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My Projects ▾
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✨ Star Project ✨
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Loan Credit Risk
As a Data Scientist Intern at ID/X Partners, my task is to create a prediction model for a multi-finance client using data from 2007-2014. The model will be used to predict if future borrowers will default their loans or not. The model has Kappa score of 98%, MAE score as low as 0.08, and chanced of misclassification at 6%.
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Employee Attrition Prediction
The model is deployed as a Web app in Netlify for the frontend, and GCP for the backend. I created a model that predicts whether or not an employee is at a higher risk of leaving the company or not. This project uses a classification machine learning algorithmm which are:KNN, Decision Tree, Random Forrest, SVC, and XGBoost. The best model happens to be SVC which has Area Under Curve score of 84%.
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Other Projects
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Financial Crisis Analysis
The 2008 financial crisis has bring the world into financial ruins across many industries. The stock I happen to analyze is from an oil and gas company. This project is divided into two parts: analysis of the stock price, and time-series prediction using seasonality
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Employee Clustering
This is a group project where we create a Machine Learning Clustering algorithm that can group employees in our data based on their performance and other factors such as hours worked, salary, overtime, etc. The cluster numbers that we found to be appropriate by considering business knowledge and the machine's statistical preference are 4. In the later section, we will give recommendation on how managers and HR teams can do to these groups of employees.
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Watch Market Analysis
This project mainly showcases my analysis skills along with creating a pipeline using airflow and DAG. The main brand that I happen to analyze is Rolex to show insights regarding their competitiveness and marketshares.