Data science modules - Regression Analysis Tutorial

Are you searching for the best resource to understand Regression Analysis? If yes, then welcome to the world of regression in Data Science. In this tutorial, you’ll learn what regression analysis is, why it’s important, its different types, and some practical examples. This guide is prepared by experts from one of India’s top Data Science training institutes.

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What is Regression Analysis?

Regression analysis is a vital concept in statistics and machine learning. It is widely used to study the relationship between variables. Among the different methods available, Linear Regression is the most common and simple one.

In general, regression is applied when you have a number of observations, each containing two or more features. The assumption is that at least one feature depends on the others, and your goal is to build a function that best explains this relationship.

In short, regression analysis helps you map independent variables (inputs) to dependent variables (outputs).

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Real-Life Examples of Regression

Imagine you’re analyzing the salaries of employees in a company. You want to check how factors such as work experience, education, designation, and city affect salary levels.

• Salary → Dependent variable

• Experience, education, role, city → Independent variables (predictors)

Here, each employee represents one observation. Such problems are called regression problems.

📌 Notes:

• Dependent features = Dependent Variables

• Independent features = Predictors / Inputs

• Independent data can be continuous, discrete, or even categorical (like gender, country, or brand).

It’s a common practice to denote:

• Output as y

• Input as x (or as a vector when multiple inputs are involved, x = (x₁, x₂, …, xᵣ)).

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Why Do We Use Regression?

Regression is used whenever you need to know whether and how certain factors influence others, or how different variables are related.

Example 1:

Checking how experience or gender influences employee salaries.

Example 2:

Forecasting water usage of a household next week based on factors like outdoor temperature, family size, and time of day.

Because of its predictive power, regression is used in multiple fields such as economics, social sciences, computer science, and business analytics. Its importance keeps growing with the massive amounts of data being generated today.

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Types of Regression Analysis

1. Linear Regression

This is one of the most widely used regression techniques. Its simplicity and interpretability make it a favorite among data scientists.

Simple Linear Regression

This type of regression involves a single independent variable.
Equation:

y(x) = β₀ + β₁x

Here, you start with a dataset of input-output pairs. The regression line (estimated function) tries to minimize the error between predicted and actual values.

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Steps for Building Regression Models in Machine Learning

To get accurate predictions, the right features must be selected. Below are common feature selection techniques used in regression:

1. Forward Selection

• Start with zero features.

• Add the most significant feature at each step.

• Continue until adding a new feature doesn’t improve the model.

2. Backward Elimination

• Start with all available features.

• Gradually remove the least significant feature in each iteration.

• Stop when removing more features doesn’t improve performance.

3. Recursive Feature Elimination (RFE)

• A greedy optimization method.

• Iteratively builds models, removes the weakest features, and repeats until the best subset remains.

4. Univariate Selection

• Use statistical tests to find features strongly related to the target variable.

• In Python, the SelectKBest class in scikit-learn is often used.

5. Feature Importance

• Many models provide a feature importance score.

• Higher score = stronger influence on the dependent variable.

6. Correlation Matrix with Heatmap

• Helps visualize how variables relate to each other and the target.

• Positive correlation = increase in feature increases target.

• Negative correlation = increase in feature decreases target.

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Conclusion

We hope this Regression Analysis tutorial gave you a clear understanding of what regression is, why it’s important, and the different methods to implement it.

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