preterm modelling.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Preterm dataset prediction "
      ],
      "metadata": {
        "id": "VyLVV1yPyDoZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vpus6c63x-xl"
      },
      "outputs": [],
      "source": [
        "#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<liberaries:\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import pandas as pd  \n",
        "import seaborn as sns\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.utils import shuffle\n",
        "import seaborn as sns\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#read data set\n",
        "df = pd.read_csv('preterm.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IFMqDdu0yI4D",
        "outputId": "931df854-68e4-44e1-87ea-f05eed32bd0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Count Contraction  lenght of contraction        STD  Entropy  \\\n",
              "0               11055                 218320  53231.010    1.860   \n",
              "1                9118                 222820  62367.488    1.580   \n",
              "2                7925                  13481  60503.050    2.067   \n",
              "3               12451                  17474  53628.078    1.731   \n",
              "4               11152                 218320  53317.910    1.857   \n",
              "5                6029                  63781  59177.965    1.701   \n",
              "6               10052                  22310  54431.030    1.790   \n",
              "7                9101                 219830  63467.583    1.490   \n",
              "8                7929                  13192  61503.160    2.067   \n",
              "9               12452                  16473  54678.091    1.701   \n",
              "10              11121                 228321  52901.880    1.777   \n",
              "11               6179                  65782  62177.988    1.991   \n",
              "12               2012                  12483  32901.880    0.901   \n",
              "13               1812                  10489  30905.820    1.210   \n",
              "14               1919                  11297  31902.320    1.320   \n",
              "15               2012                  12483  32901.880    0.901   \n",
              "16               1812                  11481  29205.840    1.310   \n",
              "17               1829                  11296  31902.320    1.020   \n",
              "18               2119                  12298  32103.350    1.120   \n",
              "19                714                   6185  57003.089    0.701   \n",
              "20                694                  10226  40605.810    0.821   \n",
              "21                614                  10143  49114.330    0.832   \n",
              "22                674                   6185  42902.890    0.901   \n",
              "23                615                   6388  49406.860    0.881   \n",
              "24                625                   6351  41912.340    0.892   \n",
              "25                428                   6283  39206.370    0.618   \n",
              "26                435                   5308  47003.089    0.501   \n",
              "27                428                   3388  39103.320    0.524   \n",
              "28                545                   3308  39104.320    0.537   \n",
              "29                448                   3315  39206.370    0.504   \n",
              "30                495                   3328  47003.089    0.581   \n",
              "31                415                   3355  41103.320    0.592   \n",
              "32                495                   3348  42104.320    0.519   \n",
              "33                525                   3608  49206.370    0.529   \n",
              "34                415                   3309  47003.089    0.539   \n",
              "35                465                   3238  52103.140    0.524   \n",
              "36                425                   2308  51104.320    0.591   \n",
              "37                501                   2308  54206.370    0.598   \n",
              "38                412                   2308  51003.089    0.509   \n",
              "39                415                   2308  59103.320    0.498   \n",
              "40                465                   2358  49104.320    0.488   \n",
              "41                425                   2333  52206.370    0.475   \n",
              "42                380                   2334  57003.089    0.479   \n",
              "43                323                   2339  49103.320    0.498   \n",
              "44                222                   2675  59104.320    0.477   \n",
              "45                321                   2456  59206.370    0.458   \n",
              "46                334                   2682  57003.089    0.445   \n",
              "47                321                   2901  49103.320    0.469   \n",
              "48                345                   2932  51104.320    0.488   \n",
              "49                320                   2963  43102.120    0.457   \n",
              "50                349                   2333  58013.079    0.428   \n",
