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🐍 Python - Kiwilytics Project

This repository contains a Python project developed as part of the Kiwilytics Data Engineering Course.
The project demonstrates practical data analysis and manipulation techniques using Python and Pandas.

📘 Project Overview

The goal of this project is to perform essential data engineering tasks, including:

  • Handling missing values
  • Calculating total prices and revenues
  • Grouping and aggregating data
  • Identifying insights such as top-selling products and highest-spending customers

All tasks were implemented in a Jupyter Notebook to demonstrate the workflow clearly.

🧩 Main Tasks Performed

  1. Data Cleaning and Handling Missing Values

    • Filled missing unit_price values using the average price per product.

  2. Feature Engineering

    • Created a new column total_price by multiplying unit_price and quantity.

  3. Revenue Calculation

    • Calculated total revenue across all orders.

  4. Data Analysis and Insights

    • Identified which product has the highest total quantity sold.
    • Determined which customer has the highest total spending.

🛠️ Tools and Libraries Used

  • Python 3
  • Pandas
  • Jupyter Notebook

🧠 Skills Demonstrated

  • Data cleaning and transformation
  • Use of groupby(), fillna(), and transform() in Pandas
  • Logical problem solving for real-world data engineering tasks

🧩 Dataset

The dataset used in this project is stored in the /data folder: data/kiwilytics_orders.csv

📝 The dataset was provided by the course instructors for educational purposes and does not contain any real or sensitive information.

📂 Files in This Repository

File/Folder Description
Kiwilytics-Python-Project.ipynb Main Jupyter Notebook containing all Python code, data cleaning, and analysis
data/kiwilytics_orders.csv Dataset used for the analysis (sample data provided by the course)
README.md Project documentation, objectives, and instructions

🚀 How to Run the Project

  1. Clone or download this repository.
  2. Open the Jupyter Notebook file: Kiwilytics-Python-Project.ipynb
  3. Make sure the CSV file exists in the /data folder: data/kiwilytics_orders.csv
  4. Run all notebook cells sequentially to reproduce the results.