This Post is a continuation of the part 1 where we discussed about the various steps that should be kept in mind before starting a project in machine learning. To have a high level recap, below are the steps that we need to follow:
- Understand the overall context.
- Gather relevant data.
- Analyze and visualize the data for insights.
- Process the data for machine learning applications.
- Choose a model and train it.
- Optimize your model for performance.
- Share your solution effectively.
- Implement, monitor, and sustain your system.
Now that we have already gone through the theoretical part, let get to the implementation part of it. We will implement different strategies for pre-processing the dataset. Keep in mind that pre-processing is the most critical step of any machine learning project. A data scientist spends most of his/her time cleaning and pre-processing the dataset. You have to have a very solid understanding of each of the features and its importance, Without a deep understanding of the data set, it will be a garbage in garbage out model. So, without any further ado, let’s dive into it.
