bank-analysis

Bank data analysis(Customer Churn Analysis)

This project focuses on analyzing customer churn and predicting whether a customer is likely to churn using machine learning techniques. The analysis is implemented in Python, utilizing popular libraries for data preprocessing, visualization, and modeling.

Project Overview

Customer churn is a critical issue for businesses, as retaining customers is often more cost-effective than acquiring new ones. This project aims to:

1. Analyze customer churn data.
2. Preprocess data using encoding and scaling techniques.
3. Train a predictive model using the K-Nearest Neighbors (KNN) algorithm.
4. Visualize results to interpret insights.

---

Features

• Data Preprocessing:
  • Handling categorical variables with LabelEncoder.
  • Scaling features using MinMaxScaler for normalization.
• Modeling:
  • Predictive modeling using KNeighborsClassifier from the sklearn library.
• Visualization:
  • Insights and trends visualized using matplotlib.

---

Setup and Installation

1. Clone the repository:

   git clone https://github.com/anurag815311/bank-data-analysis.git
   

2. Navigate to the project directory: ```bash cd churn-analysis

---

Usage

1. Data Loading: Ensure your dataset is in the same directory or update the data path in the notebook.

2. Run the Notebook: Use Jupyter Notebook or Jupyter Lab to execute the Analysis_churn_dataset.ipynb.

3. Model Training: The notebook guides you through training the KNN model and evaluating its performance.

4. Visualization: Generate and interpret visualizations for churn trends.

---

Libraries Used

• pandas: Data manipulation and analysis.
• numpy: Numerical computations.
• matplotlib: Data visualization.
• scikit-learn:
  • Preprocessing with LabelEncoder and MinMaxScaler.
  • Modeling with KNeighborsClassifier.

---

Results and Insights

• Predictive accuracy and model evaluation metrics are discussed in the notebook.
• Key churn predictors identified through the analysis.

---

Future Work

• Extend the analysis to include other machine learning models (e.g., logistic regression, random forests).
• Improve feature engineering for better predictions.
• Deploy the model as a web application for real-time predictions.

---

Acknowledgments

• Thanks to the open-source community for tools and resources.
• Dataset and domain inspiration from various churn analysis studies.