👩💻 Professional Data Scientist 👩💻
🌍 As a seasoned Data Scientist, I specialize in data preprocessing, imputation, wrangling, and transforming data into actionable insights. I also develop robust machine learning models.
🎨 I tackle diverse projects, analyzing complex business challenges and delivering strategic recommendations.
📚 Feel free to explore my repositories to discover the projects I've completed.
💡 Connect with me through my portfolio and LinkedIn. Let's collaborate and innovate together! 😄
🔥 Making some portofolio projects analysis
🔥 Actively open for Data Analyst, Business Analyst / Business Intelligence, and Data Scientist Role
✅ Predict Customer Clicked Ads Classification by Using Machine Learning: Link Project
Deveoped Logistic Regression model (Standardized) with score on Accuracy: 0.9767, precision: 0.993, and recall: 0.9600 with the best features: Daily Time Spent on Site, Daily Internet Usage, Area income, and Age, suggesting to Retargeting marketing on middle-aged adults customers (35 - 50 years old) with typical middle-lower income and spent less on site and internet.
✅ Analyzing Default Risk on Home Credit: Link Project
In collaboration with Home Credit and Rakamin, I analyzed default rates and predicted customer risk profiles. Utilizing algorithms like Logistic Regression, Decision Tree, Random Forest, AdaBoost, and XGBoost, the Logistic Regression model achieved the best performance with an ROC AUC score of 0.74.
✅ Creating a Model for Credit Risk Prediction: Link Project
Partnering with ID/X Partners and Rakamin, I developed a model to enhance credit risk evaluation. The AdaBoost algorithm excelled with an ROC AUC score of 0.965 and Recall of 0.988, optimizing business decisions and minimizing potential losses.
📜 Analyze Customer Personality to Enhance Marketing Campaigns Using Machine Learning Clustering Link Project
📜 Improving Employee Retention by Predicting Employee Attrition Link Project
⚡️Programming Language: Python.
⚡️Data Manipulation and Analysis: Pandas, Numpy, PySpark.
⚡️Data Visualization: Matplotlib, Seaborn, Looker Studio, Tableau, Power BI.
⚡️Machine Learning: Scikit-learn, Regression, Classification, Unspervised Learning.
⚡️Databases: MySQL, PostgreSQL, DBeaver.
⚡️Statistical Analysis: Hyphothesis testing, Regression Analysis.
⚡️Other: Git, A/B Testing.