main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42766 entries, 0 to 42765\n",
      "Data columns (total 73 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   nr. sessions                 42766 non-null  float64\n",
      " 1   total km                     42766 non-null  float64\n",
      " 2   km Z3-4                      42766 non-null  float64\n",
      " 3   km Z5-T1-T2                  42766 non-null  float64\n",
      " 4   km sprinting                 42766 non-null  float64\n",
      " 5   strength training            42766 non-null  float64\n",
      " 6   hours alternative            42766 non-null  float64\n",
      " 7   perceived exertion           42766 non-null  float64\n",
      " 8   perceived trainingSuccess    42766 non-null  float64\n",
      " 9   perceived recovery           42766 non-null  float64\n",
      " 10  nr. sessions.1               42766 non-null  float64\n",
      " 11  total km.1                   42766 non-null  float64\n",
      " 12  km Z3-4.1                    42766 non-null  float64\n",
      " 13  km Z5-T1-T2.1                42766 non-null  float64\n",
      " 14  km sprinting.1               42766 non-null  float64\n",
      " 15  strength training.1          42766 non-null  float64\n",
      " 16  hours alternative.1          42766 non-null  float64\n",
      " 17  perceived exertion.1         42766 non-null  float64\n",
      " 18  perceived trainingSuccess.1  42766 non-null  float64\n",
      " 19  perceived recovery.1         42766 non-null  float64\n",
      " 20  nr. sessions.2               42766 non-null  float64\n",
      " 21  total km.2                   42766 non-null  float64\n",
      " 22  km Z3-4.2                    42766 non-null  float64\n",
      " 23  km Z5-T1-T2.2                42766 non-null  float64\n",
      " 24  km sprinting.2               42766 non-null  float64\n",
      " 25  strength training.2          42766 non-null  float64\n",
      " 26  hours alternative.2          42766 non-null  float64\n",
      " 27  perceived exertion.2         42766 non-null  float64\n",
      " 28  perceived trainingSuccess.2  42766 non-null  float64\n",
      " 29  perceived recovery.2         42766 non-null  float64\n",
      " 30  nr. sessions.3               42766 non-null  float64\n",
      " 31  total km.3                   42766 non-null  float64\n",
      " 32  km Z3-4.3                    42766 non-null  float64\n",
      " 33  km Z5-T1-T2.3                42766 non-null  float64\n",
      " 34  km sprinting.3               42766 non-null  float64\n",
      " 35  strength training.3          42766 non-null  float64\n",
      " 36  hours alternative.3          42766 non-null  float64\n",
      " 37  perceived exertion.3         42766 non-null  float64\n",
      " 38  perceived trainingSuccess.3  42766 non-null  float64\n",
      " 39  perceived recovery.3         42766 non-null  float64\n",
      " 40  nr. sessions.4               42766 non-null  float64\n",
      " 41  total km.4                   42766 non-null  float64\n",
      " 42  km Z3-4.4                    42766 non-null  float64\n",
      " 43  km Z5-T1-T2.4                42766 non-null  float64\n",
      " 44  km sprinting.4               42766 non-null  float64\n",
      " 45  strength training.4          42766 non-null  float64\n",
      " 46  hours alternative.4          42766 non-null  float64\n",
      " 47  perceived exertion.4         42766 non-null  float64\n",
      " 48  perceived trainingSuccess.4  42766 non-null  float64\n",
      " 49  perceived recovery.4         42766 non-null  float64\n",
      " 50  nr. sessions.5               42766 non-null  float64\n",
      " 51  total km.5                   42766 non-null  float64\n",
      " 52  km Z3-4.5                    42766 non-null  float64\n",
      " 53  km Z5-T1-T2.5                42766 non-null  float64\n",
      " 54  km sprinting.5               42766 non-null  float64\n",
      " 55  strength training.5          42766 non-null  float64\n",
      " 56  hours alternative.5          42766 non-null  float64\n",
      " 57  perceived exertion.5         42766 non-null  float64\n",
      " 58  perceived trainingSuccess.5  42766 non-null  float64\n",
      " 59  perceived recovery.5         42766 non-null  float64\n",
      " 60  nr. sessions.6               42766 non-null  float64\n",
      " 61  total km.6                   42766 non-null  float64\n",
      " 62  km Z3-4.6                    42766 non-null  float64\n",
      " 63  km Z5-T1-T2.6                42766 non-null  float64\n",
      " 64  km sprinting.6               42766 non-null  float64\n",
      " 65  strength training.6          42766 non-null  float64\n",
      " 66  hours alternative.6          42766 non-null  float64\n",
      " 67  perceived exertion.6         42766 non-null  float64\n",
      " 68  perceived trainingSuccess.6  42766 non-null  float64\n",
      " 69  perceived recovery.6         42766 non-null  float64\n",
      " 70  Athlete ID                   42766 non-null  int64  \n",
      " 71  injury                       42766 non-null  int64  \n",
      " 72  Date                         42766 non-null  int64  \n",
      "dtypes: float64(70), int64(3)\n",
      "memory usage: 23.8 MB\n"
     ]
    }
   ],
   "source": [
    "day_ds = pd.read_csv('day_approach_maskedID_timeseries.csv')\n",
    "day_ds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr. sessions</th>\n",
       "      <th>total km</th>\n",
       "      <th>km Z3-4</th>\n",
       "      <th>km Z5-T1-T2</th>\n",
       "      <th>km sprinting</th>\n",
       "      <th>strength training</th>\n",
       "      <th>hours alternative</th>\n",
       "      <th>perceived exertion</th>\n",
       "      <th>perceived trainingSuccess</th>\n",
       "      <th>perceived recovery</th>\n",
       "      <th>...</th>\n",
       "      <th>km Z5-T1-T2.6</th>\n",
       "      <th>km sprinting.6</th>\n",
       "      <th>strength training.6</th>\n",
       "      <th>hours alternative.6</th>\n",
       "      <th>perceived exertion.6</th>\n",
       "      <th>perceived trainingSuccess.6</th>\n",
       "      <th>perceived recovery.6</th>\n",
       "      <th>Athlete ID</th>\n",
       "      <th>injury</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.8</td>\n",
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       "      <td>1.2</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    nr. sessions  total km  km Z3-4  km Z5-T1-T2  km sprinting  \\\n",
       "0            1.0       5.8      0.0          0.6           1.2   \n",
       "1            0.0       0.0      0.0          0.0           0.0   \n",
       "2            1.0       0.0      0.0          0.0           0.0   \n",
       "3            0.0       0.0      0.0          0.0           0.0   \n",
       "4            1.0       0.0      0.0          0.0           0.0   \n",
       "5            1.0      16.4     10.0          0.0           0.0   \n",
       "6            1.0       0.0      0.0          0.0           0.0   \n",
       "7            1.0       5.2      0.0          0.5           1.2   \n",
       "8            0.0       0.0      0.0          0.0           0.0   \n",
       "9            1.0       0.0      0.0          0.0           0.0   \n",
       "10           1.0      17.6      7.2          0.0           0.0   \n",
       "11           1.0       0.0      0.0          0.0           0.0   \n",
       "12           1.0      10.5      6.5          0.0           0.0   \n",
       "13           0.0       0.0      0.0          0.0           0.0   \n",
       "14           1.0       4.8      0.6          0.0           1.2   \n",
       "15           0.0       0.0      0.0          0.0           0.0   \n",
       "16           1.0       0.0      0.0          0.0           0.0   \n",
       "17           1.0      10.1      2.8          0.0           0.0   \n",
       "18           1.0       0.0      0.0          0.0           0.0   \n",
       "19           2.0       3.0      0.0          0.0           0.0   \n",
       "\n",
       "    strength training  hours alternative  perceived exertion  \\\n",
       "0                 0.0               0.00                0.11   \n",
       "1                 0.0               0.00               -0.01   \n",
       "2                 1.0               0.00                0.10   \n",
       "3                 0.0               0.00               -0.01   \n",
       "4                 0.0               1.08                0.08   \n",
       "5                 1.0               0.00                0.11   \n",
       "6                 0.0               1.00                0.10   \n",
       "7                 0.0               0.00                0.10   \n",
       "8                 0.0               0.00               -0.01   \n",
       "9                 1.0               0.00                0.10   \n",
       "10                0.0               0.00                0.11   \n",
       "11                0.0               1.00                0.08   \n",
       "12                0.0               0.00                0.09   \n",
       "13                0.0               0.00               -0.01   \n",
       "14                0.0               0.00                0.11   \n",
       "15                0.0               0.00               -0.01   \n",
       "16                1.0               0.00                0.10   \n",
       "17                0.0               0.00                0.11   \n",
       "18                0.0               1.00                0.09   \n",
       "19                1.0               0.50                0.08   \n",
       "\n",
       "    perceived trainingSuccess  perceived recovery  ...  km Z5-T1-T2.6  \\\n",
       "0                        0.00                0.18  ...            0.0   \n",
       "1                       -0.01               -0.01  ...            0.5   \n",
       "2                        0.00                0.17  ...            0.0   \n",
       "3                       -0.01               -0.01  ...            0.0   \n",
       "4                        0.00                0.18  ...            0.0   \n",
       "5                        0.00                0.17  ...            0.0   \n",
       "6                        0.00                0.15  ...            0.0   \n",
       "7                        0.00                0.17  ...            0.0   \n",
       "8                       -0.01               -0.01  ...            0.0   \n",
       "9                        0.00                0.17  ...            0.0   \n",
       "10                       0.00                0.17  ...            0.0   \n",
       "11                       0.00                0.17  ...            0.0   \n",
       "12                       0.00                0.18  ...            0.0   \n",
       "13                      -0.01               -0.01  ...            0.0   \n",
       "14                       0.00                0.18  ...            0.0   \n",
       "15                      -0.01               -0.01  ...            0.0   \n",
       "16                       0.00                0.17  ...            0.0   \n",
       "17                       0.00                0.16  ...            0.0   \n",
       "18                       0.00                0.16  ...            0.0   \n",
       "19                       0.00                0.17  ...            0.0   \n",
       "\n",
       "    km sprinting.6  strength training.6  hours alternative.6  \\\n",
       "0              0.0                  0.0                  1.0   \n",
       "1              1.2                  0.0                  0.0   \n",
       "2              0.0                  0.0                  0.0   \n",
       "3              0.0                  1.0                  0.0   \n",
       "4              0.0                  0.0                  0.0   \n",
       "5              0.0                  0.0                  1.0   \n",
       "6              0.0                  0.0                  0.0   \n",
       "7              0.0                  0.0                  0.0   \n",
       "8              1.2                  0.0                  0.0   \n",
       "9              0.0                  0.0                  0.0   \n",
       "10             0.0                  1.0                  0.0   \n",
       "11             0.0                  0.0                  0.0   \n",
       "12             0.0                  0.0                  1.0   \n",
       "13             0.0                  1.0                  0.5   \n",
       "14             0.0                  0.0                  1.0   \n",
       "15             1.0                  0.0                  0.0   \n",
       "16             0.0                  0.0                  0.0   \n",
       "17             0.0                  1.0                  0.0   \n",
       "18             0.0                  0.0                  0.0   \n",
       "19             0.0                  0.0                  1.0   \n",
       "\n",
       "    perceived exertion.6  perceived trainingSuccess.6  perceived recovery.6  \\\n",
