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
- Describing California Housing Data
- Navigating the Data Cosmos: Correlation Matrix Calculation Challenge
- Exploring Room Count Correlation
- Plotting the Data Distribution
- Navigating the Stars: Creating a Correlation Matrix
- Lesson 2: Strategies for Treatment of Missing Data in Predictive Modeling
- Counting Missing Values in the Housing Market Dataset
- Cleaning Real Estate Data by Listwise Deletion
- Enhancing Data Integrity with Mean Imputation
- Utilizing k-NN Imputation to Handle Missing Data
- Crafting Indicator Columns for Missing Data Awareness
- Lesson 3: Navigating through Data Anomalies: Outliers Detection and Treatment
- Outlier Treatment in Housing Data
- Expanding the Frontier: Elevating z-score Outlier Detection
- Adjusting Outlier Detection Sensitivity in Housing Data
- Implementing z-score for Outlier Detection
- Detecting Outliers with IQR in Housing Data
- Mitigating Outlier Impact with Log Transformation
- Lesson 4: Feature Selection Methods for Predictive Modeling
- Exploring the Stars of the Housing Market
- Expanding the Feature Selection Horizon
- Navigating the Stars of Feature Selection
- Unveiling the Most Influential Features
- Lesson 5: Mastering Feature Normalization for Predictive Accuracy
- Scaling the Space-Time: Normalizing House Ages
- Scaling the Stars: Normalization in the Housing Galaxy
- Implementing Min-Max Scaling
- Scaling Heights with Min-Max Normalization
