Welcome to the All About EDA repository — a complete beginner-to-advanced guide to Exploratory Data Analysis (EDA), Feature Engineering (FE), and Feature Selection (FS). If you're starting your journey into data science or machine learning, this is one of the first steps you must master before diving into modeling.
🔹Three Datset example of EDA and fE ,FS-- One is Zomato & Country-code and another is Blackfriday-train & Blackfriday-test dataset
🔹 Keyboardwritten Notes-- Clear and concise keyboardwritten notes are included to make concepts easier to understand — perfect for visual learners.
🔹 Practical Code Examples--
Step-by-step code for EDA, feature engineering, and feature selection using pandas, sklearn, and more.
🔹 Text Summary Files--
Quick .txt files explaining all EDA, FS & FE concepts for quick revision and interview prep.
- Missing value handling
- Outlier detection
- Distribution plots
- Correlation heatmaps
- Univarient
- Bivarient
- MultiVarient
- Categorical encoding (One-hot, Label)
- Binning
- Transformation (log, power, scaling)
- Date/time features
- Univariate selection (Chi-squared, ANOVA)
- Recursive Feature Elimination (RFE)
- Tree-based methods (Feature importance)
- Variance Thresholding
✅ Beginners who want to break into data science ✅ Intermediate learners needing a solid refresher ✅ Anyone preparing for interviews or real-world projects
⚠️ Note: It's recommended to learn EDA, FE, and FS thoroughly before jumping into model-building withscikit-learn.
Once you're comfortable with EDA & FE, check out my next repo on:
- Supervised Learning (Classification & Regression)
- Model Evaluation Metrics
- Hyperparameter Tuning
- Real-world ML Projects
Stay tuned!
If you found this helpful, ⭐️ star the repo or drop your feedback. Want to contribute? Pull requests are welcome!