Let us see how to predict the air quality index using Python. AQI is calculated based on chemical pollutant quantity. By using machine learning, we can predict the AQI.
AQI: The air quality index is an index for reporting air quality on a daily basis. In other words, it is a measure of how air pollution affects one’s health within a short time period. The AQI is calculated based on the average concentration of a particular pollutant measured over a standard time interval. Generally, the time interval is 24 hours for most pollutants, and 8 hours for carbon monoxide and ozone.
We can see how air pollution is by looking at the AQI
| AQI Level | AQI Range |
| Good | 0 – 50 |
| Moderate | 51 – 100 |
| Unhealthy | 101 – 150 |
| Unhealthy for Strong People | 151 – 200 |
| Hazardous | 201+ |
Let’s find the AQI based on Chemical pollutants using Machine Learning Concept.
Data Set Description
It contains 8 attributes, of which 7 are chemical pollution quantities and one is Air Quality Index. PM2.5-AVG, PM10-AVG, NO2-AVG, NH3-AVG, SO2-AG, OZONE-AVG are independent attributes. air_quality_index is a dependent attribute. Since air_quality_index is calculated based on the 7 attributes. You can download the dataset Here.
As the data is numeric and there are no missing values in the data, so no preprocessing is required. Our goal is to predict the AQI, so this task is either Classification or regression. So as our class label is continuous, regression technique is required.
Regression is supervised learning technique that fits the data in a given range. Example Regression techniques in Python:
- Random Forest Regressor
- Ada Boost Regressor
- Bagging Regressor
- Linear Regression etc.
Python3
# importing pandas module for data frameimport pandas as pd# loading dataset and storing in train variabletrain=pd.read_csv('AQI.csv')# display top 5 datatrain.head() |
Output:
Python3
# importing Randomforestfrom sklearn.ensemble import AdaBoostRegressorfrom sklearn.ensemble import RandomForestRegressor# creating modelm1 = RandomForestRegressor()# separating class label and other attributestrain1 = train.drop(['air_quality_index'], axis=1)target = train['air_quality_index']# Fitting the modelm1.fit(train1, target)'''RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None, oob_score=False, random_state=None, verbose=0, warm_start=False)'''# calculating the score and the score is 97.96360799890066%m1.score(train1, target) * 100# predicting the model with other values (testing the data)# so AQI is 123.71m1.predict([[123, 45, 67, 34, 5, 0, 23]])# Adaboost model# importing module# defining modelm2 = AdaBoostRegressor()# Fitting the modelm2.fit(train1, target)'''AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', n_estimators=50, random_state=None)'''# calculating the score and the score is 96.15377360010211%m2.score(train1, target)*100# predicting the model with other values (testing the data)# so AQI is 94.42105263m2.predict([[123, 45, 67, 34, 5, 0, 23]]) |
Output:
By this, we can say that by given test data we got 123 and 95 so the AQI is Unhealthy.
