EDA on Banking Data with Python: Beginner to Pro 📊

In this video we have covered -
00:00:00 - Introduction
00: 03:46 - Data overview and feature selection
00:53:41 - Feature Engineering
00:55:20 - Missing values imputation
01:26:23 - Values modification automatically in multiple columns
01:36:42 - Variables evaluation and outlier detection
01:41:15 - Binning the variables
01:57:32 - Analysing the data
02:03:25 - Univariate analysis on categorical/object/character variables
02:57:35 - Subsetting the numerical variables from a dataframe for analysis
03:03:46 - Creating correlation matrix for multiple variables | Unstacking a correlation table to make it a normal dataframe
03:16:10 - Univariate analysis on numerical variables
03:28:19 - Bivariate analysis on numeric variables
03:47:52 - Analysing the prev. application data, performing feature engineering and feature selection and merging with current app. data
04:08:54 - Doing univariate and bivariate analysis on merged dataframe
04:28:42 - Making final conclusion and preparing recommendations for bank

Individual videos playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqO6eg4mUeBDn7k21RLfti8-

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Tags -
Exploratory Data Analysis,
EDA,
Feature Engineering,
Feature Selection,
Univariate Analysis,
Bivariate Analysis,
Analysis,
Loan Default Data,
Merging,
Plotting,
Seaborn Plots,
Python,
Python Programming,
Correlation Matrix

Kaggle URL to download the data - https://www.kaggle.com/datasets/gauravduttakiit/loan-defaulter

You can download the complete script and analysis points created in this video using -
File Name - Exploratory Data Analysis - Loan Defaulter Segmentation.py
URL - https://github.com/LEARNEREA/Data_Science/tree/main/Scripts


#eda #PythonProgramming #EDA #ExploratoryDataAnalysis #Learnerea