data_cleaning_task1

Internship Task 1 — Data Cleaning and Preprocessing using Pandas

🧹 Data Cleaning and Preprocessing — Internship Task 1

This project is part of my Data Analyst Internship Task 1, where I performed data cleaning and preprocessing using Python (Pandas).
The goal was to clean a raw dataset containing missing values, duplicates, inconsistent formats, and mixed data types.

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📂 Files Included

File Name	Description
data_task1.csv	Raw dataset (with missing values, duplicates, and inconsistent formats)
task1.py	Python script used for data cleaning and preprocessing
cleaned_data_task1.csv	Final cleaned dataset ready for analysis

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🧠 Objectives

• Identify and handle missing values
• Remove duplicate rows
• Standardize text values (Gender, Country)
• Convert date formats into a consistent format
• Ensure all columns have clean and uniform names
• Correct data types (e.g., age → int, revenue → float, date → datetime)

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⚙️ Steps Performed in task1.py

1. Loaded the dataset using Pandas
2. Handled missing values using the column mean (fillna())
3. Removed duplicates with drop_duplicates()
4. Standardized text columns (converted gender and country values to consistent case)
5. Fixed inconsistent date formats using pd.to_datetime()
6. Renamed columns to lowercase and replaced spaces with underscores
7. Converted data types to ensure numeric and datetime consistency
8. Saved cleaned data into a new CSV file

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📊 Example Output (Cleaned Data)

customerid name gender country age joindate revenue 0 101 John male Usa 25 2023-05-12 4500.0 1 102 Sarah female Usa 30 2023-06-14 3260.0 2 103 John male Canada 28 2023-07-14 3200.0 3 104 Ravi male India 27 2022-03-05 2800.0 4 105 Sarah female Uk 30 2023-06-05 3000.0 5 106 Priya female India 27 2023-03-10 2800.0