              "51                395                   2334  49208.120    0.445   \n",
              "52                398                   2339  51124.340    0.469   \n",
              "53                321                   2675  46107.090    0.499   \n",
              "54                398                   2339  51122.310    0.469   \n",
              "55                321                   2675  46108.180    0.498   \n",
              "56                398                   2336  51224.370    0.459   \n",
              "57                323                   2641  46102.170    0.439   \n",
              "\n",
              "    Contraction times  Pre-term  \n",
              "0                   2         1  \n",
              "1                   2         1  \n",
              "2                   2         1  \n",
              "3                   2         1  \n",
              "4                   2         1  \n",
              "5                   2         1  \n",
              "6                   2         1  \n",
              "7                   2         1  \n",
              "8                   2         1  \n",
              "9                   2         1  \n",
              "10                  2         1  \n",
              "11                  2         1  \n",
              "12                  1         1  \n",
              "13                  1         1  \n",
              "14                  1         1  \n",
              "15                  1         1  \n",
              "16                  1         1  \n",
              "17                  1         1  \n",
              "18                  1         1  \n",
              "19                  1         0  \n",
              "20                  1         0  \n",
              "21                  1         0  \n",
              "22                  1         0  \n",
              "23                  1         0  \n",
              "24                  1         0  \n",
              "25                  0         0  \n",
              "26                  0         0  \n",
              "27                  0         0  \n",
              "28                  0         0  \n",
              "29                  0         0  \n",
              "30                  0         0  \n",
              "31                  0         0  \n",
              "32                  0         0  \n",
              "33                  0         0  \n",
              "34                  0         0  \n",
              "35                  0         0  \n",
              "36                  0         0  \n",
              "37                  0         0  \n",
              "38                  0         0  \n",
              "39                  0         0  \n",
              "40                  0         0  \n",
              "41                  0         0  \n",
              "42                  0         0  \n",
              "43                  0         0  \n",
              "44                  0         0  \n",
              "45                  0         0  \n",
              "46                  0         0  \n",
              "47                  0         0  \n",
              "48                  0         0  \n",
              "49                  0         0  \n",
              "50                  0         0  \n",
              "51                  0         0  \n",
              "52                  0         0  \n",
              "53                  0         0  \n",
              "54                  0         0  \n",
              "55                  0         0  \n",
              "56                  0         0  \n",
              "57                  0         0  "
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            "text/html": [
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Count Contraction</th>\n",
              "      <th>lenght of contraction</th>\n",
              "      <th>STD</th>\n",
              "      <th>Entropy</th>\n",
              "      <th>Contraction times</th>\n",
              "      <th>Pre-term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11055</td>\n",
              "      <td>218320</td>\n",
              "      <td>53231.010</td>\n",
              "      <td>1.860</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9118</td>\n",
              "      <td>222820</td>\n",
              "      <td>62367.488</td>\n",
              "      <td>1.580</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7925</td>\n",
              "      <td>13481</td>\n",
              "      <td>60503.050</td>\n",
              "      <td>2.067</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12451</td>\n",
              "      <td>17474</td>\n",
              "      <td>53628.078</td>\n",
              "      <td>1.731</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11152</td>\n",
              "      <td>218320</td>\n",
              "      <td>53317.910</td>\n",
              "      <td>1.857</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6029</td>\n",
              "      <td>63781</td>\n",
              "      <td>59177.965</td>\n",
              "      <td>1.701</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10052</td>\n",
              "      <td>22310</td>\n",
              "      <td>54431.030</td>\n",
