Loading...
© 2026 All rights reserved.
A data analysis and machine learning project exploring the World Happiness Report dataset. Uses linear regression, polynomial regression, and K-Means clustering to identify factors influencing global happiness scores and group countries by well-being patterns.
ai
Internal / Client
6 tools
Standard
Personal data science project exploring the World Happiness Report dataset to practice regression techniques and unsupervised learning on real-world socio-economic data.
Understanding the complex interplay of socio-economic factors that contribute to national happiness requires both regression analysis to model linear relationships and clustering to identify natural groupings among countries.
Data analyst and machine learning practitioner — performed exploratory data analysis, implemented linear and polynomial regression models, applied K-Means clustering with Elbow method optimization, and created visualizations of the results.
Applied a three-pronged analytical approach: linear and polynomial regression to model happiness factors, plus K-Means clustering with Elbow method optimization to segment countries by well-being profiles.
Identified key predictors of happiness through regression modeling and discovered three distinct country clusters with clear differences in GDP, social support, and happiness scores.
This project analyzes the World Happiness Report dataset through three complementary approaches. First, exploratory data analysis examines correlations between happiness scores and socio-economic factors including GDP per capita, social support, healthy life expectancy, freedom, generosity, and perceptions of corruption. A linear regression model quantifies the relationship between these factors and happiness scores, followed by a polynomial regression (degree 2) to capture non-linear patterns. Finally, K-Means clustering partitions countries into three distinct groups based on their socio-economic profiles, with the optimal number of clusters determined via the Elbow method. The project culminates in a visualization that maps countries by GDP per capita and happiness score, color-coded by cluster membership.
A modern web-based room reservation and management system built with FastAPI and React. Allows organizations to manage bookable spaces, browse available rooms, make reservations with interactive calendar views, and provides admin tools for managing rooms, equipment, users, and reservations.
A secure, role-based intranet portal for collaborative project management and task tracking built with native PHP. Provides administrators with full CRUD for projects, tasks, and employees, while regular employees get a personalized task dashboard with inline status updates and team visibility.