This repository contains two main data analysis (Capstone) projects developed in Python, focused on distinct areas: Stock Market Analysis and Emergency Calls (911) Analysis.
Below is a README.md template you can use in your GitHub repository.
This repository brings together practical data science projects exploring exploratory data analysis (EDA), data visualization, and financial concepts.
File: Stock-Analysis-Capstone-Project.ipynb
This project focuses on extracting and analyzing historical financial asset data using the yfinance library.
Objective:
Build a stock portfolio, analyze historical returns and volatility, and visualize asset performance over time.
Main Activities:
- Real-time data extraction via Yahoo Finance
- Daily and cumulative return calculations
- Risk vs. return analysis (Efficient Frontier)
- Visualization of adjusted closing prices and asset correlations
Technologies:
Python, Pandas, Matplotlib, Seaborn, yfinance
File: DataCapstone_Project.ipynb
A detailed analysis of a Kaggle dataset containing emergency call records from Montgomery County, PA.
Objective:
Identify temporal and geographic patterns in emergency calls to understand the main service demands (EMS, Fire, Traffic).
Main Activities:
- Data processing and cleaning (Feature Engineering with DateTime objects)
- Identification of the most frequent call types
- Temporal analysis (calls by day of week, month, and hour)
- Basic geospatial visualization based on zip codes and townships
Technologies:
Python, Pandas, NumPy, Matplotlib, Seaborn
To run the notebooks locally, it is recommended to use Anaconda or a Python virtual environment.
git clone https://github.com/your-username/repository-name.gitInstall dependencies
pip install pandas numpy matplotlib seaborn yfinanceOpen Jupyter Notebook
📈 Results and Conclusions Stocks: The notebook demonstrates how risk and return vary across different sectors (Technology, Energy, Healthcare) and how diversification impacts portfolio performance.
911: The analysis revealed that EMS (Emergency Medical Services) calls are the most common and that there are specific peak hours for traffic-related incidents during business hours.
✒️ Author Felipe Gurgel Araujo
Note: This project was developed as part of an academic activity.