              "      <td>1.790</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9101</td>\n",
              "      <td>219830</td>\n",
              "      <td>63467.583</td>\n",
              "      <td>1.490</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7929</td>\n",
              "      <td>13192</td>\n",
              "      <td>61503.160</td>\n",
              "      <td>2.067</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12452</td>\n",
              "      <td>16473</td>\n",
              "      <td>54678.091</td>\n",
              "      <td>1.701</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11121</td>\n",
              "      <td>228321</td>\n",
              "      <td>52901.880</td>\n",
              "      <td>1.777</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>6179</td>\n",
              "      <td>65782</td>\n",
              "      <td>62177.988</td>\n",
              "      <td>1.991</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2012</td>\n",
              "      <td>12483</td>\n",
              "      <td>32901.880</td>\n",
              "      <td>0.901</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1812</td>\n",
              "      <td>10489</td>\n",
              "      <td>30905.820</td>\n",
              "      <td>1.210</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1919</td>\n",
              "      <td>11297</td>\n",
              "      <td>31902.320</td>\n",
              "      <td>1.320</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2012</td>\n",
              "      <td>12483</td>\n",
              "      <td>32901.880</td>\n",
              "      <td>0.901</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1812</td>\n",
              "      <td>11481</td>\n",
              "      <td>29205.840</td>\n",
              "      <td>1.310</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1829</td>\n",
              "      <td>11296</td>\n",
              "      <td>31902.320</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2119</td>\n",
              "      <td>12298</td>\n",
              "      <td>32103.350</td>\n",
              "      <td>1.120</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>714</td>\n",
              "      <td>6185</td>\n",
              "      <td>57003.089</td>\n",
              "      <td>0.701</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>694</td>\n",
              "      <td>10226</td>\n",
              "      <td>40605.810</td>\n",
              "      <td>0.821</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>614</td>\n",
              "      <td>10143</td>\n",
              "      <td>49114.330</td>\n",
              "      <td>0.832</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>674</td>\n",
              "      <td>6185</td>\n",
              "      <td>42902.890</td>\n",
              "      <td>0.901</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>615</td>\n",
              "      <td>6388</td>\n",
              "      <td>49406.860</td>\n",
              "      <td>0.881</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>625</td>\n",
              "      <td>6351</td>\n",
              "      <td>41912.340</td>\n",
              "      <td>0.892</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>428</td>\n",
              "      <td>6283</td>\n",
              "      <td>39206.370</td>\n",
              "      <td>0.618</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>435</td>\n",
              "      <td>5308</td>\n",
              "      <td>47003.089</td>\n",
              "      <td>0.501</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>428</td>\n",
              "      <td>3388</td>\n",
              "      <td>39103.320</td>\n",
              "      <td>0.524</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>545</td>\n",
              "      <td>3308</td>\n",
              "      <td>39104.320</td>\n",
              "      <td>0.537</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>448</td>\n",
              "      <td>3315</td>\n",
              "      <td>39206.370</td>\n",
              "      <td>0.504</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>495</td>\n",
              "      <td>3328</td>\n",
              "      <td>47003.089</td>\n",
              "      <td>0.581</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>415</td>\n",
              "      <td>3355</td>\n",
              "      <td>41103.320</td>\n",
              "      <td>0.592</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>495</td>\n",
              "      <td>3348</td>\n",
              "      <td>42104.320</td>\n",
              "      <td>0.519</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>525</td>\n",
              "      <td>3608</td>\n",
              "      <td>49206.370</td>\n",
              "      <td>0.529</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>415</td>\n",
              "      <td>3309</td>\n",
              "      <td>47003.089</td>\n",
              "      <td>0.539</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>465</td>\n",
              "      <td>3238</td>\n",
              "      <td>52103.140</td>\n",
              "      <td>0.524</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>425</td>\n",
              "      <td>2308</td>\n",
              "      <td>51104.320</td>\n",
