File size: 3,874 Bytes
d636394 a538c2b d636394 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
# Credit Card Clustering with Machine Learning
This project focuses on clustering credit card customers based on their usage behavior using unsupervised machine learning techniques. The goal is to segment customers for better targeting, offers, and personalized financial services.
## 📌 Objective
- Understand customer behavior from credit card usage.
- Segment customers into clusters with similar patterns.
- Help financial institutions create targeted marketing strategies.
## 📊 Dataset
- Source: [Aman Kharwal’s GitHub Dataset](https://raw.githubusercontent.com/amankharwal/Website-data/master/credit_card.csv)
- Contains features like:
- `BALANCE`: Average balance
- `PURCHASES`: Total purchases
- `CREDIT_LIMIT`: Assigned credit limit
- `PAYMENTS`: Amount paid
- `TENURE`: Months as a customer
- `ONEOFF_PURCHASES`, `INSTALLMENTS_PURCHASES`, etc.
## 🧹 Data Preprocessing
- Checked for null values and handled them
- Dropped irrelevant columns (e.g., `CUST_ID`)
- Scaled data using `StandardScaler`
## 🧠 Clustering Algorithm
- Used **KMeans** algorithm
- Determined optimal number of clusters using:
- Elbow Method
- Silhouette Score
## 📉 Dimensionality Reduction
- Applied **PCA** for visualizing clusters in 2D space
## 📈 Results & Analysis
- Clusters represent different types of customers:
- High spenders
- Low activity users
- Customers using mostly installments
- Visualized clusters using `matplotlib` and `seaborn`
## 📦 Libraries Used
- `pandas`
- `numpy`
- `matplotlib`, `seaborn`
- `scikit-learn`
## 🔍 Future Improvements
- Try alternative clustering algorithms like DBSCAN, GMM
- Add deeper feature engineering
- Include time-based features for trend analysis
## 💻 How to Run
1. Clone the repo:
```bash
git clone https://github.com/handecrkc/credit-card-clustering.git
```
2. Install requirements:
```bash
pip install -r requirements.txt
```
3. Run the notebook:
Open `credit_card_clustering.ipynb` in Jupyter Notebook or VS Code
---
## 🧑💻 Author
- **Hande Çarkcı**
- GitHub: [github.com/handecrkc](https://github.com/handecrkc)
# 💳 Credit Card Clustering – Streamlit App
Bu proje, müşterilerin kredi kartı kullanım alışkanlıklarına göre segmentlere ayrılmasını sağlayan bir **Makine Öğrenimi** uygulamasıdır.
Streamlit ile geliştirilen bu uygulama sayesinde kullanıcıdan alınan veriye göre müşterinin ait olduğu küme tahmin edilir.
## 🎯 Proje Amacı
- Kredi kartı kullanıcılarını **benzer davranış gruplarına ayırmak**
- Finansal kurumlara **hedefli pazarlama stratejileri** sağlamak
- Kullanıcıya ait segmenti gerçek zamanlı olarak tahmin etmek
## 🧠 Kullanılan Yöntem
- **KMeans Clustering**
- **StandardScaler** ile veri ölçekleme
- **Streamlit** ile web uygulaması
## 🗃️ Kullanılan Veri Seti
- Kaynak: [`CC GENERAL.csv`](https://raw.githubusercontent.com/amankharwal/Website-data/master/credit_card.csv)
- Sütunlar: `BALANCE`, `PURCHASES`, `CREDIT_LIMIT`, `PAYMENTS`, `TENURE`, vb.
## 🚀 Uygulamayı Çalıştırmak
```bash
git clone https://github.com/kullanici_adin/credit-card-clustering-streamlit.git
cd credit-card-clustering-streamlit
pip install -r requirements.txt
streamlit run app.py
🖼️ Uygulama Görünümü
🔍 Küme Açıklamaları
Küme Açıklama
0 🟢 Düşük harcama yapan, düşük riskli müşteri
1 🟡 Orta seviyede harcama yapan müşteri
2 🔴 Yüksek harcama yapan ve aktif müşteri
3 🔵 Taksitli harcamaları yüksek olan müşteri
🛠️ Gereken Kütüphaneler
streamlit
pandas
numpy
scikit-learn
joblib
## 📜 License
This project is open-source under the MIT License.
|