I've followed lots of tutorials on Machine Learning but in each of these, they go for a different strategy so it's quite confusing for me. I want to Know that what are the operations involved and what are the correct ordering of these.

AS of now, I think the process and the ordering are ->

  1. Get Data
  2. Delete Duplicates
  3. Find Missing Values and Outliers
  4. Create New Features
  5. Deal with missing values and Outliers
  6. Build a base model
  7. Find the best features to select
  8. Try and find different Models
  9. Select the BEST model
  10. Hypertuning of the Model

Please Do Provide if something is missing and correct the sequence.


Yes learning online from different tutors can lead to different strategies. But this stats that the flow depends upon the type of data. Not everytime you have to create features or deal with outliers.

In case of images you reduce noise by applying different methods & filters, in case of text you remove stopwords, punctuation etc. So it depends upon data & its amount. General way to proceede for me is as follow

  1. Understand definition & get domain knowledge
  2. Gather data
  3. Pre-process it (Involves removing noise, applying transformation, pruning data etc)
  4. Do feature selection if necessary
  5. Do data visualisation
  6. Do different model experiments with sample data.
  7. Fix 2 3 models for case
  8. Train with real data
  9. Use different matrices to evaluate (depends upon the data & definition)
  10. Make model accessible using API/Direct to client

This is how in industry i have seen everyone around me works & i follow the same. Has helped me but this is way general, here you have much to change based upon definitions & tasks.

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Below steps are similar to the other suggestions:-

  1. Understand the problem statement and read the data.
  2. Find out the number of columns and rows for train and test data.
  3. Make a list of data that is null because we need to handle them before we start the training of the model
  4. Once identified the null values in the data, find out measures how you will fix the null values
  5. Fix the null values
  6. Do the same operation (step no 3-5) for the test data as well
  7. Now, wherever you have data of type other than integer or float, you must convert that data and make them of numeric type.
  8. So you need to select the features that you will be using for the training the model
  9. Here you may also create new features depending upon your approach.
  10. After that, we can start training the model. So we select few algorithms for it (say 2-3 algorithms)
  11. After training is done try to predict the values of test data and check the accuracy
  12. Now this accuracy will decide what we change we need to do with the features and algorithm
  13. In the case of features, you can check the relevance of the features and take the decision of dropping/including the features.
  14. Also, here to increase the performance/accuracy we can do hyper tuning of the parameters and repeat the step 11-12

These are general steps.

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