ganesh kavhar python projects..
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Updated
Jan 16, 2019 - Python
ganesh kavhar python projects..
EC60091-Machine Intelligence & Expertise Systems Course,Autumn-2019
The purpose of this project is to use machine learning to predict the presence of heart disease in patients based on various clinical features.
Machine Learning Use Cases .
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
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.
Metaheuristics feature selection library for machine learning feature selection. Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, and Ant Colony Optimization
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.
Final year engineering project based on image segmentation using multi spectral and panchromatic images of Mumbai obtained using IKONOS satellite
A comprehensive suite of Python-based machine learning models for predictive analytics, employing different evolutionary algorithms for data analysis across various topics.
End To End Machine Learning Project With Understanding
A curated list of awesome Machine Learning frameworks, libraries and software.
Back propagation algorithm to predict the weather condition(Sunny, Cold, Cloud, Rainy)
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.
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)
yet another custom data science template via cookiecutter
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