| load data (reduce memory usage) | Home Credit Default Risks |
| Comprehensive data exploration with Python | House Prices - Advanced Regression Techniques |
| Data Science Glossary on Kaggle | Multiple Data Sources |
| Home Credit : Complete EDA + Feature Importance ✓✓ | Home credit default risk |
| Resampling strategies for imbalanced datasets | portgo |
| Introduction to Manual Feature Engineering | Home Credit Default Risk |
| Automated feature engineering for Titanic dataset (Handled curse of dim) | Titanic dataset |
| Titanic (0.83253) - Comparison 20 popular models | Titanic dataset |
| Data Science for tabular data: Advanced Techniques | Multiple Data Sources |
| #1 House Prices Solution [top 1%] | House pr |
| A Guide to Handling Missing values in Python | No data |
| LightGBM (Fixing unbalanced data) | No data |
| Handle Outlier!!! The Silent Killer | Private data |
| Getting started with NLP - A general Intro | Disaster tweets |
| Comprehensive Guide on Feature Selection | _ |
| Stacked Classifier : Top 10 % on LB | Titanic dataset |
| Statistical Modeling(*Imputation&Outliers) | House Proces : advenaced regression |
| An Overview of Encoding Techniques | multiple data resources |
| 🥇Top 1st Place Solution on Private LB🏆 | categorical encoding challenge |
| Categorical Data encoding techniques | own data |
| Applying Different Encoding Methods and EDA | categorical encoding challenge |
| Scale, Standardize or Normalize with scikit-learn | own data |
| Power Transformation | No Data |