{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Formula One Project: Modeling\n",
    "\n",
    "DUE: December 4th, 2024 (Wed)  \n",
    "Name(s): Sean O'Connor, Connor Coles    \n",
    "Class: CSCI 349 - Intro to Data Mining  \n",
    "Semester: Fall 2024  \n",
    "Instructor: Brian King  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment Description\n",
    "\n",
    "Copy over the important cells from the previous step that read in and cleaned your data to this new notebook file. You do not need to copy over all your EDA and plots describing your data, only the code that prepares your data for modeling. This notebook is about exploring the development of predictive models. Some initial preliminary work on applying some modeling techniques should be completed.\n",
    "Be sure to commit and push all supporting code that you've completed in this file. Include in this notebook a summary cell at the top that details your accomplishments, challenges, and what you expect to accomplish for your final steps. Be sure to update your readme.md in your repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "import fastf1\n",
    "import fastf1.plotting\n",
    "from fastf1.ergast.structure import FastestLap\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "# FastF1 general setup\n",
    "cache_dir = '../data/cache'\n",
    "if not os.path.exists(cache_dir):\n",
    "    os.makedirs(cache_dir)\n",
    "\n",
    "fastf1.Cache.enable_cache(cache_dir)\n",
    "fastf1.plotting.setup_mpl(misc_mpl_mods=False, color_scheme=None)\n",
    "\n",
    "# Set up plot style\n",
    "# print style.available to check available styles\n",
    "print(plt.style.available)\n",
    "plt.style.use('seaborn-v0_8-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define years, sessions, and events of interest\n",
    "years = [2021, 2022, 2023, 2024]\n",
    "sessions = ['Race']\n",
    "events = ['Bahrain Grand Prix', 'British Grand Prix', 'United States Grand Prix', 'Mexico City Grand Prix', 'São Paulo Grand Prix'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2021 Bahrain Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n",
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['44', '33', '77', '4', '11', '16', '3', '55', '22', '18', '7', '99', '31', '63', '5', '47', '10', '6', '14', '9']\n",
      "core           INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2021 British Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['44', '16', '77', '4', '3', '55', '14', '18', '31', '22', '10', '63', '99', '6', '7', '11', '9', '47', '5', '33']\n",
      "core           INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2021 United States Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "core        WARNING \tDriver  7: Lap timing integrity check failed for 1 lap(s)\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['33', '44', '11', '16', '3', '77', '55', '4', '22', '5', '99', '18', '7', '63', '6', '47', '9', '14', '31', '10']\n",
      "core           INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2021 Mexico City Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['33', '44', '11', '10', '16', '55', '5', '7', '14', '4', '99', '3', '31', '18', '77', '63', '6', '9', '47', '22']\n",
      "core           INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2021 São Paulo Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['44', '33', '77', '11', '16', '55', '10', '31', '14', '4', '5', '7', '63', '99', '22', '6', '9', '47', '3', '18']\n",
      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2022 Bahrain Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['16', '55', '44', '63', '20', '77', '31', '22', '14', '24', '47', '18', '23', '3', '4', '6', '27', '11', '1', '10']\n",
      "core           INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2022 British Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['55', '11', '44', '16', '14', '4', '1', '47', '5', '20', '18', '6', '3', '22', '31', '10', '77', '63', '24', '23']\n",
      "events      WARNING \tCorrecting user input 'United States Grand Prix' to 'United States Grand Prix'\n",
      "core           INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2022 United States Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '44', '16', '11', '63', '4', '14', '5', '20', '22', '31', '24', '23', '10', '47', '3', '6', '18', '77', '55']\n",
