racePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Functions\n",
    "def getRaceResult(runner_id, raceName):\n",
    "    race_distance_miles = race_distances_df.loc[race_distances_df['Race'] == raceName, 'Distance (miles)'].values[0]\n",
    "    runner_data = run_train_data[run_train_data['athlete'] == runner_id]\n",
    "    runner_data = runner_data.sort_values(by='timestamp', ascending=True)\n",
    "    first_run_at_or_beyond_distance = runner_data[runner_data['distance (m)'] >= race_distances_df.loc[race_distances_df['Race'] == raceName, 'Distance (m)'].values[0]]\n",
    "    if not first_run_at_or_beyond_distance.empty:\n",
    "        first_run = first_run_at_or_beyond_distance.iloc[0]\n",
    "        average_mile_pace = first_run['average pace (min/mile)']\n",
    "        if pd.notna(average_mile_pace):\n",
    "            race_time_minutes = race_distance_miles * average_mile_pace\n",
    "            race_time_hours = race_time_minutes / 60\n",
    "            return race_time_hours\n",
    "    return None\n",
    "\n",
    "def getRaceDate(runner_id, raceName):\n",
    "    race_distance_m = race_distances_df.loc[race_distances_df['Race'] == raceName, 'Distance (m)'].values[0]\n",
    "    runner_data = run_train_data[run_train_data['athlete'] == runner_id]\n",
    "    runner_data = runner_data.sort_values(by='timestamp', ascending=True)\n",
    "    first_run_at_or_beyond_distance = runner_data[runner_data['distance (m)'] >= race_distance_m]\n",
    "    if not first_run_at_or_beyond_distance.empty:\n",
    "        first_run = first_run_at_or_beyond_distance.iloc[0]\n",
    "        return first_run['timestamp']\n",
    "    return None\n",
    "\n",
    "def averagePace(distance_m, time_s):\n",
    "    distance_miles = distance_m / 1609.34\n",
    "    time_minutes = time_s / 60\n",
    "    pace = time_minutes / distance_miles\n",
    "    return pace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runModel(race_type):\n",
    "    ## Drop the NaN values\n",
    "    training_results_merged_df_dropped = race_runners_dfs[race_type].dropna(subset=['distance (m)', 'elapsed time (s)', 'elevation gain (m)', 'average heart rate (bpm)'])\n",
    "    training_results_merged_df_dropped = training_results_merged_df_dropped.sort_values(by='timestamp')\n",
    "\n",
    "    # Select features (X) and target variable (y)\n",
    "    X = training_results_merged_df_dropped[['distance (m)', 'elapsed time (s)', 'elevation gain (m)']]\n",
    "    y = training_results_merged_df_dropped[race_type]\n",
    "\n",
    "    # Determine the split point\n",
    "    split_index = int(len(X) * 0.8)\n",
    "\n",
    "    # Split the data into training (80%) and testing (20%) sets\n",
    "    X_train, X_test = X[:split_index], X[split_index:]\n",
    "    y_train, y_test = y[:split_index], y[split_index:]\n",
    "\n",
    "    # Initialize the Linear Regression model\n",
    "    model = LinearRegression()\n",
    "\n",
    "    # Train the model with the training data\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions on the test set\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # Evaluate the model\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    return mse, r2\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTrainingWeeksBeforeRace(run_data, race_date, athlete):\n",
    "    athlete_data = run_data[(run_data['timestamp'] <= race_date) & (run_data['athlete'] == athlete)].copy()\n",
    "    return athlete_data\n",
    "\n",
    "def getConsecutiveTrainingWeeks(athlete_data):\n",
    "    df = pd.DataFrame()\n",
    "\n",
    "    athlete_data['week_diff'] = athlete_data['week'].astype(np.int64).diff().fillna(999)\n",
    "    athlete_data['week_diff'] = athlete_data['week_diff'].apply(lambda x: x + 52 if x < 0 and x > -52 else x)\n",
    "    athlete_data.loc[:, 'consecutive_week_flag'] = (athlete_data['week_diff'] <= 1).astype(int)\n",
    "\n",
    "    for index, row in athlete_data.iterrows():\n",
    "        if row['consecutive_week_flag'] == 1:\n",
    "            df = pd.concat([df, row.to_frame().T])\n",
    "        else:\n",
    "            df = pd.DataFrame()\n",
    "    return df \n",
    "\n",
    "def getTrainingData(run_data, race_date, athlete):\n",
    "    training_data = getTrainingWeeksBeforeRace(run_data, race_date, athlete)\n",
    "    consecutive_training_data = getConsecutiveTrainingWeeks(training_data)\n",
    "    return consecutive_training_data\n",
    "\n",
    "\n",
    "def getTrainingMetrics(athlete_training_data):\n",
    "    # Calculate metrics\n",
    "    training_weeks_sum = athlete_training_data['athlete'].count()\n",
    "    training_distance_sum = athlete_training_data['distance (miles)'].sum()\n",
    "    training_time_sum = athlete_training_data['elapsed time (hrs)'].sum()\n",
    "    training_elevation_gain_sum = athlete_training_data['elevation gain (m)'].sum()\n",
    "    \n",
    "    weekly_run_count_mean = athlete_training_data.groupby('week').size().mean()\n",
    "    weekly_run_count_std = athlete_training_data.groupby('week').size().std()\n",
    "    \n",
    "    weekly_distance_mean = athlete_training_data.groupby('week')['distance (miles)'].sum().mean()\n",
    "    weekly_distance_std = athlete_training_data.groupby('week')['distance (miles)'].sum().std()\n",
    "    \n",
    "    weekly_time_mean = athlete_training_data.groupby('week')['elapsed time (hrs)'].sum().mean()\n",
    "    weekly_time_std = athlete_training_data.groupby('week')['elapsed time (hrs)'].sum().std()\n",
    "    \n",
    "    weekly_pace_mean = athlete_training_data.groupby('week')['average pace (min/mile)'].mean().mean()\n",
    "    weekly_pace_std = athlete_training_data.groupby('week')['average pace (min/mile)'].mean().std()\n",
    "    \n",
