Tanzania E-commerce Price Intelligence Platform
# ποΈ Tanzania E-commerce Price Intelligence System
π **Advanced AI-powered price tracking and prediction system for Tanzanian e-commerce platforms**
## π Project Overview
This system tracks product prices across major Tanzanian e-commerce platforms, stores historical data, and uses machine learning to predict future price trends. It provides valuable insights for both sellers and buyers in the Tanzanian market.
## π― Problem Statement
In Tanzania's rapidly growing e-commerce sector, prices fluctuate frequently due to:
- Supply and demand variations
- Import cost changes (USD/TZS fluctuations)
- Regional price differences (Dar es Salaam vs Dodoma vs Mwanza)
**Current Challenges:**
- Sellers lack tools to track competitor prices
- Buyers struggle to find optimal purchasing times
- No centralized system for price comparison and prediction
## π οΈ Tech Stack
### Backend
- **Python** (FastAPI/Flask)
- **Machine Learning**: Scikit-learn, Prophet, ARIMA
- **Database**: SQLite/PostgreSQL
- **Web Scraping**: BeautifulSoup, Selenium, Scrapy
### Frontend
- **React.js** with modern hooks
- **Tailwind CSS** for styling
- **Recharts** for data visualization
- **Chart.js** for interactive charts
### Data Processing
- **Pandas** for data manipulation
- **NumPy** for numerical operations
- **Matplotlib/Seaborn** for visualization
## ποΈ System Architecture
```
[Web Scrapers] β [Database] β [ML Models]
β
[Backend API]
β
[Frontend Dashboard]
```
## π Features
### π Data Collection
- **Multi-platform scraping**: Jumia Tanzania, ZoomTanzania, Kilimo Mart
- **Real-time price tracking**
- **Historical data storage**
- **Product categorization**
### π€ Machine Learning
- **Price prediction** using Linear Regression, Random Forest, XGBoost
- **Time series forecasting** with ARIMA and LSTM
- **Trend analysis** and anomaly detection
- **Regional price comparison**
### π Analytics Dashboard
- **Interactive price charts**
- **Product search and filtering**
- **City-wise price compa β¦