Data Mining Project - Regression Analysis

Overview

This project focuses on performing a detailed regression analysis of the "meta" dataset. The primary aim is to apply regression techniques to understand the characteristics of literature datasets processed by a learning algorithm.

Phases of the Project

1. Dataset Analysis

• Load the dataset using scikit-learn's fetch_openml.
• Organize data into a pandas DataFrame and display the first few rows.

2. Preprocessing

• Remove nominal features and handle missing values.
• Create two datasets: D1 (missing values removed) and D2 (interpolated values).
• Apply standardization.

3. Regression

• Split datasets into training and test sets.
• Apply regression models (Linear Regression, Logistic Regression, etc.).
• Report results using MAE, MAPE, and SMAPE.
• Include appropriate graphs for analysis.