This R markdown file contains code for ‘Digit Recognizer’ problem on Kaggle. In this model I have used Random Forest Algorithm and ‘tune’ function to identify the best parameters for Random Forest. The dataset contains around 780 attributes per observation. Each of these attribute is a pixel intensity of a particular point on the grid. As the dataset had lots of attributes per observation Principal Component Analysis was used for dimensionality reduction. Achieved 100% accuracy on train dataset and 93% accuracy on test dataset.

This R markdown file contains the code for Digit Recognizer problem available on Kaggle. This model identifies the digit (0-9) based on the data given for the pixels intensities. I have used Support Vector Machines and Principal Component Analysis to create this algorithm.

This R markdown file contains the code for Leaf Classifier problem available on Kaggle.This model classifies leaves into 99 distinct categories with 89% accuracy on the test dataset. I have used XGBoost and Principal Component Analysis to solve the same.