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Heart Disease Prediction using machine and deep learning techniques works on heart dataset

This project aims to forecast weekly sales for retail stores using historical sales and economic data. By applying advanced time series forecasting models, we enable better inventory management, demand planning, and revenue optimization for retail chains. The project includes both traditional statistical models and deep learning techniques.

This a multiple disease prediction based on user input which can predict upto 40 disease and trained on 131 parameters

this is combination of 3 different disease prediction system check readme for details

Developed a constraint satisfaction problem model for deciding what projects can be taken on and what companies need to be contracted to deliver on these projects.

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this repository is part of multiple disease prediction system repository

This GitHub repository contains an example demonstrating the application of fundamental image processing filters (Mean, Median, Gaussian) using Python and OpenCV, along with the addition of Salt and Pepper Noise.

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Intelli-Helmet is a stress monitoring system using the brain wave data form EEG headset and physiological data (Heart Rate) from wearable device.

Pychram was used to create the AI voice assistant Mike. It is able to carry out fundamental client-designed tasks and comprehend human speech. The Mike is activated and starts carrying out user orders when the user specifies the right trigger words.

Geocoding-Python Code-Enter the Street address to get the latitude and longitude

Value Inc is a retail store that sells household items all over the world by bulk. The Sales Manager has no sales reporting but he has a brief idea of current sales. He also has no idea of the monthly cost, profit and top selling products. He wants a dashboard on this and says the data is currently stored in an excel sheet.

Working_with_data_found_in_the_netCDF_format

It was the machine learning project of Speech Emotion Analyzer to find the emotion by extracting the features of the voices.

Indoor PM2.5 source detection algorithm using unsupervised clustering ML method (k-means clustering)

Customer Churn Analysis - Telecom Retention