Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.
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Updated
Dec 29, 2021 - Jupyter Notebook
Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.
Machine learning utility functions and classes.
In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
Machine Learning/Pattern Recognition Models to analyze and predict if a client will subscribe for a term deposit given his/her marketing campaign related data
EasyTorch is a research-oriented pytorch prototyping framework with a straightforward learning curve. It is highly robust and contains almost everything needed to perform any state-of-the-art experiments.
Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.
2nd ranked at Cameroon - Fraud Detection in Electricity and Gas Consumption Challenge
Recurrent Capsule Network for Text Classification
Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library
Neural Network based classifier that can determine whether a customer will leave the bank or not in the next 6 months
Estimated probability models that help target consumers to build brand loyalty
The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).
Loan Approval Prediction
Advanced feature selection techniques for selecting the most optimal features for any machine learning models.
Evaluation of Machine Learning Models with Yellowbrick
calculate ROC curve and find threshold for given accuracy
A wide variety of supervised and unsupervised machine learning methods using the scikit-learn library
Analysing the telecom customer churn data
Used the Global Terrorism Database to Explore Features of Suicide Bombings
Data analysis, visualization and prediction for the prevention of heart disease using ML models
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