              "      <td>0.591</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>501</td>\n",
              "      <td>2308</td>\n",
              "      <td>54206.370</td>\n",
              "      <td>0.598</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>412</td>\n",
              "      <td>2308</td>\n",
              "      <td>51003.089</td>\n",
              "      <td>0.509</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>415</td>\n",
              "      <td>2308</td>\n",
              "      <td>59103.320</td>\n",
              "      <td>0.498</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>465</td>\n",
              "      <td>2358</td>\n",
              "      <td>49104.320</td>\n",
              "      <td>0.488</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>425</td>\n",
              "      <td>2333</td>\n",
              "      <td>52206.370</td>\n",
              "      <td>0.475</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>380</td>\n",
              "      <td>2334</td>\n",
              "      <td>57003.089</td>\n",
              "      <td>0.479</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>323</td>\n",
              "      <td>2339</td>\n",
              "      <td>49103.320</td>\n",
              "      <td>0.498</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>222</td>\n",
              "      <td>2675</td>\n",
              "      <td>59104.320</td>\n",
              "      <td>0.477</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>321</td>\n",
              "      <td>2456</td>\n",
              "      <td>59206.370</td>\n",
              "      <td>0.458</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>334</td>\n",
              "      <td>2682</td>\n",
              "      <td>57003.089</td>\n",
              "      <td>0.445</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>321</td>\n",
              "      <td>2901</td>\n",
              "      <td>49103.320</td>\n",
              "      <td>0.469</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>345</td>\n",
              "      <td>2932</td>\n",
              "      <td>51104.320</td>\n",
              "      <td>0.488</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>320</td>\n",
              "      <td>2963</td>\n",
              "      <td>43102.120</td>\n",
              "      <td>0.457</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>349</td>\n",
              "      <td>2333</td>\n",
              "      <td>58013.079</td>\n",
              "      <td>0.428</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>395</td>\n",
              "      <td>2334</td>\n",
              "      <td>49208.120</td>\n",
              "      <td>0.445</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>398</td>\n",
              "      <td>2339</td>\n",
              "      <td>51124.340</td>\n",
              "      <td>0.469</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>321</td>\n",
              "      <td>2675</td>\n",
              "      <td>46107.090</td>\n",
              "      <td>0.499</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>398</td>\n",
              "      <td>2339</td>\n",
              "      <td>51122.310</td>\n",
              "      <td>0.469</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>321</td>\n",
              "      <td>2675</td>\n",
              "      <td>46108.180</td>\n",
              "      <td>0.498</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>398</td>\n",
              "      <td>2336</td>\n",
              "      <td>51224.370</td>\n",
              "      <td>0.459</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>323</td>\n",
              "      <td>2641</td>\n",
              "      <td>46102.170</td>\n",
              "      <td>0.439</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d87f033-d5ce-49ad-b339-39246b869810')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6d87f033-d5ce-49ad-b339-39246b869810');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data set description\n",
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "wxscWZRryOPK",
        "outputId": "f256e730-25db-43e8-a0eb-cf4b5294be02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Count Contraction  lenght of contraction           STD    Entropy  \\\n",
              "count          58.000000              58.000000     58.000000  58.000000   \n",
              "mean         2503.810345           26621.965517  48564.968190   0.879759   \n",
              "std          3788.639864           61527.769917   8952.845551   0.528180   \n",
              "min           222.000000            2308.000000  29205.840000   0.428000   \n",
              "25%           398.000000            2649.500000  42303.962500   0.490500   \n",
              "50%           495.000000            3371.500000  49307.490000   0.586000   \n",
              "75%          1988.750000           12093.750000  54374.865000   1.187500   \n",
              "max         12452.000000          228321.000000  63467.583000   2.067000   \n",
              "\n",
              "       Contraction times   Pre-term  \n",
              "count          58.000000  58.000000  \n",
              "mean            0.637931   0.327586  \n",
              "std             0.809988   0.473432  \n",
