Exploratory Data Analysis (EDA) is a crucial step in data science that helps uncover patterns, detect anomalies, and summarize key characteristics of a dataset. This repository contains various EDA projects covering different domains, including global happiness, hotel bookings, and the Titanic disaster.
This project explores the World Happiness Report 2019 to identify factors affecting happiness across different countries.
🔹 Key Insights:
- Higher GDP per capita, social support, and healthy life expectancy are positively correlated with happiness.
- Countries in North America, Australia, and Europe score higher in happiness compared to Asia and Africa.
- Visualizations include happiness distribution, GDP correlation, and global happiness maps.
This project examines hotel booking data to analyze booking trends, customer behavior, and cancellation rates.
🔹 Key Insights:
- City Hotels have a higher cancellation rate than Resort Hotels.
- Families with children prefer Resort Hotels.
- Peak booking months occur in July and August.
This project explores survival patterns in the Titanic dataset, analyzing how factors like gender, class, and age influenced survival rates.
🔹 Key Insights:
- Women had a 74% survival rate, while men had only 19%.
- First-class passengers had a higher chance of survival (63%) than those in third-class (24%).
- Children (0-12 years) had better survival rates than adults.
- Passengers who paid higher fares were more likely to survive.
- Python Libraries:
pandas,numpy,matplotlib,seaborn,plotly - Statistical Analysis: Chi-square tests, correlation analysis
- Visualization: Count plots, heatmaps, box plots, regression plots
- Expand trend analysis across multiple datasets.
- Implement predictive modeling in another repository (Predictive Analysis ).
- Conduct hypothesis testing to validate insights.
- Datasets sourced from Kaggle.
- Inspired by real-world data science applications.