n",
-       "      <td>0.17000</td>\n",
-       "      <td>0.15780</td>\n",
-       "      <td>0.08089</td>\n",
-       "      <td>0.2087</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15.47</td>\n",
-       "      <td>23.75</td>\n",
-       "      <td>103.40</td>\n",
-       "      <td>741.6</td>\n",
-       "      <td>0.1791</td>\n",
-       "      <td>0.5249</td>\n",
-       "      <td>0.5355</td>\n",
-       "      <td>0.1741</td>\n",
-       "      <td>0.3985</td>\n",
-       "      <td>0.12440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>844359</th>\n",
-       "      <td>M</td>\n",
-       "      <td>18.25</td>\n",
-       "      <td>19.98</td>\n",
-       "      <td>119.60</td>\n",
-       "      <td>1040.0</td>\n",
-       "      <td>0.09463</td>\n",
-       "      <td>0.10900</td>\n",
-       "      <td>0.11270</td>\n",
-       "      <td>0.07400</td>\n",
-       "      <td>0.1794</td>\n",
-       "      <td>...</td>\n",
-       "      <td>22.88</td>\n",
-       "      <td>27.66</td>\n",
-       "      <td>153.20</td>\n",
-       "      <td>1606.0</td>\n",
-       "      <td>0.1442</td>\n",
-       "      <td>0.2576</td>\n",
-       "      <td>0.3784</td>\n",
-       "      <td>0.1932</td>\n",
-       "      <td>0.3063</td>\n",
-       "      <td>0.08368</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84458202</th>\n",
-       "      <td>M</td>\n",
-       "      <td>13.71</td>\n",
-       "      <td>20.83</td>\n",
-       "      <td>90.20</td>\n",
-       "      <td>577.9</td>\n",
-       "      <td>0.11890</td>\n",
-       "      <td>0.16450</td>\n",
-       "      <td>0.09366</td>\n",
-       "      <td>0.05985</td>\n",
-       "      <td>0.2196</td>\n",
-       "      <td>...</td>\n",
-       "      <td>17.06</td>\n",
-       "      <td>28.14</td>\n",
-       "      <td>110.60</td>\n",
-       "      <td>897.0</td>\n",
-       "      <td>0.1654</td>\n",
-       "      <td>0.3682</td>\n",
-       "      <td>0.2678</td>\n",
-       "      <td>0.1556</td>\n",
-       "      <td>0.3196</td>\n",
-       "      <td>0.11510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>844981</th>\n",
-       "      <td>M</td>\n",
-       "      <td>13.00</td>\n",
-       "      <td>21.82</td>\n",
-       "      <td>87.50</td>\n",
-       "      <td>519.8</td>\n",
-       "      <td>0.12730</td>\n",
-       "      <td>0.19320</td>\n",
-       "      <td>0.18590</td>\n",
-       "      <td>0.09353</td>\n",
-       "      <td>0.2350</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15.49</td>\n",
-       "      <td>30.73</td>\n",
-       "      <td>106.20</td>\n",
-       "      <td>739.3</td>\n",
-       "      <td>0.1703</td>\n",
-       "      <td>0.5401</td>\n",
-       "      <td>0.5390</td>\n",
-       "      <td>0.2060</td>\n",
-       "      <td>0.4378</td>\n",
-       "      <td>0.10720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84501001</th>\n",
-       "      <td>M</td>\n",
-       "      <td>12.46</td>\n",
-       "      <td>24.04</td>\n",
-       "      <td>83.97</td>\n",
-       "      <td>475.9</td>\n",
-       "      <td>0.11860</td>\n",
-       "      <td>0.23960</td>\n",
-       "      <td>0.22730</td>\n",
-       "      <td>0.08543</td>\n",
-       "      <td>0.2030</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15.09</td>\n",
-       "      <td>40.68</td>\n",
-       "      <td>97.65</td>\n",
-       "      <td>711.4</td>\n",
-       "      <td>0.1853</td>\n",
-       "      <td>1.0580</td>\n",
-       "      <td>1.1050</td>\n",
-       "      <td>0.2210</td>\n",
-       "      <td>0.4366</td>\n",
-       "      <td>0.20750</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
-       "id                                                                         \n",
-       "842517           M        20.57         17.77          132.90     1326.0   \n",
-       "84300903         M        19.69         21.25          130.00     1203.0   \n",
-       "84348301         M        11.42         20.38           77.58      386.1   \n",
-       "84358402         M        20.29         14.34          135.10     1297.0   \n",
-       "843786           M        12.45         15.70           82.57      477.1   \n",
-       "844359           M        18.25         19.98          119.60     1040.0   \n",
-       "84458202         M        13.71         20.83           90.20      577.9   \n",
-       "844981           M        13.00         21.82           87.50      519.8   \n",
-       "84501001         M        12.46         24.04           83.97      475.9   \n",
-       "\n",
-       "          smoothness_mean  compactness_mean  concavity_mean  \\\n",
-       "id                                                            \n",