    "    return {\n",
    "        'training_weeks_sum': training_weeks_sum,\n",
    "        'training_distance_sum': training_distance_sum,\n",
    "        'training_time_sum': training_time_sum,\n",
    "        'training_elevation_gain_sum': training_elevation_gain_sum,\n",
    "        'weekly_run_count_mean': weekly_run_count_mean,\n",
    "        'weekly_run_count_std': weekly_run_count_std,\n",
    "        'weekly_distance_mean': weekly_distance_mean,\n",
    "        'weekly_distance_std': weekly_distance_std,\n",
    "        'weekly_time_mean': weekly_time_mean,\n",
    "        'weekly_time_std': weekly_time_std,\n",
    "        'weekly_pace_mean': weekly_pace_mean,\n",
    "        'weekly_pace_std': weekly_pace_std\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_consecutive_weeks(run_train_data, race_date):\n",
    "    consecutive_weeks = 0\n",
    "    for index, row in run_train_data.iterrows():\n",
    "        if row['consecutive_week_flag'] == 1:\n",
    "            consecutive_weeks += 1\n",
    "        else:\n",
    "            consecutive_weeks = 0\n",
    "        if row['timestamp'] == race_date:\n",
    "            return consecutive_weeks\n",
    "    return consecutive_weeks\n",
    "\n",
    "\n",
    "def getConsecutiveTrainingWeeksOLD(run_train_data, race_date, athlete):\n",
    "    \n",
    "    # Filter data for the specified athlete up until the race date\n",
    "    filtered_data = run_train_data[(run_train_data['timestamp'] <= race_date) & (run_train_data['athlete'] == athlete)].copy()\n",
    "    \n",
    "    filtered_data.drop_duplicates(subset=['week'], keep='first', inplace=True)\n",
    "    filtered_data['week_diff'] = filtered_data.groupby('athlete')['week'].diff().fillna(0)\n",
    "    filtered_data['week_diff'] = np.where(filtered_data['week_diff'] < 0, filtered_data['week_diff'] + 52, filtered_data['week_diff'])\n",
    "    filtered_data['consecutive_week_flag'] = (filtered_data['week_diff'] == 1).astype(int)\n",
    "    \n",
    "    consecutive_training_weeks = calculate_consecutive_weeks(filtered_data, race_date)\n",
    "    \n",
    "    return consecutive_training_weeks\n",
    "\n",
    "\n",
    "## Writing out some sudo code\n",
    "\n",
    "def getTrainingWeeks(run_train_data, race_date, athlete):\n",
    "    # Filter data for the specified athlete up until the race date\n",
    "    filtered_data = run_train_data[(run_train_data['timestamp'] <= race_date) & (run_train_data['athlete'] == athlete)].copy()\n",
    "    filtered_data.drop_duplicates(subset=['week'], keep='first', inplace=True)\n",
    "    filtered_data['week_diff'] = filtered_data.groupby('athlete')['week'].diff().fillna(0)\n",
    "    filtered_data['week_diff'] = np.where(filtered_data['week_diff'] < 0, filtered_data['week_diff'] + 52, filtered_data['week_diff'])\n",
    "    filtered_data['consecutive_week_flag'] = (filtered_data['week_diff'] == 1).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getRace(run_race_data, race_name):\n",
    "    race_data = run_race_data[['athlete', race_name + '_result', race_name + '_race_date']]\n",
    "    race_data = race_data.dropna(subset=[race_name + '_result'])\n",
    "    return race_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Race</th>\n",
       "      <th>Distance (miles)</th>\n",
       "      <th>Distance (m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5K</td>\n",
       "      <td>3.10686</td>\n",
       "      <td>4970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10K</td>\n",
       "      <td>6.21371</td>\n",
       "      <td>9656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Half Marathon</td>\n",
       "      <td>13.10940</td>\n",
       "      <td>21097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marathon</td>\n",
       "      <td>26.21880</td>\n",
       "      <td>42195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50K</td>\n",
       "      <td>31.06860</td>\n",
       "      <td>49700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50M</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>80467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100K</td>\n",
       "      <td>62.13710</td>\n",
       "      <td>100320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Race  Distance (miles)  Distance (m)\n",
       "0             5K           3.10686          4970\n",
       "1            10K           6.21371          9656\n",
       "2  Half Marathon          13.10940         21097\n",
       "3       Marathon          26.21880         42195\n",
       "4            50K          31.06860         49700\n",
       "5            50M          50.00000         80467\n",
       "6           100K          62.13710        100320"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Basic info about types of races\n",
    "race_distances = {\n",
    "    'Race': ['5K', '10K', 'Half Marathon', 'Marathon','50K', '50M', '100K'],\n",
    "    'Distance (miles)': [3.10686, 6.21371, 13.1094, 26.2188, 31.0686, 50, 62.1371],\n",
    "    'Distance (m)': [4970, 9656, 21097, 42195, 49700, 80467, 100320]\n",
    "}\n",
    "\n",
    "race_distances_df = pd.DataFrame(race_distances)\n",
    "\n",
    "race_distances_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>athlete</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>distance (m)</th>\n",
       "      <th>elapsed time (s)</th>\n",
       "      <th>elevation gain (m)</th>\n",
       "      <th>average heart rate (bpm)</th>\n",
       "      <th>distance (miles)</th>\n",
       "      <th>elapsed time (hrs)</th>\n",
       "      <th>average pace (min/mile)</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9519</th>\n",
       "      <td>771514</td>\n",
       "      <td>2015-09-17 15:19:00</td>\n",