       "0                   0.10                         0.00                  0.15   \n",
       "1                   0.10                         0.00                  0.17   \n",
       "2                  -0.01                        -0.01                 -0.01   \n",
       "3                   0.10                         0.00                  0.17   \n",
       "4                   0.11                         0.00                  0.17   \n",
       "5                   0.08                         0.00                  0.17   \n",
       "6                   0.09                         0.00                  0.18   \n",
       "7                  -0.01                        -0.01                 -0.01   \n",
       "8                   0.11                         0.00                  0.18   \n",
       "9                  -0.01                        -0.01                 -0.01   \n",
       "10                  0.10                         0.00                  0.17   \n",
       "11                  0.11                         0.00                  0.16   \n",
       "12                  0.09                         0.00                  0.16   \n",
       "13                  0.08                         0.00                  0.17   \n",
       "14                  0.08                         0.00                  0.18   \n",
       "15                  0.09                         0.00                  0.17   \n",
       "16                 -0.01                        -0.01                 -0.01   \n",
       "17                  0.11                         0.00                  0.18   \n",
       "18                 -0.01                        -0.01                 -0.01   \n",
       "19                  0.09                         0.00                  0.18   \n",
       "\n",
       "    Athlete ID  injury  Date  \n",
       "0            0       0     0  \n",
       "1            0       0     1  \n",
       "2            0       0     2  \n",
       "3            0       0     3  \n",
       "4            0       0     4  \n",
       "5            0       0     5  \n",
       "6            0       0     6  \n",
       "7            0       0     7  \n",
       "8            0       0     8  \n",
       "9            0       0     9  \n",
       "10           0       0    10  \n",
       "11           0       0    11  \n",
       "12           0       0    12  \n",
       "13           0       0    13  \n",
       "14           0       0    14  \n",
       "15           0       0    15  \n",
       "16           0       0    16  \n",
       "17           0       0    17  \n",
       "18           0       0    18  \n",
       "19           0       0    19  \n",
       "\n",
       "[20 rows x 73 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_ds.head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with high correlation with the target variable:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "perceived exertion             0.039748\n",
       "perceived exertion.4           0.037525\n",
       "perceived exertion.5           0.037154\n",
       "perceived exertion.2           0.036932\n",
       "perceived trainingSuccess.4    0.034931\n",
       "perceived trainingSuccess      0.034880\n",
       "perceived exertion.6           0.034781\n",
       "perceived exertion.3           0.033723\n",
       "perceived trainingSuccess.3    0.032156\n",
       "perceived recovery.4           0.031308\n",
       "Name: injury, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_variable = 'injury'\n",
    "\n",
    "# Compute correlation coefficients\n",
    "correlation_matrix = day_ds.corr()\n",
    "\n",
    "# Extract correlation coefficients with the target variable\n",
    "correlation_with_target = correlation_matrix[target_variable].drop(target_variable)\n",
    "\n",
    "# Sort the correlation coefficients in descending order\n",
    "correlation_with_target_sorted = correlation_with_target.abs().sort_values(ascending=False)\n",
    "\n",
    "# Print the features with high correlation with the target variable\n",
    "print(\"Features with high correlation with the target variable:\")\n",
    "correlation_with_target_sorted.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42798 entries, 0 to 42797\n",
      "Data columns (total 72 columns):\n",
      " #   Column                                         Non-Null Count  Dtype  \n",
      "---  ------                                         --------------  -----  \n",
      " 0   nr. sessions                                   42798 non-null  float64\n",
      " 1   nr. rest days                                  42798 non-null  float64\n",
      " 2   total kms                                      42798 non-null  float64\n",
      " 3   max km one day                                 42798 non-null  float64\n",
      " 4   total km Z3-Z4-Z5-T1-T2                        42798 non-null  float64\n",
      " 5   nr. tough sessions (effort in Z5, T1 or T2)    42798 non-null  float64\n",
      " 6   nr. days with interval session                 42798 non-null  float64\n",
      " 7   total km Z3-4                                  42798 non-null  float64\n",
      " 8   max km Z3-4 one day                            42798 non-null  float64\n",
      " 9   total km Z5-T1-T2                              42798 non-null  float64\n",
      " 10  max km Z5-T1-T2 one day                        42798 non-null  float64\n",
      " 11  total hours alternative training               42798 non-null  float64\n",
      " 12  nr. strength trainings                         42798 non-null  float64\n",
      " 13  avg exertion                                   42798 non-null  float64\n",
      " 14  min exertion                                   42798 non-null  float64\n",
      " 15  max exertion                                   42798 non-null  float64\n",
      " 16  avg training success                           42798 non-null  float64\n",
      " 17  min training success                           42798 non-null  float64\n",
      " 18  max training success                           42798 non-null  float64\n",
      " 19  avg recovery                                   42798 non-null  float64\n",
      " 20  min recovery                                   42798 non-null  float64\n",
      " 21  max recovery                                   42798 non-null  float64\n",
      " 22  nr. sessions.1                                 42798 non-null  float64\n",
      " 23  nr. rest days.1                                42798 non-null  float64\n",
      " 24  total kms.1                                    42798 non-null  float64\n",
      " 25  max km one day.1                               42798 non-null  float64\n",
      " 26  total km Z3-Z4-Z5-T1-T2.1                      42798 non-null  float64\n",
      " 27  nr. tough sessions (effort in Z5, T1 or T2).1  42798 non-null  float64\n",
      " 28  nr. days with interval session.1               42798 non-null  float64\n",
      " 29  total km Z3-4.1                                42798 non-null  float64\n",
      " 30  max km Z3-4 one day.1                          42798 non-null  float64\n",
      " 31  total km Z5-T1-T2.1                            42798 non-null  float64\n",
      " 32  max km Z5-T1-T2 one day.1                      42798 non-null  float64\n",
      " 33  total hours alternative training.1             42798 non-null  float64\n",
      " 34  nr. strength trainings.1                       42798 non-null  float64\n",
      " 35  avg exertion.1                                 42798 non-null  float64\n",
      " 36  min exertion.1                                 42798 non-null  float64\n",
      " 37  max exertion.1                                 42798 non-null  float64\n",
      " 38  avg training success.1                         42798 non-null  float64\n",
      " 39  min training success.1                         42798 non-null  float64\n",
      " 40  max training success.1                         42798 non-null  float64\n",
      " 41  avg recovery.1                                 42798 non-null  float64\n",
      " 42  min recovery.1                                 42798 non-null  float64\n",
      " 43  max recovery.1                                 42798 non-null  float64\n",
      " 44  nr. sessions.2                                 42798 non-null  float64\n",
      " 45  nr. rest days.2                                42798 non-null  float64\n",
      " 46  total kms.2                                    42798 non-null  float64\n",
      " 47  max km one day.2                               42798 non-null  float64\n",
      " 48  total km Z3-Z4-Z5-T1-T2.2                      42798 non-null  float64\n",
      " 49  nr. tough sessions (effort in Z5, T1 or T2).2  42798 non-null  float64\n",
      " 50  nr. days with interval session.2               42798 non-null  float64\n",
      " 51  total km Z3-4.2                                42798 non-null  float64\n",
      " 52  max km Z3-4 one day.2                          42798 non-null  float64\n",
      " 53  total km Z5-T1-T2.2                            42798 non-null  float64\n",
      " 54  max km Z5-T1-T2 one day.2                      42798 non-null  float64\n",
      " 55  total hours alternative training.2             42798 non-null  float64\n",
      " 56  nr. strength trainings.2                       42798 non-null  float64\n",
      " 57  avg exertion.2                                 42798 non-null  float64\n",
      " 58  min exertion.2                                 42798 non-null  float64\n",
      " 59  max exertion.2                                 42798 non-null  float64\n",
      " 60  avg training success.2                         42798 non-null  float64\n",
      " 61  min training success.2                         42798 non-null  float64\n",
      " 62  max training success.2                         42798 non-null  float64\n",
      " 63  avg recovery.2                                 42798 non-null  float64\n",
      " 64  min recovery.2                                 42798 non-null  float64\n",
      " 65  max recovery.2                                 42798 non-null  float64\n",
      " 66  Athlete ID                                     42798 non-null  int64  \n",
      " 67  injury                                         42798 non-null  int64  \n",
      " 68  rel total kms week 0_1                         42798 non-null  float64\n",
      " 69  rel total kms week 0_2                         42798 non-null  float64\n",
      " 70  rel total kms week 1_2                         42798 non-null  float64\n",
      " 71  Date                                           42798 non-null  int64  \n",
      "dtypes: float64(69), int64(3)\n",
      "memory usage: 23.5 MB\n"
     ]
    }
   ],
   "source": [
    "week_ds=pd.read_csv('week_approach_maskedID_timeseries.csv')\n",
    "week_ds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr. sessions</th>\n",
       "      <th>total km</th>\n",
       "      <th>km Z3-4</th>\n",
       "      <th>km Z5-T1-T2</th>\n",
       "      <th>km sprinting</th>\n",
       "      <th>strength training</th>\n",
       "      <th>hours alternative</th>\n",
       "      <th>perceived exertion</th>\n",
       "      <th>perceived trainingSuccess</th>\n",
       "      <th>perceived recovery</th>\n",
       "      <th>...</th>\n",
       "      <th>km Z5-T1-T2.6</th>\n",
       "      <th>km sprinting.6</th>\n",
       "      <th>strength training.6</th>\n",
       "      <th>hours alternative.6</th>\n",
       "      <th>perceived exertion.6</th>\n",
       "      <th>perceived trainingSuccess.6</th>\n",
       "      <th>perceived recovery.6</th>\n",
       "      <th>Athlete ID</th>\n",
       "      <th>injury</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "      <td>42766.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.829561</td>\n",
       "      <td>7.038187</td>\n",
       "      <td>0.691381</td>\n",
       "      <td>0.579930</td>\n",
       "      <td>0.073016</td>\n",
       "      <td>0.116237</td>\n",
       "      <td>0.163492</td>\n",
       "      <td>0.247788</td>\n",
       "      <td>0.349802</td>\n",
       "      <td>0.195898</td>\n",
       "      <td>...</td>\n",
       "      <td>0.580347</td>\n",
       "      <td>0.072595</td>\n",
       "      <td>0.116120</td>\n",
       "      <td>0.162308</td>\n",
       "      <td>0.247550</td>\n",
       "      <td>0.349503</td>\n",
       "      <td>0.196224</td>\n",
       "      <td>34.550858</td>\n",
       "      <td>0.013632</td>\n",
       "      <td>1228.039892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.580696</td>\n",
       "      <td>7.473216</td>\n",
       "      <td>2.317657</td>\n",
       "      <td>1.811938</td>\n",
       "      <td>0.483480</td>\n",
       "      <td>0.326010</td>\n",
       "      <td>0.549664</td>\n",
       "      <td>0.257262</td>\n",
       "      <td>0.368300</td>\n",
       "      <td>0.190321</td>\n",
       "      <td>...</td>\n",
