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