       "      <td>3927.8</td>\n",
       "      <td>3484</td>\n",
       "      <td>29.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.440621</td>\n",
       "      <td>0.967778</td>\n",
       "      <td>23.791759</td>\n",
       "      <td>17</td>\n",
       "      <td>2015</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9518</th>\n",
       "      <td>771514</td>\n",
       "      <td>2015-10-29 18:40:00</td>\n",
       "      <td>1391.1</td>\n",
       "      <td>644</td>\n",
       "      <td>32.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.864389</td>\n",
       "      <td>0.178889</td>\n",
       "      <td>12.417246</td>\n",
       "      <td>29</td>\n",
       "      <td>2015</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9517</th>\n",
       "      <td>771514</td>\n",
       "      <td>2016-07-11 11:58:00</td>\n",
       "      <td>2116.8</td>\n",
       "      <td>779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.315318</td>\n",
       "      <td>0.216389</td>\n",
       "      <td>9.870869</td>\n",
       "      <td>11</td>\n",
       "      <td>2016</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9516</th>\n",
       "      <td>771514</td>\n",
       "      <td>2017-02-15 16:25:00</td>\n",
       "      <td>5355.4</td>\n",
       "      <td>3371</td>\n",
       "      <td>81.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.327690</td>\n",
       "      <td>0.936389</td>\n",
       "      <td>16.883583</td>\n",
       "      <td>15</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9515</th>\n",
       "      <td>771514</td>\n",
       "      <td>2017-06-26 17:31:00</td>\n",
       "      <td>2110.3</td>\n",
       "      <td>683</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.311279</td>\n",
       "      <td>0.189722</td>\n",
       "      <td>8.681090</td>\n",
       "      <td>26</td>\n",
       "      <td>2017</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      athlete           timestamp  distance (m)  elapsed time (s)  \\\n",
       "9519   771514 2015-09-17 15:19:00        3927.8              3484   \n",
       "9518   771514 2015-10-29 18:40:00        1391.1               644   \n",
       "9517   771514 2016-07-11 11:58:00        2116.8               779   \n",
       "9516   771514 2017-02-15 16:25:00        5355.4              3371   \n",
       "9515   771514 2017-06-26 17:31:00        2110.3               683   \n",
       "\n",
       "      elevation gain (m)  average heart rate (bpm)  distance (miles)  \\\n",
       "9519                29.2                       NaN          2.440621   \n",
       "9518                32.1                       NaN          0.864389   \n",
       "9517                 0.0                       NaN          1.315318   \n",
       "9516                81.6                       NaN          3.327690   \n",
       "9515                 6.0                       NaN          1.311279   \n",
       "\n",
       "      elapsed time (hrs)  average pace (min/mile)  day  year  week  male  \\\n",
       "9519            0.967778                23.791759   17  2015    38     1   \n",
       "9518            0.178889                12.417246   29  2015    44     1   \n",
       "9517            0.216389                 9.870869   11  2016    28     1   \n",
       "9516            0.936389                16.883583   15  2017     7     1   \n",
       "9515            0.189722                 8.681090   26  2017    26     1   \n",
       "\n",
       "      female  \n",
       "9519       0  \n",
       "9518       0  \n",
       "9517       0  \n",
       "9516       0  \n",
       "9515       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Run training data for each runner\n",
    "run_train_data = pd.read_csv('raw-data-kaggle.csv', delimiter=';')\n",
    "\n",
    "run_train_data['distance (miles)'] = run_train_data['distance (m)'] * 0.000621371\n",
    "run_train_data['elapsed time (hrs)'] = run_train_data['elapsed time (s)'] / 3600\n",
    "run_train_data['average pace (min/mile)'] = (run_train_data['elapsed time (s)'] / 60) / run_train_data['distance (miles)']\n",
    "\n",
    "run_train_data['timestamp'] = pd.to_datetime(run_train_data['timestamp'], dayfirst=True)\n",
    "run_train_data['day'] = run_train_data['timestamp'].dt.day\n",
    "run_train_data['year'] = run_train_data['timestamp'].dt.year\n",
    "run_train_data['week'] = run_train_data['timestamp'].dt.isocalendar().week\n",
    "\n",
    "run_train_data.sort_values(by=['athlete', 'year', 'week', 'day'], ascending=[True, True, True, True], inplace=True)\n",
    "\n",
    "run_train_data['male'] = run_train_data['gender'].apply(lambda x: 1 if x == 'M' else 0)\n",
    "run_train_data['female'] = run_train_data['gender'].apply(lambda x: 1 if x == 'F' else 0)\n",
    "run_train_data = run_train_data.drop('gender', axis=1)\n",
    "\n",
    "run_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>athlete</th>\n",
       "      <th>5K_result</th>\n",
       "      <th>5K_race_date</th>\n",
       "      <th>10K_result</th>\n",
       "      <th>10K_race_date</th>\n",
       "      <th>Half Marathon_result</th>\n",
       "      <th>Half Marathon_race_date</th>\n",
       "      <th>Marathon_result</th>\n",
       "      <th>Marathon_race_date</th>\n",
       "      <th>50K_result</th>\n",
       "      <th>50K_race_date</th>\n",
       "      <th>50M_result</th>\n",
       "      <th>50M_race_date</th>\n",
       "      <th>100K_result</th>\n",
       "      <th>100K_race_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>771514</td>\n",
       "      <td>0.874249</td>\n",
       "      <td>2017-02-15 16:25:00</td>\n",
       "      <td>1.009510</td>\n",
       "      <td>2017-07-12 16:55:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1776170</td>\n",