-       "842517            0.08474           0.07864         0.08690   \n",
-       "84300903          0.10960           0.15990         0.19740   \n",
-       "84348301          0.14250           0.28390         0.24140   \n",
-       "84358402          0.10030           0.13280         0.19800   \n",
-       "843786            0.12780           0.17000         0.15780   \n",
-       "844359            0.09463           0.10900         0.11270   \n",
-       "84458202          0.11890           0.16450         0.09366   \n",
-       "844981            0.12730           0.19320         0.18590   \n",
-       "84501001          0.11860           0.23960         0.22730   \n",
-       "\n",
-       "          concave points_mean  symmetry_mean           ...             \\\n",
-       "id                                                     ...              \n",
-       "842517                0.07017         0.1812           ...              \n",
-       "84300903              0.12790         0.2069           ...              \n",
-       "84348301              0.10520         0.2597           ...              \n",
-       "84358402              0.10430         0.1809           ...              \n",
-       "843786                0.08089         0.2087           ...              \n",
-       "844359                0.07400         0.1794           ...              \n",
-       "84458202              0.05985         0.2196           ...              \n",
-       "844981                0.09353         0.2350           ...              \n",
-       "84501001              0.08543         0.2030           ...              \n",
-       "\n",
-       "          radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
-       "id                                                                   \n",
-       "842517           24.99          23.41           158.80      1956.0   \n",
-       "84300903         23.57          25.53           152.50      1709.0   \n",
-       "84348301         14.91          26.50            98.87       567.7   \n",
-       "84358402         22.54          16.67           152.20      1575.0   \n",
-       "843786           15.47          23.75           103.40       741.6   \n",
-       "844359           22.88          27.66           153.20      1606.0   \n",
-       "84458202         17.06          28.14           110.60       897.0   \n",
-       "844981           15.49          30.73           106.20       739.3   \n",
-       "84501001         15.09          40.68            97.65       711.4   \n",
-       "\n",
-       "          smoothness_worst  compactness_worst  concavity_worst  \\\n",
-       "id                                                               \n",
-       "842517              0.1238             0.1866           0.2416   \n",
-       "84300903            0.1444             0.4245           0.4504   \n",
-       "84348301            0.2098             0.8663           0.6869   \n",
-       "84358402            0.1374             0.2050           0.4000   \n",
-       "843786              0.1791             0.5249           0.5355   \n",
-       "844359              0.1442             0.2576           0.3784   \n",
-       "84458202            0.1654             0.3682           0.2678   \n",
-       "844981              0.1703             0.5401           0.5390   \n",
-       "84501001            0.1853             1.0580           1.1050   \n",
-       "\n",
-       "          concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
-       "id                                                                       \n",
-       "842517                  0.1860          0.2750                  0.08902  \n",
-       "84300903                0.2430          0.3613                  0.08758  \n",
-       "84348301                0.2575          0.6638                  0.17300  \n",
-       "84358402                0.1625          0.2364                  0.07678  \n",
-       "843786                  0.1741          0.3985                  0.12440  \n",
-       "844359                  0.1932          0.3063                  0.08368  \n",
-       "84458202                0.1556          0.3196                  0.11510  \n",
-       "844981                  0.2060          0.4378                  0.10720  \n",
-       "84501001                0.2210          0.4366                  0.20750  \n",
-       "\n",
-       "[9 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv('breast-cancer.csv', index_col=0)\n",
-    "data[1:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "outdoor-element",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lb = LabelEncoder()\n",
-    "data['diagnosis'] = lb.fit_transform(data['diagnosis'])\n",
-    "features = data.iloc[:, 1:32].values\n",
