AQI-Prediction-Model-Of-India / model_results.csv
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Rank,Model,MAE,RMSE,R2,MAPE
1,6. CatBoost,11.618839584154964,22.36982355433378,0.9283483612722022,12.949346523067659
2,5. LightGBM,11.503239934613108,22.851387982211644,0.9252302069667496,11.474268093137393
3,8. XGBoost (Tuned),11.42431432723999,22.9023514889352,0.9248963296544095,11.683626656796037
4,4. XGBoost,12.050947629321705,23.552496266323278,0.9205717676355128,13.757844179829744
5,3. Random Forest,11.692200914100031,24.18867580067332,0.9162229241147566,12.53473769988607
6,1. Linear Regression,16.05899452779877,29.16817347471017,0.8781797521133908,15.149842577745524
7,7. LSTM,20.220279770634694,34.68869624459838,0.815499888213518,23.82036169947373
8,2. Decision Tree,17.435805216948435,41.96907822641348,0.7477914112114947,17.145157057820587