This repository contains the code and dataset for predicting wine quality using various machine learning algorithms. The goal of this project is to explore the performance of different algorithms in predicting the quality of wine based on given features. The algorithms investigated include Ridge Regression, Lasso Regression, Support Vector Machines (SVM), and Decision Trees.
Wine quality prediction is a classic problem in machine learning where the objective is to predict the quality of wine based on various chemical and physical properties. This project focuses on implementing and evaluating the performance of different machine learning algorithms for this task.
The dataset used in this project is the Wine Quality Dataset, which contains information about various attributes of wines along with their corresponding quality ratings. The dataset is included in the repository (wine_quality.csv
), and it is sourced from Wine Quality Dataset.
The following machine learning algorithms are implemented and compared in this project:
-
Ridge Regression: A linear regression technique that incorporates regularization to prevent overfitting.
-
Lasso Regression: Similar to Ridge Regression but uses L1 regularization, which can lead to sparse coefficient values.
-
Support Vector Machines (SVM): A supervised learning algorithm for classification and regression tasks, effective in high-dimensional spaces.
-
Decision Trees: A non-linear model that makes decisions based on the values of features, forming a tree-like structure.
To run this project locally, follow these steps:
- Clone this repository:
git clone https://github.com/Sukanyasingh3/Prediction-ML-algorithms.git
After setting up the project, you can use the provided Jupyter notebooks or Python scripts to train and evaluate the models. The main entry point is the main.py
script.
python main.py
The results of each algorithm are documented in the results
directory. This includes performance metrics, visualizations, and any relevant analysis.
Feel free to explore each project's respective notebook or document for a more in-depth understanding of the datasets and insights derived through exploratory data analysis.
If you would like to contribute to the project, follow these steps:
-
Fork the repository.
-
Create a new branch for your feature or bug fix.
-
Make your changes and submit a pull request.
Happy coding!