title
Coronavirus Outbreak Prediction Using Machine Learning | Predicted vs Actual | Simplilearn

description
🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearning-sHWKN5dakPw&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube 🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=MachineLearning-sHWKN5dakPw&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube Coronavirus (Covid-19) has become the most buzzed topic these days. Its outbreak has taken the world by storm. In this video, we'll see what Coronavirus is, how did it emerge, and what are its symptoms. Then, we will see what has been its impact so far and analyze the outbreak of Coronavirus across various regions, visualize them using charts and graphs, and predict the number of upcoming confirmed cases using the Linear Regression model in Python. Finally, we’ll look at the various safety measures that you can take to save yourself from getting attacked by Coronavirus. Dataset Link - https://drive.google.com/drive/folders/1H83jc6SgCJbYB5ApOAXmT3f-n-ZeAeHq Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 To access the slides, click here: https://www.slideshare.net/Simplilearn/coronavirus-outbreak-prediction-using-machine-learning-covid19-outbreak-prediction-simplilearn/Simplilearn/coronavirus-outbreak-prediction-using-machine-learning-covid19-outbreak-prediction-simplilearn Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Coronavirus-outbreak-prediction-sHWKN5dakPw&utm_medium=Tutorials&utm_source=youtube You can also go through the Slides here: https://goo.gl/m5Txob Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearning #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse 🔥Free Machine Learning Course: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=Description&utm_source=youtube ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearning-sHWKN5dakPw&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

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{'title': 'Coronavirus Outbreak Prediction Using Machine Learning | Predicted vs Actual | Simplilearn', 'heatmap': [{'end': 487.065, 'start': 460.354, 'weight': 0.784}, {'end': 538.911, 'start': 506.169, 'weight': 0.72}, {'end': 584.301, 'start': 554.406, 'weight': 0.709}, {'end': 634.646, 'start': 616.497, 'weight': 0.71}, {'end': 718.777, 'start': 688.777, 'weight': 0.745}, {'end': 845.706, 'start': 796.42, 'weight': 0.839}, {'end': 1303.533, 'start': 1194.296, 'weight': 0.881}], 'summary': 'Discusses the global impact of coronavirus, utilizing machine learning algorithms to predict upcoming confirmed cases for the next 10 days and emphasizes safety measures. it provides an overview of covid-19 symptoms, transmission, and outbreak, analyzes covid-19 datasets, visualizes data through charts and graphs, and demonstrates svm parameters and model building for coronavirus prediction.', 'chapters': [{'end': 85.73, 'segs': [{'end': 85.73, 'src': 'embed', 'start': 27.185, 'weight': 0, 'content': [{'end': 33.308, 'text': 'In this session, we will learn about what Coronavirus really is, how did it emerge and what are its symptoms.', 'start': 27.185, 'duration': 6.123}, {'end': 38.431, 'text': 'Then we will see what has been its impact so far in terms of the total cases emerging,', 'start': 33.789, 'duration': 4.642}, {'end': 42.834, 'text': 'the total number of deaths reported and the total number of recoveries across the globe.', 'start': 38.911, 'duration': 3.923}, {'end': 46.897, 'text': 'We will analyze the outbreak of coronavirus across different regions,', 'start': 43.334, 'duration': 3.563}, {'end': 52.721, 'text': 'visualize them using charts and graphs and predict the number of upcoming confirmed cases for the next 10 days, that is,', 'start': 46.897, 'duration': 5.824}, {'end': 59.066, 'text': 'between the 16th of March to the 25th of March, using linear regression model and support vector machines in Python.', 'start': 52.721, 'duration': 6.345}, {'end': 65.411, 'text': "Finally, we'll look at the various safety measures that you can take to save yourself from getting attacked by coronavirus.", 'start': 59.546, 'duration': 5.865}, {'end': 69.187, 'text': 'Before we begin,', 'start': 67.967, 'duration': 1.22}, {'end': 76.189, 'text': 'let me clarify that this video majorly focuses on how you can use machine learning algorithms to make predictions on such sensitive issues.', 'start': 69.187, 'duration': 7.002}, {'end': 82.31, 'text': 'We will certainly cover the current scenario and the impact it has created worldwide, but there is nothing to