💄 Skincare & Makeup Products — Exploratory Data Analysis

Exploratory Data Analysis (EDA) on a real-world skincare and makeup products dataset — uncovering pricing trends, brand performance, ratings patterns, and product insights through data cleaning and visualization.

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📌 Project Overview

This project performs a full EDA on a skincare & makeup products dataset. The goal is to explore product trends, understand pricing behavior, and extract actionable insights about the beauty industry using Python.

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🎯 Key Objectives

• 🧹 Clean and preprocess raw product data (handle nulls, duplicates, data types)
• 📊 Analyze price distribution across product categories
• ⭐ Explore the relationship between ratings and price
• 🏷️ Compare brand performance by product count and average rating
• 📈 Visualize trends to tell a data-driven story

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🔍 Dataset

Feature	Details
Domain	Beauty & Skincare / Makeup
Data Type	Product listings
Key Columns	Product name, Brand, Category, Price, Rating, Reviews

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🛠️ Tech Stack

Tool	Purpose
Python 3	Core programming language
Pandas	Data manipulation & cleaning
Matplotlib	Data visualization
Seaborn	Statistical plotting
Jupyter Notebook	Interactive analysis environment

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📋 Project Workflow

1.	Data Loading & Initial Exploration

└── Shape, dtypes, missing values overview 2. Data Cleaning ├── Handle missing values ├── Remove duplicates ├── Fix data types (prices, ratings) └── Standardize text columns 3. Exploratory Data Analysis ├── Univariate Analysis (distributions) ├── Bivariate Analysis (price vs rating) └── Category & Brand comparisons 4. Data Visualization ├── Price distribution histogram ├── Top brands by product count ├── Rating distribution ├── Category-wise price boxplot └── Correlation heatmap 5. Key Insights & Conclusions

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💡 Key Insights

• 💰 Most products are priced in the mid-range segment, with few luxury outliers
• ⭐ Higher price does not always correlate with higher customer ratings
• 🏆 A small number of brands dominate the product catalog
• 🧴 Skincare products tend to have higher average ratings than makeup products
• 🔍 Several columns had significant missing data requiring careful handling

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📁 Project Structure

data-analysis-eda-python/ │ ├── project file.ipynb ← Main analysis notebook ├── README.md ← Project documentation └── data/ ← Dataset files (if applicable)

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🚀 How to Run

# 1. Clone the repository
git clone https://github.com/aseelaburabie/data-analysis-eda-python.git

# 2. Navigate to the project
cd data-analysis-eda-python

# 3. Install required libraries
pip install pandas matplotlib seaborn jupyter

# 4. Launch Jupyter Notebook
jupyter notebook "project file.ipynb"


👩‍💻 About the Author
Aseel Abu Rabie
AI Engineering Student | Data Analyst
📍 Gaza, Palestine



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