📈 Data-Science-Linear-regression

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🎯 Overview

This repository is your go-to resource for mastering linear regression, a cornerstone of statistical modeling and machine learning. Designed for beginners, it combines theory with practical coding exercises to help you:

• Understand the math behind linear relationships.
• Build, interpret, and validate regression models.
• Apply your knowledge to real-world problems.

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Learning Objectives

By the end of this course, you will be able to:

1. Identify linear trends in data.
2. Distinguish between independent (explanatory) and dependent (response) variables.
3. Fit a regression model using Python and libraries like pandas and scikit-learn.
4. Interpret coefficients (slope and intercept) to explain variable relationships.
5. Evaluate model fit using R² and diagnose potential issues.
6. Predict outcomes for new data points.

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📚 Course Outline

1. Introduction to Linear Relationships

• What is a linear relationship?
• Scatterplots and correlation analysis.

2. Variables in Regression

• Explanatory vs. Response Variables.
• Feature and target selection.

3. Model Building

• Equation of a line: $y = \beta_0 + \beta_1x$.
• Least squares method for fitting.

4. Model Interpretation

• Slope ($\beta_1$): "For every unit increase in X, Y changes by...".
• Intercept ($\beta_0$): Baseline value.

5. Model Evaluation

• R²: Proportion of variance explained.
• Residual analysis: Checking assumptions.

6. Predictions & Applications

• Using the model for forecasting.
• Case study: Predicting house prices.

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