Step-1 : Frame the Problem
As a first step, you need to arculate your problem by
identifying the type which depends on your business
problem.
Type can be anything like Binary classification,
Unidimensional regression, Multi-class single-label
classification, Multi-class multi-label classification,
Multidimensional regression, Clustering (unsupervised),
Other (translation, parsing, bounding box id, etc.)
Step-2 : Get the Data
The next step is to get the data and store it the right
format according to your problem statement.Analyse your data to check whether you have enough
data or not and also check the quality of the data.The quality of the data fundamentally determines if
you will be able to solve the problem at all or not.
Step-3 : Data Pre-Processing
After having the data next step is to analyse it and
extract insights to make business decisions.Also, apply basic data preprocessing operations to
bring the data in a go to go format.Choose the right library.
Step-4 : Evaluation Metric
The most important step is to know how to evaluate
our results.We need to choose the right evaluation metric
according to the problem we are going to solve.For example - if we have an imbalance dataset then
we usually choose the ROC-AUC metric.
Step-5 : Split the data
In any machine learning problem, we split the data
into multiple sets like training, validation and test.Stratified splitting is mostly used for classification
problems and K-Fold for regression problems.The most important thing to note is whatever
operations you apply on the train set must be
applied to validation and test set.
Step-6 : Apply ML Algorithm
And finally, we will apply ML models to the data. We
can't say which models work best it's just hit and
trial.Applying multiple algorithms do hyperparameter
tuning, evaluate the results and choose the best
model which gives satisfying results.Benchmark your solution based on your selected
evaluation metric.
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