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DATA-ANALYTICS Project
BMW Car Market Insights Data Analysis Final Year Project Source Code
Get runnable BMW Car Market Insights Data Analysis 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
BMW Car Market Insights is a Python-based final year project developed for analyzing used BMW car market data and predicting vehicle prices using machine learning. This major project loads BMW vehicle data from a CSV file, cleans categorical text values, performs exploratory data analysis, generates statistical summaries, creates 13 visualization charts, trains a Random Forest Regressor, evaluates the model using R², RMSE, MAE, and five-fold cross-validation, and displays sample price predictions. The project analyzes model-wise prices, year-wise price variation, transmission type, fuel type, mileage, engine size, MPG, tax, model popularity, feature importance, and actual-versus-predicted prices. This BMW car market insights 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.
| Account Type | Username | Password |
|---|---|---|
| Administrator | Not applicable | Not applicable |
| User | Not applicable | Not applicable |
| Dealer | Not applicable | Not applicable |
Credential note:
No admin panel, user login, dealer panel, password, or database authentication is included. The project runs locally as a command-line analytics project.
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Project Features in this Final Year Project
- BMW used-car market data analysis
- CSV-based dataset loading
- Data cleaning and preparation
- Text column whitespace removal
- Dataset shape inspection
- Data type inspection
- First records preview
- Numerical descriptive statistics
- Categorical frequency analysis
- Missing-value analysis
- Thirteen graphical outputs
- Automatic
output_plotsfolder creation - Model-wise price analysis
- Year-wise price analysis
- Transmission-wise price comparison
- Fuel-type price comparison
- Mileage versus price analysis
- Engine size versus price analysis
- MPG versus price analysis
- Correlation heatmap
- BMW model popularity analysis
- Listings by manufacturing year
- Label encoding of categorical features
- Train-test split
- Feature scaling
- Random Forest regression model
- Training and testing prediction
- R², RMSE, and MAE evaluation
- Five-fold cross-validation
- Feature importance ranking
- Actual-versus-predicted graph
- Sample car-price prediction output
- Automatic market insights summary
- Fully local command-line execution
Data Analytics / EDA Features in this Final Year Project
-
Loads:
data.csv -
Cleans text values in:
model transmission fuelType - Displays dataset rows and columns
- Displays column names
- Displays non-null counts
- Displays memory usage
- Displays first five records
- Displays numerical summary using
describe() - Analyzes categorical value counts
- Analyzes BMW model frequency
- Analyzes transmission frequency
- Analyzes fuel type frequency
- Checks missing values column-wise
- Displays total missing cell count
- Verifies loaded data types
- Prints final market insights summary
Visualization Features in this Final Year Project
The project generates 13 PNG charts inside:
output_plots/
- Price Distribution
File:01_price_distribution.png
Chart type: Histogram
Shows BMW car price distribution with average price line. - Average Price by Model
File:02_price_by_model.png
Chart type: Horizontal bar chart
Compares average selling price of BMW models. - Price Distribution by Year
File:03_price_vs_year.png
Chart type: Box plot
Shows price variation across manufacturing years. - Price by Transmission Type
File:04_price_by_transmission.png
Chart type: Box plot
Compares prices for automatic, manual, semi-auto, and other transmission types. - Price by Fuel Type
File:05_price_by_fuel_type.png
Chart type: Box plot
Compares vehicle prices across petrol, diesel, hybrid, electric, or other fuel categories. - Mileage versus Price
File:06_mileage_vs_price.png
Chart type: Scatter plot with trend line
Studies relationship between mileage and price. - Engine Size versus Price
File:07_engine_vs_price.png
Chart type: Scatter plot
Compares engine size with price, grouped by fuel type. - MPG versus Price
File:08_mpg_vs_price.png
Chart type: Scatter plot
Studies relationship between fuel efficiency and car price. - Correlation Heatmap
File:09_correlation_heatmap.png
Chart type: Heatmap
Displays correlation among numerical variables. - BMW Model Popularity
File:10_model_popularity.png
Chart type: Horizontal bar chart
Shows listing count for each BMW model. - Listings by Year
File:11_year_distribution.png
Chart type: Bar chart
Shows number of vehicle listings by manufacturing year. - Feature Importance
File:12_feature_importance.png
Chart type: Horizontal bar chart
Shows feature contribution in Random Forest price prediction. - Actual versus Predicted Price
File:13_actual_vs_predicted.png
Chart type: Scatter plot
Compares actual test-set prices with predicted prices.
