Wine Quality Analysis

Description

Comprehensive analysis and modeling of the Wine Quality dataset, including exploratory data analysis (EDA), data preprocessing, model training, and performance evaluation using MSE and RMSE.

Project Overview

Exploratory Data Analysis (EDA)

• Conducted thorough EDA to understand data distribution and relationships between features.
• Visualized data using Python libraries like Matplotlib and Seaborn.

Data Preprocessing

• Addressed missing values and outliers to ensure data quality.
• Applied normalization and scaling techniques to prepare data for modeling.

Model Training

• Implemented various machine learning models including Linear Regression, Decision Trees, and Random Forest using Scikit-Learn.
• Performed cross-validation to enhance model robustness.

Performance Evaluation

• Evaluated models using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
• Compared different models to identify the best-performing one.

Skills Demonstrated

• Data Analysis: Proficient in data manipulation and visualization using Pandas, Matplotlib, and Seaborn.

• Machine Learning: Experienced in implementing and tuning machine learning models with Scikit-Learn.

• Model Evaluation: Skilled in evaluating model performance using appropriate metrics.

• Data Preprocessing: Expertise in preparing datasets for analysis and modeling.

• Ahmad Ali CV