      "core           INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2022 Mexico City Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '44', '11', '63', '55', '16', '3', '31', '4', '77', '10', '23', '24', '5', '18', '47', '20', '6', '14', '22']\n",
      "core           INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2022 São Paulo Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['63', '44', '55', '16', '14', '1', '11', '31', '77', '18', '5', '24', '47', '10', '23', '6', '22', '4', '20', '3']\n",
      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2023 Bahrain Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '11', '14', '55', '44', '18', '63', '77', '10', '23', '22', '2', '20', '21', '27', '24', '4', '31', '16', '81']\n",
      "core           INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2023 British Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '4', '44', '81', '63', '11', '14', '23', '16', '55', '2', '77', '27', '18', '24', '22', '21', '10', '20', '31']\n",
      "events      WARNING \tCorrecting user input 'United States Grand Prix' to 'United States Grand Prix'\n",
      "core           INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2023 United States Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '4', '55', '11', '63', '10', '18', '22', '23', '2', '27', '77', '24', '20', '3', '14', '81', '31', '44', '16']\n",
      "core           INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2023 Mexico City Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '44', '16', '55', '4', '63', '3', '81', '23', '31', '10', '22', '27', '24', '77', '2', '18', '14', '20', '11']\n",
      "core           INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2023 São Paulo Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '4', '14', '11', '18', '55', '10', '44', '22', '31', '2', '27', '3', '81', '63', '77', '24', '20', '23', '16']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2024 Bahrain Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n",
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '11', '55', '16', '63', '4', '44', '81', '14', '18', '24', '20', '3', '22', '23', '27', '31', '10', '77', '2']\n",
      "core           INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2024 British Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['44', '1', '4', '81', '55', '27', '18', '14', '23', '22', '2', '20', '3', '16', '77', '31', '11', '24', '63', '10']\n",
      "events      WARNING \tCorrecting user input 'United States Grand Prix' to 'United States Grand Prix'\n",
      "core           INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2024 United States Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['16', '55', '1', '4', '81', '63', '11', '27', '30', '43', '20', '10', '14', '22', '18', '23', '77', '31', '24', '44']\n",
      "core           INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2024 Mexico City Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['55', '4', '16', '44', '63', '1', '20', '81', '27', '10', '18', '43', '31', '77', '24', '30', '11', '14', '23', '22']\n",
      "core           INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 2024 São Paulo Grand Prix - Race\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "core        WARNING \tNo lap data for driver 23\n",
      "core        WARNING \tFailed to perform lap accuracy check - all laps marked as inaccurate (driver 23)\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '31', '10', '63', '16', '4', '22', '81', '30', '44', '11', '50', '77', '14', '24', '55', '43', '23', '18', '27']\n"
     ]
    }
   ],
   "source": [
    "# Get data from FastF1 API\n",
    "\n",
    "# Data containers\n",
    "weather_data_list = []\n",
    "lap_data_list = []\n",
    "\n",
    "# Loop through years and sessions\n",
    "for year in years:\n",
    "    for event_name in events:   \n",
    "        for session_name in sessions:\n",
    "            try:\n",
    "                print(f\"Processing {year} {event_name} - {session_name}\")\n",
    "                \n",
    "                # Load the session\n",
    "                session = fastf1.get_session(year, event_name, session_name, backend='fastf1')\n",
    "                session.load()\n",
    "                \n",
    "                # Process weather data\n",
    "                weather_data = session.weather_data\n",
    "                if weather_data is not None:\n",
    "                    weather_df = pd.DataFrame(weather_data)\n",
    "                    # Add context columns\n",
