Dee-elugs/Nigeria-inflation-cost-of-living-prediction

### **Project Overview** This project analyzes Nigeria’s inflation trends and cost-of-living dynamics using historical economic data. It explores how changes in consumer price indices (CPI), food prices, and crude oil prices relate to headline inflation, and builds a machine learning model to predict short-term inflation trends. The goal of the project is to demonstrate a complete data science workflow, from data cleaning and exploratory analysis to feature engineering and predictive modeling, using real Nigerian macroeconomic data. ------- ### **Dataset** The dataset contains monthly Nigerian economic indicators, including: Headline inflation rate Consumer Price Index (CPI) components (Food, Energy, Transport, Health, etc.) Crude oil prices, production, and exports ------- ### ** Key note:** CPI values represent price index levels, not inflation rates. CPI-based inflation rates were calculated using month-on-month percentage changes. ------- ### **Tools & Technologies** Python Pandas, NumPy Matplotlib, Seaborn Scikit-learn Jupyter Notebook ------- ### **Exploratory Data Analysis** **The analysis focuses on:** Long-term trends in Nigeria’s headline inflation Cost-of-living pressures across key CPI categories The relationship between food prices, energy costs, and inflation volatility **Key visualizations include:** Headline inflation trends over time CPI component trends for food, energy, and transport Comparison of actual vs predicted inflation values ------- ### **Feature Engineering** To prepare the data for modeling: CPI index values were converted to monthly inflation rates using percentage change Lag features were created to capture inflation persistence: Previous month inflation Previous month food inflation Previous month crude oil price Rows with missing values introduced by lagging were removed ------- ### **Modeling Approach** A Random Forest Regressor was used to predict Nigeria’s headline inflation rate. Train-test split respected time ord …