This is our Machine Learning project. We want to use Linear Regression/LASSO/Neural Network to predict PM2.5 level by related climate attributes.
This project try to predict the PM2.5 Level by other related climate attributes by regression. In this Project, we will try a number of regression methods we obtained from the lecture, and we try to compare their performance.
The data from Harvard CGA website includes 10 attributes and 1 target value: Atttibutes: (PM10 level, O3, NO2, SO2, CO, Temperature, Dew point, Pressure, Humidity, Wind) Target value: PM2.5 level
At the beginning of our project, we need to find a huge property data Source and the data also need some attibutes, which can support us to do more fancy test in Machine Learning. Because a small data set always cases data overfit. Fortunately, we found 2 million rows dat of PM2.5 in Harvard CGA website. Now let's us to do the project !
Because GitHub's file size limit of 100MB, you can find the original pm2.5 data source here:
http://aqi.cga.harvard.edu/china/cumulative/We cleaned the data in data_clean.ipynb After we get the data. First, we decide to only use One hundred thousand data, Because it's need long time to process two million data.
Second, we find there are lots of invalid rows, which have '-' or NaN. Before we do Machine Learning, we need to clean the data. So we write a data clean function to help us clean and standardize data.
Now we can get all the valid data set, it has 93790 rows, 1 column output,and 10 column attributes.
Finally, we want to make more attributes.So what we have done is to add 2-order and 3-order attributes to our data set. now it have 30 attributes.
With cleaned data, we are now able to build multiple different models to predict the PM 2.5 Level
More detail in PM2.5 Multi-method Regression.ipynb Final Result:
| Model | Normalized RSS |
|---|---|
| Simple first-order linear regression | 0.542290 |
| Third-order linear regression | 0.417474 |
| Third-order linear regression with LASSO L1 Regularization | 0.417535 |
| Neural Network with 10 hidden-units | 0.404074 |
| Neural Network with 20 hidden-units | 0.391214 |
| Neural Network with 40 hidden-units | 0.393047 |
| Neural Network with 80 hidden-units | 0.406128 |