This repository contains a Jupyter Notebook for analyzing wine quality data and predicting wine quality based on various physicochemical properties using machine learning models. The analysis includes data preprocessing, exploratory data analysis (EDA), and building predictive models with scikit-learn.
- Data Preprocessing: Handling missing values, feature scaling, and data balancing.
- Exploratory Data Analysis (EDA): Using seaborn and matplotlib for visualizing the data.
- Machine Learning Models: Utilizes K-Nearest Neighbors (KNN), Decision Trees, and Multi-Layer Perceptron (MLP) classifiers.
- Model Evaluation: Assessing model performance with classification reports.
To run this notebook, you need to have Python installed on your system. It is recommended to use a virtual environment. Then, you can install the dependencies as follows:
pip install -r requirements.txt
After installing the required packages, open the Jupyter Notebook (wine_notebook.ipynb) in your Jupyter environment:
jupyter notebook wine_notebook.ipynb