Skip to content

Commit 52ca58c

Browse files
Md-Emon-HasanMd-Emon-Hasan
authored
Create README.md
1 parent 32353be commit 52ca58c

File tree

1 file changed

+211
-0
lines changed

1 file changed

+211
-0
lines changed

‎README.md‎

Lines changed: 211 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,211 @@
1+
# 📊 **Telcom Customer Churn Prediction Project**
2+
3+
Welcome to the **Telcom Customer Churn Prediction** repository! This project aims to predict customer churn (when a customer decides to stop using a company's service) in the telecom industry. By leveraging machine learning, this system helps telecom companies identify at-risk customers, allowing them to take proactive measures to retain customers.
4+
5+
![Capture](https://github.com/user-attachments/assets/d60dee57-2b7c-4165-8b35-6132de5468e1)
6+
7+
## 📋 Contents
8+
9+
- [Introduction](#introduction)
10+
- [Topics Covered](#topics-covered)
11+
- [Getting Started](#getting-started)
12+
- [Machine Learning Model](#machine-learning-model)
13+
- [Data](#data)
14+
- [Best Practices](#best-practices)
15+
- [FAQ](#faq)
16+
- [Troubleshooting](#troubleshooting)
17+
- [Contributing](#contributing)
18+
- [Additional Resources](#additional-resources)
19+
- [Challenges Faced](#challenges-faced)
20+
- [Lessons Learned](#lessons-learned)
21+
- [Why I Created This Repository](#why-i-created-this-repository)
22+
- [License](#license)
23+
- [Contact](#contact)
24+
25+
---
26+
27+
## 📖 Introduction
28+
29+
This repository showcases a **Telcom Customer Churn Prediction** system that uses machine learning to predict whether a customer will churn based on their characteristics and usage patterns. By utilizing this system, telecom companies can minimize churn rates and improve customer retention strategies.
30+
31+
---
32+
33+
## 🔍 Topics Covered
34+
35+
- **Churn Prediction Models:** Building machine learning models like Logistic Regression, Random Forest, and XGBoost for churn prediction.
36+
- **Data Preprocessing:** Handling missing values, encoding categorical variables, and balancing the dataset.
37+
- **Model Evaluation:** Using performance metrics such as accuracy, recall, precision, ROC-AUC, and F1-score.
38+
- **Model Deployment:** Exploring deployment options for practical usage, whether through APIs or dashboard applications.
39+
40+
---
41+
42+
## 🚀 Getting Started
43+
44+
To get started with this project, follow these steps:
45+
46+
1. **Clone the repository:**
47+
48+
```bash
49+
git clone https://github.com/Md-Emon-Hasan/ML-Projects-Telcom-Customer-Churn-Prediction.git
50+
```
51+
52+
2. **Navigate to the project directory:**
53+
54+
```bash
55+
cd ML-Projects-Telcom-Customer-Churn-Prediction
56+
```
57+
58+
3. **Create and activate a virtual environment:**
59+
60+
```bash
61+
python -m venv venv
62+
source venv/bin/activate # For Windows: `venv\Scripts\activate`
63+
```
64+
65+
4. **Install the dependencies:**
66+
67+
```bash
68+
pip install -r requirements.txt
69+
```
70+
71+
5. **Run the application (if there is a UI component):**
72+
73+
```bash
74+
python app.py
75+
```
76+
77+
6. **Access the application at:**
78+
79+
```
80+
http://127.0.0.1:5000/
81+
```
82+
83+
---
84+
85+
## 🧠 Machine Learning Model
86+
87+
The project utilizes machine learning models to predict customer churn, enabling telecom companies to identify at-risk customers.
88+
89+
### Key Components:
90+
91+
- **Data Exploration:** Analyzing the customer features, such as contract types, payment methods, and usage statistics, to detect patterns that correlate with churn.
92+
- **Model Training:** Implementing algorithms such as Logistic Regression, Decision Trees, and Random Forest to predict churn.
93+
- **Model Evaluation:** Using evaluation metrics like accuracy, precision, recall, ROC-AUC, and F1-score to assess the model's performance.
94+
95+
---
96+
97+
## 📊 Data
98+
99+
- The dataset used for this project is publicly available and can be found [here](https://www.kaggle.com/datasets/blastchar/telco-customer-churn).
