Linear Regression Model using Kaggle Dataset

This repository contains the code and documentation for building a linear regression model using a sample dataset from Kaggle. The model is implemented in Python, leveraging popular libraries for data manipulation, visualization, and machine learning.

Project Overview

The goal of this project is to demonstrate the process of building a linear regression model from start to finish. This includes:

1. Importing Required Libraries:

   • The necessary Python libraries are imported at the beginning of the notebook/script.

2. Importing Dataset:

   • The sample dataset from Kaggle is used for training the linear regression model.

3. Data Preprocessing and Feature Engineering:

   • Data is cleaned and preprocessed to handle missing values and outliers.
   • Feature engineering is performed to extract meaningful features from the dataset.

4. Data Visualization:

   • Seaborn is utilized for data visualization to gain insights into the relationships between different variables.

5. Standardization:

   • The values in the dataset are standardized to ensure consistent scaling for the model.

6. Model Training:

   • The linear regression model is trained on the preprocessed and standardized data.

7. Model Evaluation:

   • The performance of the model is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R^2) metrics.