ML Classification project
The goal of this project is to predict the success of a Kickstarter campaign. The datasets used in this project came from Web Robots website from January through April 2021. I used various classification algorithms such as KNN, Logistic Regression, Decision Tree, Random Forest, Naive Bayes, and XGBoost. My final classification model was XGBoost that has a F1 score of 0.80 and AUC score of 0.82. The Model was interpreted using SHAP values metrics to understand which features have higher importance for success. Lastly, a Flask app was built using the final model after retraining it with the entire dataset.
To learn more, see my blog post and presentation slides .
- Code (in Workflow Folder)
- Flask app (in app Folder)
- SQLite, sqlalchemy
- Python (Pandas, numpy)
- Matplotlib, Seaborn
- Tableau
- Scikit-learn
- Flask
Classification Algorithms:
- KNN
- Logistic Regression
- Decision Tree, Random Forest
- Naive Bayes- Gaussian, Bernoulli
- XGBoost
Metrics:
- ROC-AUC curve
- F1 score
- Confusion matrix