Unveil how preprocessing refines data to make predictive models more effective. Learn to handle missing values, outliers and categorical variables, ensuring data consistency and integrity.
Overview
Syllabus
- Lesson 1: Deep Dive with Pandas into the California Housing Dataset
- Lesson 2: Strategies for Treatment of Missing Data in Predictive Modeling
- Lesson 3: Navigating through Data Anomalies: Outliers Detection and Treatment
- Lesson 4: Feature Selection Methods for Predictive Modeling
- Lesson 5: Mastering Feature Normalization for Predictive Accuracy
