The below steps give you the brief idea how to develop Machine Learning Models in any Industry like Banking, Healthcare, E commerce ,Telecom Domains etc.
-
Identify the Problem Statement/UseCases with respect to your Domain and Validate with your Domain Expert in that particular field.
-
Proceed your ideas/Usecases to Business Review and explain them in layman terms and get their Approval.
-
Once idea is finalized, Go for Data Collection for your Use Case/Idea by having conversion with your BIGData/DataLake Team.
-
Explore and do Data Analysis with your Collected Data and try to identify the correct features and remove the unnecessary features according to your problem statement/usecase.
-
Do Feature Engineering like Data Quality Check & Data Validation and Finalize the DataSets
-
Split your final DataSets into Train Dataset (70%) and Test Dataset(30%)
-
TrainDataSet (70%) make your selected Machine Learning model to understand the Data Pattern for Prediction and Classification and give good accuracy during TestDataSet Execution.
-
Even DataSet can be cross validated using KFold Validation for Better Accuracy
-
Fit your Train and Test Dataset into Python-ScikitLearn Fit Method to feed your input data into your selected Machine Learning Model and try to understand the Math behind your selected ML Model.
-
Get the Predicted output using Python-Scikit Learn Predict Method.
-
Compare the TestDataSet and Predicted Output for Accuracy-score using Python-ScikitLearn Metrics.
-
Repeat the step 9 to 11 with different Machine Learning Models to find out which model give more Accuracy