Data Cleaning and Purification with Python

Beginner

In this project, you will learn how to clean and purify CSV data by removing incomplete, incorrect, and invalid data. The goal is to create a clean dataset from the raw data, which can be used for further analysis or processing.

PythonMachine Learning

Introduction

In this project, you will learn how to clean and purify CSV data by removing incomplete, incorrect, and invalid data. The goal is to create a clean dataset from the raw data, which can be used for further analysis or processing.

🎯 Tasks

In this project, you will learn:

  • How to set up the project environment and prepare the necessary files
  • How to import the required libraries for data cleaning
  • How to read and process the raw data, checking for various types of dirty data
  • How to write the cleaned data to a new CSV file

🏆 Achievements

After completing this project, you will be able to:

  • Use Python and its standard library to work with CSV data
  • Apply techniques for validating and cleaning data, such as checking for missing values, invalid formats, and unrealistic data
  • Implement a data cleaning process to create a high-quality dataset
  • Generate a new CSV file with the cleaned data

Teacher

labby

Labby

Labby is the LabEx teacher.