Task 1: Data Cleaning and Preprocessing

Internship Task

This repository contains my submission for Task 1: Data Cleaning and Preprocessing completed as part of my Data Analyst Internship.

Objective

The objective of this task is to clean and prepare a raw dataset by handling missing values, checking duplicates, standardizing column names, converting date formats, and verifying data types.

Dataset Used

• Dataset Name: Netflix Movies and TV Shows
• Source: Kaggle
• File: netflix_titles.csv

Repository Structure

Task-1-Data-Cleaning-and-Preprocessing/
│
├── dataset/
│   ├── netflix_titles.csv
│   └── cleaned_netflix_titles.csv
│
├── code/
│   └── data_cleaning.py
│
├── notebook/
│   └── data_cleaning.ipynb
│
├── report/
│   └── data_cleaning_summary.txt
│
└── README.md

Steps Performed

1. Handled missing values:

   • director → Unknown
   • cast → Unknown
   • country → Unknown
   • rating → Not Rated
   • duration → Unknown

2. Checked for duplicate records:

   • Found 0 duplicate rows

3. Standardized column names:

   • Converted all column headers to lowercase
   • Replaced spaces with underscores

4. Converted date_added column:

   • Changed to datetime format using Pandas

5. Verified data types:

   • Ensured all columns had appropriate data types after cleaning

Cleaning Summary

Metric	Value
Original rows	8807
Final rows	8807
Duplicate rows removed	0

Tools and Libraries Used

• Python 3
• Pandas
• Jupyter Notebook

How to Run

1. Clone this repository:

   git clone https://github.com/Kushankumarag/Task-1-Data-Cleaning-and-Preprocessing.git

2. Install dependencies:

   pip install pandas

3. Run the script:

   python code/data_cleaning.py

Key Learning Outcomes

• Identified and treated missing values across multiple columns
• Detected and handled duplicate records
• Standardized column names for consistency
• Converted date columns to proper datetime format
• Improved overall dataset quality for downstream analysis

Author

Kushan Kumar
Data Analyst Internship | Task 1 Submission