       "      <td>0.460295</td>\n",
       "      <td>2012-11-05 17:50:00</td>\n",
       "      <td>0.941100</td>\n",
       "      <td>2012-11-06 20:12:00</td>\n",
       "      <td>1.566968</td>\n",
       "      <td>2013-10-27 09:59:00</td>\n",
       "      <td>3.207050</td>\n",
       "      <td>2013-11-24 08:00:00</td>\n",
       "      <td>8.342531</td>\n",
       "      <td>2016-07-16 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013538</td>\n",
       "      <td>0.549371</td>\n",
       "      <td>2015-10-18 08:52:00</td>\n",
       "      <td>1.098740</td>\n",
       "      <td>2015-10-18 08:52:00</td>\n",
       "      <td>2.788450</td>\n",
       "      <td>2018-05-27 06:59:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2081128</td>\n",
       "      <td>0.475112</td>\n",
       "      <td>2013-04-21 08:03:00</td>\n",
       "      <td>0.955490</td>\n",
       "      <td>2013-05-05 07:15:00</td>\n",
       "      <td>2.142375</td>\n",
       "      <td>2015-05-10 07:09:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2138770</td>\n",
       "      <td>0.789604</td>\n",
       "      <td>2014-05-19 08:08:00</td>\n",
       "      <td>1.579204</td>\n",
       "      <td>2014-05-19 08:08:00</td>\n",
       "      <td>3.331733</td>\n",
       "      <td>2014-05-19 08:08:00</td>\n",
       "      <td>5.981852</td>\n",
       "      <td>2016-05-07 05:43:00</td>\n",
       "      <td>8.165714</td>\n",
       "      <td>2017-05-26 03:00:00</td>\n",
       "      <td>11.836341</td>\n",
       "      <td>2018-06-22 21:05:00</td>\n",
       "      <td>14.709518</td>\n",
       "      <td>2018-06-22 21:05:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   athlete  5K_result        5K_race_date  10K_result       10K_race_date  \\\n",
       "0   771514   0.874249 2017-02-15 16:25:00    1.009510 2017-07-12 16:55:00   \n",
       "1  1776170   0.460295 2012-11-05 17:50:00    0.941100 2012-11-06 20:12:00   \n",
       "2  2013538   0.549371 2015-10-18 08:52:00    1.098740 2015-10-18 08:52:00   \n",
       "3  2081128   0.475112 2013-04-21 08:03:00    0.955490 2013-05-05 07:15:00   \n",
       "4  2138770   0.789604 2014-05-19 08:08:00    1.579204 2014-05-19 08:08:00   \n",
       "\n",
       "   Half Marathon_result Half Marathon_race_date  Marathon_result  \\\n",
       "0                   NaN                     NaT              NaN   \n",
       "1              1.566968     2013-10-27 09:59:00         3.207050   \n",
       "2              2.788450     2018-05-27 06:59:00              NaN   \n",
       "3              2.142375     2015-05-10 07:09:00              NaN   \n",
       "4              3.331733     2014-05-19 08:08:00         5.981852   \n",
       "\n",
       "   Marathon_race_date  50K_result       50K_race_date  50M_result  \\\n",
       "0                 NaT         NaN                 NaT         NaN   \n",
       "1 2013-11-24 08:00:00    8.342531 2016-07-16 06:00:00         NaN   \n",
       "2                 NaT         NaN                 NaT         NaN   \n",
       "3                 NaT         NaN                 NaT         NaN   \n",
       "4 2016-05-07 05:43:00    8.165714 2017-05-26 03:00:00   11.836341   \n",
       "\n",
       "        50M_race_date  100K_result      100K_race_date  \n",
       "0                 NaT          NaN                 NaT  \n",
       "1                 NaT          NaN                 NaT  \n",
       "2                 NaT          NaN                 NaT  \n",
       "3                 NaT          NaN                 NaT  \n",
       "4 2018-06-22 21:05:00    14.709518 2018-06-22 21:05:00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_race_data = pd.DataFrame()\n",
    "\n",
    "# Add each unique value in run_train_data['athlete'] to run_race_data\n",
    "run_race_data['athlete'] = run_train_data['athlete'].unique()\n",
    "\n",
    "## Add a new column for each race type with the result as its value or NaN if the runner did not complete that race\n",
    "for race in race_distances_df['Race']:\n",
    "    run_race_data[race + '_result'] = np.nan\n",
    "    run_race_data[race + '_race_date'] = np.nan\n",
    "\n",
    "for race in race_distances_df['Race']:\n",
    "    run_race_data[race + '_race_date'] = pd.to_datetime(run_race_data[race + '_race_date'], errors='coerce')\n",
    "\n",
    "for runner_id in run_train_data['athlete'].unique():\n",
    "    for race in race_distances_df['Race']:\n",
    "        result = getRaceResult(runner_id, race)\n",
    "        race_date = getRaceDate(runner_id, race)\n",
    "        run_race_data.loc[run_race_data['athlete'] == runner_id, race + '_result'] = result\n",
    "        run_race_data.loc[run_race_data['athlete'] == runner_id, race + '_race_date'] = race_date\n",
    "\n",
    "run_race_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>athlete</th>\n",
       "      <th>Half Marathon_result</th>\n",
       "      <th>Half Marathon_race_date</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1776170</td>\n",
       "      <td>1.566968</td>\n",
       "      <td>2013-10-27 09:59:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013538</td>\n",
       "      <td>2.788450</td>\n",
       "      <td>2018-05-27 06:59:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2081128</td>\n",
       "      <td>2.142375</td>\n",
       "      <td>2015-05-10 07:09:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2138770</td>\n",
       "      <td>3.331733</td>\n",
       "      <td>2014-05-19 08:08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2683270</td>\n",
       "      <td>1.520652</td>\n",
       "      <td>2015-06-07 07:01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   athlete  Half Marathon_result Half Marathon_race_date  male  female\n",
