Big Mart Sales Prediction Using R

IntermediateLevel
15616+Students Enrolled
30 MinsDuration
4.6Average Rating

About
Learning Outcomes
Who Should Enroll
Course Curriculum
Instructors

About this Course

Learn Big Mart Sales Prediction in R using real-world datasets. Understand data cleaning, feature engineering & model building to forecast sales accurately with hands-on project.
Explore end-to-end sales prediction workflow — from importing the dataset for sales prediction to evaluating regression models and improving prediction accuracy using R.
Understand practical skills in Big Mart sales prediction using R by applying data visualization, statistical analysis, and machine learning techniques on real sales datasets.

Learning Outcomes

Regression in R

Master regression techniques using R for sales prediction.

Sales Prediction

Apply data science skills to a real-life sales prediction problem.

Predictive Modeling

Build and evaluate regression models in R with evaluation metrics.

Who Should Enroll

People starting in Data Science and Machine Learning and doesn't have any prior experience.
Individuals looking to master regression techniques in R, Use R to solve Big Mart Sales Predifor real-world data.
Professionals aiming to solve real-world sales prediction problems using R with proper syntax and modeling techniques.

Course Curriculum

Explore a comprehensive curriculum covering R programming, data preprocessing, regression models, and real-world Big Mart sales prediction using datasets for accurate and data-driven business forecasting.

1. Overview of the Course
2. Table of Contents
3. Problem Statement
4. Hypothesis Generation
5. Loading Packages & Data
6. Understanding the Data
7. Univariate Analysis
8. Bivariate Analysis
9. Missing Value Treatment
10. Feature Engineering
11. Encoding Categorical Variables
12. Preprocessing Data
13. Model Building
14. Linear Regression
15. Regularized Linear Regression
16. Random Forest
17. XGBoost Algorithm
18. Summary of the Course

Meet the instructor

Our instructor and mentors carry years of experience in data industry

Kunal Jain

CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.

Get this Course Now

With this course you’ll get

30 Mins
Duration
Kunal Jain
Instructor
Intermediate
Level

Certificate of completion

Earn a professional certificate upon course completion

Industry-Recognized Credential
Career Advancement Credential
Shareable Achievement

Frequently Asked Questions

Looking for answers to other questions?

Big Mart Sales Prediction in R involves analyzing a sales prediction dataset to forecast future sales. Using regression models in R, learners identify sales patterns, optimize pricing, and make data-driven business decisions efficiently.

Learners will explore real-world data, apply regression models, and evaluate performance metrics. The project builds strong foundations in R-based analytics and enhances understanding of predictive modeling for Big Mart Sales Prediction using R.

The accuracy of Big Mart sales prediction models in R depends on data quality and chosen algorithms. By applying feature engineering, data preprocessing, and fine-tuning, you can achieve reliable predictions for real-world retail datasets.

The process includes importing the sales prediction dataset, data cleaning, exploratory data analysis, feature engineering, model training, and evaluation. Each step is implemented in R to build an end-to-end Big Mart sales prediction pipeline.

R’s caret and mlr packages allow grid search and cross-validation for tuning model parameters. Optimizing these values significantly enhances Big Mart sales prediction accuracy and model generalization on unseen data.

Visualizations created using ggplot2 help explore sales trends, outlet performance, and feature relationships. This visual analysis strengthens insights and aids in refining R models for Big Mart sales prediction accuracy.

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