https://github.com/mounishvatti/machine_learning-football-prediction-
This repository consists of prediction of the football team winners using historical data with the help of machine learning algorithms
https://github.com/mounishvatti/machine_learning-football-prediction-
learning machine-learning machine-learning-algorithms pandas python sklearn-metrics
Last synced: 2 months ago
JSON representation
This repository consists of prediction of the football team winners using historical data with the help of machine learning algorithms
- Host: GitHub
- URL: https://github.com/mounishvatti/machine_learning-football-prediction-
- Owner: mounishvatti
- Created: 2023-04-07T10:50:58.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-01-28T16:21:41.000Z (over 1 year ago)
- Last Synced: 2025-01-15T20:20:36.633Z (4 months ago)
- Topics: learning, machine-learning, machine-learning-algorithms, pandas, python, sklearn-metrics
- Language: Jupyter Notebook
- Homepage:
- Size: 295 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Football Prediction using Machine learning algorithms
## Tech Stack
   
### Project Overview:
1. **Objective:**
- The primary goal of this project is to predict the winners of English Premier League (EPL) matches using a combination of machine learning, data scraping, and data cleaning techniques.
2. **Methodology:**
- We employ machine learning algorithms to analyze historical EPL match data, extracting valuable insights to enhance prediction accuracy.
- Data scraping techniques are utilized to gather comprehensive information from various sources, ensuring a diverse and rich dataset.
3. **Data Cleaning Techniques:**
- Rigorous data cleaning processes are implemented to handle missing values, outliers, and inconsistencies, ensuring the reliability of the dataset.
- Standardization and normalization techniques are applied to ensure uniformity and enhance the performance of machine learning models.
4. **Pattern Identification:**
- Through in-depth analysis of historical data, our aim is to identify patterns and correlations that can serve as key indicators for predicting future match outcomes.
- Feature engineering is employed to extract relevant information and create informative variables for the machine learning models.
5. **Machine Learning Models:**
- Various machine learning models, such as regression or classification algorithms, are explored and tested to determine the most effective approach for predicting match winners.
- Model hyperparameter tuning and optimization are performed to enhance predictive accuracy.
6. **GitHub Repository Contents:**
- This repository houses the codebase, datasets, and documentation related to the project.
- Users can find detailed information on the implemented algorithms, data preprocessing steps, and model evaluation metrics.
7. **Usage Instructions:**
- Clear instructions and documentation are provided to guide users on replicating the analysis, running the models, and interpreting the results.
- Dependencies and system requirements are outlined to facilitate easy setup and execution.
8. **Contributions and Feedback:**
- Contributions from the community are welcomed through pull requests.
- Users are encouraged to provide feedback, report issues, or suggest enhancements to foster collaborative improvement.
By combining these elements, we aim to create a robust and insightful predictive modeling framework for EPL match outcomes.