    "                    weather_df['Year'] = year\n",
    "                    weather_df['Event'] = event_name\n",
    "                    weather_df['Session'] = session_name\n",
    "                    weather_data_list.append(weather_df)\n",
    "\n",
    "                # Process lap data\n",
    "                lap_data = session.laps\n",
    "                if lap_data is not None:\n",
    "                    lap_df = pd.DataFrame(lap_data)\n",
    "                    # Add context columns\n",
    "                    lap_df['Year'] = year\n",
    "                    lap_df['Event'] = event_name\n",
    "                    lap_df['Session'] = session_name\n",
    "                    # Ensure driver information is included\n",
    "                    if 'Driver' not in lap_df.columns:\n",
    "                        lap_df['Driver'] = lap_df['DriverNumber'].map(session.drivers)\n",
    "                    # Add team information if available\n",
    "                    if 'Team' not in lap_df.columns:\n",
    "                        lap_df['Team'] = lap_df['Driver'].map(session.drivers_info['TeamName'])\n",
    "                    lap_data_list.append(lap_df)\n",
    "        \n",
    "            except Exception as e:\n",
    "                print(f\"Error with {event_name} {session_name} ({year}): {e}\")\n",
    "\n",
    "# Combine data into DataFrames\n",
    "if weather_data_list:\n",
    "    weather_data_combined = pd.concat(weather_data_list, ignore_index=True)\n",
    "    # Ensure consistent column ordering\n",
    "    weather_cols = ['Time', 'Year', 'Event', 'Session', \n",
    "                   'AirTemp', 'Humidity', 'Pressure', 'Rainfall', \n",
    "                   'TrackTemp', 'WindDirection', 'WindSpeed']\n",
    "    weather_data_combined = weather_data_combined[weather_cols]\n",
    "    \n",
    "if lap_data_list:\n",
    "    lap_data_combined = pd.concat(lap_data_list, ignore_index=True)\n",
    "    # Ensure consistent column ordering\n",
    "    lap_cols = ['Time', 'Year', 'Event', 'Session', \n",
    "                'Driver', 'Team', 'LapNumber', 'LapTime',\n",
    "                'Sector1Time', 'Sector2Time', 'Sector3Time',\n",
    "                'Compound', 'TyreLife', 'FreshTyre',\n",
    "                'SpeedI1', 'SpeedI2', 'SpeedFL', 'SpeedST']\n",
    "    # Only include columns that exist\n",
    "    existing_cols = [col for col in lap_cols if col in lap_data_combined.columns]\n",
    "    lap_data_combined = lap_data_combined[existing_cols]\n",
    "    \n",
    "# Time conversion\n",
    "# Function to convert timedelta to datetime\n",
    "def convert_timedelta_to_datetime(df, base_date='2021-01-01'):\n",
    "    if 'Time' in df.columns:\n",
    "        # Create a base datetime and add the timedelta\n",
    "        base = pd.Timestamp(base_date)\n",
    "        if df['Time'].dtype == 'timedelta64[ns]':\n",
    "            df['Time'] = base + df['Time']\n",
    "    return df\n",
    "\n",
    "# Apply conversion to both dataframes\n",
    "weather_data_combined = convert_timedelta_to_datetime(weather_data_combined)\n",
    "lap_data_combined = convert_timedelta_to_datetime(lap_data_combined)\n",
    "\n",
    "# Remove missing values\n",
    "weather_data_combined = weather_data_combined.dropna()\n",
    "lap_data_combined = lap_data_combined.dropna()\n",
    "\n",
    "# Create a new column for lap time in seconds\n",
    "lap_data_combined['LapTime_seconds'] = lap_data_combined['LapTime'].dt.total_seconds()\n",
    "\n",
    "# Merge the data\n",
    "merged_data = pd.merge_asof(\n",
    "    lap_data_combined.sort_values('Time'),\n",
    "    weather_data_combined.sort_values('Time'),\n",
    "    on='Time',\n",
    "    by=['Event', 'Year'],  # Match within same event and year\n",
    "    direction='nearest',\n",
    "    tolerance=pd.Timedelta('1 min')  # Allow matching within 1 minute\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Engineering\n",
    "def engineer_features(df):\n",
    "    # Normalize lap times per track\n",
    "    df['NormalizedLapTime'] = df.groupby('Event')['LapTime_seconds'].transform(\n",
    "        lambda x: (x - x.mean()) / x.mean()\n",
    "    )\n",
    "    \n",
    "    # Driver performance metrics (now track-specific)\n",
    "    df['DriverTrackAvg'] = df.groupby(['Driver', 'Event'])['LapTime_seconds'].transform('mean')\n",
    "    df['DriverTrackStd'] = df.groupby(['Driver', 'Event'])['LapTime_seconds'].transform('std')\n",
