Hi there, and welcome to AnalyticalAnuj!

In this uncut video, we dive deep into RAW data analysis for UPI Payment Systems, a crucial first step in any machine learning project. This is Part 1 of a hands-on tutorial where we walk you through the entire data analysis process, focusing on real-world techniques that are frequently asked in job interviews for data science and AI roles.

Whether you’re a Python developer, a data science student, or a professional looking to refine your skills, this video will give you the practical knowledge you need to analyze data for Unified Payment Interfaces (UPI) and other financial systems. If you haven’t already, make sure to check out my other videos on setting up Python, Anaconda Navigator, and Jupyter Notebooks for this project.

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Timestamps:

0:00 Introduction
2:10 Agenda for the video
3:27 Launching a Jupyter notebook & Extensions on VS Code
6:32 Installing libraries & Data Collection
09:41 Importing and exploring the data (pd.head(), pd.columns(), pd.info())
12:08 Project Objective: Unified Payment Interface
16:50 Descriptive Analysis (pd.describe(), pd.duplicated(), pd.drop(), pd.isnull())
21:30 Handling Missing Values & Dataset Shape (df.shape)
26:22 Unique values and accessing columns
34:17 Working with Cross-Sectional Data (Accessing columns, Datetime module)
48:30 Creating and formatting new columns
52:13 Using the map function in pandas
57:00 Target variable distribution
59:00 Data visualisation using seaborn

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