Predicting the future sounds like magic whether it be detecting in advance the intent of a potential customer to purchase your product or figuring out where the price of a stock is headed. If we can reliably predict the future of something, then we own a massive advantage. Machine learning has only served to amplify this magic and mystery.
Applications
The main objective of sports prediction is to improve team performance and enhance the chances of winning the game. The value of a win takes on different forms like trickles down to the fans filling the stadium seats, television contracts, fan store merchandise, parking, concessions, sponsorships, enrollment and retention.
Environment and tools
- Spyder
- Numpy
- Pandas
- Scikit-learn
- HTML
- CSS
- JavaScript
- Flask
- Python
Prerequisites : This post assumes familiarity with basic Machine Learning concepts Regressions like Linear Regression , Logistic Regression , SVM , Decision Tree , Randomn Forest etc. we used Linear Regression because it works better on the dataset. you can use any Regression Technique for your Purpose.
Front end :-knowledge of HTML CSS JS Booststrap
Backend :- Python programing and Flask.
How to Run
- Clone the repository
- open cmd prompt
- Create a new enviornment
- write the command --> pip install -r requirements.txt
- Run crc.py
Creators :