IPL Prediction using Machine Learning

Overview

This project aims to predict the outcomes of Indian Premier League (IPL) matches using machine learning techniques. The prediction model utilizes historical data, player statistics, team performance, and various features to make predictions about match results. Features

Data Collection: The project involves gathering comprehensive data on past IPL matches, including player performance, team statistics, venue details, and match outcomes.

Data Preprocessing: The collected data undergoes thorough preprocessing to handle missing values, outliers, and normalization. This ensures that the data is in a suitable format for machine learning algorithms.

Feature Engineering: Relevant features are selected, and new features may be engineered to improve the model's predictive performance. This step involves a deep understanding of the game and its influencing factors.

Model Selection: Various machine learning models are experimented with and evaluated to determine the most suitable algorithm for IPL match prediction. Common models include Decision Trees, Random Forest, Support Vector Machines, and Neural Networks.

Training and Testing: The dataset is split into training and testing sets to train the model on historical data and evaluate its performance on unseen data.

Evaluation Metrics: The model's performance is assessed using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score. This helps in understanding how well the model is performing and where it might need improvement.

Deployment: The model is deployed using streamlit to get real-time predictions for upcoming IPL matches.

Requirements

Python 3.x
Jupyter Notebook (for development)
Necessary Python libraries (NumPy, Pandas, Scikit-learn, etc.)