-    "labels = np.array(data['diagnosis'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "buried-community",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2.057e+01, 1.777e+01, 1.329e+02, 1.326e+03, 8.474e-02, 7.864e-02,\n",
-       "        8.690e-02, 7.017e-02, 1.812e-01, 5.667e-02, 5.435e-01, 7.339e-01,\n",
-       "        3.398e+00, 7.408e+01, 5.225e-03, 1.308e-02, 1.860e-02, 1.340e-02,\n",
-       "        1.389e-02, 3.532e-03, 2.499e+01, 2.341e+01, 1.588e+02, 1.956e+03,\n",
-       "        1.238e-01, 1.866e-01, 2.416e-01, 1.860e-01, 2.750e-01, 8.902e-02],\n",
-       "       [1.969e+01, 2.125e+01, 1.300e+02, 1.203e+03, 1.096e-01, 1.599e-01,\n",
-       "        1.974e-01, 1.279e-01, 2.069e-01, 5.999e-02, 7.456e-01, 7.869e-01,\n",
-       "        4.585e+00, 9.403e+01, 6.150e-03, 4.006e-02, 3.832e-02, 2.058e-02,\n",
-       "        2.250e-02, 4.571e-03, 2.357e+01, 2.553e+01, 1.525e+02, 1.709e+03,\n",
-       "        1.444e-01, 4.245e-01, 4.504e-01, 2.430e-01, 3.613e-01, 8.758e-02],\n",
-       "       [1.142e+01, 2.038e+01, 7.758e+01, 3.861e+02, 1.425e-01, 2.839e-01,\n",
-       "        2.414e-01, 1.052e-01, 2.597e-01, 9.744e-02, 4.956e-01, 1.156e+00,\n",
-       "        3.445e+00, 2.723e+01, 9.110e-03, 7.458e-02, 5.661e-02, 1.867e-02,\n",
-       "        5.963e-02, 9.208e-03, 1.491e+01, 2.650e+01, 9.887e+01, 5.677e+02,\n",
-       "        2.098e-01, 8.663e-01, 6.869e-01, 2.575e-01, 6.638e-01, 1.730e-01],\n",
-       "       [2.029e+01, 1.434e+01, 1.351e+02, 1.297e+03, 1.003e-01, 1.328e-01,\n",
-       "        1.980e-01, 1.043e-01, 1.809e-01, 5.883e-02, 7.572e-01, 7.813e-01,\n",
-       "        5.438e+00, 9.444e+01, 1.149e-02, 2.461e-02, 5.688e-02, 1.885e-02,\n",
-       "        1.756e-02, 5.115e-03, 2.254e+01, 1.667e+01, 1.522e+02, 1.575e+03,\n",
-       "        1.374e-01, 2.050e-01, 4.000e-01, 1.625e-01, 2.364e-01, 7.678e-02],\n",
-       "       [1.245e+01, 1.570e+01, 8.257e+01, 4.771e+02, 1.278e-01, 1.700e-01,\n",
-       "        1.578e-01, 8.089e-02, 2.087e-01, 7.613e-02, 3.345e-01, 8.902e-01,\n",
-       "        2.217e+00, 2.719e+01, 7.510e-03, 3.345e-02, 3.672e-02, 1.137e-02,\n",
-       "        2.165e-02, 5.082e-03, 1.547e+01, 2.375e+01, 1.034e+02, 7.416e+02,\n",
-       "        1.791e-01, 5.249e-01, 5.355e-01, 1.741e-01, 3.985e-01, 1.244e-01],\n",
-       "       [1.825e+01, 1.998e+01, 1.196e+02, 1.040e+03, 9.463e-02, 1.090e-01,\n",
-       "        1.127e-01, 7.400e-02, 1.794e-01, 5.742e-02, 4.467e-01, 7.732e-01,\n",
-       "        3.180e+00, 5.391e+01, 4.314e-03, 1.382e-02, 2.254e-02, 1.039e-02,\n",
-       "        1.369e-02, 2.179e-03, 2.288e+01, 2.766e+01, 1.532e+02, 1.606e+03,\n",
-       "        1.442e-01, 2.576e-01, 3.784e-01, 1.932e-01, 3.063e-01, 8.368e-02],\n",
-       "       [1.371e+01, 2.083e+01, 9.020e+01, 5.779e+02, 1.189e-01, 1.645e-01,\n",
-       "        9.366e-02, 5.985e-02, 2.196e-01, 7.451e-02, 5.835e-01, 1.377e+00,\n",
-       "        3.856e+00, 5.096e+01, 8.805e-03, 3.029e-02, 2.488e-02, 1.448e-02,\n",
-       "        1.486e-02, 5.412e-03, 1.706e+01, 2.814e+01, 1.106e+02, 8.970e+02,\n",
-       "        1.654e-01, 3.682e-01, 2.678e-01, 1.556e-01, 3.196e-01, 1.151e-01],\n",
-       "       [1.300e+01, 2.182e+01, 8.750e+01, 5.198e+02, 1.273e-01, 1.932e-01,\n",
-       "        1.859e-01, 9.353e-02, 2.350e-01, 7.389e-02, 3.063e-01, 1.002e+00,\n",
-       "        2.406e+00, 2.432e+01, 5.731e-03, 3.502e-02, 3.553e-02, 1.226e-02,\n",
-       "        2.143e-02, 3.749e-03, 1.549e+01, 3.073e+01, 1.062e+02, 7.393e+02,\n",
-       "        1.703e-01, 5.401e-01, 5.390e-01, 2.060e-01, 4.378e-01, 1.072e-01],\n",
-       "       [1.246e+01, 2.404e+01, 8.397e+01, 4.759e+02, 1.186e-01, 2.396e-01,\n",
-       "        2.273e-01, 8.543e-02, 2.030e-01, 8.243e-02, 2.976e-01, 1.599e+00,\n",
-       "        2.039e+00, 2.394e+01, 7.149e-03, 7.217e-02, 7.743e-02, 1.432e-02,\n",
-       "        1.789e-02, 1.008e-02, 1.509e+01, 4.068e+01, 9.765e+01, 7.114e+02,\n",
-       "        1.853e-01, 1.058e+00, 1.105e+00, 2.210e-01, 4.366e-01, 2.075e-01]])"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features[1:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "id": "incredible-quantum",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 1, 1, 1, 1, 1, 1, 1, 1])"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels[1:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "brazilian-butler",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, random_state=42, shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "exotic-method",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Training\n",
-    "model = Model(features_train.shape[1])\n",
-    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
-    "loss_fn = nn.CrossEntropyLoss()\n",
