scus3/wage-regression-analysis

This project provides a comprehensive guide to implementing PCA from scratch and validating it using scikit-learn's implementation. The visualizations help in understanding the data's variance and the effectiveness of dimensionality reduction.

Wage Regression Analysis

This repository contains a Python implementation of wage regression analysis using Linear Regression and Support Vector Regression (SVR) models. The project aims to predict hourly wages based on various factors such as education level, years of experience, and job type.

Project Overview

The project includes the following key components:

1. Data Loading and Preprocessing: Loading the training and test datasets using Pandas, preprocessing the data, and computing the hourly wage.

2. Linear Regression Model: Training a Linear Regression model to predict hourly wages and evaluating its performance on both training and test datasets.

3. Support Vector Regression (SVR): Training SVR models with polynomial and radial basis function (RBF) kernels and evaluating their performance.

Features

• Data Loading and Preprocessing: Loading CSV files using Pandas and preprocessing missing values.
• Linear Regression: Training a Linear Regression model to predict hourly wages and evaluating its performance.
• Support Vector Regression (SVR): Training SVR models with polynomial and RBF kernels and evaluating their performance.
• Error Calculation: Computing the sum of squared errors (SSE) for model evaluation.

Technologies Used

• Python
• Pandas
• scikit-learn

Getting Started

Prerequisites

Ensure you have Python and the following libraries installed:

• Pandas
• scikit-learn

You can install the required libraries using pip:

pip install pandas scikit-learn