Summer Bootcamp Training Project
SRMCEM, Lucknow
This project is a comprehensive data science analysis focused on understanding patterns and risk factors related to heart attacks. Our team explored multiple patient data sources and developed visualizations and reports using Python, SQL, Power BI, and Jupyter Notebooks.
The core objective of the project was to identify causes, patterns, and contributing factors related to heart attacks, using real-world clinical datasets. We aimed to derive insights that can aid in early detection, better diagnosis, and healthcare planning.
🧩 Project Goals
- Understand patient demographics and medical history
- Identify key diagnostic markers and abnormal lab results
- Explore the relationship between medications and diagnoses
- Analyze visit patterns and hospitalization frequency
- Integrate and visualize data through dashboards
📚 Datasets Used
patients.csv– Patient demographics and IDsvisits.csv– Details of hospital visits and admissionsdiagnosis.csv– Medical diagnoses related to cardiovascular conditionslab_record.csv– Lab test results (e.g., cholesterol, ECG, blood pressure)medication.csv– Prescribed drugs and treatment histories
🔬 Key Analysis Areas
- Patient-level profiling
- Temporal patterns in visits and treatments
- Correlation between diagnoses and lab findings
- Effectiveness and frequency of specific medications
- Data integration across files for comprehensive case analysis
The project involved SQL-based data extraction, Python-based analysis using libraries such as Pandas, Matplotlib, and Seaborn, as well as the development of a Power BI dashboard for executive-level visual insights.
📁 Summer-Bootcamp-Training-Project/
│
├── 📁 CSV/
│ ├── diagnoses+lab_results.csv
│ ├── diagnoses_updated.csv
│ ├── medications.csv
│ ├── patient+visit.csv
│ ├── patients+visits.csv
│ ├── updated_lab_results.csv
│ └── updated_patients.csv
│ └── visits.csv
│
├── 📁 Code/
│ ├──Heart_Disease_(Lab_result+Medication+Diagnoses).ipynb/
│ ├── diagnoses+lab_result.ipynb
│ ├── paitents_.analysis.ipynb
│ ├── patients+visits.ipynb
│ └── visit_analysis.ipynb
│
├── 📁 SQL/
│ ├── heart_disease.sql
│ └── SQL_Report.pdf
│
├── 📁 PDF/
│ ├── Heart Disease Analysis Project.pdf
│ ├── Heart Disease.pdf
│ ├── Heart_Disease_Report.pdf
│ ├── Paitent_View.pdf
│ └── lab_diagnosis_analysis.pdf
│
├── 📁 PowerBI/
│ └── Heart Disease Analysis Project.pbix
│
└── README.md
This project includes a detailed exploration of heart disease data using Power BI and SQL.
- 🧠 Dashboard File:
Heart Disease Analysis Project.pbix - 📄 CSV Files: Click here to view the datasets
- Data Source: Data was gathered from various online health datasets.
- Data Files: 5 key CSVs including:
diagnoses_updated.csvmedications.csvupdated_lab_results.csvupdated_patients.csvvisits.csv
- Analysis Tools:
- Python libraries:
pandas,numpy,seaborn,matplotlib,fpdf,warnings - SQL for querying structured data
- Jupyter Notebooks for visual exploratory analysis
- Power BI for interactive dashboards and stakeholder reporting
- Python libraries:
- Identification of critical lab results and diagnosis combinations that correlate with heart attacks.
- Frequency and type of medications administered.
- Patient visit trends and relationships between comorbidities and heart disease.
- Interactive dashboards summarizing findings visually.
| Tool/Library | Purpose |
|---|---|
| Python (Pandas, NumPy, Matplotlib, Seaborn) | Data manipulation & visualization |
| SQL | Data querying & exploration |
| Jupyter Notebook | Interactive data analysis |
| Power BI | Dashboard creation |
| FPDF | PDF report generation |
| Name | GitHub Username |
|---|---|
| Aditi Agnihotri | aditi549 |
| Aditi Pandey | Aditi14319 |
| Anshika Dubey | Anshika-Dubey |
| Ishita Aggarwal | ishita009A |
| Nirnay Awasthi | NirnayAwasthi |
| Shashank Mishra | ShashankMishra9696 |
| Shivam Kumar Dubey | kuro-shiv |
| Suryakant Mishra | mishrasuryakant |
| Vanshika Gupta | vanshikagpt1204 |
Special thanks to our mentor Saurabh Aggarwal Sir for his valuable guidance throughout the project.
"This bootcamp was a highly enriching and insightful experience. Working on a real-world case study helped us enhance both technical and analytical skills, and taught us how to collaborate on industry-level data science workflows."
— Team Feedback
This project is for academic and educational purposes only.
git clone https://github.com/kuro-shiv/Summer-Bootcamp-Training-Project
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Tada here
