Data Science: Predict the eligibility of potential creditors

This project aims to develop a machine learning model that can make predictions regarding the credit risk of potential borrowers. Using historical borrower data, the model will be trained to identify the creditworthiness of potential creditors.

Data Analyst: Netflix Data Visualization

I did Exploration Data Analysis using data from netflix, and in this EDA I used python to clean data, understand data, and explore data.

Data Science: Customer Segmentation Using K-Means Clustering with RFM Analysis

For Customer Segmentation on the Online Retail Dataset. We will know each customer segmentation’s characteristics using RFM Analysis.

Data Science & Data Analyst: Kalbe Nutritionals Data Scientist Project Based Internship

As the final assignment of my virtual internship at Kalbe Nutritionals, I developed a predictive data model to improve the company's business and then prepared visual media.

Data Science: Home Credit Indonesia Data Scientist Project Based Internship

Home Credit has a problem where it requires efficiency and speed in receiving application credit from each customer. As a Data Science Intern from Home Credit, we will to process data and create models that are able to predict and assess optimal credit applications and predict credit score customer.

Data Analyst: Best Product Sales (EDA)

I performed EDA (Exploratory Data Analysis) on the sales data, added columns for date, month, day, and day of the week, and performed analysis to gain insights into products, revenue, and sales patterns.

Data Analyst: Coffe Sales Dashboard (Excel)

I managed to create an informative and effective Coffee Order dashboard using Microsoft Excel, making good use of Excel's features to analyze and visualize coffee sales data.

Data Science: Customer churn

By understanding the behavior patterns of users who tend to churn, companies can take proactive measures, such as offering special promotions, providing better service, or adjusting pricing plans to retain customers. These measures are expected to help reduce churn rates and keep customer retention

Data Analyst: King County House Sales (Tableau)

Sales Dashboard

Data Science: Customer Personality Analysis Segmentation (Clustering)

The purpose of customer segmentation is to divide customers into many different ways. Using customer segmentation in marketing means that you can target the right people with the right messaging about your products. This will increase the success of your marketing campaigns.

Machine Learning: Regression

Data Science: Credit Card Fraud

This project classifies the records of transaction data as a fraudulent transaction or a real transaction.

Data Analyst: Game Sales (Tableau)

Data Analyst: Job Salary (Tableau)

Machine Learning: Tree Ensemble Learning

Ensemble learning is a machine learning technique that combines several models to improve predictive performance. One of the most popular ensemble methods is Random Forest, which is a type of decision tree ensemble. Another popular method is Gradient Boosting.

Power BI: Sales Dashboard

Machine Learning: Optimizing-KNN-Algorithm-Parameters-with-GridSearchCV

Bangkit Academy Capstone Project : Localbite food recommendation application based on disease