       "1  1776170              1.566968     2013-10-27 09:59:00     1       0\n",
       "2  2013538              2.788450     2018-05-27 06:59:00     1       0\n",
       "3  2081128              2.142375     2015-05-10 07:09:00     1       0\n",
       "4  2138770              3.331733     2014-05-19 08:08:00     1       0\n",
       "5  2683270              1.520652     2015-06-07 07:01:00     1       0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_race_data_half_marathon = getRace(run_race_data, 'Half Marathon')\n",
    "\n",
    "run_race_data_half_marathon['male'] = run_race_data_half_marathon['athlete'].apply(lambda x: run_train_data[run_train_data['athlete'] == x]['male'].iloc[0])\n",
    "run_race_data_half_marathon['female'] = run_race_data_half_marathon['athlete'].apply(lambda x: run_train_data[run_train_data['athlete'] == x]['female'].iloc[0])\n",
    "\n",
    "run_race_data_half_marathon = run_race_data_half_marathon.dropna(subset=['Half Marathon_result'])\n",
    "run_race_data_half_marathon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>athlete</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>distance (m)</th>\n",
       "      <th>elapsed time (s)</th>\n",
       "      <th>elevation gain (m)</th>\n",
       "      <th>average heart rate (bpm)</th>\n",
       "      <th>distance (miles)</th>\n",
       "      <th>elapsed time (hrs)</th>\n",
       "      <th>average pace (min/mile)</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
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       "      <th>female</th>\n",
       "      <th>week_diff</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32101</th>\n",
       "      <td>11284478</td>\n",
       "      <td>2014-12-29 11:19:00</td>\n",
       "      <td>5645.2</td>\n",
       "      <td>2116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.507764</td>\n",
       "      <td>0.587778</td>\n",
       "      <td>10.053889</td>\n",
       "      <td>29</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32105</th>\n",
       "      <td>11284478</td>\n",
       "      <td>2014-12-20 19:07:00</td>\n",
       "      <td>9370.7</td>\n",
       "      <td>2879</td>\n",
       "      <td>51.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.822681</td>\n",
       "      <td>0.799722</td>\n",
       "      <td>8.240763</td>\n",
       "      <td>20</td>\n",
       "      <td>2014</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32106</th>\n",
       "      <td>11284478</td>\n",
       "      <td>2014-12-20 18:38:00</td>\n",
       "      <td>1941.1</td>\n",
       "      <td>711</td>\n",
       "      <td>17.7</td>\n",
       "      <td>152.1</td>\n",
       "      <td>1.206143</td>\n",
       "      <td>0.197500</td>\n",
       "      <td>9.824704</td>\n",
       "      <td>20</td>\n",
       "      <td>2014</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32104</th>\n",
       "      <td>11284478</td>\n",
       "      <td>2014-12-21 16:19:00</td>\n",
       "      <td>4858.9</td>\n",
       "      <td>1814</td>\n",
       "      <td>23.8</td>\n",
       "      <td>147.1</td>\n",
       "      <td>3.019180</td>\n",
       "      <td>0.503889</td>\n",
       "      <td>10.013758</td>\n",
       "      <td>21</td>\n",
       "      <td>2014</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32103</th>\n",
       "      <td>11284478</td>\n",
       "      <td>2014-12-24 08:41:00</td>\n",
       "      <td>9595.9</td>\n",
       "      <td>8584</td>\n",
       "      <td>979.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.962614</td>\n",
       "      <td>2.384444</td>\n",
       "      <td>23.993951</td>\n",
       "      <td>24</td>\n",
       "      <td>2014</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete           timestamp  distance (m)  elapsed time (s)  \\\n",
       "32101  11284478 2014-12-29 11:19:00        5645.2              2116   \n",
       "32105  11284478 2014-12-20 19:07:00        9370.7              2879   \n",
       "32106  11284478 2014-12-20 18:38:00        1941.1               711   \n",
       "32104  11284478 2014-12-21 16:19:00        4858.9              1814   \n",
       "32103  11284478 2014-12-24 08:41:00        9595.9              8584   \n",
       "\n",
       "       elevation gain (m)  average heart rate (bpm)  distance (miles)  \\\n",
       "32101                 0.0                       NaN          3.507764   \n",
       "32105                51.6                       NaN          5.822681   \n",
       "32106                17.7                     152.1          1.206143   \n",
       "32104                23.8                     147.1          3.019180   \n",
       "32103               979.6                       NaN          5.962614   \n",
       "\n",
       "       elapsed time (hrs)  average pace (min/mile)  day  year  week  male  \\\n",
       "32101            0.587778                10.053889   29  2014     1     1   \n",
       "32105            0.799722                 8.240763   20  2014    51     1   \n",
       "32106            0.197500                 9.824704   20  2014    51     1   \n",
       "32104            0.503889                10.013758   21  2014    51     1   \n",
       "32103            2.384444                23.993951   24  2014    52     1   \n",
       "\n",
       "       female  week_diff  consecutive_week_flag  \n",
       "32101       0      999.0                      0  \n",
       "32105       0       50.0                      0  \n",
       "32106       0        0.0                      1  \n",
       "32104       0        0.0                      1  \n",
       "32103       0        1.0                      1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter data for the specified athlete up until the race date\n",
