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https://github.com/sanskaryo/customer_offer_predict
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https://github.com/sanskaryo/customer_offer_predict
customer offer-prediction random-forest sklearn streamlit
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/sanskaryo/customer_offer_predict
- Owner: sanskaryo
- Created: 2024-11-12T06:25:53.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T08:49:54.000Z (about 2 months ago)
- Last Synced: 2024-11-12T09:20:18.627Z (about 2 months ago)
- Topics: customer, offer-prediction, random-forest, sklearn, streamlit
- Language: Jupyter Notebook
- Homepage: https://ecommerce-offer-predict.streamlit.app/
- Size: 91.8 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Customer Offer Prediction 🎁
Welcome to the **Customer Offer Prediction** repository! This project is a machine learning-powered application built with **Streamlit** to predict whether an e-commerce customer is eligible for an offer based on their behavioral metrics. The app uses a **Random Forest classifier** to determine offer eligibility, providing valuable insights for marketing and customer engagement.
## 📌 Project Overview
In e-commerce, personalizing offers based on customer behavior can significantly increase customer retention and satisfaction. This project predicts whether a customer should receive an offer based on specific parameters, such as:
- Time spent on the website
- Time spent on the app
- Yearly amount spent
- Length of membership
By analyzing these factors, the app aims to help businesses make data-driven decisions to boost customer loyalty and engagement.
## 🎯 Key Features
- **Real-time Predictions**: Input customer metrics to get an immediate prediction of offer eligibility.
- **Streamlit Interface**: An intuitive and interactive web-based interface.
- **Customizable Parameters**: Adjust sliders to simulate different customer profiles.
- **Styled Results Display**: Visually distinct "Offer" and "No Offer" indicators for clear insights.
## 🛠️ Technology Stack
- **Python**: Core programming language.
- **Streamlit**: Frontend for building a responsive web application.
- **Scikit-Learn**: Random Forest classifier for predictive modeling.
- **Pickle**: Model serialization for easy deployment.
## Page View - 
## 🚀 Getting Started
### Prerequisites
Ensure you have **Python 3.7+** installed. Then, install the necessary packages:
```bash
pip install streamlit scikit-learn pandas
```
### Installation
1. **Clone the Repository**:
```bash
git clone https://github.com/sanskaryo/Customer_offer_predict.git
```
2. **Navigate to the Project Directory**:
```bash
cd Customer_offer_predict
```
3. **Run the App**:
```bash
streamlit run app.py
```
4. **Access the Application**: Go to `http://localhost:8501` in your browser.
### Input Parameters
The app requires the following inputs:
- **Time on Website**: Time spent on the website (0-40 minutes).
- **Yearly Amount Spent**: Total amount spent annually by the customer (0-800 USD).
- **Time on App**: Time spent on the app (0-20 minutes).
- **Length of Membership**: Duration of membership (0-8 years).
### Example Usage
After inputting the parameters, click **"Predict Eligibility"** to see if the customer qualifies for an offer. Results are displayed with color-coded visuals:
- **Green**: Eligible for offer
- **Red**: Not eligible for offer
## 📂 Project Structure
```
.
├── app.py # Main Streamlit application
├── model.pkl # Serialized Random Forest model
├── README.md # Project documentation
└── requirements.txt # List of dependencies
```
## 📈 Model Information
The model was trained using a Random Forest classifier with historical customer data to identify patterns in spending and activity. This model can be further fine-tuned by adding more relevant features or experimenting with different algorithms for better accuracy.
## ✨ Future Enhancements
Some potential enhancements include:
- **Improving Model Accuracy**: Experiment with additional features.
- **Enhanced UI**: Add charts and interactive components.
- **Backend Integration**: Store customer data in a database for real-time analytics.
## 🤝 Contributing
Feel free to fork this repository and create a pull request if you'd like to contribute. Any ideas for improving this project are welcome!
## 📄 License
This project is licensed under the MIT License.
## 📝 Acknowledgments
Thanks to the open-source community and **Streamlit** for making this project possible.
---
Enjoy the app, and happy predicting! 🎉