👩💻 Results-Oriented Analyst with a Focus on Data-Driven Decision Making and Predictive Analytics 👩💻
🌍 As a Professional 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! 😄
🔥 Actively open for Data Analyst, Business Analyst / Business Intelligence, and Data Scientist Role
🔥 Further learning on AI & Machine Learning
✅ Analyze Customer Personality to Enhance Marketing Campaigns Using Machine Learning Clustering: Link Project
Creating customer personality using Machine Learning resulting 4 type of customers consist of: Cluster 0 (Middle-Low Income & Low Spend), Cluster 1 (Low Income & Spend), Cluster 2 (Middle-high Income & High Spend), and Cluster 3 (High Income & Spend).
✅ Performance Marketing Dashboard Kimia Farma 2020 - 2023 (Project Based Intern Kimia Farma with Rakamin): Link Project
Creating performance marketing dashboard on Kimia Farma using Tableau
✅ 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 (Project Based Intern Home Credit with Rakamin): 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 (Project Based Intern IDX Partners with Rakamin): Link Project
📜 Improving Employee Retention by Predicting Employee Attrition Link Project
📜 Dashboarding with Google Looker Studio (Project Based Intern Bank Muamalat with Rakamin)
⚡️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.