Customer Churn Prediction

This project predicts whether a customer is likely to churn (leave the bank) based on various features like age, geography, balance, and activity. The model is built using a machine learning classifier and is deployed as a web application using Streamlit.

Project Overview

The goal of this project is to build a machine learning model to predict customer churn for a bank. Customer churn prediction helps the bank to identify at-risk customers and take measures to retain them.

Dataset

The dataset contains several features related to customer demographics, account information, and activity. Key columns include:

• RowNumber: A unique identifier for each row
• CustomerId: Unique ID for the customer
• Surname: The surname of the customer
• CreditScore: Credit score of the customer
• Geography: The location (country) of the customer
• Gender: Gender of the customer (Male or Female)
• Age: Age of the customer
• Tenure: Number of years the customer has been with the bank
• Balance: The account balance of the customer
• NumOfProducts: Number of products the customer is using
• HasCrCard: Whether the customer holds a credit card (1 = Yes, 0 = No)
• IsActiveMember: Whether the customer is an active member (1 = Yes, 0 = No)
• EstimatedSalary: Estimated annual salary of the customer
• Exited: The target variable (1 = Exited, 0 = Stayed), indicating whether the customer churned or not

Installation

Requirements

• Python 3.7+
• Streamlit
• Scikit-learn
• Pandas
• NumPy
• Matplotlib
• Pickle

Setup Instructions

1. Clone the repository:

   git clone https://github.com/Shyam1719/Customer_churn_prediction.git

2. Navigate to the project directory:

   cd Customer_churn_prediction

3. **Set up a virtual environment **:

   python -m venv myenv
    source myenv/bin/activate  # On Windows use: myenv\Scripts\activate

4. Install the required dependencies:

    pip install -r requirements.txt
   

How to Run

Train the model (if needed):

• Run the Jupyter Notebook to preprocess the dataset and train the machine learning model.
• The trained model will be saved as churn_model.pkl.
• And the encoders and standarization will be saved as one_hot_geo.pkl , gender_encoder.pkl , scaler.pkl .

Run the Streamlit app locally:

Once you have your model and the necessary libraries installed, you can launch the app:

• Make sure that place the all the pickle files and streamlit_app.py in one folder to avoid error

streamlit run streamlit_app.py

After running the above command, you will be provided with a local URL (usually http://localhost:8501). Open this URL in your browser to interact with the app.