diff --git a/README.md b/README.md index 39c0783..13f6942 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ - Connor Coles ### Project Summary -We are conducting a data mining project focused on analyzing driver performance in Formula One racing. Our goal is to correlate driver performance with track and weather conditions, and to predict future race results using these correlations. We will apply various data mining techniques to extract meaningful insights from the dataset. +We are conducting a data mining project focused on analyzing driver performance in Formula One racing, with the goal to correlate driver performance with track and weather conditions, and to predict future race results using these correlations. We will apply various data mining techniques learned throughout the course to extract meaningful insights from the dataset. ### Important Dates - **Data Selection Due:** November 13, 2024 @@ -23,6 +23,8 @@ Directories: - pandas - numpy - matplotlib +- rapidfuzz +- fastf1 ### Video Presentation @@ -35,5 +37,6 @@ Our video presentation will be linked here. - **Video Presentation** - A recorded video summarizing our project, linked above. ### Important Links -- [Dataset Source](https://openf1.org) +- [Dataset Source](https://github.com/theOehrly/Fast-F1) +- [Dataset Documentation](https://docs.fastf1.dev/index.html) - [GitLab Repository](https://gitlab.bucknell.edu/sso005/csci349_final_project) \ No newline at end of file diff --git a/project/DataPrep_EDA.ipynb b/project/DataPrep_EDA.ipynb index a11ea2c..e5b94f0 100644 --- a/project/DataPrep_EDA.ipynb +++ b/project/DataPrep_EDA.ipynb @@ -26,11 +26,106 @@ "- In your final cells, discuss the modeling methods you expect to use. Start by clearly explaining if this is a classification, regression, clustering, or association rule mining problem? Justify. You have much of the framework to apply most algorithms, even those beyond what we covered in class. Feel free to explore different methods if you have good justification for doing so. If there are any papers of significance that have been published with these data, then discuss the ones most interesting/relevant to the team.\n", "- Finally, what is your overarching aim with this project? What are you hoping to learn? Or, what hypothesis are you using the data to confirm or disprove? What challenges do you foresee on this project? Discuss your concerns. How will you get your work done? Give a reasonable list of milestones to reach to arrive at the final deadline for the project." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem Summary\n", + "We are conducting a data mining project focused on analyzing driver performance in Formula One. Our goal is to correlate driver performance with track and weather conditions, and to predict future race results using these correlations. We will apply various data mining techniques learned throughout the course to extract meaningful insights from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "core INFO \tLoading data for Italian Grand Prix - Qualifying [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 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', '44', '77', '5', '3', '27', '55', '23', '18', '7', '99', '20', '26', '4', '10', '8', '11', '63', '88', '33']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# FastF1 Example\n", + "import fastf1\n", + "import fastf1.plotting\n", + "\n", + "fastf1.plotting.setup_mpl(misc_mpl_mods=False, color_scheme=None)\n", + "\n", + "session = fastf1.get_session(2019, 'Monza', 'Q')\n", + "\n", + "session.load()\n", + "fast_leclerc = session.laps.pick_drivers('LEC').pick_fastest()\n", + "lec_car_data = fast_leclerc.get_car_data()\n", + "t = lec_car_data['Time']\n", + "vCar = lec_car_data['Speed']\n", + "\n", + "# The rest is just plotting\n", + "fig, ax = plt.subplots()\n", + "ax.plot(t, vCar, label='Fast')\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('Speed [Km/h]')\n", + "ax.set_title('Leclerc is')\n", + "ax.legend()\n", + "plt.show()" + ] } ], "metadata": { + "kernelspec": { + "display_name": "csci349", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "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,