https://github.com/sehaj003/telco-churn-analysis
This repository contains files (dataset and Jupyter codebooks) for a project aimed to build machine learning models to predict customer churn based on given parameters.
https://github.com/sehaj003/telco-churn-analysis
data-science data-visualization exploratory-data-analysis machine-learning machine-learning-models predictive-modeling principal-component-analysis python
Last synced: about 1 month ago
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This repository contains files (dataset and Jupyter codebooks) for a project aimed to build machine learning models to predict customer churn based on given parameters.
- Host: GitHub
- URL: https://github.com/sehaj003/telco-churn-analysis
- Owner: sehaj003
- Created: 2024-05-30T14:33:59.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-27T20:41:59.000Z (over 1 year ago)
- Last Synced: 2025-02-28T05:26:50.546Z (over 1 year ago)
- Topics: data-science, data-visualization, exploratory-data-analysis, machine-learning, machine-learning-models, predictive-modeling, principal-component-analysis, python
- Language: Jupyter Notebook
- Homepage:
- Size: 777 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Teleco-Customer-Churn-Exploratory-Data-Analysis-and-Predictive-Analysis
This Project presents a comprehensive analysis of Telco customer churn, aiming to understand and predict customer attrition for a telecom company. It covers a range of data analysis and machine learning techniques implemented in Python, utilizing various libraries like pandas, numpy, matplotlib, seaborn, tensorflow, and scikit-learn. The analysis delves into exploratory data analysis (EDA), feature engineering, data preprocessing, and the implementation of multiple machine learning models, including K-Nearest Neighbors, Neural Network, Random Forests, Decision Trees, Naive Bayes, Stochastic Gradient Descent Classifier, Logistic Regression, and Support Vector Machines. The GitHub readme provides an in-depth walkthrough of the project, detailing each step from data collection and cleaning to model evaluation and conclusion