This project focuses on predicting the market values of football players using machine learning techniques. By analyzing various player performance statistics such as age, goals, dribbling, and reputation, the system provides estimates of their market worth. The project includes both server-side and client-side implementations, with a Flask-based RESTful API for the server and web and mobile interfaces for the client.
This project involves building a house price prediction model for Mumbai using data from Kaggle. The process includes data exploration and cleaning, feature engineering, and model selection (Linear Regression, Random Forest, Decision Tree) with hyperparameter tuning. The Random Forest Regressor performed the best. The final trained model is saved as a pickle file for easy predictions
Sparkyy has different fun commands,anime commands,meme commands,supercell game commands etc.
Created this 2d game which has the same concept that we have played when we were young.Wasn't able to put much time into this as i had other deadlines too(lol)
This is created using HTML,Bootstrap CSS, JavaScript and
Rapid-Api.
Made this as I wanted to learn how to fetch data from an API in
javascript