Data Exploration and Cleaning Project
Fajar Satria
Data Exploration and Cleaning
📌 Project Overview
This project focuses on exploring and cleaning datasets using Python (Pandas). The analysis includes:
Exploratory Data Analysis (EDA) on a Coffee Bean Sales Dataset.
Data Cleaning & Transformation on a Students Dataset.
📂 Datasets Used
Coffee Bean Sales Dataset (https://www.kaggle.com/datasets/halaturkialotaibi/coffee-bean-sales-dataset)
Contains sales data for coffee over 2 years in Saudi Arabia.
Includes information on customers, product categories, and sales amount.
Students Dataset (https://drive.google.com/file/d/1ZWpS_EKAvbsvuigBXDaH0p01FahpHeh8/view)
Contains student performance data.
Includes details like lunch category, reading score, and grade.
🔍 Analysis Performed
1️⃣ Coffee Bean Sales Analysis
Filtering data based on relevant columns.
Sorting by quantity to find best-selling products.
Grouping data by city to analyze total sales.
Additional insights: Average price per product & product category distribution.
2️⃣ Students Data Cleaning
Checking missing values and handling them appropriately:
Mode for categorical data (e.g., lunch category).
Mean for numerical data (e.g., reading score).
Median for ordinal data (e.g., grade).
Using alternative missing value handling techniques:
Forward fill & Backward fill
Interpolation
Dropping missing values
🛠️ Technologies Used
Python (Pandas, NumPy)
Jupyter Notebook / Google Colab
🚀 How to Run
Clone the repository:
Install dependencies (if required):
Open the Jupyter Notebook and run the analysis.
📜 License
This project is for educational purposes only. Feel free to modify and explore!
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Posted Jun 7, 2025
Explored and cleaned datasets using Python for data analysis.
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