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DATA-ANALYTICS Project
Olympic Data Analytics Project Final Year Project Source Code
Get runnable Olympic Data Analytics Project Final Year Project Source Code with database files, project setup instructions, live demo options and installation support. This project resource helps students understand the implementation, modules, workflow and technical architecture of a complete DATA-ANALYTICS project.
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Project's Overview
Olympic Data Analytics Project is a Python-based final year project developed for analyzing 120 years of Olympic history from 1896 to 2016. This major project performs data loading, exploratory data analysis, country-wise medal comparison, gender participation analysis, sport-wise age analysis, medal trend visualization, athlete participation growth analysis, machine learning-based medal prediction, and future medal forecasting. The project includes Python scripts, Jupyter Notebook workflow, visualization outputs, machine learning evaluation, confusion matrix, ROC curve, and forecast graph. This data analytics project 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, visualization, and machine learning.
Login Credentials
This project has no login credentials because it is not a web application.
Credential note:
Kaggle API credentials are optional and only required if the dataset is downloaded automatically through Kaggle API. There are no shared or default Kaggle credentials included in the project.
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- Olympic dataset analysis from 1896 to 2016
- Automated data loading from local CSV
- Kaggle API fallback for dataset download
- Country name enrichment using NOC region mapping
- Exploratory data analysis report
- Dataset shape analysis
- Column data type analysis
- Sample row display
- Descriptive statistics
- Missing value analysis
- Unique athlete, country, sport, and year analysis
- Medal distribution analysis
- Gender participation analysis
- Summer and Winter season distribution analysis
- Six comparative visualization graphs
- Country-wise medal comparison
- Medal trend over time
- Gender comparison using pie and bar charts
- Age distribution analysis by sport
- Country medal heatmap
- Athlete participation growth analysis
- Machine learning medal prediction
- Random Forest binary classifier
- Feature scaling using StandardScaler
- Label encoding for sport and NOC
- Train-test split with stratification
- Accuracy score calculation
- ROC-AUC score calculation
- Classification report generation
- Feature importance analysis
- Confusion matrix visualization
- ROC curve visualization
- Medal forecast module
- Linear regression-based medal trend forecasting
- User-selected forecast year input
- Default forecast years from 2022 to 2031
- Forecast graph generation
- Jupyter Notebook support
- IDLE / script mode support
- Environment-aware plot display
- Output PNG graph generation
- Kaggle dataset helper script
- Legacy batch script support
- Dataset row and column count analysis
- Numeric column summary
- Categorical column summary
- Missing data inspection
- Athlete participation count
- Country participation count
- Sport count analysis
- Year range identification
- Medal count distribution
- Gold, Silver, and Bronze medal analysis
- Male vs female participation analysis
- Summer vs Winter Olympics comparison
- Country-wise medal points calculation
- Olympic participation trend analysis
- Sport-wise age distribution analysis
- Top country medal comparison
- Top 15 countries by medal points using horizontal bar chart
- Total medals awarded per year using line chart
- Summer vs Winter medals over time comparison
- Gender participation share using pie chart
- Medals won by gender using bar chart
- Age distribution of medal winners by sport using box plot
- Top 10 countries × medal type using heatmap
- Athlete participation growth using stacked area chart
- Machine learning confusion matrix
- Machine learning ROC curve
- Medal forecast graph
- All generated charts saved in the
output/folder - Predicts whether an athlete wins a medal or not
- Uses Random Forest Classifier
- Uses 100 trees with max depth 15
- Uses athlete features such as age, height, weight, sex, sport, and NOC
- Encodes categorical columns
- Handles large dataset through sampling
- Uses 75% training and 25% testing split
- Prints model accuracy
- Prints ROC-AUC score
- Prints classification report
- Displays feature importance
- Generates confusion matrix
- Generates ROC curve
- Supports medal forecast using Linear Regression
- Forecasts total medal count for selected future years
Option 1: Run main Python script
-
Open project folder:
cd "olympic-data-analysis" -
Install dependencies:
pip install -r requirements.txt -
Place dataset files inside:
data/ -
Run main project:
python olympic_analysis_complete.py - Close each graph window to continue to the next output.
Option 2: Run Jupyter Notebook
-
Open project folder:
cd "olympic-data-analysis" -
Install requirements:
pip install -r requirements.txt -
Start Jupyter Notebook:
python -m jupyter notebook -
Open:
olympic_analysis.ipynb - Run all cells.
Option 3: Download dataset using Kaggle API
-
Configure Kaggle API token:
kaggle.json -
Place it in:
C:\Users\<YourUsername>\.kaggle\kaggle.json -
Run:
python download_dataset.py
README Included
Setup Support Available
This project has no login credentials because it is not a web application.
Credential note:
Kaggle API credentials are optional and only required if the dataset is downloaded automatically through Kaggle API. There are no shared or default Kaggle credentials included in the project.