A data-driven analysis project examining the cost of living trends in Bengaluru, Karnataka. This project utilizes Python data science libraries to visualize expenses, calculate correlations between lifestyle factors, and provide actionable insights for residents and students.
Bengaluru is known as the Silicon Valley of India, but it is also one of the most expensive cities to live in. This project analyzes a dataset of various expense categories—including rent, transport, food, and utilities—to determine the minimum and average budget required for students and working professionals.
Key Objectives:
- Analyze the distribution of expenses across different neighborhoods.
- Visualize the correlation between lifestyle choices and monthly budget.
- Provide a clear breakdown of "Needs" vs. "Wants" for financial planning.
College: K.S. School of Engineering and Management
- Pranav (Lead Developer / Data Analyst)
- Syed (Data Collection & Research)
- Supreeth (Visualization & Reporting)
- Rohith R. (Documentation & Analysis)
- Correlation Analysis: Generated Heatmaps (Correlation Matrices) to identify how rent prices influence overall monthly expenditure.
- Expense Distribution: Pie charts and Bar graphs visualizing the percentage of income spent on Transport (Metro/Bus) vs. Food/Rent.
- Budget Calculator: A logic-based model to estimate monthly costs based on user inputs (e.g., "Student" vs. "Professional").
- Data Cleaning: Pre-processing scripts using Pandas to handle missing values and outliers in the price data.
- Language: Python 3.x
- Data Manipulation: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
- IDE: VS Code / Jupyter Notebook
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Clone the repository:
git clone [https://github.com/yourusername/bengaluru-cost-of-living.git](https://github.com/yourusername/bengaluru-cost-of-living.git) cd bengaluru-cost-of-living -
Install dependencies: Ensure you have the required Python libraries installed:
pip install pandas numpy matplotlib seaborn
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Run the Analysis: Open the main script (or Jupyter Notebook) to see the visualizations:
python main_analysis.py
Note: Below is a summary of the insights generated by our code.
- Rent vs. Location: A scatter plot showing the variance in rent across Tier-1 and Tier-2 areas.
- Transport Costs: Analysis of Metro vs. Private vehicle daily commute expenses (e.g., Average ₹66/day for Metro).
- Correlation Matrix: A heatmap highlighting the strong relationship between "Dining Out" frequency and "Total Monthly Savings".
This was a collaborative academic project. If you wish to improve the data or add new parameters (e.g., Inflation rates), feel free to fork the repo and submit a Pull Request!
© 2025 K.S. School of Engineering and Management Group Project