Machine Learning projects (Personal Practice)
Within each repository, you will need a folder for each of the projects, have a standard way of naming them i.e
Week 1 Boston Regression
Week 2 Credit Risk Classification etc.
Within each week, have a readme of the project, detail what you did, what was the outcome and what were the key concepts/lessons learnt.
The Readme here is incomplete. Your readme needs to have the following sections
Title and Description (What the project is about and what it seeks to solve, i.e the objectives)
Data Source
Insights about the data (Key ones only, accompanied with some charts)
The product, what was the ML development steps and the outcomes to the same (output can be a graph eg a confusion matrix plot, with explanations below it)
When doing EDA, I would advice against splitting the data first before exploring it, as some features would need to be explored in the context of the independent variable y
What is the full meaning of this "OOPs for plots"
For each plot you have to note what you have observed
This step 'Summary statistics for each numerical feature' can easily be done by the .info() method
Are your features linearly separable, that should be intention of this plot : sns.pairplot(Clients,vars=['Age', 'AnnualIncome','FamilyMembers'], hue='TravelInsurance')
This step (Converting the categorial features into binary) can easily be done by an encoder - use that.
You data is so small relatively speaking, thus instead of going with hold out sets, I would advice that you do cross val scores which give a realistic value for the model performance
There is no tuning of parameters that you have done here, so the section " Defining models while tuning their hyperparameters" is misleading
This model evaluation metric selected is not the best, you need to use a more balance metric eg F1_score
Your model performance on train data is wanting, remember, I mentioned you need to aim for 90% and above, given this work has been done in many platforms.