-    "epochs = 100\n",
-    "\n",
-    "def print_(loss):\n",
-    "    print (\"The loss calculated: \", loss)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "sharp-month",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch # 1\n",
-      "The loss calculated:  0.922476053237915\n",
-      "Epoch # 2\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 3\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 4\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 5\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 6\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 7\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 8\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 9\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 10\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 11\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 12\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 13\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 14\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 15\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 16\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 17\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 18\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 19\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 20\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 21\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 22\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 23\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 24\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 25\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 26\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 27\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 28\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 29\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 30\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 31\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 32\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 33\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 34\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 35\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 36\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 37\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 38\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 39\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 40\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 41\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 42\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 43\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 44\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 45\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 46\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 47\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 48\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 49\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 50\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 51\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 52\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 53\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 54\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 55\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 56\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 57\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 58\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 59\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 60\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 61\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 62\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 63\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 64\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 65\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 66\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 67\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 68\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 69\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 70\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 71\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 72\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 73\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 74\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 75\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 76\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 77\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 78\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 79\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 80\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 81\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 82\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 83\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 84\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 85\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 86\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 87\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 88\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 89\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 90\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 91\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 92\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 93\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 94\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 95\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 96\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 97\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 98\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 99\n",
-      "The loss calculated:  0.9223369359970093\n",
-      "Epoch # 100\n",
-      "The loss calculated:  0.9223369359970093\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
-      "  # This is added back by InteractiveShellApp.init_path()\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Not using dataloader\n",
-    "x_train, y_train = Variable(torch.from_numpy(features_train)).float(), Variable(torch.from_numpy(labels_train)).long()\n",
-    "for epoch in range(1, epochs+1):\n",
-    "    print (\"Epoch #\",epoch)\n",
-    "    y_pred = model(x_train)\n",
-    "    loss = loss_fn(y_pred, y_train)\n",
-    "    print_(loss.item())\n",
-    "    \n",
-    "    # Zero gradients\n",
-    "    optimizer.zero_grad()\n",
-    "    loss.backward() # Gradients\n",
-    "    optimizer.step() # Update"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "mechanical-humidity",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
-      "  # This is added back by InteractiveShellApp.init_path()\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Prediction\n",
-    "x_test = Variable(torch.from_numpy(features_test)).float()\n",
-    "pred = model(x_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "based-charleston",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1., 0., 0.],\n",
-       "       [1., 0., 0.],\n",
-       "       [1., 0., 0.],\n",
-       "       [1., 0., 0.],\n",
-       "       [1., 0., 0.],\n",
-       "       [1., 0., 0.],\n",
-       "       [1., 0., 0.],\n",
-       "       [1., 0., 0.],\n",
-       "       [1., 0., 0.]], dtype=float32)"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pred = pred.detach().numpy()\n",
-    "pred[1:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "dried-accessory",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The accuracy is 0.6223776223776224\n"
-     ]
-    }
-   ],
-   "source": [
-    "print (\"The accuracy is\", accuracy_score(labels_test, np.argmax(pred, axis=1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "effective-characterization",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels_test[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "oriented-determination",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.save(model, \"travel_insurance-pytorch.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "infectious-wagon",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "saved_model = torch.load(\"travel_insurance-pytorch.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "built-contributor",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
-      "  # This is added back by InteractiveShellApp.init_path()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(saved_model(x_test[0]).detach().numpy(), axis=0)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/classification_net.ipynb b/classification_net.ipynb
new file mode 100644
index 0000000..67b646d
--- /dev/null
+++ b/classification_net.ipynb
@@ -0,0 +1,530 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "forty-fault",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kaggle datasets download -d kukuroo3/body-performance-data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "pediatric-tuesday",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!unzip -o body-performance-data.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "interstate-presence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report\n",
+    "import torch\n",
+    "from torch import nn, optim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "structural-trigger",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13393, 12)"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('bodyPerformance.csv')\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "turkish-category",
