This repository contains three data mining projects that address important aspects of data preprocessing and modeling. Each project focuses on a specific question related to handling missing data, categorical variables, feature scaling, handling outlier data, and implementing multiple linear regression.
In this project, we explore various techniques and strategies for handling missing data. We examine the impact of missing data on the overall analysis and present effective methods for imputation or removal of missing values.
Categorical variables pose unique challenges in data analysis. In this project, we delve into the reasons why categorical variables cannot be directly used in certain machine learning algorithms and propose approaches for encoding and handling these variables to enable their effective utilization.
Feature scaling plays a crucial role in the performance of machine learning models. In this project, we investigate the importance of feature scaling and its impact on multiple linear regression. We demonstrate the implementation of feature scaling techniques and how they contribute to accurate model predictions.
Outliers can significantly affect the integrity and reliability of data analysis. In this project, we explore techniques for detecting and handling outlier data points, ensuring robustness and accuracy in the subsequent analysis.
This project focuses on the implementation of multiple linear regression, a widely used technique for predicting outcomes based on multiple independent variables. We provide a step-by-step guide on how to apply multiple linear regression on a dataset, assuming that all available data is for training purposes.