    "    \n",
    "    # Calculate driver's performance relative to track average\n",
    "    df['DriverTrackPerformance'] = df.groupby(['Event', 'Year'])['LapTime_seconds'].transform(\n",
    "        lambda x: (x - x.mean()) / x.mean()\n",
    "    )\n",
    "    \n",
    "    # Tire performance degradation (exponential, track-specific)\n",
    "    # Different tracks have different tire wear characteristics\n",
    "    df['TyreAgeFactor'] = df.groupby('Event')['TyreLife'].transform(\n",
    "        lambda x: np.exp(-0.02 * x)  # Could vary coefficient by track type\n",
    "    )\n",
    "    \n",
    "    # Track evolution (grip improvement, track-specific)\n",
    "    df['TrackEvolution'] = df.groupby(['Event', 'Year'])['LapNumber'].transform(\n",
    "        lambda x: (x - x.min()) / (x.max() - x.min())\n",
    "    )\n",
    "    \n",
    "    # Weather impact (track-specific)\n",
    "    df['TempDelta'] = df['TrackTemp'] - df['AirTemp']\n",
    "    \n",
    "    # Fuel effect (track-specific due to different fuel consumption rates)\n",
    "    df['FuelEffect'] = df.groupby('Event')['LapNumber'].transform(\n",
    "        lambda x: 1 - (x / x.max())\n",
    "    )\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data for modeling\n",
    "def prepare_modeling_data(df):\n",
    "    # Engineer features\n",
    "    data = engineer_features(df)\n",
    "    \n",
    "    # Train separate models for each track\n",
    "    track_models = {}\n",
    "    track_results = {}\n",
    "    \n",
    "    for event in data['Event'].unique():\n",
    "        print(f\"\\nProcessing {event}\")\n",
    "        track_data = data[data['Event'] == event].copy()\n",
    "        \n",
    "        # Select features for modeling\n",
    "        feature_columns = [\n",
    "            'DriverTrackAvg', 'DriverTrackStd',\n",
    "            'DriverTrackPerformance',\n",
    "            'TrackTemp', 'AirTemp', 'Humidity', 'WindSpeed',\n",
    "            'TyreLife', 'TyreAgeFactor', 'TrackEvolution',\n",
    "            'TempDelta', 'FuelEffect', 'SpeedI1', 'SpeedI2'\n",
    "        ]\n",
    "        \n",
    "        # Create dummy variables for categorical features\n",
    "        track_data = pd.get_dummies(track_data, columns=['Compound'])\n",
    "        feature_columns.extend([col for col in track_data.columns if col.startswith('Compound_')])\n",
    "        \n",
    "        # Split and scale data for this track\n",
    "        X = track_data[feature_columns]\n",
    "        y = track_data['LapTime_seconds']\n",
    "        \n",
    "        X_train, X_test, y_train, y_test = train_test_split(\n",
    "            X, y, test_size=0.2, random_state=42\n",
    "        )\n",
    "        \n",
    "        scaler = StandardScaler()\n",
    "        X_train_scaled = scaler.fit_transform(X_train)\n",
    "        X_test_scaled = scaler.transform(X_test)\n",
    "        \n",
    "        # Train models for this track\n",
    "        models = {\n",
    "            'Linear Regression': LinearRegression(),\n",
    "            'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42),\n",
    "            'XGBoost': xgb.XGBRegressor(n_estimators=100, random_state=42),\n",
    "            'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, random_state=42)\n",
    "        }\n",
    "        \n",
    "        track_results[event] = {}\n",
    "        for name, model in models.items():\n",
    "            # Train model\n",
    "            model.fit(X_train_scaled, y_train)\n",
    "            \n",
    "            # Make predictions\n",
    "            y_pred = model.predict(X_test_scaled)\n",
    "            \n",
    "            # Calculate metrics\n",
    "            mse = mean_squared_error(y_test, y_pred)\n",
    "            rmse = np.sqrt(mse)\n",
    "            r2 = r2_score(y_test, y_pred)\n",
    "            \n",
    "            track_results[event][name] = {\n",
    "                'RMSE': rmse,\n",
    "                'R2': r2,\n",
    "                'model': model,\n",
    "                'scaler': scaler,\n",
    "                'features': feature_columns\n",
    "            }\n",
    "            \n",
    "        # Print results for this track\n",
    "        print(f\"\\nResults for {event}:\")\n",
    "        for name, metrics in track_results[event].items():\n",
    "            print(f\"{name}:\")\n",
