https://github.com/azizbekavazov/eda-uci-retail-dataset
Exploratory Data Analysis (EDA) on UCI Online Retail Dataset. Customer insights, product trends, sales patterns and product recommendations.
https://github.com/azizbekavazov/eda-uci-retail-dataset
customer-insights data-analysis data-visualization eda exploratory-data-analysis jupyter-notebook matplotlib pandas personalized-recommendations product-recommendation python recommendation-system retail-analytics seaborn uci-online-retail
Last synced: 3 months ago
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Exploratory Data Analysis (EDA) on UCI Online Retail Dataset. Customer insights, product trends, sales patterns and product recommendations.
- Host: GitHub
- URL: https://github.com/azizbekavazov/eda-uci-retail-dataset
- Owner: AzizbekAvazov
- Created: 2025-03-22T14:27:51.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-04-09T10:19:02.000Z (6 months ago)
- Last Synced: 2025-04-10T22:48:50.534Z (6 months ago)
- Topics: customer-insights, data-analysis, data-visualization, eda, exploratory-data-analysis, jupyter-notebook, matplotlib, pandas, personalized-recommendations, product-recommendation, python, recommendation-system, retail-analytics, seaborn, uci-online-retail
- Language: Jupyter Notebook
- Homepage:
- Size: 22.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# EDA on UCI Online Retail Dataset
## 📌 Project Overview
This project performs an **Exploratory Data Analysis (EDA)** on the [UCI Online Retail Dataset](https://archive.ics.uci.edu/dataset/352/online+retail) to uncover key insights related to customers, products, and temporal sales patterns.
### 🎯 Objectives
- **Data Quality Report:** Identify missing data, duplicates, and outliers.
- **Customer Insights:** Analyze purchasing behavior, frequency, and high-value customers.
- **Product Analysis:** Determine top-selling products, revenue drivers, and return-prone items.
- **Temporal Patterns:** Examine sales cycles, peak times, and seasonal trends.
- **Actionable Insights:** Provide recommendations for improving sales, reducing returns, and enhancing customer retention.
- **Product Recommendation System:** Recommending products to customers based on purchase patterns.
---
## 📂 Dataset Information
- **Source:** UCI Machine Learning Repository
- **Dataset Name:** Online Retail (11/5/2015)
- **Time Period:** 01/12/2010 - 09/12/2011
- **Description:** The dataset contains transactional data for a UK-based online retail store selling unique all-occasion gifts. Many customers are wholesalers.
🔗 **[Dataset Link](https://archive.ics.uci.edu/dataset/352/online+retail)**
---
## 🛠️ Setup & Installation
### 1️⃣ Clone the repository
```bash
git clone https://github.com/AzizbekAvazov/eda-uci-retail-dataset.git
cd eda-uci-retail-dataset
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run the Jupyter Notebook
```bash
jupyter notebook notebooks/01_data_exploration.ipynb
```
Ensure the dataset file is present at:
```
data/uci_online_retail.xlsx
```
---
## 📊 Folder Structure
```bash
.
├── data/
│ └── uci_online_retail.xlsx # Raw dataset
├── notebooks/
│ └── 01_data_exploration.ipynb # EDA + Recommendation System
├── requirements.txt # Dependencies
└── README.md
```
---
## 🧠 Key Features
- 📉 Data cleaning, anomaly detection using Isolation Forest
- 🔄 Time-series and trend analysis
- 📊 Rich visualizations (Seaborn, Matplotlib)
- 🧑🤝🧑 Collaborative Filtering Recommendation System using KNN and Cosine Similarity
---
## ✅ License
This project is open source and free to use under the MIT License.