Part 3 - Loan Default Classification Project | Kaggle | Analysis | Encoding | Outliers | Modelling

Here is the new project on Loan Default classification. In this project, we are going to explore these things:

How to do EDA (Exploratory Data Analysis) using graphs and complex queries?
How to clean the dataset, which involves dealing with missing values, variable encoding, etc?
How to build multiple models in a single line of code?

Check out my other playlist:

1) Deep Learning with TensorFlow

2) Hands-On with OpenCV

3) Image classification with Keras

4) Fake News Detection

5) Data Science Project

Follow me on:

#DataScience
#Datacleaning
#Machinelearning

[samcavalera]

Thanks so much brother! Keep up the excellent job!

[infinitedata]

Less than 9 min 11 sec you have explained a lot, i saw many youtubers & your are among those who is mastered in solving problem in optimized way

[dineshkumard]

Thank you very much, the video length is just 9 minutes but you explained a lot.

[infinitedata]

Thank you very much for your effort, keep it up bro.
I like the way you explains & solve the problems.

[VarunSharma-ymns]

predict = (model.predict(X_test) > 0.5).astype("int32") this code will work

[tejasjadhav]

Man you're a gem! Aspiring to be a data scientist, have an interview scheduled for data analyst role, any last minute tips?

[anandanarayanan]

is there any technique is used here to balance the dataset ?