https://github.com/shridhar1504/churn-prediction-using-pca-datascience-project
This project predicts customer churn in a bank using Principal Component Analysis (PCA). This can be helpful for churn prediction, as it can help to identify the most important features that contribute to customer churn.
https://github.com/shridhar1504/churn-prediction-using-pca-datascience-project
classification-algorithm classification-models data-science dimensionality-reduction machine-learning principal-component-analysis supervised-learning
Last synced: about 1 year ago
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This project predicts customer churn in a bank using Principal Component Analysis (PCA). This can be helpful for churn prediction, as it can help to identify the most important features that contribute to customer churn.
- Host: GitHub
- URL: https://github.com/shridhar1504/churn-prediction-using-pca-datascience-project
- Owner: shridhar1504
- Created: 2023-06-23T07:38:50.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-02T07:22:28.000Z (almost 3 years ago)
- Last Synced: 2025-02-15T06:29:04.604Z (over 1 year ago)
- Topics: classification-algorithm, classification-models, data-science, dimensionality-reduction, machine-learning, principal-component-analysis, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 180 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Churn-Prediction-using-PCA-Datascience-Project
This project predicts customer churn in a bank using Principal Component Analysis. PCA is a dimensionality reduction technique that can be used to reduce the number of features in a dataset while preserving as much of the information as possible.This can be helpful for churn prediction, as it can help to identify the most important features that contribute to customer churn.
## Problem Statement:
The problem statement for this project is to build a machine learning model to predict customer churn in a bank. The model should be able to make accurate predictions based on customer gender, account information.
## Solution Approach:
The solution approach for this project is to use PCA to reduce the dimensionality of the bank data, and then build a machine learning model to predict customer churn. The PCA step will help to identify the most important features that contribute to customer churn, and the machine learning model will use these features to make predictions.
## Observations:
The observations from this project are that the most important features for churn prediction are related to customer gender, account information. These features can be used to identify customers who are at risk of churning, and targeted interventions can be used to prevent these customers from churning.
## Findings:
The findings of this project are that PCA can be a helpful tool for churn prediction. The PCA step helped to identify the most important features that contribute to customer churn, and the machine learning model was able to make accurate predictions based on these features.
## Insights:
The insights from this project are that PCA can be a valuable tool for churn prediction. By identifying the most important features that contribute to customer churn, PCA can help to improve the accuracy of churn prediction models.
## Conclusion :
Achieved in developing a predictive model to predict that whether the customer will stop using the product or continue using it PCA Dimensionality Reduction Technique with accuracy of 75.83% (Logistic Regression)