       "      <td>1.814538</td>\n",
       "      <td>0.483691</td>\n",
       "      <td>0.326016</td>\n",
       "      <td>0.554031</td>\n",
       "      <td>0.256718</td>\n",
       "      <td>0.368042</td>\n",
       "      <td>0.190568</td>\n",
       "      <td>19.050033</td>\n",
       "      <td>0.115960</td>\n",
       "      <td>807.021168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>436.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>55.900000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2673.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nr. sessions      total km       km Z3-4   km Z5-T1-T2  km sprinting  \\\n",
       "count  42766.000000  42766.000000  42766.000000  42766.000000  42766.000000   \n",
       "mean       0.829561      7.038187      0.691381      0.579930      0.073016   \n",
       "std        0.580696      7.473216      2.317657      1.811938      0.483480   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        1.000000      6.000000      0.000000      0.000000      0.000000   \n",
       "75%        1.000000     12.000000      0.000000      0.000000      0.000000   \n",
       "max        2.000000     55.900000     42.200000     48.000000     40.000000   \n",
       "\n",
       "       strength training  hours alternative  perceived exertion  \\\n",
       "count       42766.000000       42766.000000        42766.000000   \n",
       "mean            0.116237           0.163492            0.247788   \n",
       "std             0.326010           0.549664            0.257262   \n",
       "min             0.000000           0.000000           -0.010000   \n",
       "25%             0.000000           0.000000           -0.010000   \n",
       "50%             0.000000           0.000000            0.160000   \n",
       "75%             0.000000           0.000000            0.440000   \n",
       "max             2.000000          10.220000            1.000000   \n",
       "\n",
       "       perceived trainingSuccess  perceived recovery  ...  km Z5-T1-T2.6  \\\n",
       "count               42766.000000        42766.000000  ...   42766.000000   \n",
       "mean                    0.349802            0.195898  ...       0.580347   \n",
       "std                     0.368300            0.190321  ...       1.814538   \n",
       "min                    -0.010000           -0.010000  ...       0.000000   \n",
       "25%                    -0.010000           -0.010000  ...       0.000000   \n",
       "50%                     0.260000            0.160000  ...       0.000000   \n",
       "75%                     0.720000            0.300000  ...       0.000000   \n",
       "max                     1.000000            1.000000  ...      48.000000   \n",
       "\n",
       "       km sprinting.6  strength training.6  hours alternative.6  \\\n",
       "count    42766.000000         42766.000000         42766.000000   \n",
       "mean         0.072595             0.116120             0.162308   \n",
       "std          0.483691             0.326016             0.554031   \n",
       "min          0.000000             0.000000             0.000000   \n",
       "25%          0.000000             0.000000             0.000000   \n",
       "50%          0.000000             0.000000             0.000000   \n",
       "75%          0.000000             0.000000             0.000000   \n",
       "max         40.000000             2.000000            20.000000   \n",
       "\n",
       "       perceived exertion.6  perceived trainingSuccess.6  \\\n",
       "count          42766.000000                 42766.000000   \n",
       "mean               0.247550                     0.349503   \n",
       "std                0.256718                     0.368042   \n",
       "min               -0.010000                    -0.010000   \n",
       "25%               -0.010000                    -0.010000   \n",
       "50%                0.160000                     0.260000   \n",
       "75%                0.440000                     0.720000   \n",
       "max                1.000000                     1.000000   \n",
       "\n",
       "       perceived recovery.6    Athlete ID        injury          Date  \n",
       "count          42766.000000  42766.000000  42766.000000  42766.000000  \n",
       "mean               0.196224     34.550858      0.013632   1228.039892  \n",
       "std                0.190568     19.050033      0.115960    807.021168  \n",
       "min               -0.010000      0.000000      0.000000      0.000000  \n",
       "25%               -0.010000     20.000000      0.000000    436.000000  \n",
       "50%                0.170000     34.000000      0.000000   1256.000000  \n",
       "75%                0.300000     50.000000      0.000000   1913.000000  \n",
       "max                1.000000     73.000000      1.000000   2673.000000  \n",
       "\n",
       "[8 rows x 73 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_ds.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>nr. sessions</th>\n",
       "      <th>nr. rest days</th>\n",
       "      <th>total kms</th>\n",
       "      <th>max km one day</th>\n",
       "      <th>total km Z3-Z4-Z5-T1-T2</th>\n",
       "      <th>nr. tough sessions (effort in Z5, T1 or T2)</th>\n",
       "      <th>nr. days with interval session</th>\n",
       "      <th>total km Z3-4</th>\n",
       "      <th>max km Z3-4 one day</th>\n",
       "      <th>total km Z5-T1-T2</th>\n",
       "      <th>...</th>\n",
       "      <th>max training success.2</th>\n",
       "      <th>avg recovery.2</th>\n",
       "      <th>min recovery.2</th>\n",
       "      <th>max recovery.2</th>\n",
       "      <th>Athlete ID</th>\n",
       "      <th>injury</th>\n",
       "      <th>rel total kms week 0_1</th>\n",
       "      <th>rel total kms week 0_2</th>\n",
       "      <th>rel total kms week 1_2</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>42798.000000</td>\n",
       "      <td>4.279800e+04</td>\n",
       "      <td>4.279800e+04</td>\n",
       "      <td>4.279800e+04</td>\n",
       "      <td>42798.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.809337</td>\n",
       "      <td>1.874667</td>\n",
       "      <td>49.543911</td>\n",
       "      <td>14.009255</td>\n",
       "      <td>9.433621</td>\n",
       "      <td>0.930184</td>\n",
       "      <td>1.672531</td>\n",
       "      <td>4.859398</td>\n",
       "      <td>3.456888</td>\n",
       "      <td>4.063970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.525709</td>\n",
       "      <td>0.255089</td>\n",
       "      <td>0.184927</td>\n",
       "      <td>0.343522</td>\n",
       "      <td>34.538249</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>4.408628e+05</td>\n",
       "      <td>9.014685e+05</td>\n",
       "      <td>4.803623e+05</td>\n",
       "      <td>1227.733422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.484234</td>\n",
       "      <td>1.853287</td>\n",
       "      <td>36.715017</td>\n",
       "      <td>9.071678</td>\n",
       "      <td>8.887120</td>\n",
       "      <td>1.040631</td>\n",
       "      <td>1.263528</td>\n",
       "      <td>6.984670</td>\n",
       "      <td>4.577423</td>\n",
       "      <td>5.645305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390604</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.127996</td>\n",
       "      <td>0.219536</td>\n",
       "      <td>19.020826</td>\n",
       "      <td>0.115130</td>\n",
       "      <td>4.328234e+06</td>\n",
       "      <td>6.945503e+06</td>\n",
       "      <td>4.656806e+06</td>\n",
       "      <td>806.495152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.955245e-01</td>\n",
       "      <td>6.723968e-01</td>\n",
       "      <td>6.923077e-01</td>\n",
       "      <td>437.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.800000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.891862e-01</td>\n",
       "      <td>1.001431e+00</td>\n",
       "      <td>9.897523e-01</td>\n",
       "      <td>1254.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>70.100000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.320515e+00</td>\n",
       "      <td>1.422171e+00</td>\n",
       "      <td>1.327586e+00</td>\n",
       "      <td>1913.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>79.800000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.096000e+08</td>\n",
       "      <td>2.176000e+08</td>\n",
       "      <td>2.096000e+08</td>\n",
       "      <td>2673.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nr. sessions  nr. rest days     total kms  max km one day  \\\n",
       "count  42798.000000   42798.000000  42798.000000    42798.000000   \n",
       "mean       5.809337       1.874667     49.543911       14.009255   \n",
       "std        2.484234       1.853287     36.715017        9.071678   \n",
       "min        0.000000       0.000000      0.000000        0.000000   \n",
       "25%        5.000000       1.000000     22.800000        9.000000   \n",
       "50%        6.000000       1.000000     44.800000       13.400000   \n",
       "75%        7.000000       3.000000     70.100000       18.300000   \n",
       "max       14.000000       7.000000    242.000000      131.000000   \n",
       "\n",
       "       total km Z3-Z4-Z5-T1-T2  nr. tough sessions (effort in Z5, T1 or T2)  \\\n",
       "count             42798.000000                                 42798.000000   \n",
       "mean                  9.433621                                     0.930184   \n",
       "std                   8.887120                                     1.040631   \n",
       "min                   0.000000                                     0.000000   \n",
       "25%                   1.000000                                     0.000000   \n",
       "50%                   8.000000                                     1.000000   \n",
       "75%                  14.600000                                     2.000000   \n",
       "max                 100.000000                                     6.000000   \n",
       "\n",
       "       nr. days with interval session  total km Z3-4  max km Z3-4 one day  \\\n",
       "count                    42798.000000   42798.000000         42798.000000   \n",
       "mean                         1.672531       4.859398             3.456888   \n",
       "std                          1.263528       6.984670             4.577423   \n",
       "min                          0.000000       0.000000             0.000000   \n",
       "25%                          0.000000       0.000000             0.000000   \n",
       "50%                          2.000000       0.000000             0.000000   \n",
       "75%                          3.000000       8.000000             6.300000   \n",
       "max                          7.000000      79.800000            75.000000   \n",
       "\n",
       "       total km Z5-T1-T2  ...  max training success.2  avg recovery.2  \\\n",
       "count       42798.000000  ...            42798.000000    42798.000000   \n",
       "mean            4.063970  ...                0.525709        0.255089   \n",
       "std             5.645305  ...                0.390604        0.153214   \n",
       "min             0.000000  ...                0.000000        0.000000   \n",
       "25%             0.000000  ...                0.000000        0.150000   \n",
       "50%             1.500000  ...                0.730000        0.220000   \n",
       "75%             6.300000  ...                0.840000        0.360000   \n",
       "max            80.000000  ...                1.000000        0.900000   \n",
       "\n",
       "       min recovery.2  max recovery.2    Athlete ID        injury  \\\n",
       "count    42798.000000    42798.000000  42798.000000  42798.000000   \n",
       "mean         0.184927        0.343522     34.538249      0.013435   \n",
       "std          0.127996        0.219536     19.020826      0.115130   \n",
       "min          0.000000        0.000000      0.000000      0.000000   \n",
       "25%          0.110000        0.170000     20.000000      0.000000   \n",
       "50%          0.160000        0.310000     34.000000      0.000000   \n",
       "75%          0.240000        0.520000     50.000000      0.000000   \n",
       "max          0.900000        1.000000     73.000000      1.000000   \n",
       "\n",
       "       rel total kms week 0_1  rel total kms week 0_2  rel total kms week 1_2  \\\n",
       "count            4.279800e+04            4.279800e+04            4.279800e+04   \n",
       "mean             4.408628e+05            9.014685e+05            4.803623e+05   \n",
       "std              4.328234e+06            6.945503e+06            4.656806e+06   \n",
       "min              0.000000e+00            0.000000e+00            0.000000e+00   \n",
       "25%              6.955245e-01            6.723968e-01            6.923077e-01   \n",
       "50%              9.891862e-01            1.001431e+00            9.897523e-01   \n",