panic at all.', 'start': 76.729, 'duration': 5.581}, {'end': 85.73, 'text': 'Make sure you take all the necessary precautions to stay safe.', 'start': 82.89, 'duration': 2.84}], 'summary': 'Learn about coronavirus, its impact, analyze outbreak, and use ml for predictions.', 'duration': 58.545, 'max_score': 27.185, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw27185.jpg'}], 'start': 9.437, 'title': 'The impact and prediction of coronavirus', 'summary': 'Discusses the global impact of coronavirus with data on total cases, deaths, and recoveries, and also highlights the use of machine learning algorithms to predict upcoming confirmed cases for the next 10 days. it emphasizes the importance of safety measures and precautions.', 'chapters': [{'end': 85.73, 'start': 9.437, 'title': 'Coronavirus: impact and prediction', 'summary': 'Discusses the impact of coronavirus, including total cases, deaths, and recoveries globally, and the use of machine learning algorithms to predict upcoming confirmed cases for the next 10 days, while emphasizing the importance of safety measures and precautions.', 'duration': 76.293, 'highlights': ['The session delves into the impact of Coronavirus, covering the total cases, deaths, and recoveries globally, and utilizes machine learning algorithms to predict upcoming confirmed cases for the next 10 days.', 'Emphasizes the importance of safety measures and precautions to stay safe from Coronavirus.', 'Provides an overview of what Coronavirus is, its emergence, symptoms, and the outbreak analysis across different regions.']}], 'duration': 76.293, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw9437.jpg', 'highlights': ['Utilizes machine learning algorithms to predict upcoming confirmed cases for the next 10 days.', 'Covers the total cases, deaths, and recoveries globally.', 'Emphasizes the importance of safety measures and precautions to stay safe from Coronavirus.', 'Provides an overview of what Coronavirus is, its emergence, symptoms, and the outbreak analysis across different regions.']}, {'end': 420.605, 'segs': [{'end': 152.075, 'src': 'embed', 'start': 127.902, 'weight': 0, 'content': [{'end': 135.666, 'text': "In December 2019, the city of Wuhan in Hubei province, which is China's seventh largest city, comprising 11 million residents,", 'start': 127.902, 'duration': 7.764}, {'end': 138.848, 'text': 'became the center of a pneumonia outbreak of unknown cause.', 'start': 135.666, 'duration': 3.182}, {'end': 147.172, 'text': 'The Chinese health authorities conducted an immediate investigation of these cases to identify and control its spread by isolating suspected patients,', 'start': 139.368, 'duration': 7.804}, {'end': 152.075, 'text': 'closely monitoring their contacts and obtaining detailed clinical and epidemiologic data.', 'start': 147.172, 'duration': 4.903}], 'summary': 'Wuhan, china: 2019 pneumonia outbreak prompts swift response by health authorities.', 'duration': 24.173, 'max_score': 127.902, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw127902.jpg'}, {'end': 212.595, 'src': 'embed', 'start': 172.965, 'weight': 1, 'content': [{'end': 177.788, 'text': 'Illness can be more severe for some people and can lead to pneumonia or breathing difficulties.', 'start': 172.965, 'duration': 4.823}, {'end': 186.293, 'text': 'Older people and people with other medical conditions such as asthma, diabetes, or heart disease may be more vulnerable to becoming severely ill.', 'start': 178.368, 'duration': 7.925}, {'end': 190.956, 'text': 'The novel coronavirus is now a public health emergency of international concern.', 'start': 186.973, 'duration': 3.983}, {'end': 196.36, 'text': 'People across the globe are racing to cut short their vacations and rebook flights home,', 'start': 191.716, 'duration': 4.644}, {'end': 200.604, 'text': 'as most countries continue to lock down cities and towns amid the spread of coronavirus.', 'start': 196.36, 'duration': 4.244}, {'end': 210.232, 'text': 'As per the World Health Organization, till the 15th of March, the total deaths reported globally stood at 5, 735, while more than 100,', 'start': 201.045, 'duration': 9.187}, {'end': 212.595, 'text': '000 people were infected worldwide.', 'start': 210.232, 'duration': 2.363}], 'summary': 'Covid-19 poses severe risk, with 5,735 deaths and 100,000+ global infections reported by march 15th.', 'duration': 39.63, 'max_score': 172.965, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw172965.jpg'}, {'end': 297.708, 'src': 'embed', 'start': 255.568, 'weight': 2, 'content': [{'end': 