Welcome to the College Football Game Outcome Prediction repository! In this project, I have utilized the power of Python, specifically the pandas and numpy libraries, to predict the outcomes of college football games in the 2022 season. By analyzing data from 2000 onwards, I aimed to identify key statistics that serve as strong predictors of game winners, considering both home field and away field data.
College football games are filled with excitement and uncertainty. However, with the right data and analysis, we can gain insights into factors that significantly influence game outcomes. In this project, I have leveraged the pandas library for effective data wrangling and utilized numpy to identify statistical predictors of the final scores.
To predict game outcomes, I have collected and processed a comprehensive dataset spanning from 2000 to the present. This dataset contains valuable information about teams, their performance, and various game-related statistics. By applying data wrangling techniques and feature engineering, I have identified key indicators that strongly correlate with game results.
Through this project, I have achieved the following learning goals:
- Proficiently working with the pandas library for data manipulation and analysis.
- Utilizing numpy to perform advanced numerical computations and identify key statistics.
- Implementing effective data wrangling techniques to prepare the dataset for analysis.
- Identifying and evaluating predictors of game outcomes based on home field and away field data.
The predictive model developed in this project provides valuable insights into the outcome of college football games. By considering various statistical factors, we can make informed predictions about winners and gain a deeper understanding of the dynamics of the game.
Feel free to explore the code and analysis in the repository. If you have any questions or would like to discuss this project further, please don't hesitate to reach out.
Husain