Please find instructions for this assignment in the word document. Make sure you submit everything including the final report, ppts, scripts and codes, datasets, references. Thank you very much!
COVID-19 Machine Learning Analytical Report In this assignment, you are required to perform a machine learning analysis on COVID-19 datasets. You can choose any methods and algorithms which you feel confident at, the emphasis is on the report. Make sure you follow the outline to write your report carefully. When you complete the final report please also create a 5-7 pages of ppt slides to present and explain this project report. You can use the dataset uploaded but feel free to use any other sources as you like. You can refer, edit, copy and use those documents uploaded. Those are each small parts that have been worded previously so far This report should be 7-9 pages including words, codes and visualizations contains following parts: -Abstract -Introduction 1. Project Background and Execute Summary Project background, needs and importance, targeted project problem, motivations and goals. Planned project approaches and method. Expected project contributions and applications. 2. Project Deliverables Deliverables include reports, prototypes, development applications, and/or production applications. -Problem Definition and Data Exploration 1. Data Management Plan Data collection approaches, management methods, storage methods, and usage mechanisms. 2. Project Development Methodology Data analytics with intelligent system development cycle; planned development processes and activities. 3. Project Organization Plan Work breakdown structure presenting the hierarchical and incremental decomposition of the project into phases, deliverables and work packages. 4. Project Resource Requirements and Plan Required hardware, software, tools and licenses including specifications, costs and justification. -Data Processing 1. Data Process Decide the approaches and steps of deriving raw, training, validation and test datasets in order to enable the models to meet the project requirements. 2. Data Collection Define the sources, parameters and quantity of raw datasets; collect necessary and sufficient raw datasets; present samples from raw datasets. 3. Data Pre-processing Pre-process collected raw data with cleaning and validation tools; present samples from pre-processed datasets. 4. Data Transformation Transform pre-processed datasets to desired formats with tools and scripts; present samples from transformed datasets. 5. Data Preparation Prepare training, validation and test datasets from transformed datasets; present samples from training, validation and test datasets. -Model Selection and Development 1. Model Proposals Propose models to solve targeted problems in detailed terms of concepts, features, algorithms etc. 2. Model Supports Describe the platform, environment and tools to support the development and execution of each model; provide diagrams of architecture, components, data flows, etc. 3. Model Comparison and Justification Compare models regarding function and non-functional characteristics including strengths and targeted problems, approaches, data types, limitations; provide justification for each model. 4. Model Evaluation Methods Present evaluation methods and metrics for each model, e.g., accuracy, loss, ROC/AOC, MSRE, etc. -Data Analytics Demonstration of analysis performed in this project -Results and Visualization 1. Analysis of Model Execution/Evaluation Results Evaluate whether the output match tagged/labelled targets. Describe the methodology of measuring accuracy/loss, precision/recall/F-score, or AUC, confusion metrics, etc. 2. Achievements and Constraints Demonstrate and compare that the problem has been solved, made advances have been made, and/or any limitations have been acknowledged. 3. Quality Evaluation of Model Functions and Performance Other than the correctness of the model, evaluate whether the run-time performance meet timing requirement. -Conclusion 1. Summary Explain what the research has achieved; revisit key points in each section and summary of major findings, and implications for the field if any. 2. Experience and Lessons Learned Discuss and summarize the experience and lessons learned from this project 3. Recommendations for Future Work Provide recommendations for future project works and extensions. -References and Appendices 1. Appendix A – System Testing Present the test results of required use cases in terms of a sequence of GUI screens for each required use case. 2. Appendix B – Project Data Source and Management Store Upload and Provide the required project data source information in a designated Google Drive Link URL: https://drive.google.com/drive/u/0/folders/1Ghbhi1qhHYKbEeTC4Lx9gsEDUqD1OwBT In this report we will try to analyse the data which is best over the Covid patients across the world. The data has different attributes, and it shows information about the code patients. It shows the data on the basis of different state in regions from where the information about the confirm, deaths, recovered patients have been used and it will use date feature also. I selected this data from the Kaggle website which provides open-source data free of cost. I selected this data because it has null values also so it will be easier to implement and demonstrate the usage of exploratory data analysis over the information. The exploratory data analysis