261.832, 'text': 'where we will analyze the outbreak of coronavirus cases across different countries and visualize them using different charts and graphs.', 'start': 255.568, 'duration': 6.264}, {'end': 266.534, 'text': 'The data has information from the 22nd of January till the 15th of March.', 'start': 262.512, 'duration': 4.022}, {'end': 268.195, 'text': 'Using this data,', 'start': 267.134, 'duration': 1.061}, {'end': 275.239, 'text': 'we will predict the upcoming cases for the next 10 days using algorithms such as linear regression and support vector machines in Python.', 'start': 268.195, 'duration': 7.044}, {'end': 280.042, 'text': 'Now let me go ahead and show you the data sets that we will be using for this analysis.', 'start': 275.879, 'duration': 4.163}, {'end': 283.983, 'text': "So for this analysis, we'll be using three CSV files.", 'start': 280.982, 'duration': 3.001}, {'end': 288.525, 'text': 'So here you can see I have opened all the three files.', 'start': 285.944, 'duration': 2.581}, {'end': 291.366, 'text': 'The first data set is regarding the confirmed cases.', 'start': 289.105, 'duration': 2.261}, {'end': 297.708, 'text': 'It has columns such as province or the state country or the region, latitude,', 'start': 291.906, 'duration': 5.802}], 'summary': 'Analyzing coronavirus outbreak data, predicting cases for next 10 days using algorithms, and utilizing three csv files for the analysis.', 'duration': 42.14, 'max_score': 255.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw255568.jpg'}], 'start': 86.391, 'title': 'Covid-19 outbreak and global impact', 'summary': 'Provides an overview of the emergence of covid-19, its symptoms, transmission, and the initial outbreak in wuhan, china in 2019. it also discusses the global impact, with 5,735 reported deaths and over 100,000 infections worldwide as of march 15, 2021, focusing on highly affected countries such as china, italy, iran, and the united states. it also covers the use of algorithms to predict future cases and the utilization of csv files for analysis.', 'chapters': [{'end': 190.956, 'start': 86.391, 'title': 'Covid-19 outbreak overview', 'summary': 'Discusses the emergence of covid-19, including its symptoms, transmission, and the initial outbreak in wuhan, china in 2019, which became a public health emergency of international concern.', 'duration': 104.565, 'highlights': ['COVID-19 is characterized by symptoms such as cold, cough, running nose, sore throat, and fever. Describes the symptoms of COVID-19, including common cold symptoms and potential severity for some individuals.', 'The city of Wuhan in Hubei province, China, with 11 million residents, became the center of a pneumonia outbreak of unknown cause in December 2019. Highlights the significance of Wuhan as the initial epicenter of the COVID-19 outbreak.', 'Coronaviruses, first discovered in 1960s, are a family of viruses that cause illness ranging from common cold to severe diseases such as Middle East respiratory syndrome and severe acute respiratory syndrome. Provides an overview of coronaviruses, emphasizing their potential to cause a range of illnesses.', 'The source of coronavirus 2019 or COVID-19 is believed to be a wet market in Wuhan which sold both dead and live animals including fish and birds. Explains the suspected origin of COVID-19, linking it to a wet market in Wuhan.', 'Older people and people with other medical conditions such as asthma, diabetes, or heart disease may be more vulnerable to becoming severely ill from COVID-19. Highlights the increased vulnerability of certain individuals to severe illness from COVID-19.']}, {'end': 420.605, 'start': 191.716, 'title': 'Global coronavirus impact analysis', 'summary': 'Discusses the global impact of coronavirus, including 5,735 reported deaths and over 100,000 infections worldwide as of march 15, 2021, with specific focus on the most affected countries like china, italy, iran, and the united states. additionally, it describes the use of algorithms to predict future cases and the utilization of csv files for analysis.', 'duration': 228.889, 'highlights': ['The total deaths reported globally stood at 5,735, while more than 100,000 people were infected worldwide as of March 15, 2021, according to the World Health Organization.', 'China, Italy, Iran, and the United States of America have the highest number of deaths reported till March 13, 2021.', 'China, Italy, Iran, and South Korea have the most number of patients who have recovered from coronavirus till March 13, 2021.', 'The chapter discusses the use of algorithms such as linear regression and support vector machines in Python to predict the upcoming cases for the next 10 days, utilizing data from January 22 to March 15, 2021.', 'The analysis utilizes three CSV files containing data related to confirmed cases, deaths, and recovered cases, and describes the implementation of code on Jupyter Notebook using libraries like numpy, pandas, matplotlib, and sklearn.']