    "athlete_data_full = run_train_data[(run_train_data['timestamp'] <= '2016-06-05 07:15:00') & (run_train_data['athlete'] == 11284478)].copy()\n",
    "\n",
    "# Calculate the difference in weeks, cast to int64 to handle potential negative differences\n",
    "athlete_data_full['week_diff'] = athlete_data_full['week'].astype(np.int64).diff().fillna(999)\n",
    "athlete_data_full['week_diff'] = athlete_data_full['week_diff'].apply(lambda x: x + 52 if x < 0 and x > -52 else x)\n",
    "athlete_data_full.loc[:, 'consecutive_week_flag'] = (athlete_data_full['week_diff'] <= 1).astype(int)\n",
    "\n",
    "athlete_data_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of runs: 37\n",
      "Total distance run (miles): 190.7887965579\n",
      "Total elapsed time (hrs): 36.42916666666667\n",
      "Total elevation gain (m): 6365.6\n",
      "Mean number of runs per week: 2.0555555555555554\n",
      "Standard deviation of weekly run counts: 0.802365782965103\n",
      "Mean weekly distance (miles): 10.599377586549998\n",
      "Standard deviation of weekly distances: 4.9376312284254915\n",
      "Mean weekly elapsed time (hrs): 2.023842592592592\n",
      "Standard deviation of weekly elapsed times: 1.0870563905862711\n",
      "Mean weekly pace (min/mile): 11.280297634759897\n",
      "Standard deviation of weekly pace (min/mile): 2.885467106827181\n"
     ]
    }
   ],
   "source": [
    "athlete_half_marathon_training_data = getTrainingData(run_train_data, '2015-04-18 07:17:00', 11284478)\n",
    "\n",
    "training_weeks_sum = athlete_half_marathon_training_data['athlete'].count()\n",
    "training_distance_sum = athlete_half_marathon_training_data['distance (miles)'].sum()\n",
    "training_time_sum = athlete_half_marathon_training_data['elapsed time (hrs)'].sum()\n",
    "training_elevation_gain_sum = athlete_half_marathon_training_data['elevation gain (m)'].sum()\n",
    "\n",
    "weekly_run_count_mean = athlete_half_marathon_training_data.groupby('week').size().mean()\n",
    "weekly_run_count_std = athlete_half_marathon_training_data.groupby('week').size().std()\n",
    "\n",
    "weekly_distance_mean = athlete_half_marathon_training_data.groupby('week')['distance (miles)'].sum().mean()\n",
    "weekly_distance_std = athlete_half_marathon_training_data.groupby('week')['distance (miles)'].sum().std()\n",
    "\n",
    "weekly_time_mean = athlete_half_marathon_training_data.groupby('week')['elapsed time (hrs)'].sum().mean()\n",
    "weekly_time_std = athlete_half_marathon_training_data.groupby('week')['elapsed time (hrs)'].sum().std()\n",
    "\n",
    "weekly_pace_mean = athlete_half_marathon_training_data.groupby('week')['average pace (min/mile)'].mean().mean()\n",
    "weekly_pace_std = athlete_half_marathon_training_data.groupby('week')['average pace (min/mile)'].mean().std()\n",
    "\n",
    "# Print results\n",
    "print(f\"Total number of runs: {training_weeks_sum}\")\n",
    "print(f\"Total distance run (miles): {training_distance_sum}\")\n",
    "print(f\"Total elapsed time (hrs): {training_time_sum}\")\n",
    "print(f\"Total elevation gain (m): {training_elevation_gain_sum}\")\n",
    "\n",
    "print(f\"Mean number of runs per week: {weekly_run_count_mean}\")\n",
    "print(f\"Standard deviation of weekly run counts: {weekly_run_count_std}\")\n",
    "\n",
    "print(f\"Mean weekly distance (miles): {weekly_distance_mean}\")\n",
    "print(f\"Standard deviation of weekly distances: {weekly_distance_std}\")\n",
    "\n",
    "print(f\"Mean weekly elapsed time (hrs): {weekly_time_mean}\")\n",
    "print(f\"Standard deviation of weekly elapsed times: {weekly_time_std}\")\n",
    "\n",
    "print(f\"Mean weekly pace (min/mile): {weekly_pace_mean}\")\n",
    "print(f\"Standard deviation of weekly pace (min/mile): {weekly_pace_std}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>training_weeks_sum</th>\n",
       "      <th>training_distance_sum</th>\n",
       "      <th>training_time_sum</th>\n",
       "      <th>training_elevation_gain_sum</th>\n",
       "      <th>weekly_run_count_mean</th>\n",
       "      <th>weekly_run_count_std</th>\n",
       "      <th>weekly_distance_mean</th>\n",
       "      <th>weekly_distance_std</th>\n",
       "      <th>weekly_time_mean</th>\n",
       "      <th>weekly_time_std</th>\n",
       "      <th>weekly_pace_mean</th>\n",
       "      <th>weekly_pace_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [training_weeks_sum, training_distance_sum, training_time_sum, training_elevation_gain_sum, weekly_run_count_mean, weekly_run_count_std, weekly_distance_mean, weekly_distance_std, weekly_time_mean, weekly_time_std, weekly_pace_mean, weekly_pace_std]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athlete_summary_data = pd.DataFrame()\n",
    "athlete_summary_data['training_weeks_sum'] = None\n",
    "athlete_summary_data['training_distance_sum'] = None\n",
    "athlete_summary_data['training_time_sum'] = None\n",
    "athlete_summary_data['training_elevation_gain_sum'] = None\n",
    "athlete_summary_data['weekly_run_count_mean'] = None\n",
    "athlete_summary_data['weekly_run_count_std'] = None\n",
    "athlete_summary_data['weekly_distance_mean'] = None\n",
    "athlete_summary_data['weekly_distance_std'] = None\n",
    "athlete_summary_data['weekly_time_mean'] = None\n",
    "athlete_summary_data['weekly_time_std'] = None\n",
    "athlete_summary_data['weekly_pace_mean'] = None\n",
    "athlete_summary_data['weekly_pace_std'] = None\n",
    "\n",
    "athlete_summary_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vw/yyqdn1290jq54yhkbdwgt8b00000gp/T/ipykernel_11784/764386749.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  runner_data['timestamp'] = pd.to_datetime(runner_data['timestamp'], format='%m/%d/%Y %H:%M')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select a runner to plot\n",
