Bank Customer Churn Model | Real World Example | Project | Hands-On | Machine Learning | Python

In this Real world Problem on Bank Customer Churn Model you will learn the Data Encoding, Feature Scaling, Handling Imbalanced Data, Support Vector Machine Classifier and Grid Search for Hyperparameter tuning from scratch with detailed explanation of the concept.

00:00 - Introduction
00:26 - Learning Objective
01:43 - Import Library
02:02 - Import Data URL
02:15 - Analyze Data
04:26 - Data Encoding
10:58 - Define Label and Features
11:53 - Handling Imbalance Data
12:56 - Undersampling and Oversampling
14:40 - Random Under Sampling
16:42 - Random Over Sampling
18:25 - Train Test Split Dataset
19:14 - Standardize Features
20:45 - Support Vector Machine Classifier with Raw Data
21:21 - Model Accuracy
22:53 - Hyperparameter Tuning
24:05 - Model with Random Under Sampling
24:26 - Model Accuracy
25:07 - Hyperparameter Tuning
26:03 - Model with Random Over Sampling
26:32 - Model Accuracy
27:02 - Hyperparameter Tuning
28:56 - Summary


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