Data Preprocessing for Predictive Modeling
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.
Lessons and practices
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
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