How to approach a challenge?

The model development cycle goes through various stages, starting from data collection to model building. Most of us admit that data exploration needs more attention to unleashing the hidden story of data but before exploring the data to understand relationships (in variables), It’s always recommended to perform hypothesis generation. (To know more about hypothesis generation, refer to this link).

It is important that you spend time thinking about the given problem and gaining the domain knowledge. So, how does it help?

This practice usually helps in building better features later on, which are not biased by the data available in the dataset. This is a crucial step which usually improves a model’s accuracy.

At this stage, you are expected to apply structured thinking to the problem i.e. a thinking process which takes into consideration all the possible aspects of a particular problem.

Which of the following has the right order of model building life cycle?

This exercise is part of the course

Introduction to Python & Machine Learning (with Analytics Vidhya Hackathons)

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Exercise instructions

Data Collection --> Data Exploration --> Hypothesis Generation --> Model Building --> Prediction,Data Collection --> Hypothesis Generation --> Data Exploration --> Model Building --> Prediction,Hypothesis Generation --> Data Collection --> Data Exploration --> Model Building --> Prediction

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