       "75%              1.320515e+00            1.422171e+00            1.327586e+00   \n",
       "max              2.096000e+08            2.176000e+08            2.096000e+08   \n",
       "\n",
       "               Date  \n",
       "count  42798.000000  \n",
       "mean    1227.733422  \n",
       "std      806.495152  \n",
       "min        0.000000  \n",
       "25%      437.000000  \n",
       "50%     1254.000000  \n",
       "75%     1913.000000  \n",
       "max     2673.000000  \n",
       "\n",
       "[8 rows x 72 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_ds.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of two numerical variables\n",
    "\"\"\" plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x='max exertion', y='injury', data=week_ds)\n",
    "plt.title('Scatter Plot of Two Numerical Columns')\n",
    "plt.xlabel('max exertion')\n",
    "plt.ylabel('injury')\n",
    "plt.show()\n",
    " \"\"\"\n",
    "week_ds.plot(kind='hist', x='', y='max exertion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with high correlation with the target variable:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "max exertion                0.051565\n",
       "avg exertion                0.048771\n",
       "max exertion.1              0.045636\n",
       "avg exertion.1              0.043397\n",
       "max recovery                0.043163\n",
       "max exertion.2              0.040890\n",
       "avg exertion.2              0.038910\n",
       "max training success        0.038485\n",
       "max recovery.1              0.036539\n",
       "max training success.1      0.035923\n",
       "avg training success        0.035395\n",
       "max training success.2      0.034510\n",
       "max recovery.2              0.034062\n",
       "avg recovery                0.033640\n",
       "avg training success.1      0.033339\n",
       "nr. rest days               0.032846\n",
       "avg training success.2      0.031828\n",
       "avg recovery.1              0.030573\n",
       "nr. strength trainings.1    0.029773\n",
       "Date                        0.029498\n",
       "Name: injury, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute correlation coefficients\n",
    "correlation_matrix = week_ds.corr()\n",
    "\n",
    "# Extract correlation coefficients with the target variable\n",
    "correlation_with_target = correlation_matrix[target_variable].drop(target_variable)\n",
    "\n",
    "# Sort the correlation coefficients in descending order\n",
    "correlation_with_target_sorted = correlation_with_target.abs().sort_values(ascending=False)\n",
    "\n",
    "# Print the features with high correlation with the target variable\n",
    "print(\"Features with high correlation with the target variable:\")\n",
    "correlation_with_target_sorted.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>max exertion</th>\n",
       "      <th>injury</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>injury</th>\n",
       "      <td>0.051565</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max exertion</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051565</td>\n",
       "      <td>0.634115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg exertion</th>\n",
       "      <td>0.917739</td>\n",
       "      <td>0.048771</td>\n",
       "      <td>0.600287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max exertion.1</th>\n",
       "      <td>0.849678</td>\n",
       "      <td>0.045636</td>\n",
       "      <td>0.629762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg exertion.1</th>\n",
       "      <td>0.814261</td>\n",
       "      <td>0.043397</td>\n",
       "      <td>0.596485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max recovery</th>\n",
       "      <td>0.744581</td>\n",
       "      <td>0.043163</td>\n",
       "      <td>0.555967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max exertion.2</th>\n",
       "      <td>0.819560</td>\n",
       "      <td>0.040890</td>\n",
       "      <td>0.627481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg exertion.2</th>\n",
       "      <td>0.788268</td>\n",
       "      <td>0.038910</td>\n",
       "      <td>0.594640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max training success</th>\n",
       "      <td>0.824201</td>\n",
       "      <td>0.038485</td>\n",
       "      <td>0.725109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max recovery.1</th>\n",
       "      <td>0.684907</td>\n",
       "      <td>0.036539</td>\n",
       "      <td>0.555776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      max exertion    injury      Date\n",
       "injury                    0.051565  1.000000  0.029498\n",
       "max exertion              1.000000  0.051565  0.634115\n",
       "avg exertion              0.917739  0.048771  0.600287\n",
       "max exertion.1            0.849678  0.045636  0.629762\n",
       "avg exertion.1            0.814261  0.043397  0.596485\n",
       "max recovery              0.744581  0.043163  0.555967\n",
       "max exertion.2            0.819560  0.040890  0.627481\n",
       "avg exertion.2            0.788268  0.038910  0.594640\n",
       "max training success      0.824201  0.038485  0.725109\n",
       "max recovery.1            0.684907  0.036539  0.555776"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix[['max exertion', 'injury', 'Date']].abs().sort_values(by='injury', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='max exertion', ylabel='Date'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_ds.plot(kind='scatter', x='max exertion', y='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nr. sessions                   0\n",
       "total km                       0\n",
       "km Z3-4                        0\n",
       "km Z5-T1-T2                    0\n",
       "km sprinting                   0\n",
       "                              ..\n",
       "perceived trainingSuccess.6    0\n",
       "perceived recovery.6           0\n",
       "Athlete ID                     0\n",
       "injury                         0\n",
       "Date                           0\n",
       "Length: 73, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of missing data points per column\n",
    "missing_values_count = day_ds.isnull().sum()\n",
    "missing_values_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nr. sessions               0\n",
       "nr. rest days              0\n",
       "total kms                  0\n",
       "max km one day             0\n",
       "total km Z3-Z4-Z5-T1-T2    0\n",
       "                          ..\n",
       "injury                     0\n",
       "rel total kms week 0_1     0\n",
       "rel total kms week 0_2     0\n",
       "rel total kms week 1_2     0\n",
       "Date                       0\n",
       "Length: 72, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of missing data points per column\n",
    "missing_values_count = week_ds.isnull().sum()\n",
    "missing_values_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 424829093205019488\n",
      "xla_global_id: -1\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 5671747584\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 16164555492905696902\n",
      "physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 4070 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9\"\n",
      "xla_global_id: 416903419\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Athlete 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>310.0</td>\n",
       "      <td>157.122581</td>\n",
       "      <td>96.110160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.25</td>\n",
       "      <td>154.5</td>\n",
       "      <td>231.75</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>461.0</td>\n",
       "      <td>258.943601</td>\n",
       "      <td>192.768100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.00</td>\n",
       "      <td>230.0</td>\n",
       "      <td>345.00</td>\n",
       "      <td>765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1291.0</td>\n",
       "      <td>822.561580</td>\n",
       "      <td>484.371439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>368.50</td>\n",
       "      <td>887.0</td>\n",
       "      <td>1252.50</td>\n",
       "      <td>1624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>344.0</td>\n",
       "      <td>214.781977</td>\n",
       "      <td>162.391643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.75</td>\n",
       "      <td>171.5</td>\n",
       "      <td>343.25</td>\n",
       "      <td>547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>681.0</td>\n",
       "      <td>529.609398</td>\n",
       "      <td>410.207412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.00</td>\n",
       "      <td>383.0</td>\n",
       "      <td>921.00</td>\n",
       "      <td>1589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>313.0</td>\n",
       "      <td>2471.795527</td>\n",
       "      <td>119.808647</td>\n",
       "      <td>2121.0</td>\n",
       "      <td>2354.00</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>2574.00</td>\n",
       "      <td>2652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>327.0</td>\n",
       "      <td>2354.623853</td>\n",
       "      <td>141.605742</td>\n",
       "      <td>2101.0</td>\n",
       "      <td>2229.50</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>2450.50</td>\n",
       "      <td>2641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>392.0</td>\n",
       "      <td>2386.739796</td>\n",
       "      <td>150.178985</td>\n",
       "      <td>2101.0</td>\n",
       "      <td>2247.75</td>\n",
       "      <td>2388.5</td>\n",
       "      <td>2529.25</td>\n",
       "      <td>2673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>216.0</td>\n",
       "      <td>2544.500000</td>\n",
       "      <td>62.498000</td>\n",
       "      <td>2437.0</td>\n",
       "      <td>2490.75</td>\n",
       "      <td>2544.5</td>\n",
       "      <td>2598.25</td>\n",
       "      <td>2652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>192.0</td>\n",
       "      <td>2556.500000</td>\n",
       "      <td>55.569776</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>2508.75</td>\n",
       "      <td>2556.5</td>\n",
       "      <td>2604.25</td>\n",
       "      <td>2652.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date                                                             \\\n",
       "             count         mean         std     min      25%     50%      75%   \n",
       "Athlete ID                                                                      \n",
       "0            310.0   157.122581   96.110160     0.0    77.25   154.5   231.75   \n",
       "1            461.0   258.943601  192.768100     0.0   115.00   230.0   345.00   \n",
       "2           1291.0   822.561580  484.371439     0.0   368.50   887.0  1252.50   \n",
       "3            344.0   214.781977  162.391643     0.0    85.75   171.5   343.25   \n",
       "4            681.0   529.609398  410.207412     0.0   170.00   383.0   921.00   \n",
       "...            ...          ...         ...     ...      ...     ...      ...   \n",
       "69           313.0  2471.795527  119.808647  2121.0  2354.00  2496.0  2574.00   \n",
       "70           327.0  2354.623853  141.605742  2101.0  2229.50  2369.0  2450.50   \n",
       "71           392.0  2386.739796  150.178985  2101.0  2247.75  2388.5  2529.25   \n",
       "72           216.0  2544.500000   62.498000  2437.0  2490.75  2544.5  2598.25   \n",
       "73           192.0  2556.500000   55.569776  2461.0  2508.75  2556.5  2604.25   \n",
       "\n",
       "                    \n",
       "               max  \n",
       "Athlete ID          \n",
       "0            528.0  \n",
       "1            765.0  \n",
       "2           1624.0  \n",
       "3            547.0  \n",
       "4           1589.0  \n",
       "...            ...  \n",
       "69          2652.0  \n",
       "70          2641.0  \n",
       "71          2673.0  \n",
       "72          2652.0  \n",
       "73          2652.0  \n",
       "\n",
       "[74 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_ds[['Athlete ID', 'Date']].groupby('Athlete ID').describe() # looks like there are gaps in the Dates for all the athletes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_ds.pivot(index='Date', columns='Athlete ID', values='max exertion').plot(legend=False, figsize=[16, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>nr. sessions</th>\n",
       "      <th>nr. rest days</th>\n",
       "      <th>total kms</th>\n",
       "      <th>max km one day</th>\n",
       "      <th>total km Z3-Z4-Z5-T1-T2</th>\n",
       "      <th>nr. tough sessions (effort in Z5, T1 or T2)</th>\n",
       "      <th>nr. days with interval session</th>\n",
       "      <th>total km Z3-4</th>\n",
       "      <th>max km Z3-4 one day</th>\n",
       "      <th>total km Z5-T1-T2</th>\n",
       "      <th>...</th>\n",