    "runner_id = 1776170\n",
    "\n",
    "# Filter the data for the selected runner\n",
    "runner_data = run_train_data[run_train_data['athlete'] == runner_id]\n",
    "\n",
    "# Convert the timestamp to datetime for plotting\n",
    "runner_data['timestamp'] = pd.to_datetime(runner_data['timestamp'], format='%m/%d/%Y %H:%M')\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(runner_data['timestamp'], runner_data['distance (miles)'], marker='o', linestyle='None')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Distance (miles)')\n",
    "plt.title(f'Runner {runner_id} - Distance over Time')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot the average pace per mile\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(runner_data['timestamp'], runner_data['average pace (min/mile)'], marker='o', linestyle='None')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Average Pace (min/mile)')\n",
    "plt.title(f'Runner {runner_id} - Average Pace over Time')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>athlete</th>\n",
       "      <th>gender</th>\n",
       "      <th>distance (m)</th>\n",
       "      <th>elapsed time (s)</th>\n",
       "      <th>elevation gain (m)</th>\n",
       "      <th>average heart rate (bpm)</th>\n",
       "      <th>time</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>5K</th>\n",
       "      <th>10K</th>\n",
       "      <th>Half Marathon</th>\n",
       "      <th>Marathon</th>\n",
       "      <th>50K</th>\n",
       "      <th>50M</th>\n",
       "      <th>100K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7824</th>\n",
       "      <td>30467841</td>\n",
       "      <td>M</td>\n",
       "      <td>3480.4</td>\n",
       "      <td>1482</td>\n",
       "      <td>64.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16:49:00</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.550278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7825</th>\n",
       "      <td>30467841</td>\n",
       "      <td>M</td>\n",
       "      <td>5573.3</td>\n",
       "      <td>1981</td>\n",
       "      <td>136.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17:09:00</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.550278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7826</th>\n",
       "      <td>30467841</td>\n",
       "      <td>M</td>\n",
       "      <td>3760.1</td>\n",
       "      <td>1444</td>\n",
       "      <td>36.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13:40:00</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.550278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7827</th>\n",
       "      <td>30467841</td>\n",
       "      <td>M</td>\n",
       "      <td>7218.7</td>\n",
       "      <td>3050</td>\n",
       "      <td>142.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13:06:00</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.550278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7828</th>\n",
       "      <td>30467841</td>\n",
       "      <td>M</td>\n",
       "      <td>5888.4</td>\n",
       "      <td>2216</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17:03:00</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.550278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       athlete gender  distance (m)  elapsed time (s)  elevation gain (m)  \\\n",
       "7824  30467841      M        3480.4              1482                64.9   \n",
       "7825  30467841      M        5573.3              1981               136.1   \n",
       "7826  30467841      M        3760.1              1444                36.1   \n",
       "7827  30467841      M        7218.7              3050               142.9   \n",
       "7828  30467841      M        5888.4              2216                60.0   \n",
       "\n",
       "      average heart rate (bpm)      time  day  month  year        5K  10K  \\\n",
       "7824                       NaN  16:49:00   27     12  2018  0.550278  NaN   \n",
       "7825                       NaN  17:09:00   11     10  2018  0.550278  NaN   \n",
       "7826                       NaN  13:40:00   13      8  2018  0.550278  NaN   \n",
       "7827                       NaN  13:06:00   11      8  2018  0.550278  NaN   \n",
       "7828                       NaN  17:03:00   14      7  2018  0.550278  NaN   \n",
       "\n",
       "      Half Marathon  Marathon  50K  50M  100K  \n",
       "7824            NaN       NaN  NaN  NaN   NaN  \n",
       "7825            NaN       NaN  NaN  NaN   NaN  \n",
       "7826            NaN       NaN  NaN  NaN   NaN  \n",
       "7827            NaN       NaN  NaN  NaN   NaN  \n",
       "7828            NaN       NaN  NaN  NaN   NaN  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create separate dataframes for each race type based on the highest race completed by the runner\n",
    "race_types = ['5K', '10K', 'Half Marathon', 'Marathon', '50K', '50M', '100K']\n",
    "\n",
    "# Initialize an empty dictionary to store the dataframes\n",
    "race_runners_dfs = {}\n",
    "\n",
    "# Iterate through each race type and create a dataframe for runners whose highest completed race is that type\n",
    "for race in race_types:\n",
    "    # Filter runners who have completed the current race type\n",
    "    race_runners = run_train_data.dropna(subset=[race])\n",
    "    \n",
    "    # Check if the current race type is the highest completed race for each runner\n",
    "    for other_race in race_types:\n",
    "        if race_types.index(other_race) > race_types.index(race):\n",
    "            race_runners = race_runners[race_runners[other_race].isna()]\n",
    "    \n",
    "    # Store the dataframe in the dictionary\n",
