Yohannes022/food-price-analysis-ethiopia

# 🇪🇹 Food Price Analysis in Ethiopia ## 📊 Project Overview This project analyzes food price trends across Ethiopia using real-world data. The goal is to understand price behavior, identify regional differences, and build predictive models for food pricing. --- ## 🎯 Objectives * Analyze food price trends over time * Identify regional and market-level price variations * Understand key factors influencing food prices * Build machine learning models to predict prices --- ## 🧹 Data Processing * Cleaned raw dataset by handling missing values and removing duplicates * Converted date fields and engineered time-based features (year, month) * Filtered dataset to focus on retail prices --- ## 📊 Exploratory Data Analysis (EDA) Key findings: * Food prices show a consistent upward trend, indicating inflation * Price distribution is highly skewed due to high-value livestock commodities * Significant regional and market-level price differences exist * Maize appears to be a widely available staple with relatively stable prices --- ## 🤖 Modeling Approach ### Model Type * Random Forest Regressor ### Strategy To improve model performance, the dataset was segmented into: * **Food commodities model** * **Livestock commodities model** --- ## 📈 Model Performance | Model | MAE | R² | | --------------- | ------- | ---- | | Food Model | 446.86 | 0.94 | | Livestock Model | 2990.70 | 0.89 | --- ## 🧠 Key Insights * Commodity type is the most influential factor in price prediction * Livestock commodities dominate the high-price range * Time (year) significantly impacts prices, confirming inflation trends * Segmenting the dataset improves model performance and interpretability --- ## ⚠️ Limitations * Dataset includes both food and livestock, which differ significantly in price scale * External factors (transport, demand, seasonality) are not included * Some extreme values may influence model performance --- ## 🛠️ Tools & Technologies * Python (P …