How to build a machine learning model? (Summary in 12 lines)
The below steps give you the brief idea how to develop MachineLearning Models in any Industry like Banking, Healthcare, E commerce ,Telecom Domains etc.
1.Identify the Problem Statement/UseCases with respect to your Domain and Validate with your Domain Expert in that particular field.
2.Proceed your ideas/Usecases to Business Review and explain them in layman terms and get their Approval.
3.Once idea is finalized, Go for Data Collection for your Use Case/Idea by having conversion with your BIGData/DataLake Team.
4.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.
5. Do Feature Engineering like Data Quality Check & Data Validation and Finalize the DataSets
6.Split your final DataSets into Train Dataset (70%) and Test Dataset(30%)
7. TrainDataSet(70%) make your selected Machine Learning model to understand the DataPattern for Prediction and Classification and give good accuracy during TestDataSet Execution.
8.Even DataSet can be cross validated using KFold Validation for Better Accuracy
9. 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.
10.Get the Predicted output using Python-ScikitLearn Predict Method.
11.Compare the TestDataSet and Predicted Output for Accuracy-score using Python-ScikitLearn Metrics.
12. Repeat the step 9 to 11 with different MachineLearning Models to find out which model give more Accuracy.
1.Identify the Problem Statement/UseCases with respect to your Domain and Validate with your Domain Expert in that particular field.
2.Proceed your ideas/Usecases to Business Review and explain them in layman terms and get their Approval.
3.Once idea is finalized, Go for Data Collection for your Use Case/Idea by having conversion with your BIGData/DataLake Team.
4.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.
5. Do Feature Engineering like Data Quality Check & Data Validation and Finalize the DataSets
6.Split your final DataSets into Train Dataset (70%) and Test Dataset(30%)
7. TrainDataSet(70%) make your selected Machine Learning model to understand the DataPattern for Prediction and Classification and give good accuracy during TestDataSet Execution.
8.Even DataSet can be cross validated using KFold Validation for Better Accuracy
9. 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.
10.Get the Predicted output using Python-ScikitLearn Predict Method.
11.Compare the TestDataSet and Predicted Output for Accuracy-score using Python-ScikitLearn Metrics.
12. Repeat the step 9 to 11 with different MachineLearning Models to find out which model give more Accuracy.
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