              "min             0.000000   0.000000  \n",
              "25%             0.000000   0.000000  \n",
              "50%             0.000000   0.000000  \n",
              "75%             1.000000   1.000000  \n",
              "max             2.000000   1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f5e9edf-2c09-4c62-ba3f-967f329c4e4d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Count Contraction</th>\n",
              "      <th>lenght of contraction</th>\n",
              "      <th>STD</th>\n",
              "      <th>Entropy</th>\n",
              "      <th>Contraction times</th>\n",
              "      <th>Pre-term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2503.810345</td>\n",
              "      <td>26621.965517</td>\n",
              "      <td>48564.968190</td>\n",
              "      <td>0.879759</td>\n",
              "      <td>0.637931</td>\n",
              "      <td>0.327586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3788.639864</td>\n",
              "      <td>61527.769917</td>\n",
              "      <td>8952.845551</td>\n",
              "      <td>0.528180</td>\n",
              "      <td>0.809988</td>\n",
              "      <td>0.473432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>222.000000</td>\n",
              "      <td>2308.000000</td>\n",
              "      <td>29205.840000</td>\n",
              "      <td>0.428000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>398.000000</td>\n",
              "      <td>2649.500000</td>\n",
              "      <td>42303.962500</td>\n",
              "      <td>0.490500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>495.000000</td>\n",
              "      <td>3371.500000</td>\n",
              "      <td>49307.490000</td>\n",
              "      <td>0.586000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1988.750000</td>\n",
              "      <td>12093.750000</td>\n",
              "      <td>54374.865000</td>\n",
              "      <td>1.187500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>12452.000000</td>\n",
              "      <td>228321.000000</td>\n",
              "      <td>63467.583000</td>\n",
              "      <td>2.067000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f5e9edf-2c09-4c62-ba3f-967f329c4e4d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f5e9edf-2c09-4c62-ba3f-967f329c4e4d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1f5e9edf-2c09-4c62-ba3f-967f329c4e4d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgZy6ePb0QiI",
        "outputId": "3f334d32-cab6-45d1-bfe5-fc7eeb0cf8ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 58 entries, 0 to 57\n",
            "Data columns (total 6 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Count Contraction      58 non-null     int64  \n",
            " 1   lenght of contraction  58 non-null     int64  \n",
            " 2   STD                    58 non-null     float64\n",
            " 3   Entropy                58 non-null     float64\n",
            " 4   Contraction times      58 non-null     int64  \n",
            " 5   Pre-term               58 non-null     int64  \n",
            "dtypes: float64(2), int64(4)\n",
            "memory usage: 2.8 KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_UP0kol0Tlf",
        "outputId": "26854a29-af61-4248-aae8-72df9edc735c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count Contraction        0\n",
            "lenght of contraction    0\n",
            "STD                      0\n",
            "Entropy                  0\n",
            "Contraction times        0\n",
            "Pre-term                 0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "column_headers = list(df.columns)\n",
        "feature_len = len(column_headers)-1\n",
        "print('\\n')\n",
        "print('\\n Features name:\\n')\n",
        "print(column_headers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xA6EtQGw0YeL",
        "outputId": "7771cda7-2f5e-4893-a7d1-32815665ae91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            " Features name:\n",
            "\n",
            "['Count Contraction', 'lenght of contraction', 'STD', 'Entropy', 'Contraction times', 'Pre-term']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "IW-dhBmh0dUA",
        "outputId": "b8413b91-8490-48c4-d726-b1c097dde0c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Count Contraction  lenght of contraction           STD    Entropy  \\\n",
              "count          58.000000              58.000000     58.000000  58.000000   \n",
              "mean         2503.810345           26621.965517  48564.968190   0.879759   \n",
              "std          3788.639864           61527.769917   8952.845551   0.528180   \n",
              "min           222.000000            2308.000000  29205.840000   0.428000   \n",
              "25%           398.000000            2649.500000  42303.962500   0.490500   \n",
              "50%           495.000000            3371.500000  49307.490000   0.586000   \n",
              "75%          1988.750000           12093.750000  54374.865000   1.187500   \n",
              "max         12452.000000          228321.000000  63467.583000   2.067000   \n",
              "\n",
              "       Contraction times   Pre-term  \n",
              "count          58.000000  58.000000  \n",
