Data Scientist, SwissRe
Data Scientist, Credit Suisse
Dev Advocate, Pinecone and Freelance ML
Principal Software Engineer, Afiniti
In this Machine Learning Project, you will learn how to build a simple logistic regression model in PyTorch for customer churn prediction.
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Overview
Logistic regression is a probabilistic model of modeling the probabilities of the discrete outcomes given the input variables. Despite the regression name, it is a classification model rather than a regression model. In simpler terms, logistic regression is linear regression but for classification. It uses the sigmoid function to calculate the probabilities of the outcome. Unlike linear regression, logistic regression doesn’t require linear relationships between input and output variables because of the non-linear transformation of the input variable.
In the previous project of the series, we have built a linear regression model in Pytorch. This project focuses on building a logistic regression model for predicting customer churn in PyTorch.
Aim
To understand the logistic regression and sigmoid function
To predict the customer churn using logistic regression in PyTorch
Data Description
The dataset used in this project has information about the customer churn based on various features.
Tech Stack
Language: Python
Libraries: pandas, pytorch, matplotlib, sci-kit learn, numpy, torchvision, seaborn
Approach
Data Cleaning
Data Preprocessing
Building Logistic Regression Model
Model Training
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