Binary-Classification-XGB

Assessment solution for Arya-ai Binary classification problem

---

Project File Struture:

Root /fldr

• Alpha&Omega.ipynb <---- Main Jupyter notebook (Forgot to change name)
• Assignement - Data Scientist (1).docx <---- Assessment problem document
• Testing_predicitons.csv <---- Target class outputs of the Test Data
• README.md <---- this very file
• XGBFTW.sav <---- XGBoost model export done over using pickle
• requirements.txt <---- Environment Screenshot
• essentials_only_req.txt <---- ipynb specific requirements
• Data /fldr
  • Training_set.csv <---- Training Dataset
  • Test_set.csv <---- Testing Dataset

---

Data Stats:

• Train Dataset Shape -> (3910,58)
• Test Dataset Shape -> (691,57)
• Dataset is Sparse and High Dimensional
• Features are highly skewed

---

Key Decisions:

• Used RandomForest Classifier for feature selection.
• Selected top 30 features with respect to their feature importance.
• For metric considered Binary CrossEntropy | LogLoss and ROC-AUC score.
• Model of choice is Xgboost.

---

Process Flow - Main.ipynb (Alpha&Omega.ipynb)

1. EDA
2. Splitting the data
3. Feature Selection
4. Data Scaling - Normalization
5. Model Training
6. Prediction Metrics
7. Processing and Predicting on Test Data
8. Saving Model for Future Usage
9. Exporting Y_test Predicted scores
10. Generating requirements. #Has an important Note. Must Read!

---

Process Flow - Performance_print.py

1. Splitting the Data
2. Feature Selection
3. Importing Presaved model
4. Using presaved model to generate scores
5. Using Prettytable to print output table

---

Had fun making this!!