Age-Based Purchase Simulation

This project simulates purchase behavior across different age groups using two probabilistic models: a weighted probability model and a uniform probability model.

📌 Objective

To visualize and compare how weighted vs uniform probability distributions affect purchasing behavior, using simulated data grouped by age.

🛠️ Tools & Libraries

• Python 3.x
• Pandas
• NumPy
• Seaborn
• Matplotlib
• Jupyter Notebook

🔍 Methodology

1. Simulated Dataset of 40,000 users, categorized into age groups (20, 30, ..., 70)
2. Weighted Model: Each age group is assigned a purchase probability (e.g., 70-year-olds have 90%)
3. Uniform Model: All users have the same purchase probability equal to the weighted average
4. Visualization: Line graph comparing actual probabilities between models

📈 Results

• Older age groups showed significantly higher purchase rates in the weighted model
• The uniform model flattens out the variation between age groups
• Graph clearly shows divergence between assumptions

📁 Output Files

• Project_Analysis.ipynb: Main notebook with code and plots
• Conditional_Probability_Graph.png: Graph comparing both models
• Report.txt: 200-word results summary (won't be uploaded here)

✅ Key Insight

Using a uniform distribution can oversimplify customer behavior. Weighted models more accurately reflect real-world differences across demographics. Detailed analysis will be found in the expected report.

💡 Future Ideas

• Add gender/income factors
• Fit logistic regression models
• Create an interactive Streamlit dashboard