       "      <th>max training success.2</th>\n",
       "      <th>avg recovery.2</th>\n",
       "      <th>min recovery.2</th>\n",
       "      <th>max recovery.2</th>\n",
       "      <th>Athlete ID</th>\n",
       "      <th>injury</th>\n",
       "      <th>rel total kms week 0_1</th>\n",
       "      <th>rel total kms week 0_2</th>\n",
       "      <th>rel total kms week 1_2</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42223</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.9</td>\n",
       "      <td>14.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.925729</td>\n",
       "      <td>9.432430</td>\n",
       "      <td>10.189186</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42224</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.629213</td>\n",
       "      <td>0.968300</td>\n",
       "      <td>1.538905</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42225</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>1.076923</td>\n",
       "      <td>2.538461</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42226</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.810997</td>\n",
       "      <td>1.053571</td>\n",
       "      <td>1.299107</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42227</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.794595</td>\n",
       "      <td>1.017301</td>\n",
       "      <td>1.280277</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42228</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.619318</td>\n",
       "      <td>0.823699</td>\n",
       "      <td>0.508671</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42229</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.570732</td>\n",
       "      <td>1.292818</td>\n",
       "      <td>2.265193</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42230</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>1.206186</td>\n",
       "      <td>0.938144</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42231</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.870229</td>\n",
       "      <td>1.488550</td>\n",
       "      <td>765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42232</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.054054</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.692307</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42233</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.615942</td>\n",
       "      <td>0.522113</td>\n",
       "      <td>0.847666</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42234</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.415584</td>\n",
       "      <td>1.211111</td>\n",
       "      <td>0.855556</td>\n",
       "      <td>1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42235</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.005291</td>\n",
       "      <td>0.860702</td>\n",
       "      <td>0.856172</td>\n",
       "      <td>1513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42236</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.384615</td>\n",
       "      <td>1.082207</td>\n",
       "      <td>0.453829</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42237</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>45.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.340708</td>\n",
       "      <td>2.890710</td>\n",
       "      <td>1.234973</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42238</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.572127</td>\n",
       "      <td>0.934593</td>\n",
       "      <td>0.594477</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42239</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>24.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896092</td>\n",
       "      <td>0.938124</td>\n",
       "      <td>1.046906</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42240</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.866782</td>\n",
       "      <td>0.889086</td>\n",
       "      <td>1.025732</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42241</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.971831</td>\n",
       "      <td>1.169492</td>\n",
       "      <td>1.203390</td>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42242</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.35</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.988506</td>\n",
       "      <td>1.036145</td>\n",
       "      <td>1.048193</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nr. sessions  nr. rest days  total kms  max km one day  \\\n",
       "42223           6.0            1.0       34.9            14.5   \n",
       "42224           6.0            1.0       33.6            15.2   \n",
       "42225           1.0            6.0        7.0             7.0   \n",
       "42226           7.0            0.0       47.2            12.5   \n",
       "42227           9.0            1.0       29.4             7.5   \n",
       "42228           7.0            0.0       28.5             6.9   \n",
       "42229           3.0            4.0       23.4            12.0   \n",
       "42230           6.0            1.0      117.0            22.0   \n",
       "42231           6.0            1.0      114.0            30.0   \n",
       "42232           6.0            2.0       78.0            24.0   \n",
       "42233           6.0            1.0       85.0            25.0   \n",
       "42234          10.0            1.0       54.5            18.0   \n",
       "42235           5.0            2.0       76.0            21.0   \n",
       "42236           8.0            0.0       96.1            21.0   \n",
       "42237           8.0            0.0      105.8            20.8   \n",
       "42238           8.0            0.0       64.3            17.1   \n",
       "42239           7.0            0.0       94.0            22.3   \n",
       "42240           8.0            0.0      100.2            22.2   \n",
       "42241           5.0            2.0       69.0            19.0   \n",
       "42242           6.0            1.0       86.0            19.0   \n",
       "\n",
       "       total km Z3-Z4-Z5-T1-T2  nr. tough sessions (effort in Z5, T1 or T2)  \\\n",
       "42223                     10.4                                          0.0   \n",
       "42224                     15.3                                          1.0   \n",
       "42225                      0.0                                          0.0   \n",
       "42226                     14.7                                          0.0   \n",
       "42227                      3.9                                          3.0   \n",
       "42228                     10.8                                          1.0   \n",
       "42229                      1.9                                          1.0   \n",
       "42230                     20.0                                          0.0   \n",
       "42231                     10.0                                          1.0   \n",
       "42232                     15.0                                          1.0   \n",
       "42233                     21.0                                          0.0   \n",
       "42234                     26.5                                          1.0   \n",
       "42235                     16.0                                          0.0   \n",
       "42236                     22.6                                          3.0   \n",
       "42237                     45.5                                          3.0   \n",
       "42238                     17.8                                          4.0   \n",
       "42239                     24.6                                          2.0   \n",
       "42240                     10.2                                          2.0   \n",
       "42241                     14.0                                          1.0   \n",
       "42242                     12.0                                          2.0   \n",
       "\n",
       "       nr. days with interval session  total km Z3-4  max km Z3-4 one day  \\\n",
       "42223                             2.0            9.5                  5.0   \n",
       "42224                             2.0           12.4                  6.4   \n",
       "42225                             0.0            0.0                  0.0   \n",
       "42226                             3.0           14.0                  6.0   \n",
       "42227                             3.0            0.0                  0.0   \n",
       "42228                             4.0           10.0                  4.5   \n",
       "42229                             2.0            1.5                  1.5   \n",
       "42230                             2.0           20.0                 11.0   \n",
       "42231                             1.0            0.0                  0.0   \n",
       "42232                             2.0            9.0                  9.0   \n",
       "42233                             1.0           21.0                 21.0   \n",
       "42234                             4.0           22.5                 10.0   \n",
       "42235                             2.0           16.0                 10.0   \n",
       "42236                             4.0            8.0                  8.0   \n",
       "42237                             6.0           19.9                  8.3   \n",
       "42238                             5.0           10.0                  7.0   \n",
       "42239                             3.0            3.3                  3.3   \n",
       "42240                             2.0            0.0                  0.0   \n",
       "42241                             2.0            6.0                  6.0   \n",
       "42242                             2.0            0.0                  0.0   \n",
       "\n",
       "       total km Z5-T1-T2  ...  max training success.2  avg recovery.2  \\\n",
       "42223                0.0  ...                    0.00            0.18   \n",
       "42224                1.8  ...                    0.00            0.18   \n",
       "42225                0.0  ...                    0.00            0.18   \n",
       "42226                0.0  ...                    0.00            0.17   \n",
       "42227                2.5  ...                    0.00            0.18   \n",
       "42228                0.8  ...                    0.00            0.17   \n",
       "42229                0.4  ...                    0.00            0.17   \n",
       "42230                0.0  ...                    0.80            0.35   \n",
       "42231               10.0  ...                    0.89            0.22   \n",
       "42232                6.0  ...                    0.63            0.36   \n",
       "42233                0.0  ...                    0.89            0.27   \n",
       "42234                4.0  ...                    0.79            0.28   \n",
       "42235                0.0  ...                    0.76            0.34   \n",
       "42236               12.4  ...                    0.00            0.10   \n",
       "42237               23.8  ...                    0.00            0.09   \n",
       "42238                5.8  ...                    0.00            0.10   \n",
       "42239               21.0  ...                    0.00            0.13   \n",
       "42240                9.3  ...                    0.00            0.13   \n",
       "42241                8.0  ...                    0.85            0.16   \n",
       "42242               12.0  ...                    0.71            0.26   \n",
       "\n",
       "       min recovery.2  max recovery.2  Athlete ID  injury  \\\n",
       "42223            0.17            0.18           0       1   \n",
       "42224            0.17            0.18           0       1   \n",
       "42225            0.18            0.18           0       1   \n",
       "42226            0.15            0.18           0       1   \n",
       "42227            0.17            0.18           0       1   \n",
       "42228            0.16            0.17           0       1   \n",
       "42229            0.17            0.18           0       1   \n",
       "42230            0.19            0.48           1       1   \n",
       "42231            0.06            0.46           1       1   \n",
       "42232            0.36            0.36           2       1   \n",
       "42233            0.21            0.35           2       1   \n",
       "42234            0.20            0.37           2       1   \n",
       "42235            0.27            0.48           2       1   \n",
       "42236            0.09            0.11           3       1   \n",
       "42237            0.08            0.10           3       1   \n",
       "42238            0.08            0.11           3       1   \n",
       "42239            0.12            0.14           4       1   \n",
       "42240            0.12            0.14           4       1   \n",
