Analyze and Predict Prices Using Regression Techniques

Overview

Learners will analyze real-world datasets, prepare and transform features, and apply regression algorithms to predict numerical outcomes with confidence. By the end of this course, learners will be able to structure datasets for modeling, handle missing and inconsistent data, encode categorical variables appropriately, and evaluate regression models using training and test data. This course is designed to build practical, job-ready skills in predictive analytics by walking learners through the complete regression workflow. Rather than focusing only on theory, the course emphasizes hands-on data preparation techniques such as imputation, feature replacement, ordinal encoding, and dataset validation. Learners gain a clear understanding of how real-world data issues impact model performance and how to address them systematically. What makes this course unique is its end-to-end, implementation-driven approach. Each concept is reinforced through realistic data scenarios that mirror industry practices in pricing analytics. By completing this course, learners will be able to confidently design, train, and evaluate regression models, making them well prepared for applied data science, business analytics, and machine learning roles where accurate price prediction is essential.

Syllabus

Foundations of Price Prediction with Regression

This module introduces learners to the fundamentals of predicting prices using regression techniques. Learners explore how real-world factors influence pricing, learn to structure datasets correctly for regression analysis, and apply essential data preparation techniques such as indexing, test value setup, and missing-value handling. By the end of the module, learners will be able to transform raw data into a regression-ready format while avoiding common data quality and evaluation pitfalls.

Feature Engineering and Regression Modeling

This module focuses on advanced data preparation and modeling techniques required for effective regression-based price prediction. Learners perform feature engineering, convert categorical variables into quantitative and ordinal forms, validate dataset structure, and apply proper train–test splitting to evaluate model performance. The module concludes with executing a regression algorithm to generate and interpret predicted values.