A machine learning model for Covid case prediction

Overview

CovidcasePrediction

A machine learning model for Covid case prediction

Problem Statement Using regression algorithms we can able to track the active covid cases

Problem oppertunity We need to get data. We'll use a sample data set for that. The raw data is completely infused in the workspace. Preprocessing is done before the analyzing part. The data must be clean so that the model can analyze correctly. For that the rows with empty values are removed. A module is added fothat process. Individual measurable properties are called features. Each row is representing an automobile and each column represents the feature of that automobile. The model is build accordingly. Now that the data is ready, constructing a predictive model consists of training and testing. We'll use our data to train the model, and then we'll test the model to see how closely it's able to predict prices. Because we want to predict no of cases, which is a number, we'll use a regression algorithm.

Azure Machine learning studio

Here we are using 2 Algorithms

  1. Linear Regression
  2. Neural Network Regression

image

image

image

  1. The input values are to be found
  2. The csv file is converted to dataset
  3. The data miss is cleaned
  4. Split data
  5. The regression algorithms Linear Regression and Neural Network Regression takes place
  6. The data is trained
  7. Train model
  8. Score model
  9. Evaluate Model
  10. Output values obtained

In this case the both the training experiment and predicive experiments are done.

The API key: 8Lnx6+i4W7R2i7aFyGh+gmbhrnEpHrdFzd84kmvka7yEKTnt8P8EEKK46oXxmHHQphffTh9FvdPA2g3FEpCkgw==

https://studio.azureml.net/Home/ViewWorkspaceCached/5343e8d2284d47de9d5a3c941a85e8bf#Workspaces/Experiments/Experiment/5343e8d2284d47de9d5a3c941a85e8bf.f-id.06f7bb2e287e4d74aeaaf2223be0b151/ViewExperiment

The data prediction part is done as

  1. Cough
  2. Fever
  3. Sore Throat
  4. Shortness of Breath
  5. Headache
  6. Age 60 or Above
  7. Corona Result

By giving these values as sample we can predict.

https://studio.azureml.net/Home/ViewWorkspaceCached/5343e8d2284d47de9d5a3c941a85e8bf#Workspaces/Projects/Project/66450ebc-1dd6-44c3-a580-808dfc470798/ProjectDetails image

After that Deploy web service part is done the project is published to gallery. https://gallery.cortanaintelligence.com/Experiment/Covid-19-prediction-two-algos-Predictive-Exp

Owner
VijayAadhithya2019rit
VijayAadhithya2019rit
Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)

sklearn-compatible Random Bits Forest Scikit-learn compatible wrapper of the Random Bits Forest program written by Wang et al., 2016, available as a b

Tamas Madl 8 Jul 24, 2021
Python package for causal inference using Bayesian structural time-series models.

Python Causal Impact Causal inference using Bayesian structural time-series models. This package aims at defining a python equivalent of the R CausalI

Thomas Cassou 219 Dec 11, 2022
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms

LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query

5 Aug 06, 2022
Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo

2.5k Dec 28, 2022
A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

Demand-Forecasting Business Problem A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

Ayşe Nur Türkaslan 3 Mar 06, 2022
stability-selection - A scikit-learn compatible implementation of stability selection

stability-selection - A scikit-learn compatible implementation of stability selection stability-selection is a Python implementation of the stability

185 Dec 03, 2022
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

AI Fairness 360 (AIF360) The AI Fairness 360 toolkit is an extensible open-source library containg techniques developed by the research community to h

1.9k Jan 06, 2023
Summer: compartmental disease modelling in Python

Summer: compartmental disease modelling in Python Summer is a Python-based framework for the creation and execution of compartmental (or "state-based"

6 May 13, 2022
Magenta: Music and Art Generation with Machine Intelligence

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new

Magenta 18.1k Dec 30, 2022
#30DaysOfStreamlit is a 30-day social challenge for you to build and deploy Streamlit apps.

30 Days Of Streamlit 🎈 This is the official repo of #30DaysOfStreamlit — a 30-day social challenge for you to learn, build and deploy Streamlit apps.

Streamlit 53 Jan 02, 2023
ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.

ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. It has a simple, flexible syntax, is cloud and tool agnostic, and has interfaces/abstraction

ZenML 2.6k Jan 08, 2023
End to End toy example of MLOps

churn_model MLOps Toy Example End to End You might find below links useful Connect VSCode to Git MLFlow Port Heroku App Project Organization ├── LICEN

Ashish Tele 6 Feb 06, 2022
ML-powered Loan-Marketer Customer Filtering Engine

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very length

Sagnik Roy 13 Jul 02, 2022
Time series changepoint detection

changepy Changepoint detection in time series in pure python Install pip install changepy Examples from changepy import pelt from cha

Rui Gil 92 Nov 08, 2022
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Responsible AI Workshop Responsible innovation is top of mind. As such, the tech industry as well as a growing number of organizations of all kinds in

Microsoft 9 Sep 14, 2022
[HELP REQUESTED] Generalized Additive Models in Python

pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized

daniel servén 747 Jan 05, 2023
Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale.

Model Search Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers sp

AriesTriputranto 1 Dec 13, 2021
Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining

**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.** S

Sebastian Raschka 4k Dec 30, 2022
This repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

B DEVA DEEKSHITH 1 Nov 03, 2021
Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

Machine Learning Engineering for Production (MLOps) Specialization on Coursera (offered by deeplearning.ai) Programming assignments from all courses i

Aman Chadha 173 Jan 05, 2023