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Free Certificate
Linear Regression - Step by Step Guide
Build a strong foundation in predictive modeling with this linear regression free course—learn simple and multiple regression, performance metrics, and applications across data science domains.
21 hours of learning
Data Manipulation
Data Cleaning
Problem Solving
For enquiries call:
18002102020
Key Highlights Of This Linear Regression Free Course
What You Will Learn
Introduction to Simple Linear Regression
Gain foundational insights into machine learning by learning how to build and interpret simple linear regression models for predicting continuous outcomes based on one predictor variable.
Topics Covered:
- Introduction to Machine Learning
Machine learning forms the backbone of modern predictive modeling. In this course, we introduce the core concept of machine learning, explaining the difference between supervised and unsupervised learning. Supervised learning, where the model is trained on labeled data, is central to linear regression, making it an essential technique for prediction. - Supervised and Unsupervised Learning Methods
Supervised learning uses input-output pairs to train the model, while unsupervised learning deals with unlabeled data to find inherent structures. This section emphasizes the importance of supervised learning for predictive analytics, explaining how linear regression fits into this paradigm. - The Linear Regression Model
The linear regression model is the simplest and most widely used algorithm for predicting continuous outcomes. Learn the mathematical foundation of the linear regression equation (y = mx + b), where ‘y’ is the predicted outcome, ‘x’ is the predictor variable, and ‘m’ and ‘b’ are the model parameters (slope and intercept). - Residuals
Residuals are the differences between observed and predicted values. In regression analysis, residuals help us understand how well the model fits the data. Small residuals indicate a good fit, while large residuals suggest a poor model fit. We'll examine how to compute and interpret residuals. - Residual Sum of Squares (RSS) and R² (R-squared)
RSS quantifies the total deviation of the data points from the fitted line, indicating model accuracy. R² measures the proportion of variance in the dependent variable explained by the independent variable. We’ll discuss how these metrics help evaluate model performance and decide whether to improve or adjust the model.
Simple Linear Regression in Python
Experience hands-on training by using Python to implement a simple linear regression model. Explore a real dataset to predict sales based on TV ad spend using libraries like Pandas and sklearn.
Topics Covered:
- Reading & Understanding the Data
Data preparation is crucial in regression modeling. In this topic, we’ll use Python's Pandas library to read and explore the dataset, examining the relationship between advertising spend and sales. Learn techniques like data cleaning, handling missing values, and basic data visualization to understand the dataset’s structure. - Hypothesis Testing
Hypothesis testing helps validate the assumptions of regression models. You’ll learn how to test the statistical significance of your predictor variable (e.g., TV advertising budget) using t-tests and p-values. This will allow you to determine if the predictor variable has a meaningful relationship with the dependent variable (sales). - Building a Linear Model
We’ll guide you through the process of building a simple linear regression model using the statsmodels and scikit-learn libraries. Learn to interpret model outputs like coefficients, intercepts, and p-values, and understand how these components contribute to the prediction process. - Linear Regression using SKLearn
Using scikit-learn, a popular Python library for machine learning, we’ll walk you through training a linear regression model, making predictions, and evaluating model performance using metrics like Mean Absolute Error (MAE) and R-squared. Learn how to split the dataset into training and testing sets for better validation.
Multiple Linear Regression
Learn how to build more complex models using multiple variables, such as TV, radio, and newspaper marketing budgets. Master the challenges and techniques for selecting optimal features.
Topics Covered:
- Multicollinearity
Multicollinearity occurs when independent variables are highly correlated with each other, causing instability in regression models. We’ll cover how to detect multicollinearity using metrics like the Variance Inflation Factor (VIF), and strategies to address it, such as removing redundant variables or applying principal component analysis. - Dealing with Categorical Variables
Many datasets include categorical data (e.g., gender, region, product type) that cannot be used directly in regression models. Learn techniques like one-hot encoding, label encoding, and ordinal encoding to convert categorical variables into numerical representations, making them usable in regression analysis. - Model Assessment
Learn how to assess the performance of your multiple regression model. We'll cover important evaluation metrics such as Adjusted R², F-statistics, and the Root Mean Squared Error (RMSE), which give deeper insights into how well your model generalizes to unseen data and whether any adjustments are needed. - Feature Selection
In multiple regression models, having too many variables can lead to overfitting, where the model is too complex and fits the noise in the data. Learn methods like stepwise regression, backward elimination, and forward selection to identify the most important predictors, ensuring a simpler and more efficient model.
Multiple Linear Regression in Python
Learn to implement a multiple linear regression model using a real dataset. Use Python’s data analysis tools to prepare data, build models, and assess performance.
Topics Covered:
- Data Preparation
In this section, we’ll focus on preparing a real-world dataset, such as housing prices, for analysis. Learn how to clean the data, deal with missing values, and scale features before fitting a regression model. We’ll also cover feature engineering techniques like creating new variables that improve model performance. - Building the Model
Once the data is prepared, we’ll guide you through the process of building a multiple linear regression model using Python’s scikit-learn. You’ll learn to fit the model on training data and use it to make predictions. We’ll also discuss how to interpret model coefficients and assess the goodness-of-fit. - Residual Analysis & Predictions
After building the model, it’s crucial to perform residual analysis to check for patterns that indicate model weaknesses, such as heteroscedasticity or non-linearity. Learn how to visualize residuals and refine the model for better predictive power, ensuring that the predictions are accurate and reliable. - Variable Selection using RFE
Recursive Feature Elimination (RFE) is a method for improving model accuracy by selecting only the most important features. We’ll show you how to use RFE to iteratively remove features and select the best subset of variables for your model, improving performance while reducing overfitting.
Industry Relevance of Linear Regression
Understand how linear regression models are applied across various industries. Explore case studies and learn to use regression for decision-making in business scenarios.
Topics Covered:
- Prediction vs Projection
Understand the difference between prediction and projection in the context of linear regression. While predictions are typically based on observed data, projections extend to future trends. Learn how regression can be used in both contexts for business forecasting and planning. - Media Company Case Study
In this case study, we will analyze how a media company uses regression to allocate its advertising budget. Learn how regression models can optimize spending across multiple channels (e.g., TV, radio) to maximize revenue and reach, providing valuable insights into resource allocation. - Exploratory Data Analysis (EDA)
Before building a regression model, it’s essential to understand the data. This session covers the techniques of EDA—visualizing data distributions, identifying outliers, and detecting correlations—providing you with the tools to explore datasets thoroughly before fitting models. - Model Building
We will demonstrate how linear regression models are built and used in industry to solve real business problems. From initial data exploration to final model deployment, you’ll learn the step-by-step process of developing a regression model that can be implemented in real-world business scenarios.
What Are the Benefits of This Course?
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