Data Cleaning in Python | Handling Missing Values, Duplicates & Outliers

🚀 Data Cleaning in Python | Complete Beginner-Friendly Tutorial

In this video, you will learn Data Cleaning and its practical implementation using Python. Data cleaning is one of the most important steps in Data Science and Machine Learning because raw data often contains missing values, duplicate records, and outliers.

This tutorial demonstrates practical techniques to clean and prepare data for better analysis and model performance.

📚 Topics Covered:

00:00 - Introduction
00:12 - What is Data Cleaning
01:57 - Implementation
11:17 - Search and Remove Duplicates
14:57 - Deal with Missing Values
24:11 - Filter the Outliers

💡 What you’ll learn:
Importance of Data Cleaning
Removing duplicate records
Handling missing values
Detecting and filtering outliers
Practical implementation using Python
🎯 Who is this video for?
Beginners in Data Science
Python learners
Students & job seekers
Anyone learning data preprocessing

👉 Full Data Science Playlist:
https://youtube.com/playlist?list=PLIooqaX_EcWMTJM0eKiQjj78ynEIhfaP_&si=RR84eIuKI08-0Gfw

👉 Source Code:
https://github.com/Rhythmbellic/Data_science-youtube/blob/main/Data_cleaning.ipynb

👉 Dataset Used:
https://www.kaggle.com/datasets/kunjadiyarohit/flats-uncleaned-dataset

🔍 Keywords:

data cleaning python, handling missing values python, remove duplicates pandas, outlier detection python, data preprocessing tutorial

Видео Data Cleaning in Python | Handling Missing Values, Duplicates & Outliers канала Mr. Bellic