Analyzing The Death Ratio of Covid Patients Using Multiple Logistic Regression in Comparison with Linear Regression for Improving Accuracy
Objective
The main objective of this project is to compare the performance of the models (accuracies) like Multiple logistic regression, linear regression, Bayesian regression, Lasso regression and proposes that our Multiple Logistic Regression models outperforms all the other models in terms of accuracies.
Abstract
The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. Machine learning (ML) based forecasting mechanisms have proved their significance to
Anticipate in perioperative outcomes to improve the decision making on the future course of actions. In this project we are using Multiple Logistic regression algorithm as our proposed algorithm by comparing with linear regression, Lasso Regression, Logistic Regression and Bayesian Ridge Regression algorithms. In this project the dataset was taken from publicly available dataset. Our proposed algorithm outperforms the remaining algorithms in terms of r2 scores.
KEYWORDS: Machine Learning, Covid-19, Multiple Logistic Regression, Regression, r2score.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
H/W SPECIFICATIONS:
- Processor: I3/Intel Processor
- RAM : 8GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
S/W SPECIFICATIONS:
- Operating System: Windows 10
- Server-side Script: Python 3.6
- IDE : Jupyter Notebook or colab.
- Libraries Used : Pandas, NumPy, Scikit-Learn, Seaborn, Matplotlib
Learning Outcomes
· About Classification in machine learning.
· About preprocessing techniques.
· About Random Forest Regressor.
· About Decision Tree Regressor.
· About Bagging Regressor.
· About XGBoost.
· About Gradient Boosting Regressor.
· About CatBoost Regressor.
· About K Neighbors Regressor
· About SVR.
· About Extra Tree Regressor.
· About StackingRegressors.
· Knowledge on PyCharm Editor.
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
