Multiple Linear Regression Project

This project focuses on developing and analyzing a multiple linear regression model to understand how multiple independent variables influence a target variable and evaluate model accuracy.

Project Overview

Multiple linear regression models the relationship between one dependent variable and multiple independent variables. It is used for predictive analytics in fields like finance, marketing, and engineering.

Tools and Libraries

• Python: Programming language
• Pandas: Data manipulation
• NumPy: Numerical computations
• Matplotlib & Seaborn: Data visualization
• Scikit-Learn: Machine learning library
• Jupyter Notebook: Development and documentation environment

Techniques and Methodology

1. Data Preprocessing

• Load and understand dataset structure
• Handle missing values and outliers
• Encode categorical variables
• Split data into training and testing sets

2. Exploratory Data Analysis (EDA)

• Visualize distributions and relationships
• Analyze correlations for feature selection

3. Feature Engineering

• Select/transform features for better performance
• Remove multicollinearity by checking correlations

4. Model Building and Training

• Implement and train the multiple linear regression model

5. Model Evaluation

• Evaluate performance using MSE, RMSE, and R-squared
• Interpret coefficients to understand variable influence

Conclusion

The model provides insights into predictor relationships, and evaluation metrics help assess its performance. Future improvements include feature engineering, model optimization, and cross-validation.