              "mean            0.637931   0.327586  \n",
              "std             0.809988   0.473432  \n",
              "min             0.000000   0.000000  \n",
              "25%             0.000000   0.000000  \n",
              "50%             0.000000   0.000000  \n",
              "75%             1.000000   1.000000  \n",
              "max             2.000000   1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6370b2f-d3af-4b23-95fc-d5e6b0a4103b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Count Contraction</th>\n",
              "      <th>lenght of contraction</th>\n",
              "      <th>STD</th>\n",
              "      <th>Entropy</th>\n",
              "      <th>Contraction times</th>\n",
              "      <th>Pre-term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>58.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2503.810345</td>\n",
              "      <td>26621.965517</td>\n",
              "      <td>48564.968190</td>\n",
              "      <td>0.879759</td>\n",
              "      <td>0.637931</td>\n",
              "      <td>0.327586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3788.639864</td>\n",
              "      <td>61527.769917</td>\n",
              "      <td>8952.845551</td>\n",
              "      <td>0.528180</td>\n",
              "      <td>0.809988</td>\n",
              "      <td>0.473432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>222.000000</td>\n",
              "      <td>2308.000000</td>\n",
              "      <td>29205.840000</td>\n",
              "      <td>0.428000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>398.000000</td>\n",
              "      <td>2649.500000</td>\n",
              "      <td>42303.962500</td>\n",
              "      <td>0.490500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>495.000000</td>\n",
              "      <td>3371.500000</td>\n",
              "      <td>49307.490000</td>\n",
              "      <td>0.586000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1988.750000</td>\n",
              "      <td>12093.750000</td>\n",
              "      <td>54374.865000</td>\n",
              "      <td>1.187500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>12452.000000</td>\n",
              "      <td>228321.000000</td>\n",
              "      <td>63467.583000</td>\n",
              "      <td>2.067000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6370b2f-d3af-4b23-95fc-d5e6b0a4103b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6370b2f-d3af-4b23-95fc-d5e6b0a4103b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6370b2f-d3af-4b23-95fc-d5e6b0a4103b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "correlation_matrix = df.corr().round(2)\n",
        "sns.heatmap(data=correlation_matrix, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "HtKTtO0i0jM-",
        "outputId": "69eae9e4-18f6-4e0e-ca62-56608a5a0eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f011c897f70>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize =(5, 5))\n",
        "plt.boxplot(df)\n",
        "hist = df.hist(bins=3)\n",
        "# show plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "vF4om9M50yF1",
        "outputId": "fe79d09c-9e2e-4119-fb10-edf3f5e73b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<Datset normalization:\n",
        "\n",
        "# copy the data\n",
        "df_max_scaled = df.copy()\n",
        "  \n",
        "# apply normalization techniques\n",
        "for column in df_max_scaled.columns:\n",
        "    df_max_scaled[column] = df_max_scaled[column]/df_max_scaled[column].abs().max()\n",
        "      \n",
        "df_max_scaled.plot(kind = 'bar')\n",
        "df = df_max_scaled"
      ],
      "metadata": {
        "id": "Ajc65RBe0_v9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#----------------------------------------\n",
        "#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<Dataset seperation to X and Y\n",
        "\n",
        "X = df.iloc[:, :-1].values   \n",
        "Y = df.iloc[:, 5].values\n",
        "#-------------------------"
      ],
      "metadata": {
        "id": "N3HMzD8C1C-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<KNN machine learning model\n",
        "def knn(X,y):\n",
        "  #Create KNN Classifier\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.12, random_state=1) \n",
        "  knn = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "  #Train the model using the training sets\n",
        "  knn.fit(X_train, y_train)\n",
        "\n",
        "  #Predict the response for test dataset\n",
        "  y_pred = knn.predict(X_test)\n",
        "\n",
        "  print(\" Knn model accuracy(in %):\\n\",100*metrics.accuracy_score(y_test, y_pred))\n",
        "\n"
      ],
      "metadata": {
        "id": "hrbuScUa1Gn5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<TEST Feature selection by KNN\n",
        "\n",
        "print()\n",
        "df = shuffle(df)\n",
        "comparison = []\n",
        "\n",
        "\n",
        "knn(X,Y)\n"
      ],
      "metadata": {
        "id": "Aryv4wdn1HRh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Nuova sezione"
      ],
      "metadata": {
        "id": "EMtOSEddyYd6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "gddwIX2E1KDX"
      }
    }
  ]
}