This project aims to build a regression model from scratch using Ridge, Lasso, and Elastic Net regularization techniques. Additionally, it involves data cleaning, exploration, and visualization to enhance model accuracy.

Regression analysis is a powerful statistical method used to examine the relationship between one dependent variable and one or more independent variables. Regularization techniques like Ridge, Lasso, and Elastic Net are employed to prevent overfitting and improve the generalization of regression models.

This project will cover the following steps:

1. Data Collection: Obtain the dataset for regression analysis.
2. Data Cleaning: Handle missing values, outliers, and other inconsistencies in the dataset.
3. Data Exploration: Understand the dataset's structure, relationships between variables, and identify patterns.
4. Data Visualization: Visualize the data to gain insights and understand the distributions.
5. Model Building: Implement Ridge, Lasso, and Elastic Net regression models from scratch.
6. Model Evaluation: Evaluate the models' performance and compare their accuracies.
7. Fine-tuning: Adjust hyperparameters and optimize the models for better results.
8. Conclusion: Summarize findings and discuss the effectiveness of the models.

• Python
• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• Lasso
• Ridge
• Elastic Net

To run this project locally, follow these steps:

1. Clone this repository.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run the Jupyter notebook or Python scripts provided.

The dataset used in this project contains [describe your dataset here, including features and target variable].

Data cleaning involves:

• Handling missing values
• Removing outliers
• Standardizing or normalizing data if required

Data exploration includes:

• Statistical summary of variables
• Correlation analysis
• Distribution of variables
• Visualization of relationships between variables

Visualizations such as scatter plots, histograms, and heatmaps will be used to explore the data and identify patterns.

Ridge, Lasso, and Elastic Net regression models will be implemented from scratch using Python.

Model performance will be evaluated using metrics like

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Mean Absolute Error (MAE)
• R-squared
• Cross-Validation scores

This project demonstrates how to build regression models using Ridge, Lasso, and Elastic Net from scratch and showcases the importance of data cleaning, exploration, and visualization in improving model accuracy.

For more details, refer to the Jupyter notebook or Python scripts provided in this repository.