This is a Python repository implementing a K-Nearest Neighbors (KNN) algorithm for predictive modeling using car data. This repository serves as a tool for predictive modeling and analysis in the automotive domain. By leveraging machine learning techniques, users can gain insights into various aspects of cars and make informed decisions.The repository contains code that preprocesses car-related datasets, trains a KNN model on the processed data, and provides functionality for making predictions about different classes of cars. The KNN algorithm is utilized for its simplicity and effectiveness in making predictions based on similarity to other data points.
- Ensure that Python and the required packages (pandas, numpy, sklearn, matplotlib) are installed.
- Download the "car.data" dataset or use your own dataset with similar structure.
- Execute the provided Python script in an environment with the necessary packages installed.
- car.data: Sample dataset used for the model (You can replace with your own dataset)
- knn.py: Python script containing the entire process for prediction using KNN classification
- This project is inspired by the work of various contributors in the fields of machine learning, data analysis, and education research.
- Feel free to explore, use, and modify the code as needed. If you have any questions or suggestions, please feel free to reach out and contribute to this project. Thank you for your interest in the student performance prediction model!