+   "metadata": {},
+   "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>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>height_cm</th>\n",
+       "      <th>weight_kg</th>\n",
+       "      <th>body fat_%</th>\n",
+       "      <th>diastolic</th>\n",
+       "      <th>systolic</th>\n",
+       "      <th>gripForce</th>\n",
+       "      <th>sit and bend forward_cm</th>\n",
+       "      <th>sit-ups counts</th>\n",
+       "      <th>broad jump_cm</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>172.3</td>\n",
+       "      <td>75.24</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>54.9</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>217.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>55.80</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>36.4</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>229.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>179.6</td>\n",
+       "      <td>78.00</td>\n",
+       "      <td>20.1</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>44.8</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>174.5</td>\n",
+       "      <td>71.10</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>41.4</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>173.8</td>\n",
+       "      <td>67.70</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>43.5</td>\n",
+       "      <td>27.1</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>217.0</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    age gender  height_cm  weight_kg  body fat_%  diastolic  systolic  \\\n",
+       "0  27.0      M      172.3      75.24        21.3       80.0     130.0   \n",
+       "1  25.0      M      165.0      55.80        15.7       77.0     126.0   \n",
+       "2  31.0      M      179.6      78.00        20.1       92.0     152.0   \n",
+       "3  32.0      M      174.5      71.10        18.4       76.0     147.0   \n",
+       "4  28.0      M      173.8      67.70        17.1       70.0     127.0   \n",
+       "\n",
+       "   gripForce  sit and bend forward_cm  sit-ups counts  broad jump_cm class  \n",
+       "0       54.9                     18.4            60.0          217.0     C  \n",
+       "1       36.4                     16.3            53.0          229.0     A  \n",
+       "2       44.8                     12.0            49.0          181.0     C  \n",
+       "3       41.4                     15.2            53.0          219.0     B  \n",
+       "4       43.5                     27.1            45.0          217.0     B  "
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "received-absence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['gender', 'height_cm', 'weight_kg', 'body fat_%', 'sit-ups counts', 'broad jump_cm']\n",
+    "df = df[cols]\n",
+    "\n",
+    "# male - 0, female - 1\n",
+    "df['gender'].replace({'M': 0, 'F': 1}, inplace = True)\n",
+    "df = df.dropna(how='any')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "excited-parent",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.632196\n",
+       "1    0.367804\n",
+       "Name: gender, dtype: float64"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.gender.value_counts() / df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "extended-cinema",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[['height_cm', 'weight_kg', 'body fat_%', 'sit-ups counts', 'broad jump_cm']]\n",
+    "y = df[['gender']]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "animated-farming",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([10714, 5]) torch.Size([10714])\n",
+      "torch.Size([2679, 5]) torch.Size([2679])\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train = torch.from_numpy(np.array(X_train)).float()\n",
+    "y_train = torch.squeeze(torch.from_numpy(y_train.values).float())\n",
+    "\n",
+    "X_test = torch.from_numpy(np.array(X_test)).float()\n",
+    "y_test = torch.squeeze(torch.from_numpy(y_test.values).float())\n",
+    "\n",
+    "print(X_train.shape, y_train.shape)\n",
+    "print(X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "technical-wallet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Net(nn.Module):\n",
+    "  def __init__(self, n_features):\n",
+    "    super(Net, self).__init__()\n",
+    "    self.fc1 = nn.Linear(n_features, 5)\n",
+    "    self.fc2 = nn.Linear(5, 3)\n",
+    "    self.fc3 = nn.Linear(3, 1)\n",
+    "  def forward(self, x):\n",
+    "    x = F.relu(self.fc1(x))\n",
+    "    x = F.relu(self.fc2(x))\n",
+    "    return torch.sigmoid(self.fc3(x))\n",