100+
- It includes features like customer demographic data, service details, account status, and usage statistics.
101+
- Preprocessing steps involve handling missing values, encoding categorical variables, and feature scaling.
102+
103+
---
104+
105+
## 🌟 Best Practices
106+
107+
Suggestions for maintaining and improving this project:
108+
109+
- **Feature Engineering:** Continue to refine feature selection and extraction to improve model accuracy.
110+
- **Model Tuning:** Experiment with hyperparameter tuning and other advanced techniques like GridSearchCV for better results.
111+
- **Model Retraining:** Retrain the model periodically with new data to ensure it remains accurate over time.
112+
- **Data Privacy:** Handle customer data securely, ensuring privacy and compliance with data protection regulations.
113+
114+
---
115+
116+
## ❓ FAQ
117+
118+
**Q: What is the purpose of this project?**
119+
A: This project predicts customer churn in telecom companies, providing valuable insights that can help reduce churn rates and improve retention strategies.
120+
121+
**Q: How can I contribute to this repository?**
122+
A: See the [Contributing](#contributing) section for more details.
123+
124+
**Q: Can I deploy the model in production?**
125+
A: Yes, the model can be deployed in production using APIs, dashboards, or integrated into customer management systems.
126+
127+
---
128+
129+
## 🛠️ Troubleshooting
130+
131+
Common issues and solutions:
132+
133+
- **Issue: Low Model Performance**
134+
*Solution:* Experiment with different models, hyperparameter tuning, or feature engineering to improve performance.
135+
136+
- **Issue: Dependencies Not Installed**
137+
*Solution:* Ensure the virtual environment is activated and install all dependencies with `pip install -r requirements.txt`.
138+
139+
---
140+
141+
## 🤝 Contributing
142+
143+
Contributions are welcome! Here’s how to get started:
144+
145+
1. **Fork the repository.**
146+
2. **Create a branch:**
147+
148+
```bash
149+
git checkout -b feature/new-feature
150+
```
151+
152+
3. **Make changes.**
153+
4. **Commit:**
154+
155+
```bash
156+
git commit -m 'Add feature or fix issue'
157+
```
158+
159+
5. **Push and create a pull request.**
160+
161+
---
162+
163+
## 📚 Additional Resources
164+
165+
For further reading on churn prediction and machine learning:
166+
167+
- **Churn Prediction Dataset:** [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)
168+
- **Machine Learning Course:** [Coursera](https://www.coursera.org/learn/machine-learning)
169+
- **Flask Documentation:** [flask.palletsprojects.com](https://flask.palletsprojects.com/)
170+
171+
---
172+
173+
## 💪 Challenges Faced
174+
175+
Key challenges during development:
176+
177+
- Handling class imbalance (more non-churning customers than churning customers).
178+
- Dealing with missing values and incomplete data.
179+
180+
---
181+
182+
## 📚 Lessons Learned
183+
184+
Valuable insights gained:
185+
186+
- The importance of feature engineering and selecting the right features for better model performance.
187+
- The role of model evaluation metrics in choosing the best model for predicting churn.
188+
189+
---
190+
191+
## 🌟 Why I Created This Repository
192+
193+
This repository was created to explore how machine learning can be used to solve business challenges in the telecom industry by predicting customer churn, which helps improve retention strategies and overall business performance.
194+
195+
---
196+
197+
## 📝 License
198+
199+
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for details.
200+
201+
---
202+
203+
## 📬 Contact
204+
205+
- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
206+
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
207+
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
208+
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
209+
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
210+
211+
---

0 commit comments

Comments
 (0)