Cleaning your dataset

Collecting and storing the data for your project is one step but cleaning it is a whole other step in the creation of your DSML workflow. That is why papAI introduces some indispensable tools to correctly prepare your datasets effortlessly for ML training.

Normally, the preprocessing step in the flow is a complex and demanding step for a data scientist since there is a need to explore what is available and what can be exploited to assess the necessary cleaning steps and obtain the perfect dataset to train the best model possible in the end.

In papAI, the cleaning steps include :

• Preprocessing basic operations
• Array / Map operations
• Time-Series specific operations
• Complex operations through Python and R recipes