       "42241            0.13            0.22           4       1   \n",
       "42242            0.18            0.35           4       1   \n",
       "\n",
       "       rel total kms week 0_1  rel total kms week 0_2  rel total kms week 1_2  \\\n",
       "42223                0.925729                9.432430               10.189186   \n",
       "42224                0.629213                0.968300                1.538905   \n",
       "42225                0.424242                1.076923                2.538461   \n",
       "42226                0.810997                1.053571                1.299107   \n",
       "42227                0.794595                1.017301                1.280277   \n",
       "42228                1.619318                0.823699                0.508671   \n",
       "42229                0.570732                1.292818                2.265193   \n",
       "42230                1.285714                1.206186                0.938144   \n",
       "42231                0.584615                0.870229                1.488550   \n",
       "42232                1.054054                6.000000                5.692307   \n",
       "42233                0.615942                0.522113                0.847666   \n",
       "42234                1.415584                1.211111                0.855556   \n",
       "42235                1.005291                0.860702                0.856172   \n",
       "42236                2.384615                1.082207                0.453829   \n",
       "42237                2.340708                2.890710                1.234973   \n",
       "42238                1.572127                0.934593                0.594477   \n",
       "42239                0.896092                0.938124                1.046906   \n",
       "42240                0.866782                0.889086                1.025732   \n",
       "42241                0.971831                1.169492                1.203390   \n",
       "42242                0.988506                1.036145                1.048193   \n",
       "\n",
       "       Date  \n",
       "42223   324  \n",
       "42224   353  \n",
       "42225   386  \n",
       "42226   413  \n",
       "42227   461  \n",
       "42228   490  \n",
       "42229   528  \n",
       "42230   672  \n",
       "42231   765  \n",
       "42232   680  \n",
       "42233   710  \n",
       "42234  1201  \n",
       "42235  1513  \n",
       "42236   310  \n",
       "42237   401  \n",
       "42238   476  \n",
       "42239   303  \n",
       "42240   455  \n",
       "42241   722  \n",
       "42242   747  \n",
       "\n",
       "[20 rows x 72 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_ds[(week_ds['injury']>0)].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n",
      "Epoch 1/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9701 - loss: 0.1511 - val_accuracy: 0.9862 - val_loss: 0.0736\n",
      "Epoch 2/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 770us/step - accuracy: 0.9861 - loss: 0.0721 - val_accuracy: 0.9862 - val_loss: 0.0736\n",
      "Epoch 3/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 754us/step - accuracy: 0.9867 - loss: 0.0661 - val_accuracy: 0.9862 - val_loss: 0.0724\n",
      "Epoch 4/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 806us/step - accuracy: 0.9874 - loss: 0.0614 - val_accuracy: 0.9862 - val_loss: 0.0731\n",
      "Epoch 5/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 838us/step - accuracy: 0.9872 - loss: 0.0605 - val_accuracy: 0.9862 - val_loss: 0.0746\n",
      "Epoch 6/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 865us/step - accuracy: 0.9868 - loss: 0.0599 - val_accuracy: 0.9862 - val_loss: 0.0741\n",
      "Epoch 7/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 825us/step - accuracy: 0.9875 - loss: 0.0551 - val_accuracy: 0.9862 - val_loss: 0.0741\n",
      "Epoch 8/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 827us/step - accuracy: 0.9876 - loss: 0.0533 - val_accuracy: 0.9862 - val_loss: 0.0741\n",
      "Epoch 9/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 809us/step - accuracy: 0.9865 - loss: 0.0544 - val_accuracy: 0.9862 - val_loss: 0.0745\n",
      "Epoch 10/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 775us/step - accuracy: 0.9877 - loss: 0.0507 - val_accuracy: 0.9862 - val_loss: 0.0742\n",
      "Epoch 11/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 819us/step - accuracy: 0.9875 - loss: 0.0482 - val_accuracy: 0.9862 - val_loss: 0.0741\n",
      "Epoch 12/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 825us/step - accuracy: 0.9879 - loss: 0.0446 - val_accuracy: 0.9862 - val_loss: 0.0752\n",
      "Epoch 13/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 828us/step - accuracy: 0.9879 - loss: 0.0436 - val_accuracy: 0.9862 - val_loss: 0.0752\n",
      "Epoch 14/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 809us/step - accuracy: 0.9886 - loss: 0.0413 - val_accuracy: 0.9862 - val_loss: 0.0765\n",
      "Epoch 15/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 847us/step - accuracy: 0.9884 - loss: 0.0410 - val_accuracy: 0.9862 - val_loss: 0.0781\n",
      "Epoch 16/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 888us/step - accuracy: 0.9891 - loss: 0.0400 - val_accuracy: 0.9862 - val_loss: 0.0785\n",
      "Epoch 17/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 910us/step - accuracy: 0.9893 - loss: 0.0365 - val_accuracy: 0.9860 - val_loss: 0.0778\n",
      "Epoch 18/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9905 - loss: 0.0321 - val_accuracy: 0.9862 - val_loss: 0.0783\n",
      "Epoch 19/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9902 - loss: 0.0332 - val_accuracy: 0.9862 - val_loss: 0.0801\n",
      "Epoch 20/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9920 - loss: 0.0278 - val_accuracy: 0.9860 - val_loss: 0.0810\n",
      "Epoch 21/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9914 - loss: 0.0285 - val_accuracy: 0.9862 - val_loss: 0.0824\n",
      "Epoch 22/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9924 - loss: 0.0271 - val_accuracy: 0.9862 - val_loss: 0.0818\n",
      "Epoch 23/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9923 - loss: 0.0260 - val_accuracy: 0.9862 - val_loss: 0.0826\n",
      "Epoch 24/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9935 - loss: 0.0233 - val_accuracy: 0.9860 - val_loss: 0.0851\n",
      "Epoch 25/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 989us/step - accuracy: 0.9939 - loss: 0.0218 - val_accuracy: 0.9853 - val_loss: 0.0876\n",
      "Epoch 26/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - accuracy: 0.9947 - loss: 0.0196 - val_accuracy: 0.9862 - val_loss: 0.0889\n",
      "Epoch 27/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9950 - loss: 0.0185 - val_accuracy: 0.9857 - val_loss: 0.0897\n",
      "Epoch 28/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 979us/step - accuracy: 0.9953 - loss: 0.0167 - val_accuracy: 0.9862 - val_loss: 0.0880\n",
      "Epoch 29/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9961 - loss: 0.0146 - val_accuracy: 0.9860 - val_loss: 0.0921\n",
      "Epoch 30/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9960 - loss: 0.0152 - val_accuracy: 0.9860 - val_loss: 0.0923\n",
      "Epoch 31/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9967 - loss: 0.0136 - val_accuracy: 0.9860 - val_loss: 0.0957\n",
      "Epoch 32/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9967 - loss: 0.0136 - val_accuracy: 0.9862 - val_loss: 0.1015\n",
      "Epoch 33/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9967 - loss: 0.0124 - val_accuracy: 0.9850 - val_loss: 0.0970\n",
      "Epoch 34/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9974 - loss: 0.0107 - val_accuracy: 0.9855 - val_loss: 0.0974\n",
      "Epoch 35/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 969us/step - accuracy: 0.9978 - loss: 0.0102 - val_accuracy: 0.9855 - val_loss: 0.1007\n",
      "Epoch 36/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 843us/step - accuracy: 0.9979 - loss: 0.0095 - val_accuracy: 0.9855 - val_loss: 0.1006\n",
      "Epoch 37/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 984us/step - accuracy: 0.9981 - loss: 0.0090 - val_accuracy: 0.9860 - val_loss: 0.1045\n",
      "Epoch 38/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 904us/step - accuracy: 0.9980 - loss: 0.0085 - val_accuracy: 0.9857 - val_loss: 0.1065\n",
      "Epoch 39/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 919us/step - accuracy: 0.9984 - loss: 0.0075 - val_accuracy: 0.9862 - val_loss: 0.1082\n",
      "Epoch 40/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 894us/step - accuracy: 0.9982 - loss: 0.0076 - val_accuracy: 0.9862 - val_loss: 0.1116\n",
      "Epoch 41/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - accuracy: 0.9983 - loss: 0.0070 - val_accuracy: 0.9860 - val_loss: 0.1081\n",
      "Epoch 42/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 909us/step - accuracy: 0.9989 - loss: 0.0058 - val_accuracy: 0.9857 - val_loss: 0.1119\n",
      "Epoch 43/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 873us/step - accuracy: 0.9987 - loss: 0.0062 - val_accuracy: 0.9860 - val_loss: 0.1174\n",
      "Epoch 44/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 898us/step - accuracy: 0.9989 - loss: 0.0053 - val_accuracy: 0.9857 - val_loss: 0.1176\n",
      "Epoch 45/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 884us/step - accuracy: 0.9991 - loss: 0.0045 - val_accuracy: 0.9857 - val_loss: 0.1191\n",
      "Epoch 46/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 832us/step - accuracy: 0.9993 - loss: 0.0045 - val_accuracy: 0.9862 - val_loss: 0.1201\n",
      "Epoch 47/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 828us/step - accuracy: 0.9992 - loss: 0.0041 - val_accuracy: 0.9860 - val_loss: 0.1188\n",
      "Epoch 48/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 856us/step - accuracy: 0.9993 - loss: 0.0037 - val_accuracy: 0.9857 - val_loss: 0.1217\n",
      "Epoch 49/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 836us/step - accuracy: 0.9992 - loss: 0.0033 - val_accuracy: 0.9850 - val_loss: 0.1211\n",
      "Epoch 50/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 883us/step - accuracy: 0.9992 - loss: 0.0032 - val_accuracy: 0.9860 - val_loss: 0.1260\n",
      "Epoch 51/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 864us/step - accuracy: 0.9995 - loss: 0.0029 - val_accuracy: 0.9860 - val_loss: 0.1306\n",
      "Epoch 52/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - accuracy: 0.9996 - loss: 0.0027 - val_accuracy: 0.9862 - val_loss: 0.1289\n",
      "Epoch 53/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 889us/step - accuracy: 0.9997 - loss: 0.0028 - val_accuracy: 0.9862 - val_loss: 0.1333\n",
      "Epoch 54/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 885us/step - accuracy: 0.9997 - loss: 0.0022 - val_accuracy: 0.9860 - val_loss: 0.1404\n",
      "Epoch 55/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 845us/step - accuracy: 0.9997 - loss: 0.0023 - val_accuracy: 0.9860 - val_loss: 0.1381\n",
      "Epoch 56/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 863us/step - accuracy: 0.9996 - loss: 0.0026 - val_accuracy: 0.9857 - val_loss: 0.1429\n",
      "Epoch 57/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 855us/step - accuracy: 0.9998 - loss: 0.0020 - val_accuracy: 0.9855 - val_loss: 0.1352\n",
      "Epoch 58/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 887us/step - accuracy: 0.9997 - loss: 0.0023 - val_accuracy: 0.9862 - val_loss: 0.1466\n",
      "Epoch 59/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 845us/step - accuracy: 0.9997 - loss: 0.0015 - val_accuracy: 0.9857 - val_loss: 0.1450\n",
      "Epoch 60/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 875us/step - accuracy: 0.9998 - loss: 0.0015 - val_accuracy: 0.9860 - val_loss: 0.1501\n",
      "Epoch 61/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 900us/step - accuracy: 0.9997 - loss: 0.0015 - val_accuracy: 0.9857 - val_loss: 0.1497\n",
      "Epoch 62/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 894us/step - accuracy: 0.9997 - loss: 0.0017 - val_accuracy: 0.9860 - val_loss: 0.1531\n",
      "Epoch 63/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 935us/step - accuracy: 0.9999 - loss: 0.0013 - val_accuracy: 0.9860 - val_loss: 0.1537\n",
      "Epoch 64/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 961us/step - accuracy: 0.9999 - loss: 0.0012 - val_accuracy: 0.9860 - val_loss: 0.1522\n",
      "Epoch 65/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9999 - loss: 0.0013 - val_accuracy: 0.9857 - val_loss: 0.1647\n",
      "Epoch 66/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 983us/step - accuracy: 0.9999 - loss: 0.0011 - val_accuracy: 0.9860 - val_loss: 0.1602\n",
      "Epoch 67/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 943us/step - accuracy: 0.9999 - loss: 0.0012 - val_accuracy: 0.9862 - val_loss: 0.1633\n",
