Machine Learning - Customer Segmentation
K-Means clustering was applied to Olist's transaction data, segmenting customers according to their purchasing behaviors. This segmentation strategy facilitated the identification of distinct customer groups, providing an opportunity to develop personalized marketing strategies for each segment.
Machine Learning - Predicting Apartment Price
An XGBoost-based predictive model was developed to estimate apartment prices in Daegu amid market fluctuations. It has achieved a high accuracy level, maintaining a MAPE under 20%. This tool offers valuable insights for buyers and sellers navigating the volatile Daegu real estate market.
Exploratory Data Analysis
Exploratory Data Analysis with NYC TLC Trip Data to to interpret patterns and provide actionable recommendations to inform decision-making.
Python CRUD Function
The implementation of CRUD (Create, Read, Update, Delete) in Python involves creating functions related to car rental data.
Python Transformation
Performing the transformation of tabular data into a spatial database format using Python to enhance the efficiency of the transformation process.
Scrapping Spatial Data
Creating a Python function to retrieve spatial data from OpenStreetMap by specifying the spatial data selection and the data retrieval location.
Tableau Visualization
Visualizing data in the form of pie charts, bar charts, line charts, as well as creating visualization dashboards and geodashboards to provide engaging and understandable information.