This assignment is designed to help students practice data analysis skills on a hospital transactions dataset. The tasks involve data cleaning, exploratory analysis, visualization, and insight generation. Students will gain hands-on experience working with real-world healthcare data.
The dataset contains hospital transaction records with the following columns:
| Column Name | Description |
|---|---|
| Record_ID | Unique identifier for each transaction |
| Date | Date of the transaction |
| Patient_Name | Name of the patient |
| Department | Hospital department (Cardiology, Pediatrics, Oncology, etc.) |
| Doctor_Name | Name of the attending doctor |
| Transaction_Type | Type of transaction (Appointment, Lab Test, Prescription, Billing, Donation) |
| Amount | Amount paid or donated |
| Payment_Mode | Mode of payment (Cash, Cheque, Online, Credit Card) |
| Notes | Miscellaneous notes |
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Data Cleaning and Preprocessing
- Handle missing values in key columns.
- Standardize date formats.
- Remove duplicate records.
- Ensure consistency in categorical columns.
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Exploratory Data Analysis (EDA)
- Compute total and average amounts per department.
- Identify top doctors by number of transactions.
- Analyze distribution of transaction types and payment modes.
- Detect trends in transactions over time.
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Data Visualization
- Bar chart: total transactions per department.
- Line chart: trends in donations over time.
- Pie chart: payment mode distribution.
- Box plot: distribution of amounts across departments.
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Insights and Recommendations
- Summarize key findings from the analysis.
- Provide recommendations for hospital management.
- Submit a PDF report containing all analysis, visualizations, and insights.
- Include all code used for analysis (Python, R, or Excel) in an appendix.
- Ensure the report is well-structured, readable, and clearly explains your findings.
You may use any of the following tools for this assignment:
- Python (pandas, matplotlib, seaborn)
- R (tidyverse, ggplot2)
- Excel or Google Sheets
The main goal of this assignment is to develop practical skills in handling real-world data, performing exploratory data analysis, and presenting actionable insights.