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This project, developed during my data science internship at Eisystems Technologies, aims to predict insurance purchase likelihood using a logistic regression model. The project includes a fully functional Streamlit app that allows users to interact with the model and visualize predictions. Refer to readme file for more info.
Telecom Customer Churn Prediction This repository contains a machine learning project focused on predicting customer churn in the telecommunications industry. By leveraging a dataset of customer demographics and usage patterns, we develop and deploy a predictive model to identify customers at risk of leaving the service.
A comprehensive suite of Python-based machine learning models for predictive analytics, employing different evolutionary algorithms for data analysis across various topics.
A streamlit based WebApp to automate basic Machine Learning activities like data_profiling, ML model training, Model comparison and download of the best model to predict the selected target.
In this project we are trying to solve a classification problem where we need to check that a particular wafer sensor is active or not after which we would do CICD using CircleCi
I have created face recognition system using openCV and numpy library . the project is bascially in two parts 1) collecting samples of your face(Face_Recognition_part1) and 2) Training your model and generating output as Locked(when face does not match) and unlocked (when face match) and face not found(when it could not detect your face)