You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This project involved analyzing 10,000 customer records, applying data preparation techniques, and training supervised machine learning models, achieving 94% accuracy. Model efficiency was further refined using cross-validation and hyperparameter tuning, ensuring reliability and performance
A delinquency model which can predict in terms of a probability for each loan transaction, whether the customer will be paying back the loaned amount within 5 days of insurance of loan.
Analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.
Designing strategies to pull back potential churn customers of a telecom operator by building a model which can generalize well and can explain the variance in the behavior of different churn customer. Analysis being done on large dataset which has lot of scope for cleaning and choosing the right model for prediction.
This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and data plan usage to forecast churn likelihood. Its insights aid telecom companies in proactively retaining customers and mitigating churn rates.
To explore and analyze the Telecom Churn dataset to understand factors contributing to customer churn and to develop a predictive model that can forecast customer churn with high accuracy
This repository showcases machine learning projects covering diverse topics such as book recommendations, New York Airbnb analysis, and telecom churn prediction. Each project utilizes various techniques and algorithms to tackle specific challenges and extract meaningful insights from the data.
Analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn (usage-based churn) and identify the main indicators of churn.
In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.
This is a Streamlit web application for predicting Telecom Churn. The app uses a trained machine learning model to predict whether a customer is likely to churn or not based on certain input features.
Analysing customer-level data of a leading telecom firm, building predictive models to identify customers at high risk of churn and identifying the main indicators of churn.