+    "net = Net(X_train.shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "requested-plymouth",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "criterion = nn.BCELoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "iraqi-english",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = optim.Adam(net.parameters(), lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "emerging-helmet",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "differential-aviation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = X_train.to(device)\n",
+    "y_train = y_train.to(device)\n",
+    "X_test = X_test.to(device)\n",
+    "y_test = y_test.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "ranging-calgary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = net.to(device)\n",
+    "criterion = criterion.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "iraqi-blanket",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_accuracy(y_true, y_pred):\n",
+    "  predicted = y_pred.ge(.5).view(-1)\n",
+    "  return (y_true == predicted).sum().float() / len(y_true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "robust-serbia",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0\n",
+      "Train set - loss: 1.005, accuracy: 0.37\n",
+      "Test  set - loss: 1.018, accuracy: 0.358\n",
+      "\n",
+      "epoch 100\n",
+      "Train set - loss: 0.677, accuracy: 0.743\n",
+      "Test  set - loss: 0.679, accuracy: 0.727\n",
+      "\n",
+      "epoch 200\n",
+      "Train set - loss: 0.636, accuracy: 0.79\n",
+      "Test  set - loss: 0.64, accuracy: 0.778\n",
+      "\n",
+      "epoch 300\n",
+      "Train set - loss: 0.568, accuracy: 0.839\n",
+      "Test  set - loss: 0.577, accuracy: 0.833\n",
+      "\n",
+      "epoch 400\n",
+      "Train set - loss: 0.504, accuracy: 0.885\n",
+      "Test  set - loss: 0.514, accuracy: 0.877\n",
+      "\n",
+      "epoch 500\n",
+      "Train set - loss: 0.441, accuracy: 0.922\n",
+      "Test  set - loss: 0.45, accuracy: 0.913\n",
+      "\n",
+      "epoch 600\n",
+      "Train set - loss: 0.388, accuracy: 0.944\n",
+      "Test  set - loss: 0.396, accuracy: 0.938\n",
+      "\n",
+      "epoch 700\n",
+      "Train set - loss: 0.353, accuracy: 0.954\n",
+      "Test  set - loss: 0.359, accuracy: 0.949\n",
+      "\n",
+      "epoch 800\n",
+      "Train set - loss: 0.327, accuracy: 0.958\n",
+      "Test  set - loss: 0.333, accuracy: 0.953\n",
+      "\n",
+      "epoch 900\n",
+      "Train set - loss: 0.306, accuracy: 0.961\n",
+      "Test  set - loss: 0.312, accuracy: 0.955\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def round_tensor(t, decimal_places=3):\n",
+    "  return round(t.item(), decimal_places)\n",
+    "for epoch in range(1000):\n",
+    "    y_pred = net(X_train)\n",
+    "    y_pred = torch.squeeze(y_pred)\n",
+    "    train_loss = criterion(y_pred, y_train)\n",
+    "    if epoch % 100 == 0:\n",
+    "      train_acc = calculate_accuracy(y_train, y_pred)\n",
+    "      y_test_pred = net(X_test)\n",
+    "      y_test_pred = torch.squeeze(y_test_pred)\n",
+    "      test_loss = criterion(y_test_pred, y_test)\n",
+    "      test_acc = calculate_accuracy(y_test, y_test_pred)\n",
+    "      print(\n",
+    "f'''epoch {epoch}\n",
+    "Train set - loss: {round_tensor(train_loss)}, accuracy: {round_tensor(train_acc)}\n",
+    "Test  set - loss: {round_tensor(test_loss)}, accuracy: {round_tensor(test_acc)}\n",
+    "''')\n",
+    "    optimizer.zero_grad()\n",
+    "    train_loss.backward()\n",
+    "    optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "optimum-excerpt",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# torch.save(net, 'model.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "dental-seating",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# net = torch.load('model.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "german-satisfaction",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        Male       0.97      0.96      0.96      1720\n",
+      "      Female       0.93      0.94      0.94       959\n",
+      "\n",
+      "    accuracy                           0.95      2679\n",
+      "   macro avg       0.95      0.95      0.95      2679\n",
+      "weighted avg       0.95      0.95      0.95      2679\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "classes = ['Male', 'Female']\n",
+    "y_pred = net(X_test)\n",
+    "y_pred = y_pred.ge(.5).view(-1).cpu()\n",
+    "y_test = y_test.cpu()\n",
+    "print(classification_report(y_test, y_pred, target_names=classes))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "british-incidence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('test_out.csv', 'w') as file:\n",
+    "    for y in y_pred:\n",
+    "        file.write(classes[y.item()])\n",
+    "        file.write('\\n')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}