drkbluescience/AutoGluon_Cameroon_Air_Quality

Finished 5th in the Cameroon Air Quality Prediction competition, later refining the model to achieve a score better than the 1st place submission using AutoGluon. # **Cameroon Air Quality Prediction - AutoGluon** ## Introduction This study focuses on predicting air quality in Cameroon, specifically the concentration of particulate matter (**PM2.5**), using various machine learning techniques. The dataset includes weather and air quality features collected from different cities across Cameroon. ## Leaderboard Achievement Here’s a snapshot of the position on the leaderboard during the **Cameroon Air Quality Prediction** competition, showing the score in 5th place at the end of the competition. After further model improvements, the 1st-place score was surpassed. ## Methodology The analysis began with an exploration of the dataset, where **data inconsistencies** were addressed. Features with a **single value**, such as **'sunrise'**, **'sunset'**, and **'snowfall_sum'**, were removed. Redundant variables, including **city**, **longitude**, and **latitude**, were also eliminated to reduce unnecessary complexity in the models. ### Feature Engineering Enhancing predictive power involved analyzing the distribution of **PM2.5** concentrations across different cities, leading to the creation of a new feature: - **Distance from Bafoussam**, the city with the highest PM2.5 levels. ## Models Several machine learning models were initially employed to predict **PM2.5** levels, including: - **CatBoost** - **LightGBM (LGBM)** - **XGBoost (XGB)** - **GradientBoostingRegressor** - **ExtraTreesRegressor** - **RandomForestRegressor** - **AdaBoostRegressor** - **MLPRegressor** These models were evaluated using a **9-split RepeatedKFold cross-validation** strategy to ensure reliable results. However, after initial testing, **AutoGluon** was introduced and ultimately provided the best performance, surpassing all other models in predictive accuracy. ## Results Among all models tested, **CatBoost** performed well, achieving a **root mean squared error (RMSE)** of **3.11078**. However, **AutoGluon** outperformed every other model, achie …