    "    race_runners_dfs[race] = race_runners\n",
    "\n",
    "# Access the dataframes using race_runners_dfs['5K'], race_runners_dfs['10K'], etc.\n",
    "race_runners_dfs['5K'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'timestamp'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/vw/yyqdn1290jq54yhkbdwgt8b00000gp/T/ipykernel_22493/1804796921.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Run the model for each race type and print the results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrace\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrace_types\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mmse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrunModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Race Type: {race}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Mean Squared Error: {mse}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'R^2 Score: {r2}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/vw/yyqdn1290jq54yhkbdwgt8b00000gp/T/ipykernel_22493/3368124749.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(race_type)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrunModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrace_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;31m## Drop the NaN values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mtraining_results_merged_df_dropped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrace_runners_dfs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrace_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance (m)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'elapsed time (s)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'elevation gain (m)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'average heart rate (bpm)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mtraining_results_merged_df_dropped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_results_merged_df_dropped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;31m# Select features (X) and target variable (y)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Development/racePrediction/myenv/lib/python3.10/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[1;32m   7185\u001b[0m             )\n\u001b[1;32m   7186\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7187\u001b[0m             \u001b[0;31m# len(by) == 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7189\u001b[0;31m             \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7191\u001b[0m             \u001b[0;31m# need to rewrap column in Series to apply key function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7192\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Development/racePrediction/myenv/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1907\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mother_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1908\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_level_reference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1909\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1910\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1911\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1913\u001b[0m         \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1914\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'timestamp'"
     ]
    }
   ],
   "source": [
    "# Run the model for each race type and print the results\n",
    "for race in race_types:\n",
    "    mse, r2 = runModel(race)\n",
    "    print(f'Race Type: {race}')\n",
    "    print(f'Mean Squared Error: {mse}')\n",
    "    print(f'R^2 Score: {r2}')\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was the results from the first set of data with merged race results.\n",
    "Mean Squared Error: 3.4519115491222747\n",
    "R^2 Score: 0.005312515370057724\n",
    "\n",
    "\n",
    "## Results from only the source data features\n",
    "Race Type: 5K\n",
    "Mean Squared Error: 1.232595164407831e-32\n",
    "R^2 Score: 0.0\n",
    "---\n",
    "Race Type: 10K\n",
    "Mean Squared Error: 0.047388928185275486\n",
    "R^2 Score: 0.08935285083748334\n",
    "---\n",
    "Race Type: Half Marathon\n",
    "Mean Squared Error: 0.8170558205393661\n",
    "R^2 Score: -0.0012955886806558237\n",
    "---\n",
    "Race Type: Marathon\n",
    "Mean Squared Error: 3.1623861291863853\n",
    "R^2 Score: 0.041607053936431604\n",
    "---\n",
    "Race Type: 50K\n",
    "Mean Squared Error: 2.0892964679226824\n",
    "R^2 Score: -0.01893250091885057\n",
    "---\n",
    "Race Type: 50M\n",
    "Mean Squared Error: 0.0\n",
    "R^2 Score: 1.0\n",
    "---\n",
    "Race Type: 100K\n",
    "Mean Squared Error: 7.888609052210118e-31\n",
    "R^2 Score: 0.75\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distance (m): -2.2801244016725836e-05\n",
      "elapsed time (s): 5.141540391086153e-05\n",
      "elevation gain (m): 0.00016101170928957878\n",
      "average heart rate (bpm): 0.004526336574417813\n"
     ]
    }
   ],
   "source": [
    "# Display the model coefficients\n",
    "coefficients = model.coef_\n",
    "feature_names = X.columns\n",
    "\n",
    "for feature, coef in zip(feature_names, coefficients):\n",
    "    print(f'{feature}: {coef}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 3.4519115491222747\n",
      "R^2 Score: 0.005312515370057724\n"
     ]
    }
   ],
   "source": [
    "# Create a pipeline with feature scaling and the model\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('model', LinearRegression())\n",
    "])\n",
    "\n",
    "# Fit the pipeline with the training data\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print('Mean Squared Error:', mse)\n",
    "print('R^2 Score:', r2)"
   ]
  }
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