is performed over the data set to make sure that there is no null values from the data set and if there is some null values then can we move from that help us a different questions over the data set. In this code I try to find out answer for the 5 different questions. When I was working on the Covid data the first step was to import the data into the data frame. I imported pandas and NumPy libraries the data set was used by calling the file path into the pandas data frame variable. After importing the data, I selected the head function to show the first 5 rows of the data so that it becomes easier to understand what type of values are present in it. End step was to check whether the data contains null values or not. I called the is null function to check for the null values and it shows there are 2 null values present in a column. I also used the shape function to find out the number of rows and column present in the whole data set. Shape gets the number of rows and columns present in a data set and it will become easier to understand the size of the data set to perform any other operation over it. I also wrote the code to check for the total number of null values present in the data set. We can call the sum function over is null function to get the sum of all the null values present in the data set. The output shows there are 181 null values present in the state attribute. The following are the 5 questions over which I analyse the data set and answer was found in the form of output. The first one is to show the number of all the confirmed, deaths and recovered cases present in each region. The second question was to find out the maximum number of confirmed cases confirmed cases from the region. In the 3rd question we will find out the minimum number of deaths which were recorded Indian region and it will show the name of the region along with the minimum number of deaths. The 4th question was to find out the confirm, deaths and recovered cases from any particular region on the basis of a date. For this query I selected Pakistan as the region. The last question was to remove all the records where the confirmed cases were less than 10. For this I found out all the values who were less than 10 and then removed them from the table. Analysis of COVID-19 Pandemic Based on Machine Learning Techniques The development of cyber-infrastructure to advance worldwide collaborations remains prudent in facilitating the ease with which persons can access and manage COVID-19 related data. Ongoing efforts to design diagnostic strategies using machine learning algorithms in response to COVID-19 disease accelerate diagnostic accuracy and promise to safeguard the healthcare of persons (Alimadadi et al. 2020). Within this framework, in a machine learning analysis of COVID-19, De Felice & Polimeni (2020) established evidence regarding the feasibility of implementing and scaling up networks that promote rapid sharing of data from China to allow quick assessment and evaluation of fundamental aspects of the disease. For example, machine learning strategies have allowed for sharing of COVID-19 pathogenesis as well as further the generation of specific treatments. Machine learning and deep learning approaches remain instrumental in advancing the early forecasting of COVID-19 spread to allow for the development and implementation of necessary actions to tackle the disease. Models such as polynomial regression (PR) have predicted a significant loss of lives without the adoption of COVID-19 measures such as social distancing and adhering to the lockdown initiative (Punn, Sonbhadra, & Agarwal, 2020). A robust clinical and societal response backed by intelligence from machine learning and artificial intelligence supports better utilization of scarce health resources, accelerates clinical trials, and informs policy directives. At the same time, transfer learning methods can address the issue of biased models when making predictions about individuals derived from populations (van der Schaar et al. 2021). International collaboration remains critical in yielding large datasets for machine learning training and deployment globally. The utilization of artificial intelligence models can take advantage of prevailing data infrastructure such as EHR records, data from airlines, social media, as well as cellular operators. Results from previous studies remain promising regarding the practicality of machine learning models in enabling users including physicians, patients, and policymakers to make rational decisions in mitigating the COVID-19 pandemic. References Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X. (2020). Artificial intelligence and machine learning to fight COVID-19. De Felice, F., & Polimeni, A. (2020). Coronavirus Disease (COVID-19): A Machine learning bibliometric analysis. in vivo, 34(3 suppl), 1613-1617. Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv. van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., ... & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110(1), 1-14. 1 COVID-19 ANALYSIS 1.0 Introduction The novel Corona Virus also known as Covid-19 was spread extensively throughout China at the end of 2019. It has infected a huge number of people. While the new virus was disseminating hastily in other regions, the native epidemic has been efficiently controlled. Europe had become the vulnerable spot of the then outburst of new pneumonia. In the meantime,