Using Python coding, I downloaded a folder named PRSA_Data. This folder contains all the CSV files for the dataframe (df). Here are the steps to perform:
1. Load the dataset, PRSA_Data.csv, into memory.
2. Clean the data and check for missing values in this dataset.
3. Convert all categorical variables to numerical values.
Folder Path: /Desktop/PRSA_Data
File List:
1. PRSA_Data_Aotizhongxin_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.84 MB)
2. PRSA_Data_Changping_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.72 MB)
3. PRSA_Data_Dingling_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.68 MB)
4. PRSA_Data_Dongsi_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.64 MB)
5. PRSA_Data_Guanyuan_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.7 MB)
6. PRSA_Data_Gucheng_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.65 MB)
7. PRSA_Data_Huairou_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.64 MB)
8. PRSA_Data_Nongzhanguan_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.84 MB)
9. PRSA_Data_Shunyi_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.62 MB)
10. PRSA_Data_Tiantan_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.66 MB)
11. PRSA_Data_Wanliu_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.66 MB)
12. PRSA_Data_Wanshouxigong_20130301-20170228.csv (Last Modified: 4 years ago, File size: 2.87 MB)