F1 Race Predictor Project

Overview

This project focuses on predicting the outcomes of Formula 1 races. The dataset includes information about races, drivers, teams, and various race-related metrics.

Table of Contents

1. Data Collection
2. Data Preprocessing
3. Exploratory Data Analysis (EDA)
4. Further Data Preprocessing
5. Model Building and Evaluation
6. Conclusion

Data Collection

The project begins with collecting data from various CSV files, merging them based on common identifiers, and creating a comprehensive dataset.

Data Preprocessing

Cleaning and feature engineering are performed on the dataset, including handling missing values, updating team names, and calculating driver ages.

EDA

Exploratory Data Analysis is conducted to visualize and understand the distribution of various factors such as driver nationality, age, wins, constructor wins, and DNFs.

Further Data Preprocessing

The dataset is prepared for classification models, including one-hot encoding categorical features and scaling numerical features.

Model Building and Evaluation

Several machine learning models, including Random Forest, XGBoost, SVM, KNN, and Logistic Regression, are implemented and evaluated based on training and testing accuracies.

Conclusion

The project provides insights into the relationships between different variables and aims to predict race outcomes using machine learning models.

Getting Started

Follow these steps to get started with the project:

Requirements

• Python (>=3.6)
• Required Python packages (install using pip install -r requirements.txt)

Usage

1. Clone the repository.
2. Install the required packages.
3. Run the Jupyter notebooks to explore the data, perform EDA, and build models.

Contributing

Contributions are welcome! If you have ideas for improvement or find any issues, feel free to open an issue or create a pull request.