This comprehensive course specializes in data cleaning and preprocessing techniques in Python, preparing you to apply these techniques in predictive modeling. The course covers a range of relevant topics, from missing data handling to feature engineering.
Detecting and Handling Missing Data in the Titanic Dataset
Replacing Mean Imputation with Median Imputation
Fixing Missing Data Handling in Titanic Dataset
Detecting and Filling Missing Values in the Titanic Dataset
Handling and Visualizing Missing Data in Titanic Dataset
Exploring Encoding of Categorical Data in the Titanic Dataset
Exploring Different Columns with Categorical Data Encoding in Titanic Dataset
Fixing Conditional Encoding Issue in the Titanic Dataset
Encoding Categorical Data in the Titanic Dataset
Mastering Encoding Techniques with the Titanic Data Set
Applying Scaling Techniques to Titanic Dataset
Applying Robust Scaling to 'fare' Column
Applying Robust Scaling on the Titanic Dataset
Applying Min-Max and Robust Scaling to Titanic Dataset
Applying Standard, Min-Max, and Robust Scaling Techniques to Titanic Dataset by Hand
Detecting and Handling Outliers in Titanic Dataset
Detecting and Handling Fare Outliers in the Titanic Dataset
Adjusting Fare Anomalies in the Titanic Dataset
Detecting Fare Outliers Using the Z-score Method in Titanic Dataset
Navigating the Ocean of Outliers in Age Data for Titanic Dataset
Visualizing and Handling Redundant Features in the Titanic Dataset
Analyzing Correlations in Titanic Data Excluding the Survival Rate
Unraveling the Galactic Web of Correlation with a Heatmap
Visualizing and Handling Correlated Features in the Titanic Dataset
Visualizing and Cleaning Redundant Features from Titanic Dataset
Investigating Family Sizes and Solitude on the Titanic
Changing Age Group Categories in Titanic Dataset
Debug and Perfect Titanic Dataset Feature Engineering
Adding New Features and Age Groups in Titanic Dataset
Creating Age Groups and Family Size Features in Titanic Dataset