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MSc Project Report
MSc COVID-19 Data Analysis Final Year Project Report
Complete MSc final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
COVID-19 Data Analysis Project is a Python-based final year project developed for analyzing real-world COVID-19 case data using exploratory data analysis, visualization, and machine learning. This major project loads regional time-series data, processes province/state-level observations, aggregates records into country-day and country-level snapshots, prints statistical summaries, generates 12 chart images, and trains regression models to predict reported deaths using confirmed cases, latitude, longitude, and WHO region. The project can be executed as a standalone Python script or through a Jupyter Notebook where graphs appear inline. This COVID-19 data analysis source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python, data analytics, EDA, visualization, and machine learning.
Login Credentials
This project has no login credentials because it is not a web application.
Credential note:
No admin panel, user login, password, or database authentication is included. The project runs locally as a Python data analysis script or Jupyter Notebook.
Project Features in this Final Year Project
- COVID-19 dataset loading from local CSV
- Province/state-level data processing
- Country-day aggregation
- Country-level latest snapshot creation
- Global daily series generation
- Exploratory data analysis
- Statistical summary printing
- Missing-value rate analysis
- WHO region distribution analysis
- Country-wise confirmed case ranking
- COVID-19 deaths analysis
- Recovered vs deaths comparison
- Global daily new confirmed case trend
- 7-day rolling average calculation
- Cumulative confirmed case trend analysis
- Data visualization using Matplotlib and Seaborn
- 12 PNG chart exports
- Machine learning regression model training
- Linear Regression model
- Random Forest Regression model
- Holdout train-test split
- R² score evaluation
- MAE evaluation
- RMSE evaluation
- Random Forest feature importance listing
- Jupyter Notebook support
- Standalone Python script support
- Inline notebook chart display
- Exported figures folder support
Data Analytics / EDA Features in this Final Year Project
- Raw dataset row count
- Country-day aggregate row count
- Date range identification
- Number of distinct countries/regions
- Column data type inspection
- Sample row display
- Numeric summary statistics
- Missing-value analysis
- WHO region distribution
- Top countries by confirmed cases
- Province/state level to country-day aggregation
- Latest country snapshot generation
- Global daily total calculation
- Daily new confirmed case calculation
- Rolling mean calculation
- Country-level cross-sectional analytics
Visualization Features in this Final Year Project
The project generates 12 chart images in the figures/ folder:
- Top 15 countries by confirmed cases
- Top 12 countries by total deaths
- Pie chart of confirmed case share among top 8 countries
- Scatter plot of confirmed cases vs deaths
- Hexbin density chart of confirmed cases vs deaths
- Histogram of log-transformed confirmed case distribution
- Box plot of deaths by WHO region
- Violin plot of confirmed cases by WHO region
- Correlation heatmap of numeric latest snapshot columns
- Global daily new confirmed cases with 7-day rolling mean
- Cumulative confirmed cases for selected large countries
- Grouped bar chart of recovered vs deaths for top 10 countries by confirmed cases
Machine Learning Features in this Final Year Project
- Predicts reported COVID-19 deaths
- Uses latest country-level snapshot
- Uses only rows where confirmed cases are greater than zero
- Input features:
- Confirmed cases
- Latitude
- Longitude
- WHO region
- Linear Regression model
- Random Forest Regression model
- One-hot encoding for WHO region
- Scaled numeric features for Linear Regression
- Numeric passthrough for Random Forest pipeline
- 75% training and 25% testing split
- R² score calculation
- Mean Absolute Error calculation
- Root Mean Squared Error calculation
- Random Forest feature importance output
- Avoids using Recovered and Active as input features to prevent artificially perfect correlations
Dataset Details in this Final Year Project
Dataset file:
data/data.csv
Date range:
2020-01-22 to 2020-07-27
Important columns:
Province/StateCountry/RegionLatLongDateConfirmedDeathsRecoveredActiveWHO Region
Used column names after processing:
Province_StateCountry_RegionWHO_Region
Files / Modules Included in this Final Year Project
covid19_data_analysis_project.py— main script for EDA, visualization, and MLcovid_project_loader.py— notebook bootstrap loadercovid19_analysis.ipynb— Jupyter Notebook workflowrequirements.txt— Python dependency listREADME.md— project documentationdata/data.csv— required COVID-19 datasetfigures/— generated PNG chart output folder
Output Files
The project exports chart images into:
figures/
Generated figures include:
- Top countries by confirmed cases
- Top countries by deaths
- Confirmed case share pie chart
- Confirmed vs deaths scatter plot
- Confirmed vs deaths hexbin plot
- Confirmed case histogram
- WHO region deaths box plot
- WHO region confirmed cases violin plot
- Correlation heatmap
- Global daily new confirmed trend chart
- Cumulative confirmed cases chart
- Recovered vs deaths grouped bar chart
Option 1: Run as command-line script
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Open project folder:
cd "Covid 19 data analysis" -
Create virtual environment:
python -m venv .venv -
Activate virtual environment on Windows:
.\.venv\Scripts\Activate.ps1 -
Activate virtual environment on macOS/Linux:
source .venv/bin/activate -
Install dependencies:
pip install -r requirements.txt -
Ensure dataset exists:
data/data.csv -
Run the analysis:
python covid19_data_analysis_project.py -
Check generated charts:
figures/
Option 2: Run in Jupyter Notebook
- Complete virtual environment and dependency installation.
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Register environment as Jupyter kernel:
python -m ipykernel install --user --name=covid19-analysis --display-name="Python (covid19-analysis)" -
Start Jupyter Notebook:
jupyter notebook -
Open:
covid19_analysis.ipynb - Select the project kernel.
- Run all cells from top to bottom.
- View EDA, charts, and ML results inline.
README Included
Setup Support Available
This project has no login credentials because it is not a web application.
Credential note:
No admin panel, user login, password, or database authentication is included. The project runs locally as a Python data analysis script or Jupyter Notebook.