Machine Learning Features in this Final Year Project
- Supervised machine learning regression
-
Target variable:
price -
Input features:
model year transmission mileage fuelType tax mpg engineSize - Uses Random Forest Regressor
- Categorical encoding using LabelEncoder
- Feature scaling using StandardScaler
- 80% training and 20% testing split
- Random state set to 42 for reproducibility
- Trains model on scaled training data
- Predicts training prices
- Predicts testing prices
- Reports training R² score
- Reports testing R² score
- Reports training RMSE
- Reports testing RMSE
- Reports training MAE
- Reports testing MAE
- Performs five-fold cross-validation
- Calculates feature importance values
- Ranks important price-prediction features
- Generates actual-versus-predicted visualization
- Displays sample prediction table
Random Forest Model Configuration in this Final Year Project
The project uses:
RandomForestRegressor(
n_estimators=100,
max_depth=15,
random_state=42,
n_jobs=-1
)
Why Random Forest is suitable:
- Handles non-linear relationships
- Works well on structured tabular data
- Learns interactions between vehicle features
- Performs well with mixed encoded feature sets
- Handles noisy patterns better than simple linear models
- Provides feature importance values
- Useful for used-car price prediction demonstrations
Dataset Details in this Final Year Project
Dataset file:
data.csv
Required CSV header:
model,year,price,transmission,mileage,fuelType,tax,mpg,engineSize
Dataset columns:
model— BMW vehicle model or seriesyear— manufacturing or registration yearprice— vehicle selling pricetransmission— transmission typemileage— miles already travelledfuelType— vehicle fuel categorytax— annual road taxmpg— fuel economy in miles per gallonengineSize— engine displacement in litres
Files / Modules Included in this Final Year Project
main.py— recommended name for the main scriptdata.csv— required BMW used-car datasetrequirements.txt— dependency listREADME.md— project documentationoutput_plots/— generated charts folder
Recommended structure:
bmw-car-market-insights/
|
|-- main.py
|-- data.csv
|-- requirements.txt
|-- README.md
|
`-- output_plots/
Output Files in this Final Year Project
After successful execution, generated files appear in:
output_plots/
Expected output files:
01_price_distribution.png02_price_by_model.png03_price_vs_year.png04_price_by_transmission.png05_price_by_fuel_type.png06_mileage_vs_price.png07_engine_vs_price.png08_mpg_vs_price.png09_correlation_heatmap.png10_model_popularity.png11_year_distribution.png12_feature_importance.png13_actual_vs_predicted.png
These outputs can be used in:
- Project reports
- Presentations
- Research documentation
- Viva demonstrations
- Data analysis assignments
- Portfolio case studies
-
Create project folder:
bmw-car-market-insights/ -
Place files inside the folder:
main.py data.csv requirements.txt README.md -
Open terminal in project folder:
cd "bmw-car-market-insights" -
Create virtual environment on Windows:
python -m venv venv venv\Scripts\activate -
Create virtual environment on macOS/Linux:
python3 -m venv venv source venv/bin/activate -
Install required packages:
pip install pandas numpy matplotlib seaborn scikit-learn -
Or install using requirements file:
pip install -r requirements.txt -
Ensure the dataset exists:
data.csv -
Run the project on Windows:
python main.py - Run the project on macOS/Linux:
python3 main.py
- Check generated charts:
output_plots/
README Included
Setup Support Available
This project has no login credentials because it is not a web application.
| Account Type | Username | Password |
|---|---|---|
| Administrator | Not applicable | Not applicable |
| User | Not applicable | Not applicable |
| Dealer | Not applicable | Not applicable |
Credential note:
No admin panel, user login, dealer panel, password, or database authentication is included. The project runs locally as a command-line analytics project.