➢ Flight Price Prediction
✓ Tech stack: Python, Machine learning, Jupiter notebook
✓ Objective: Building a Flight rate prediction regression model
✓ Solution: Did EDA, treated null values, removed outliers, did hyperparameter tuning, built 3-4 models based on different parameters
✓ Achievements: Decision Tree Regressor having an f1 score of 99 and cross-validation score of 95 and AUC score of 99
✓ Library: NumPy, Pandas Seaborn, Matplotlib
➢ Micro Credit Defaulter
✓ Tech stack: Python, Machine learning, Jupiter notebook
✓ Objective: Building defaulter prediction using a classification model
✓ Solution: Did EDA, treated null values, removed outliers, did hyperparameter tuning, built 3-4 models based on different parameters
✓ Achievements: Decision Tree Regressor having an f1 score of 99 and cross-validation score of 95 and AUC score of 99
✓ Library: NumPy, Pandas Seaborn, Matplotlib
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