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https://github.com/darkdk123/customer-churn-prediction-innobytes
Predicting Customer churns as an Internship project at Innobytes services.
https://github.com/darkdk123/customer-churn-prediction-innobytes
data-science python scikit-learn streamlit xgboost-classifier
Last synced: 11 days ago
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Predicting Customer churns as an Internship project at Innobytes services.
- Host: GitHub
- URL: https://github.com/darkdk123/customer-churn-prediction-innobytes
- Owner: DarkDk123
- Created: 2024-09-12T10:11:50.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-10-03T12:27:29.000Z (3 months ago)
- Last Synced: 2024-11-07T15:26:42.079Z (2 months ago)
- Topics: data-science, python, scikit-learn, streamlit, xgboost-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 2.84 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# **Customer Churn Prediction**
### A Project as an ML Intern given by ***[Innobytes Services](https://www.linkedin.com/company/innobyte-services/posts/?feedView=all)***
A Classification model that predicts whether a customer will churn or not.
**Open Notebook 🔥**
[](./Customer_Churn_Prediction.ipynb) Â Â
[](https://nbviewer.org/github/DarkDk123/Customer-churn-prediction-innobytes/blob/main/Customer_Churn_Prediction.ipynb) Â Â
[](https://colab.research.google.com/github/DarkDk123/Customer-churn-prediction-innobytes/blob/main/Customer_Churn_Prediction.ipynb)
## Predicting Customer Churn using Machine Learning
This project builds a machine learning model capable of predicting whether a customer will **churn (leave)** based on their characteristics. The data comes from a *Telco customer churn* dataset.
### **1. Problem Definition**
>Understanding how customer attributes contribute the probability of their churn, insights & patterns etc.
> How accurately can we predict customer churn based on customer attributes like usage behavior, contract type, and services used?
### **2. Data**
The dataset is sourced from Telco's customer churn records and includes the following features:
- Customer demographics (gender, senior citizen status)
- Services used (internet, phone, etc.)
- Contract types (month-to-month, one year, two years)
- Billing information (monthly and total charges)
I have used the specific dataset provided in the Internship. For more details explore below sources.
**Official Sources :** [Kaggle](https://www.kaggle.com/blastchar/telco-customer-churn) & [IBM](https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113)
### **3. Evaluation**
We evaluate the model using classification metrics such as accuracy, F1-score, precision, recall, and ROC-AUC. The primary goal is to minimize misclassification errors, **particularly false negatives**.
Also our prime focus was to maximize the **tradeoff** between **Recall & AUC Score.**
### **4. Features**
The dataset contains customer data with various service attributes. Key features include contract type, paperless billing, payment method, monthly charges, and tenure.
After **model driven EDA**, we understand the following features are crucial to predict Customer Churn!
### **5. Key Achievements**
1. **Data Preprocessing Pipeline**:
- Developed a robust preprocessing pipeline for cleaning and encoding features.
- Efficiently handled missing data and categorical variables.
2. **Machine Learning Model Development**:
- Built multiple classifiers: Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, and CatBoost.
- Applied cross-validation for model evaluation.
3. **Hyperparameter Tuning**:
- Used Bayesian optimization with `BayesSearchCV` to improve model performance by fine-tuning hyperparameters.
### **Final Result**
The final model achieved a **recall** of ***90%*** & **AUC score** of ***75%*** , successfully predicting customer churn with good accuracy.
**Confusion Matrix & ROC Curve for predictions!**
### For more details, refer to the Jupyter [Notebook](./Customer_Churn_Prediction.ipynb) file.
---
#### Run the model locally!
1. ```python
pip install -r requirements_to_run.txt # install requirements!
```
2. ```python
streamlit run Streamlit_front/main_streamlit.py # From root directory
```
3. Open the server in your browser: http://127.0.0.1:8501/
4. Get predictions for you customer either with the *form* or *upload a CSV file.*
**Other images :**
|Input data format|Uploading CSV File|
|---|---|
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#### Author: [Dipesh Rathore](https://github.com/DarkDk123) 😎
#### Complete Analysis : [Notebook](./Customer_Churn_Prediction.ipynb)
---