Key Steps:
✔ Data Wrangling & Cleaning
✔ Exploratory Data Analysis (EDA) with Python & SQL
✔ Interactive Maps (Folium) & Dashboards (Plotly Dash)
✔ Machine Learning (Classification/Regression)
✔ Presentation & Insights

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📄 README.md Template

# Applied Data Science Capstone Project  
**Course:** IBM Data Science Professional Certificate (Coursera)  
**Author:** Jefferson Firmino Mendes
**GitHub:** www.github.com/jeffthedeveloper  

## 🎯 Project Overview  
This project demonstrates an end-to-end data science workflow, from **data collection** to **predictive modeling**, as part of the IBM/Coursera Applied Data Science Capstone. It includes:  
- **Data wrangling** (cleaning, APIs, web scraping)  
- **Exploratory Data Analysis (EDA)** with Python & SQL  
- **Interactive visualizations** (Folium maps, Plotly Dash)  
- **Machine learning** (classification/regression) for predictions  

📂 Repository Structure

├── data/                    # Raw & processed datasets  
├── notebooks/               # Jupyter notebooks (EDA, ML, etc.)  
│   ├── 1_Data_Collection.ipynb  
│   ├── 2_Data_Wrangling.ipynb  
│   ├── 3_EDA_SQL_Analysis.ipynb  
│   ├── 4_Predictive_Modeling.ipynb  
├── scripts/                 # Python helper scripts  
├── docs/                    # Reports, presentations (PDF)  
├── app/                     # Plotly Dash/Flask app (if applicable)  
└── README.md  

🛠️ Tools & Technologies

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• SQL (SQLite, PostgreSQL, or IBM Db2)
• Interactive Maps: Folium
• Dashboarding: Plotly Dash
• Machine Learning: Scikit-learn, XGBoost
• Version Control: Git/GitHub

🔍 Key Findings

1. Exploratory Analysis: [Brief insight, e.g., "70% of SpaceX launches reuse the booster"]
2. Predictive Model: [e.g., "Random Forest achieved 85% accuracy in classifying accident severity"]
3. Interactive Tools: [e.g., "Folium maps revealed regional trends in accidents"]

🚀 How to Run

1. Clone the repo:

   git clone [https://github.com/jeffthedeveloper/Applied-Data-Science-Capstone-End-to-End-Analysis-with-Python-SQL-and-Machine-Learning/blob/main/README.md]

2. Install dependencies:

   pip install -r requirements.txt

3. Run Jupyter notebooks:

   jupyter lab

📜 License

This project is part of an educational coursework (MIT License).

📊 Presentation

click here ➡ [https://drive.google.com/file/d/1lgArDDKVNuzi1ucUSftZAgvOwFCHdOkG/view?usp=sharing]

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### **Customization Tips:**  

- For **SpaceX projects**, highlight:  
  - Falcon 9 landing predictions  
  - Folium launch site analysis  
- For **Accident Severity projects**, focus on:  
  - SQL queries for crash hotspots  
  - Classification model (e.g., Logistic Regression)  

Let me know if you'd like a **domain-specific version** (e.g., for SpaceX, accidents, COVID-19, etc.)! 🎯