DataMining Lab 01 - Preprocessing

How to use

Note: Feel free to pass -h option to show help each of command.

1. List column that missing data

   python3 list_missing.py test/house-prices.csv --extra

2. Count rows that missing data

   python3 count_missing.py test/house-prices.csv

3. Impute

    python3 impute.py test/house-prices.csv --method mode

4. Remove rows that have missing rate greater than a constant

   python3 remove_missing.py test/house-prices.csv 50

5. Remove cols that have missing rate greater than a constant

   python3 remove_missing.py test/house-prices.csv 50 --column

6. Remove duplicate rows

   python3 remove_dup.py test/house-prices.csv

7. Feature Scaling dataset

   python3 feature_scaling.py test/house-prices.csv --column PoolArea YrSold

   python3 feature_scaling.py test/house-prices.csv --column PoolArea YrSold --method zscore

8. Calculate the value of attributes expressions
   For Windows

   python3 calculating_attributes_expressions.py test/house-prices.csv YrSold + SalePrice * 2 --cname Total

   For Linux

   python3 calculating_attributes_expressions.py test/house-prices.csv YrSold + SalePrice \* 2 --cname Total