Python_assignment

this is assignment Python given by Aadi foundation ( Training classes )

Data Cleaning and Preparation Assignment

Introduction

This project demonstrates basic data cleaning and preparation techniques using Python and pandas. The dataset used is a sample of customer data, which contains missing values, duplicates, and potential outliers.

Dataset

The dataset contains the following fields:

• CustomerID: Unique ID of the customer.
• Name: Name of the customer.
• Age: Age of the customer (with missing values).
• Gender: Gender of the customer.
• PurchaseAmount: Amount spent by the customer (with missing values).
• City: City of residence (with missing values).

Data Cleaning Steps

1. Remove Duplicate Records: Duplicates are removed based on the CustomerID.
2. Handle Missing Values:
   • Missing Age values are filled with the average age.
   • Missing PurchaseAmount values are filled with the median purchase amount.
   • Missing City values are filled with 'Unknown'.
3. Outlier Detection: Outliers in the PurchaseAmount column were detected using the IQR method, but none were removed in this sample.
4. Data Type Correction: The data types of columns were checked, and all were found correct.

How to Run the Script

1. Install the necessary libraries:

   pip install pandas

2. Run the Python script or the Jupyter notebook.
3. The cleaned dataset will be printed and saved as a CSV file (cleaned_data.csv).

Files in the Repository

• data_cleaning.py: Python script containing the data cleaning code.
• cleaned_data.csv: The cleaned version of the dataset.
• README.md: Explanation of the assignment.