Houssam-123-ship-it/Forecasting-Air-Quality-In-Africa

# Forecasting-Air-Quality-In-Africa # 🌍 Air Quality Forecasting in Nairobi 🇰🇪 & Dar es Salaam 🇹🇿 This project was completed as part of the **Applied Data Science Lab by WorldQuant University**. It focuses on **time series forecasting** of PM2.5 pollution levels across two major African cities — **Nairobi** and **Dar es Salaam** — using real-world data from **OpenAfrica**. We developed, tuned, and validated models to predict hourly air pollution levels, while applying best practices in time series modeling, diagnostics (ACF/PACF), and walk-forward validation. --- ## 🧠 Project Overview In this end-to-end data science project, we: - Connected to a **MongoDB database** hosting air sensor data - Cleaned and resampled the data to hourly PM2.5 readings - Built and compared several models: **Linear Regression**, **AutoReg**, and **ARIMA** - Tuned hyperparameters (lags, `p`, and `q`) - Validated model performance using **walk-forward prediction** - Evaluated results using plots, residuals, and correlation diagnostics This project offers insights into modeling **time-dependent data for public health**, and the techniques here apply directly to domains like **finance**, **NLP**, and **environmental monitoring**. --- ## 🗃️ Data Source The dataset comes from OpenAfrica.net, one of the largest open data platforms in Africa. We used collections for: - **Nairobi, Kenya** - **Dar es Salaam, Tanzania** Data is stored in a **MongoDB database**, accessible and queried using `pymongo`. --- ## 💻 Technologies Used - **Languages**: Python - **Libraries**: - Data: `pandas`, `numpy`, `gzip`, `json`, `pickle` - Plotting: `matplotlib`, `seaborn`, `plotly.express` - Modeling: `statsmodels`, `scikit-learn` - Database: `pymongo` - **Environment**: Jupyter Notebook --- ## 🔧 Project Structure This project is organized into 4 main lessons and a final assignment: | Notebook | Description …