Data Cleaning using Python Pandas (Step By Step) | Jupyter Notebook | kaggle Dataset | sithtamil

Welcome to our Data Cleaning Tutorial using Python Pandas! In this video, we'll walk you through the essential steps of data cleaning using the popular Python library, Pandas.
Data_set link: www.kaggle.com...
📌 Topics Covered:
🚀 Data Cleaning/Data Preprocessing Before Building a Model - A Comprehensive Guide
We'll be working with the "Vehicle Sales and Market Trends Dataset" from Kaggle, a rich source of information on vehicle sales and market trends.
Here's what you'll learn in this tutorial:
📌Importing Libraries: We'll start by importing the necessary libraries, including Pandas, NumPy, and Matplotlib, to get started with data cleaning.
📌Loading Data: We'll load the "Vehicle Sales and Market Trends Dataset" into a Pandas DataFrame, allowing us to analyze and clean the data.
📌Exploring Data: We'll explore the dataset to understand its structure, features, and any potential issues that need to be addressed.
📌Handling Missing Values: We'll tackle missing values in the dataset, employing various strategies such as filling missing values with appropriate measures or dropping them altogether.
📌Handling Duplicate Data: We'll identify and handle duplicate rows in the dataset, ensuring data integrity and accuracy.
📌Correcting Data Types: We'll ensure that the data types of columns are correct and consistent, converting them if necessary to facilitate analysis.
📌Outlier Detection and Treatment: We'll detect outliers in the dataset and apply treatment methods to address them, ensuring robust and reliable analysis results.
By the end of this tutorial, you'll have a solid understanding of the data cleaning process using Python Pandas, equipped with practical skills to handle real-world datasets effectively. Don't forget to like, comment, and subscribe for more tutorials on data analysis, Let's dive in! 🚗📊
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