SCT_DA_Task2

Data Cleaning and Preparation using Python

Overview

This project demonstrates the process of cleaning and preparing a sales dataset using Python and Pandas. The dataset was analyzed to identify missing values, remove duplicate records, and convert data types to improve data quality and consistency.

Objectives

• Load the sales dataset using Pandas.
• Identify and handle missing values.
• Remove duplicate records.
• Convert columns to appropriate data types.
• Export the cleaned dataset for further analysis.

Tools and Technologies

• Python
• Pandas
• VS Code
• CSV Dataset

Data Cleaning Process

1. Loaded the dataset into Python.
2. Inspected the dataset structure and data types.
3. Identified missing values.
4. Handled missing values.
5. Removed duplicate rows.
6. Converted date columns to datetime format.
7. Exported the cleaned dataset as a new CSV file.

Key Outcomes

• Improved data quality and consistency.
• Removed duplicate records.
• Standardized data formats.
• Prepared the dataset for analysis and visualization.