deeprpatel700 / predictive-analysis-of-obesity-levels-with-automatic-classifiers

Classification Analysis with supervised algorithms KNN, RandomForest and SVM. Use of PCA and K-means to recognize the importance of features.

Predictive-Analysis-with-Automatic-Classifiers-and-Importance-of-features-with-PCA-and-K-means

This dataset is about various factors affecting obesity levels and use of machine learning algorithms for classification analysis and visualization of centers.

Below are briefly described important parts of this project.

1. Principal Component Analysis (PCA) to identify features with most variance and visualize the distribution of those features.
2. Unsupervised K-means algorithm to visualize the centers of features.
3. Classification Analysis with Automatic Classifiers using Training and Testing sets (Supervised algorithms KNN, RandomForest and SVM).