Introduction
The Maharashtra Health and Technical Common Entrance Test (MHT-CET) is a vital exam for students aspiring to enter engineering and pharmacy programs in Maharashtra. The 'Rank-Predictor-MHT-CET' project on GitHub, developed by Mandar Wagh, aims to provide valuable predictions of students' ranks based on their scores, aiding them in making informed decisions about their future.
Introduction to the Repository
The 'Rank-Predictor-MHT-CET' repository is a tool designed to predict students' ranks based on their MHT-CET scores. It helps estimate students' standings in the competition, allowing them to make better decisions regarding their academic pursuits.
Explore the repository here: Rank-Predictor-MHT-CET
Features of the Rank Predictor
- Data Analysis: Thorough analysis of previous years' MHT-CET data to identify trends and patterns essential for accurate rank prediction.
- Machine Learning Model: A predictive model trained on historical data, considering score distribution, percentile ranks, and competition levels for precise predictions.
- User-Friendly Interface: A simple interface for students to input their scores and receive estimated ranks, ensuring accessibility to a wide range of users.
- Detailed Documentation: Comprehensive documentation covering data preprocessing, machine learning algorithms, and model rationale.
Getting Started
Clone the repository with the following command:
git clone https://github.com/mandarwagh9/Rank-Predictor-MHT-CET-analyse.gitExplore the Python scripts and Jupyter notebooks included in the repository for data analysis and model training. The documentation makes it easy for both beginners and experienced developers to understand and contribute.
Key Components
Data Collection
Collect historical MHT-CET data, including scores and ranks, and preprocess it for analysis.
Exploratory Data Analysis (EDA)
Uncover insights through EDA, including visualizations and statistical analysis to understand score distribution and rank relationships.
Model Training
Train the machine learning model using algorithms like linear regression and decision trees. The training process involves iterative refinement to improve accuracy.
Prediction Interface
Run the prediction interface locally to input scores and receive estimated ranks, providing a convenient tool for students.
Future Enhancements
Planned enhancements for the repository include:
- Incorporating More Data: Adding data from more years and similar exams to improve prediction accuracy.
- Advanced Algorithms: Experimenting with advanced machine learning algorithms and ensemble methods to enhance performance.
- Web Application: Developing a web-based application to make the rank predictor more accessible.
Conclusion
The 'Rank-Predictor-MHT-CET' repository by Mandar Wagh is a valuable tool for predicting MHT-CET ranks using data science and machine learning. It empowers students to make informed academic decisions. For more details and to contribute, visit the GitHub repository: Rank-Predictor-MHT-CET.