Key Achievements

• Data Analysis:
  • Conducted an in-depth analysis of the dataset, including exploration of data types, shape, and descriptive statistics.
  • Explained the relevance of variables, such as wilderness area and soil type designations.
• Feature Engineering:
  • Performed outlier detection and treatment, feature transformations, and created new features.
  • Grouped soil types based on USFS ecological land type units and geological zones.
• Feature Selection:
  • Employed feature selection techniques like correlation analysis, filter methods, and recursive feature elimination to improve model performance and reduce overfitting.
• Model Building:
  • Implemented and evaluated several machine learning algorithms, including Decision Trees, XGBoost, Extra Tree Classifier, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, Logistic Regression, and Ensemble Methods.
• Dimensionality Reduction:
  • Explored techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) to reduce dimensionality and assess their impact on model performance.
• Final Submission:
  • Detailed the final model used for prediction and provided instructions for making predictions on the test set.
  • Included guidelines for submitting predictions to the Kaggle competition.

Summary

This project demonstrates the application of various machine learning techniques to a classification problem in predicting forest cover types. It encompasses data analysis, feature engineering, model building, and evaluation, culminating in a robust predictive model for forest cover type classification.