      "Epoch 68/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9999 - loss: 8.2978e-04 - val_accuracy: 0.9855 - val_loss: 0.1617\n",
      "Epoch 69/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9999 - loss: 0.0011 - val_accuracy: 0.9862 - val_loss: 0.1643\n",
      "Epoch 70/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9998 - loss: 0.0013 - val_accuracy: 0.9857 - val_loss: 0.1682\n",
      "Epoch 71/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 7.5244e-04 - val_accuracy: 0.9855 - val_loss: 0.1630\n",
      "Epoch 72/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 9.0342e-04 - val_accuracy: 0.9860 - val_loss: 0.1674\n",
      "Epoch 73/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 1.0000 - loss: 5.3284e-04 - val_accuracy: 0.9860 - val_loss: 0.1675\n",
      "Epoch 74/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9999 - loss: 0.0012 - val_accuracy: 0.9862 - val_loss: 0.1774\n",
      "Epoch 75/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.9998 - loss: 0.0015 - val_accuracy: 0.9857 - val_loss: 0.1806\n",
      "Epoch 76/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9999 - loss: 8.6508e-04 - val_accuracy: 0.9860 - val_loss: 0.1788\n",
      "Epoch 77/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9998 - loss: 0.0012 - val_accuracy: 0.9860 - val_loss: 0.1808\n",
      "Epoch 78/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.9999 - loss: 5.0692e-04 - val_accuracy: 0.9860 - val_loss: 0.1808\n",
      "Epoch 79/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9998 - loss: 8.0227e-04 - val_accuracy: 0.9860 - val_loss: 0.1840\n",
      "Epoch 80/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9998 - loss: 0.0010 - val_accuracy: 0.9857 - val_loss: 0.1807\n",
      "Epoch 81/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 808us/step - accuracy: 0.9999 - loss: 8.5654e-04 - val_accuracy: 0.9862 - val_loss: 0.1911\n",
      "Epoch 82/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 785us/step - accuracy: 0.9999 - loss: 9.7021e-04 - val_accuracy: 0.9848 - val_loss: 0.1771\n",
      "Epoch 83/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 823us/step - accuracy: 0.9997 - loss: 0.0011 - val_accuracy: 0.9853 - val_loss: 0.1852\n",
      "Epoch 84/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 773us/step - accuracy: 0.9998 - loss: 9.1053e-04 - val_accuracy: 0.9857 - val_loss: 0.1928\n",
      "Epoch 85/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 786us/step - accuracy: 0.9998 - loss: 8.6314e-04 - val_accuracy: 0.9853 - val_loss: 0.1919\n",
      "Epoch 86/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 798us/step - accuracy: 0.9998 - loss: 0.0016 - val_accuracy: 0.9855 - val_loss: 0.1911\n",
      "Epoch 87/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 781us/step - accuracy: 0.9999 - loss: 8.5735e-04 - val_accuracy: 0.9860 - val_loss: 0.1915\n",
      "Epoch 88/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 796us/step - accuracy: 0.9998 - loss: 5.5531e-04 - val_accuracy: 0.9857 - val_loss: 0.1925\n",
      "Epoch 89/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 899us/step - accuracy: 0.9999 - loss: 6.0976e-04 - val_accuracy: 0.9857 - val_loss: 0.1964\n",
      "Epoch 90/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 965us/step - accuracy: 0.9999 - loss: 5.8251e-04 - val_accuracy: 0.9855 - val_loss: 0.1944\n",
      "Epoch 91/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 862us/step - accuracy: 1.0000 - loss: 4.9445e-04 - val_accuracy: 0.9857 - val_loss: 0.1943\n",
      "Epoch 92/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 860us/step - accuracy: 0.9998 - loss: 0.0012 - val_accuracy: 0.9857 - val_loss: 0.1985\n",
      "Epoch 93/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 881us/step - accuracy: 0.9999 - loss: 6.9883e-04 - val_accuracy: 0.9853 - val_loss: 0.1979\n",
      "Epoch 94/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 964us/step - accuracy: 0.9999 - loss: 4.9644e-04 - val_accuracy: 0.9857 - val_loss: 0.2104\n",
      "Epoch 95/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 996us/step - accuracy: 0.9999 - loss: 5.8472e-04 - val_accuracy: 0.9857 - val_loss: 0.2028\n",
      "Epoch 96/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 964us/step - accuracy: 0.9999 - loss: 5.0816e-04 - val_accuracy: 0.9848 - val_loss: 0.2082\n",
      "Epoch 97/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 885us/step - accuracy: 0.9999 - loss: 7.0595e-04 - val_accuracy: 0.9857 - val_loss: 0.2091\n",
      "Epoch 98/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 970us/step - accuracy: 0.9999 - loss: 8.4281e-04 - val_accuracy: 0.9860 - val_loss: 0.2097\n",
      "Epoch 99/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 954us/step - accuracy: 0.9999 - loss: 0.0011 - val_accuracy: 0.9855 - val_loss: 0.2098\n",
      "Epoch 100/100\n",
      "\u001b[1m1070/1070\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.9999 - loss: 6.9767e-04 - val_accuracy: 0.9855 - val_loss: 0.2133\n",
      "\u001b[1m134/134\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9851 - loss: 0.2116\n",
      "Test Loss: 0.19660407304763794\n",
      "Test Accuracy: 0.9855140447616577\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n",
    "\n",
    "week_ds=pd.read_csv('week_approach_maskedID_timeseries.csv')\n",
    "# Load the dataset from CSV and sort it by Date then Athlete ID\n",
    "df = week_ds.sort_values(by=['Date', 'Athlete ID'])\n",
    "\n",
    "# Use all features except 'injury' as input\n",
    "input_data = df.drop(columns=['injury']).values\n",
    "\n",
    "# Normalize the input data using z score scaling (optional but recommended)\n",
    "input_data = (input_data - np.mean(input_data, axis=0)) / np.std(input_data, axis=0)\n",
    "\n",
    "# Use 'injury' column as the target variable y\n",
    "y = df['injury'].values\n",
    "\n",
    "# Reshape the input data for LSTM (batches, timesteps, features)\n",
    "x_tf = input_data.reshape(input_data.shape[0], 1, input_data.shape[1])\n",
    "y_tf = y\n",
    "\n",
    "# Split the data into training, validation, and test sets\n",
    "x_train, x_temp, y_train, y_temp = train_test_split(x_tf, y_tf, test_size=0.2, random_state=42)\n",
    "x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Define and compile the LSTM model\n",
    "model = Sequential([\n",
    "    LSTM(units=64), # input_shape=(x_train.shape[1], x_train.shape[2])\n",
    "    Dense(units=1, activation='sigmoid')  # Output layer for binary classification (sigmoid activation)\n",
    "])\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x=x_train, y=y_train, validation_data=(x_val, y_val), epochs=100, batch_size=32, verbose=1)\n",
    "\n",
    "# Evaluate the model on test set\n",
    "loss, accuracy = model.evaluate(x_test, y_test)\n",
    "print(f'Test Loss: {loss}')\n",
    "print(f'Test Accuracy: {accuracy}')\n",
    "\n",
    "# Save the trained model\n",
    "model.save('trained_model.keras')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n",
      "Epoch 1/100\n",
      "1070/1070 [==============================] - 1s 985us/step - loss: 0.0811 - accuracy: 0.9858 - val_loss: 0.0635 - val_accuracy: 0.9886\n",
      "Epoch 2/100\n",
      "1070/1070 [==============================] - 1s 925us/step - loss: 0.0718 - accuracy: 0.9863 - val_loss: 0.0649 - val_accuracy: 0.9886\n",
      "Epoch 3/100\n",
      "1070/1070 [==============================] - 1s 943us/step - loss: 0.0699 - accuracy: 0.9863 - val_loss: 0.0638 - val_accuracy: 0.9886\n",
      "Epoch 4/100\n",
      "1070/1070 [==============================] - 1s 947us/step - loss: 0.0685 - accuracy: 0.9863 - val_loss: 0.0622 - val_accuracy: 0.9886\n",
      "Epoch 5/100\n",
      "1070/1070 [==============================] - 1s 980us/step - loss: 0.0669 - accuracy: 0.9864 - val_loss: 0.0623 - val_accuracy: 0.9886\n",
      "Epoch 6/100\n",
      "1070/1070 [==============================] - 1s 965us/step - loss: 0.0660 - accuracy: 0.9863 - val_loss: 0.0638 - val_accuracy: 0.9886\n",
      "Epoch 7/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0648 - accuracy: 0.9863 - val_loss: 0.0636 - val_accuracy: 0.9886\n",
      "Epoch 8/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0637 - accuracy: 0.9863 - val_loss: 0.0635 - val_accuracy: 0.9886\n",
      "Epoch 9/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0626 - accuracy: 0.9864 - val_loss: 0.0632 - val_accuracy: 0.9886\n",
      "Epoch 10/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0616 - accuracy: 0.9864 - val_loss: 0.0642 - val_accuracy: 0.9883\n",
      "Epoch 11/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0608 - accuracy: 0.9864 - val_loss: 0.0636 - val_accuracy: 0.9886\n",
      "Epoch 12/100\n",
      "1070/1070 [==============================] - 1s 995us/step - loss: 0.0597 - accuracy: 0.9864 - val_loss: 0.0653 - val_accuracy: 0.9886\n",
      "Epoch 13/100\n",
      "1070/1070 [==============================] - 3s 3ms/step - loss: 0.0584 - accuracy: 0.9864 - val_loss: 0.0702 - val_accuracy: 0.9876\n",
      "Epoch 14/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0574 - accuracy: 0.9864 - val_loss: 0.0650 - val_accuracy: 0.9886\n",
      "Epoch 15/100\n",
      "1070/1070 [==============================] - 5s 4ms/step - loss: 0.0563 - accuracy: 0.9866 - val_loss: 0.0656 - val_accuracy: 0.9883\n",
      "Epoch 16/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0556 - accuracy: 0.9866 - val_loss: 0.0672 - val_accuracy: 0.9886\n",
      "Epoch 17/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0547 - accuracy: 0.9867 - val_loss: 0.0678 - val_accuracy: 0.9886\n",
      "Epoch 18/100\n",
      "1070/1070 [==============================] - 5s 4ms/step - loss: 0.0538 - accuracy: 0.9867 - val_loss: 0.0697 - val_accuracy: 0.9886\n",
      "Epoch 19/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0531 - accuracy: 0.9867 - val_loss: 0.0701 - val_accuracy: 0.9883\n",
      "Epoch 20/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0521 - accuracy: 0.9870 - val_loss: 0.0689 - val_accuracy: 0.9881\n",
      "Epoch 21/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0512 - accuracy: 0.9869 - val_loss: 0.0693 - val_accuracy: 0.9883\n",
      "Epoch 22/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0497 - accuracy: 0.9871 - val_loss: 0.0696 - val_accuracy: 0.9886\n",
      "Epoch 23/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0492 - accuracy: 0.9869 - val_loss: 0.0701 - val_accuracy: 0.9879\n",
      "Epoch 24/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0484 - accuracy: 0.9871 - val_loss: 0.0707 - val_accuracy: 0.9886\n",
      "Epoch 25/100\n",
      "1070/1070 [==============================] - 4s 3ms/step - loss: 0.0471 - accuracy: 0.9875 - val_loss: 0.0734 - val_accuracy: 0.9881\n",
      "Epoch 26/100\n",
      "1070/1070 [==============================] - 1s 995us/step - loss: 0.0471 - accuracy: 0.9874 - val_loss: 0.0710 - val_accuracy: 0.9883\n",
      "Epoch 27/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0464 - accuracy: 0.9876 - val_loss: 0.0706 - val_accuracy: 0.9879\n",
      "Epoch 28/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0453 - accuracy: 0.9879 - val_loss: 0.0728 - val_accuracy: 0.9879\n",
      "Epoch 29/100\n",
      "1070/1070 [==============================] - 1s 868us/step - loss: 0.0447 - accuracy: 0.9879 - val_loss: 0.0726 - val_accuracy: 0.9881\n",
      "Epoch 30/100\n",
      "1070/1070 [==============================] - 1s 809us/step - loss: 0.0435 - accuracy: 0.9883 - val_loss: 0.0758 - val_accuracy: 0.9876\n",
      "Epoch 31/100\n",
      "1070/1070 [==============================] - 1s 804us/step - loss: 0.0432 - accuracy: 0.9881 - val_loss: 0.0730 - val_accuracy: 0.9881\n",
      "Epoch 32/100\n",
      "1070/1070 [==============================] - 1s 801us/step - loss: 0.0427 - accuracy: 0.9884 - val_loss: 0.0741 - val_accuracy: 0.9886\n",
      "Epoch 33/100\n",
      "1070/1070 [==============================] - 1s 828us/step - loss: 0.0423 - accuracy: 0.9880 - val_loss: 0.0747 - val_accuracy: 0.9879\n",
      "Epoch 34/100\n",
      "1070/1070 [==============================] - 1s 846us/step - loss: 0.0409 - accuracy: 0.9889 - val_loss: 0.0780 - val_accuracy: 0.9874\n",
      "Epoch 35/100\n",
      "1070/1070 [==============================] - 1s 842us/step - loss: 0.0405 - accuracy: 0.9888 - val_loss: 0.0808 - val_accuracy: 0.9876\n",
      "Epoch 36/100\n",
      "1070/1070 [==============================] - 1s 857us/step - loss: 0.0400 - accuracy: 0.9888 - val_loss: 0.0773 - val_accuracy: 0.9879\n",
      "Epoch 37/100\n",
      "1070/1070 [==============================] - 1s 851us/step - loss: 0.0387 - accuracy: 0.9892 - val_loss: 0.0783 - val_accuracy: 0.9876\n",
      "Epoch 38/100\n",
      "1070/1070 [==============================] - 1s 867us/step - loss: 0.0387 - accuracy: 0.9890 - val_loss: 0.0789 - val_accuracy: 0.9876\n",
      "Epoch 39/100\n",
      "1070/1070 [==============================] - 1s 873us/step - loss: 0.0375 - accuracy: 0.9895 - val_loss: 0.0792 - val_accuracy: 0.9874\n",
      "Epoch 40/100\n",
