Overview

This repository contains the final project for the course Introduction to Statistical Learning. The project is divided into two tasks, each involving different statistical techniques to analyze and predict outcomes based on various datasets.

Task 1: Predicting Laptop Prices In this task, I applied several regression techniques to predict the prices of laptops. The following methods were used:

• Simple Linear Regression
• Multiple Linear Regression
• Ridge Regression
• Lasso Regression For each method, I performed detailed analyses, including model evaluation and interpretation of the results. Full reports are provided to offer a comprehensive understanding of the data, model selection, and the prediction of laptop prices.

Task 2: Predicting Student Academic Success or Dropout In Task 2, I focused on predicting student academic success or the likelihood of dropout using various classification methods. The following techniques were employed:

• Multinomial Logistic Regression
• Naive Bayes
• K-Nearest Neighbors (KNN) Detailed reports are included to offer deeper insights into the data and the models used, along with the interpretation of the predictions regarding student outcomes.