- General skills: Exploratory Data Analysis, Time Series Analysis, Hypothesis Testing, ETL, Machine Learning, Deep Learning
- Programming Language: Python, SQL.
- Visualization Tools: Tableau, Looker Studio.
- Libraries / Framework: TensorFlow, Scikit-learn, Streamlit, Pandas, Numpy, Matplotlib, Seaborn, Scipy, Feature-Engine.
- Techniques: NLP, Computer Vision, Time Series Analysis, Forecasting.
- Modeling Algorithms: Regression, Random Forest, Decision Trees, Support Vector Machine (SVM), KKN, Neural Networks, Clustering, and Dimensionality Reduction
- Others: Google BigQuery, Hugging Face, Docker
- Hacktiv8 Bootcamp Jakarta, Indonesia Data Science Program. Score: A(89.48%) | June 2023 - August 2023
- Telkom University Bandung Indonesia Bachelor of Physics Engineering | July 2014– August 2021
Developed algorithms using natural language processing and deep learning models for predictive cyberbullying tweets, had the ability to create and deploy predictive models and achieved 72% accuracy score.
Technology / Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-Learn, TensorFlow, Keras, Streamlit
Developed a machine learning project utilizing Artificial Neural Network to forecast customer churn for a company, based on historical customer data, and achieved a 90% accuracy score
Technology / Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, SciPy, Scikit-Learn, Feature-Engine, TensorFlow, Keras, Streamlit
Developed a machine learning project utilizing classification supervised learning to forecast airline passengers’ satisfaction for a company, base on historical passengers data, and achieved a 91% accuracy score
Technology / Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-Learn, TensorFlow, Keras, Streamlit.
Developed a clustering segmentation customer of credit card based on the financial behaviour. By using K-Means method, it was found that there are 4 clusters with customers' characteristics based on its segments.
Technology / Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, K-Means, Scikit-Learn, TensorFlow, Keras, Streamlit.
Designed and analyzed the global suicide rate utilizing using hypothesis testing with statistical methods such as t-tests, ANOVA, and chi-square tests, based on historical global suicide rate data
Technology / Tools: Tableau, Python, Pandas, Numpy, Seaborn, Matplotlib, Scikit-Learn, Statsmodels, studiolooker.
Based on the modeling that has been carried out, the XGBoosting tuning model is the best model for predicting whether a customer will subscribe to a term deposit or not. With an accuracy rate of up to 88%, a classification rate of 89%
Technology / Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, SciPy, Scikit-Learn, Feature-Engine, TensorFlow, huggingface Keras, Streamlit
TheLook's CEO plans to increase sales in the 4th quarter by targeting $250,000 due to mass celebrations like Christmas and New Year. Product categories like intimates, hoodies, sweatshirts, and shorts will be produced to increase sales.
Technology / Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-Learn.