      "1070/1070 [==============================] - 1s 866us/step - loss: 0.0376 - accuracy: 0.9895 - val_loss: 0.0786 - val_accuracy: 0.9871\n",
      "Epoch 41/100\n",
      "1070/1070 [==============================] - 1s 862us/step - loss: 0.0369 - accuracy: 0.9895 - val_loss: 0.0813 - val_accuracy: 0.9871\n",
      "Epoch 42/100\n",
      "1070/1070 [==============================] - 1s 860us/step - loss: 0.0356 - accuracy: 0.9898 - val_loss: 0.0814 - val_accuracy: 0.9881\n",
      "Epoch 43/100\n",
      "1070/1070 [==============================] - 1s 873us/step - loss: 0.0360 - accuracy: 0.9896 - val_loss: 0.0809 - val_accuracy: 0.9876\n",
      "Epoch 44/100\n",
      "1070/1070 [==============================] - 1s 873us/step - loss: 0.0350 - accuracy: 0.9901 - val_loss: 0.0906 - val_accuracy: 0.9857\n",
      "Epoch 45/100\n",
      "1070/1070 [==============================] - 1s 916us/step - loss: 0.0347 - accuracy: 0.9902 - val_loss: 0.0826 - val_accuracy: 0.9862\n",
      "Epoch 46/100\n",
      "1070/1070 [==============================] - 1s 897us/step - loss: 0.0339 - accuracy: 0.9902 - val_loss: 0.0879 - val_accuracy: 0.9869\n",
      "Epoch 47/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0334 - accuracy: 0.9904 - val_loss: 0.0859 - val_accuracy: 0.9874\n",
      "Epoch 48/100\n",
      "1070/1070 [==============================] - 1s 960us/step - loss: 0.0322 - accuracy: 0.9909 - val_loss: 0.0876 - val_accuracy: 0.9876\n",
      "Epoch 49/100\n",
      "1070/1070 [==============================] - 1s 916us/step - loss: 0.0326 - accuracy: 0.9906 - val_loss: 0.0861 - val_accuracy: 0.9876\n",
      "Epoch 50/100\n",
      "1070/1070 [==============================] - 1s 908us/step - loss: 0.0315 - accuracy: 0.9905 - val_loss: 0.0856 - val_accuracy: 0.9874\n",
      "Epoch 51/100\n",
      "1070/1070 [==============================] - 1s 932us/step - loss: 0.0322 - accuracy: 0.9907 - val_loss: 0.0847 - val_accuracy: 0.9876\n",
      "Epoch 52/100\n",
      "1070/1070 [==============================] - 1s 935us/step - loss: 0.0317 - accuracy: 0.9908 - val_loss: 0.0907 - val_accuracy: 0.9867\n",
      "Epoch 53/100\n",
      "1070/1070 [==============================] - 1s 951us/step - loss: 0.0304 - accuracy: 0.9909 - val_loss: 0.0904 - val_accuracy: 0.9879\n",
      "Epoch 54/100\n",
      "1070/1070 [==============================] - 1s 952us/step - loss: 0.0299 - accuracy: 0.9914 - val_loss: 0.0896 - val_accuracy: 0.9876\n",
      "Epoch 55/100\n",
      "1070/1070 [==============================] - 1s 931us/step - loss: 0.0288 - accuracy: 0.9916 - val_loss: 0.0940 - val_accuracy: 0.9879\n",
      "Epoch 56/100\n",
      "1070/1070 [==============================] - 1s 952us/step - loss: 0.0294 - accuracy: 0.9913 - val_loss: 0.0921 - val_accuracy: 0.9883\n",
      "Epoch 57/100\n",
      "1070/1070 [==============================] - 1s 924us/step - loss: 0.0285 - accuracy: 0.9914 - val_loss: 0.0966 - val_accuracy: 0.9862\n",
      "Epoch 58/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0286 - accuracy: 0.9917 - val_loss: 0.0941 - val_accuracy: 0.9862\n",
      "Epoch 59/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0271 - accuracy: 0.9920 - val_loss: 0.0917 - val_accuracy: 0.9869\n",
      "Epoch 60/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0272 - accuracy: 0.9920 - val_loss: 0.0927 - val_accuracy: 0.9871\n",
      "Epoch 61/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0264 - accuracy: 0.9917 - val_loss: 0.0935 - val_accuracy: 0.9879\n",
      "Epoch 62/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0261 - accuracy: 0.9921 - val_loss: 0.0945 - val_accuracy: 0.9867\n",
      "Epoch 63/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0252 - accuracy: 0.9927 - val_loss: 0.0986 - val_accuracy: 0.9876\n",
      "Epoch 64/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0254 - accuracy: 0.9929 - val_loss: 0.0982 - val_accuracy: 0.9874\n",
      "Epoch 65/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0257 - accuracy: 0.9920 - val_loss: 0.0983 - val_accuracy: 0.9857\n",
      "Epoch 66/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0251 - accuracy: 0.9926 - val_loss: 0.0970 - val_accuracy: 0.9874\n",
      "Epoch 67/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0240 - accuracy: 0.9931 - val_loss: 0.1008 - val_accuracy: 0.9879\n",
      "Epoch 68/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0237 - accuracy: 0.9931 - val_loss: 0.1031 - val_accuracy: 0.9874\n",
      "Epoch 69/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0235 - accuracy: 0.9933 - val_loss: 0.1012 - val_accuracy: 0.9862\n",
      "Epoch 70/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0226 - accuracy: 0.9937 - val_loss: 0.1020 - val_accuracy: 0.9867\n",
      "Epoch 71/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0231 - accuracy: 0.9933 - val_loss: 0.1051 - val_accuracy: 0.9855\n",
      "Epoch 72/100\n",
      "1070/1070 [==============================] - 1s 969us/step - loss: 0.0227 - accuracy: 0.9935 - val_loss: 0.1043 - val_accuracy: 0.9860\n",
      "Epoch 73/100\n",
      "1070/1070 [==============================] - 1s 967us/step - loss: 0.0217 - accuracy: 0.9935 - val_loss: 0.1032 - val_accuracy: 0.9864\n",
      "Epoch 74/100\n",
      "1070/1070 [==============================] - 1s 920us/step - loss: 0.0214 - accuracy: 0.9940 - val_loss: 0.1109 - val_accuracy: 0.9839\n",
      "Epoch 75/100\n",
      "1070/1070 [==============================] - 1s 918us/step - loss: 0.0214 - accuracy: 0.9939 - val_loss: 0.1074 - val_accuracy: 0.9864\n",
      "Epoch 76/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0209 - accuracy: 0.9940 - val_loss: 0.1108 - val_accuracy: 0.9860\n",
      "Epoch 77/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0206 - accuracy: 0.9941 - val_loss: 0.1107 - val_accuracy: 0.9848\n",
      "Epoch 78/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0204 - accuracy: 0.9940 - val_loss: 0.1115 - val_accuracy: 0.9864\n",
      "Epoch 79/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0203 - accuracy: 0.9942 - val_loss: 0.1156 - val_accuracy: 0.9860\n",
      "Epoch 80/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0202 - accuracy: 0.9942 - val_loss: 0.1146 - val_accuracy: 0.9850\n",
      "Epoch 81/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0187 - accuracy: 0.9945 - val_loss: 0.1142 - val_accuracy: 0.9850\n",
      "Epoch 82/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0195 - accuracy: 0.9943 - val_loss: 0.1148 - val_accuracy: 0.9864\n",
      "Epoch 83/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0195 - accuracy: 0.9945 - val_loss: 0.1161 - val_accuracy: 0.9867\n",
      "Epoch 84/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0182 - accuracy: 0.9950 - val_loss: 0.1219 - val_accuracy: 0.9862\n",
      "Epoch 85/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0184 - accuracy: 0.9946 - val_loss: 0.1150 - val_accuracy: 0.9874\n",
      "Epoch 86/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0180 - accuracy: 0.9949 - val_loss: 0.1183 - val_accuracy: 0.9864\n",
      "Epoch 87/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0181 - accuracy: 0.9948 - val_loss: 0.1231 - val_accuracy: 0.9860\n",
      "Epoch 88/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0179 - accuracy: 0.9948 - val_loss: 0.1147 - val_accuracy: 0.9867\n",
      "Epoch 89/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0166 - accuracy: 0.9955 - val_loss: 0.1229 - val_accuracy: 0.9860\n",
      "Epoch 90/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0172 - accuracy: 0.9950 - val_loss: 0.1218 - val_accuracy: 0.9848\n",
      "Epoch 91/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0169 - accuracy: 0.9951 - val_loss: 0.1219 - val_accuracy: 0.9855\n",
      "Epoch 92/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0170 - accuracy: 0.9955 - val_loss: 0.1199 - val_accuracy: 0.9862\n",
      "Epoch 93/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0164 - accuracy: 0.9956 - val_loss: 0.1237 - val_accuracy: 0.9874\n",
      "Epoch 94/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0167 - accuracy: 0.9953 - val_loss: 0.1364 - val_accuracy: 0.9808\n",
      "Epoch 95/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0156 - accuracy: 0.9956 - val_loss: 0.1247 - val_accuracy: 0.9864\n",
      "Epoch 96/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0163 - accuracy: 0.9955 - val_loss: 0.1288 - val_accuracy: 0.9834\n",
      "Epoch 97/100\n",
      "1070/1070 [==============================] - 1s 1ms/step - loss: 0.0149 - accuracy: 0.9960 - val_loss: 0.1268 - val_accuracy: 0.9853\n",
      "Epoch 98/100\n",
      "1070/1070 [==============================] - 2s 2ms/step - loss: 0.0163 - accuracy: 0.9956 - val_loss: 0.1287 - val_accuracy: 0.9857\n",
      "Epoch 99/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0153 - accuracy: 0.9959 - val_loss: 0.1346 - val_accuracy: 0.9874\n",
      "Epoch 100/100\n",
      "1070/1070 [==============================] - 4s 4ms/step - loss: 0.0144 - accuracy: 0.9962 - val_loss: 0.1346 - val_accuracy: 0.9850\n",
      "134/134 [==============================] - 0s 3ms/step - loss: 0.1556 - accuracy: 0.9820\n",
      "Test Loss: 0.15556031465530396\n",
      "Test Accuracy: 0.9820093512535095\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "`save_model()` using h5 format requires h5py. Could not import h5py.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 44\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTest Accuracy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maccuracy\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     43\u001b[0m \u001b[38;5;66;03m# Save the trained model\u001b[39;00m\n\u001b[1;32m---> 44\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrained_model_2.keras\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\envs\\tf\\lib\\site-packages\\keras\\saving\\hdf5_format.py:80\u001b[0m, in \u001b[0;36msave_model_to_hdf5\u001b[1;34m(model, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Saves a model to a HDF5 file.\u001b[39;00m\n\u001b[0;32m     55\u001b[0m \n\u001b[0;32m     56\u001b[0m \u001b[38;5;124;03mThe saved model contains:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[38;5;124;03m    ImportError: if h5py is not available.\u001b[39;00m\n\u001b[0;32m     77\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m h5py \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 80\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m     81\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`save_model()` using h5 format requires h5py. Could not \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     82\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimport h5py.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     83\u001b[0m     )\n\u001b[0;32m     85\u001b[0m \u001b[38;5;66;03m# TODO(psv) Add warning when we save models that contain non-serializable\u001b[39;00m\n\u001b[0;32m     86\u001b[0m \u001b[38;5;66;03m# entities like metrics added using `add_metric` and losses added using\u001b[39;00m\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# `add_loss.`\u001b[39;00m\n\u001b[0;32m     88\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(model\u001b[38;5;241m.\u001b[39mweights) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(model\u001b[38;5;241m.\u001b[39m_undeduplicated_weights):\n",
      "\u001b[1;31mImportError\u001b[0m: `save_model()` using h5 format requires h5py. Could not import h5py."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n",
    "\n",
    "df=pd.read_csv('week_approach_maskedID_timeseries.csv')\n",
    "\n",
    "# Use all features except 'injury' as input\n",
    "input_data = df.drop(columns=['injury']).values\n",
    "\n",
    "# Normalize the input data using z score scaling (optional but recommended)\n",
    "input_data = (input_data - np.mean(input_data, axis=0)) / np.std(input_data, axis=0)\n",
    "\n",
    "# Use 'injury' column as the target variable y\n",
    "y = df['injury'].values\n",
    "\n",
    "x_tf = input_data\n",
    "y_tf = y\n",
    "\n",
    "# Split the data into training, validation, and test sets\n",
    "x_train, x_temp, y_train, y_temp = train_test_split(x_tf, y_tf, test_size=0.2, random_state=42)\n",
    "x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Define and compile the model\n",
    "model = Sequential([\n",
    "    Dense(units=64, activation='relu', name=\"layer1\"),\n",
    "    Dense(units=1, activation='sigmoid', name=\"output\")  # Output layer for binary classification (sigmoid activation)\n",
    "])\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x=x_train, y=y_train, validation_data=(x_val, y_val), epochs=100, batch_size=32, verbose=1)\n",
    "\n",
    "# Evaluate the model on test set\n",
    "loss, accuracy = model.evaluate(x_test, y_test)\n",
    "print(f'Test Loss: {loss}')\n",
    "print(f'Test Accuracy: {accuracy}')\n",
    "\n",
    "# Save the trained model\n",
    "model.save('trained_model_2.keras')\n"
   ]
  }
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