    "            print(f\"RMSE: {metrics['RMSE']:.2f} seconds\")\n",
    "            print(f\"R2 Score: {metrics['R2']:.3f}\")\n",
    "    \n",
    "    return track_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing Bahrain Grand Prix\n",
      "\n",
      "Results for Bahrain Grand Prix:\n",
      "Linear Regression:\n",
      "RMSE: 1.10 seconds\n",
      "R2 Score: 0.981\n",
      "Random Forest:\n",
      "RMSE: 0.30 seconds\n",
      "R2 Score: 0.999\n",
      "XGBoost:\n",
      "RMSE: 0.32 seconds\n",
      "R2 Score: 0.998\n",
      "Gradient Boosting:\n",
      "RMSE: 0.42 seconds\n",
      "R2 Score: 0.997\n",
      "\n",
      "Processing Mexico City Grand Prix\n",
      "\n",
      "Results for Mexico City Grand Prix:\n",
      "Linear Regression:\n",
      "RMSE: 0.50 seconds\n",
      "R2 Score: 0.998\n",
      "Random Forest:\n",
      "RMSE: 0.28 seconds\n",
      "R2 Score: 0.999\n",
      "XGBoost:\n",
      "RMSE: 0.39 seconds\n",
      "R2 Score: 0.999\n",
      "Gradient Boosting:\n",
      "RMSE: 0.25 seconds\n",
      "R2 Score: 0.999\n",
      "\n",
      "Processing United States Grand Prix\n",
      "\n",
      "Results for United States Grand Prix:\n",
      "Linear Regression:\n",
      "RMSE: 0.40 seconds\n",
      "R2 Score: 0.996\n",
      "Random Forest:\n",
      "RMSE: 0.39 seconds\n",
      "R2 Score: 0.997\n",
      "XGBoost:\n",
      "RMSE: 0.47 seconds\n",
      "R2 Score: 0.995\n",
      "Gradient Boosting:\n",
      "RMSE: 0.30 seconds\n",
      "R2 Score: 0.998\n",
      "\n",
      "Processing British Grand Prix\n",
      "\n",
      "Results for British Grand Prix:\n",
      "Linear Regression:\n",
      "RMSE: 0.73 seconds\n",
      "R2 Score: 0.996\n",
      "Random Forest:\n",
      "RMSE: 0.56 seconds\n",
      "R2 Score: 0.998\n",
      "XGBoost:\n",
      "RMSE: 0.46 seconds\n",
      "R2 Score: 0.998\n",
      "Gradient Boosting:\n",
      "RMSE: 0.50 seconds\n",
      "R2 Score: 0.998\n",
      "\n",
      "Processing São Paulo Grand Prix\n",
      "\n",
      "Results for São Paulo Grand Prix:\n",
      "Linear Regression:\n",
      "RMSE: 0.81 seconds\n",
      "R2 Score: 0.995\n",
      "Random Forest:\n",
      "RMSE: 0.52 seconds\n",
      "R2 Score: 0.998\n",
      "XGBoost:\n",
      "RMSE: 0.56 seconds\n",
      "R2 Score: 0.998\n",
      "Gradient Boosting:\n",
      "RMSE: 0.59 seconds\n",
      "R2 Score: 0.997\n"
     ]
    }
   ],
   "source": [
    "# Execute modeling pipeline\n",
    "track_results = prepare_modeling_data(merged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize results across tracks\n",
    "def plot_rmse(track_results):\n",
    "    # Prepare data for plotting\n",
    "    comparison_data = []\n",
    "    for track, models in track_results.items():\n",
    "        for model_name, metrics in models.items():\n",
    "            comparison_data.append({\n",
    "                'Track': track,\n",
    "                'Model': model_name,\n",
    "                'RMSE': metrics['RMSE'],\n",
    "            })\n",
    "    \n",
    "    comparison_df = pd.DataFrame(comparison_data)\n",
    "    \n",
    "    # Plot RMSE comparison\n",
    "    plt.figure(figsize=(15, 6))\n",
    "    sns.barplot(data=comparison_df, x='Track', y='RMSE', hue='Model')\n",
    "    plt.title('Model Performance (RMSE) Across Different Tracks')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def plot_r2(track_results):\n",
    "    # Prepare data for plotting\n",
    "    comparison_data = []\n",
    "    for track, models in track_results.items():\n",
    "        for model_name, metrics in models.items():\n",
    "            comparison_data.append({\n",
    "                'Track': track,\n",
    "                'Model': model_name,\n",
    "                'R2': metrics['R2'],\n",
    "            })\n",
    "    \n",
    "    comparison_df = pd.DataFrame(comparison_data)\n",
    "    \n",
    "    # Plot R² comparison\n",
    "    plt.figure(figsize=(15, 6))\n",
    "    sns.barplot(data=comparison_df, x='Track', y='R2', hue='Model')\n",
    "    plt.title('Model Performance (R²) Across Different Tracks')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RMSE comparison across tracks\n",
    "plot_rmse(track_results)\n",
    "\n",
    "# Plot R2 comparison across tracks\n",
    "plot_r2(track_results)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "csci349",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}