Web Design
Multivariate Analysis of Physicochemical Properties for Wine Quality Dataset
Analyzed wine quality dataset through data visualization, assessed multicollinearity, and performed regression and classification tasks. Explored Multivariate Linear Regression, Decision Tree, Random Forest, and Support Vector Regression for regression; Random Forest, Gradient Boost, SVM, Naive Bayes, KNN, XGBoost, Isolation Forest, LightGBM, and Decision Tree for classification. Demonstrated the efficacy of Principal Component Analysis in improving regression and classification models
Year :
2024
Project Duration :
4 weeks
Technologies :
RStudio, Machine learning techniques, ggplot2, Data Mining, Python (Programming Language)
Project :
Performed regression and classification using multiple machine learning models in R (e.g., Random Forest, SVM, XGBoost). Applied PCA to reduce dimensionality and improve performance. Visualized feature impact using ggplot2 and assessed multicollinearity to refine predictors.
Challenge :
Balancing model accuracy while managing multicollinearity and overfitting due to a high number of features.
Summary :
Delivered interpretable and optimized predictive models for wine quality using multivariate techniques in R.
More Projects
Web Design
Multivariate Analysis of Physicochemical Properties for Wine Quality Dataset
Analyzed wine quality dataset through data visualization, assessed multicollinearity, and performed regression and classification tasks. Explored Multivariate Linear Regression, Decision Tree, Random Forest, and Support Vector Regression for regression; Random Forest, Gradient Boost, SVM, Naive Bayes, KNN, XGBoost, Isolation Forest, LightGBM, and Decision Tree for classification. Demonstrated the efficacy of Principal Component Analysis in improving regression and classification models
Year :
2024
Project Duration :
4 weeks
Technologies :
RStudio, Machine learning techniques, ggplot2, Data Mining, Python (Programming Language)
Project :
Performed regression and classification using multiple machine learning models in R (e.g., Random Forest, SVM, XGBoost). Applied PCA to reduce dimensionality and improve performance. Visualized feature impact using ggplot2 and assessed multicollinearity to refine predictors.
Challenge :
Balancing model accuracy while managing multicollinearity and overfitting due to a high number of features.
Summary :
Delivered interpretable and optimized predictive models for wine quality using multivariate techniques in R.
More Projects
Web Design
Multivariate Analysis of Physicochemical Properties for Wine Quality Dataset
Analyzed wine quality dataset through data visualization, assessed multicollinearity, and performed regression and classification tasks. Explored Multivariate Linear Regression, Decision Tree, Random Forest, and Support Vector Regression for regression; Random Forest, Gradient Boost, SVM, Naive Bayes, KNN, XGBoost, Isolation Forest, LightGBM, and Decision Tree for classification. Demonstrated the efficacy of Principal Component Analysis in improving regression and classification models
Year :
2024
Project Duration :
4 weeks
Technologies :
RStudio, Machine learning techniques, ggplot2, Data Mining, Python (Programming Language)
Project :
Performed regression and classification using multiple machine learning models in R (e.g., Random Forest, SVM, XGBoost). Applied PCA to reduce dimensionality and improve performance. Visualized feature impact using ggplot2 and assessed multicollinearity to refine predictors.
Challenge :
Balancing model accuracy while managing multicollinearity and overfitting due to a high number of features.
Summary :
Delivered interpretable and optimized predictive models for wine quality using multivariate techniques in R.