Student-Extended-dataset-EDA-and-ML-Model

This README file provides an overview of how to perform linear regression analysis with a focus on Exploratory Data Analysis (EDA). Linear regression is a powerful statistical method used for modeling the relationship between a dependent variable and one or more independent variables.

Table of content

1. Introduction
2. Exploratory Data Analysis (EDA)
3. Linear Regression Model
4. Conclusion

Introduction

Linear regression is a fundamental machine learning and statistical technique used for predictive modeling. It aims to establish a linear relationship between one or more independent variables (features) and a dependent variable (target). The model assumes that this relationship can be expressed as a straight line equation: y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope, and b is the intercept. In this README, we will walk through the process of performing linear regression, with a strong emphasis on Exploratory Data Analysis (EDA) for better understanding of the data before modeling.

Exploratory Data Analysis (EDA)

Before building a linear regression model, it's essential to thoroughly understand the dataset through EDA. EDA involves:

1. Data Cleaning: Handling missing values, duplicates, and outliers.
2. Summary Statistics: Calculating basic statistics like mean, median, and standard deviation.
3. Data Visualization: Creating plots and charts to visualize the data's distribution and relationships between variables.
4. Scatter Plots: Visualizing the relationship between the dependent and independent variables.
5. Correlation Heatmaps: Measuring the correlation between variables.

Linear Regression Model

Once we have a good grasp of our data through EDA, we can proceed to build a linear regression model. The steps involved are:

1. Data Preprocessing: Prepare the data by encoding categorical variables, handling missing values, and splitting it into training and testing sets.
2. Model Selection: Choose the appropriate type of linear regression (simple or multiple) based on the number of independent variables.
3. Model Training: Use the training data to fit the linear regression model to the data.
4. Model Evaluation: Assess the model's performance using metrics like Mean Squared Error (MSE), R-squared, and visualizations.
5. Prediction: Apply the trained model to make predictions on new data.

Conclusion

Linear regression is a valuable tool for modeling relationships between variables, and performing EDA can enhance the accuracy and interpretability of your model. By following the guidelines in this README, you can effectively use linear regression in your data analysis and predictive modeling projects.