}], 'duration': 334.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw86391.jpg', 'highlights': ['The city of Wuhan in Hubei province, China, with 11 million residents, became the center of a pneumonia outbreak of unknown cause in December 2019.', 'The total deaths reported globally stood at 5,735, while more than 100,000 people were infected worldwide as of March 15, 2021, according to the World Health Organization.', 'The chapter discusses the use of algorithms such as linear regression and support vector machines in Python to predict the upcoming cases for the next 10 days, utilizing data from January 22 to March 15, 2021.', 'Older people and people with other medical conditions such as asthma, diabetes, or heart disease may be more vulnerable to becoming severely ill from COVID-19.', 'The analysis utilizes three CSV files containing data related to confirmed cases, deaths, and recovered cases, and describes the implementation of code on Jupyter Notebook using libraries like numpy, pandas, matplotlib, and sklearn.']}, {'end': 708.075, 'segs': [{'end': 493.528, 'src': 'heatmap', 'start': 460.354, 'weight': 0, 'content': [{'end': 466.437, 'text': "After that, I'm extracting all the column names from the confirmed cases data frame using the .", 'start': 460.354, 'duration': 6.083}, {'end': 467.137, 'text': 'keys function.', 'start': 466.437, 'duration': 0.7}, {'end': 468.978, 'text': 'Let me run it.', 'start': 468.337, 'duration': 0.641}, {'end': 475.34, 'text': 'So below you can see all the columns being displayed.', 'start': 472.179, 'duration': 3.161}, {'end': 487.065, 'text': "Then, we're extracting only the date columns that have information of confirmed cases, death cases, and recovered cases using the .", 'start': 479.181, 'duration': 7.884}, {'end': 487.725, 'text': 'loc function.', 'start': 487.065, 'duration': 0.66}, {'end': 493.528, 'text': 'The first parameter, that is, colon, tells you we need all the rows,', 'start': 488.926, 'duration': 4.602}], 'summary': 'Extracted column names and date columns with confirmed, death, and recovered cases from data frame.', 'duration': 71.289, 'max_score': 460.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw460354.jpg'}, {'end': 538.911, 'src': 'heatmap', 'start': 506.169, 'weight': 0.72, 'content': [{'end': 510.391, 'text': 'You can go ahead to see the outbreak cases using Confirmed.head.', 'start': 506.169, 'duration': 4.222}, {'end': 518.495, 'text': 'Here you can see it has only the date columns and the values for each of the dates.', 'start': 513.633, 'duration': 4.862}, {'end': 532.006, 'text': 'After that, in the next cell, I am finding the total confirmed cases, death cases and the recovered cases and appending them to 4 empty lists.', 'start': 523.118, 'duration': 8.888}, {'end': 538.911, 'text': 'We are also calculating the total mortality rate which is nothing but the total death sum divided by the total confirmed cases.', 'start': 532.766, 'duration': 6.145}], 'summary': 'The transcript demonstrates using code to analyze outbreak cases and calculate mortality rate.', 'duration': 32.742, 'max_score': 506.169, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw506169.jpg'}, {'end': 646.803, 'src': 'heatmap', 'start': 616.497, 'weight': 3, 'content': [{'end': 622.96, 'text': "In the next step, I'm converting all the dates and the cases in the form of a numpy array using np.array function.", 'start': 616.497, 'duration': 6.463}, {'end': 625.181, 'text': 'Let me run it.', 'start': 624.581, 'duration': 0.6}, {'end': 630.944, 'text': 'Now, I can display all the newly created arrays by passing the variable names.', 'start': 627.282, 'duration': 3.662}, {'end': 634.646, 'text': 'So if I run days since 22nd of Jan,', 'start': 632.044, 'duration': 2.602}, {'end': 642.401, 'text': 'you can see it will list you the total number of days that were present from the 22nd of Jan till the 15th of March.', 'start': 637.079, 'duration': 5.322}, {'end': 646.803, 'text': 'Now, if I run world cases,', 'start': 645.002, 'duration': 1.801}], 'summary': 'Converting dates and cases into numpy arrays, displaying total days from jan 22 to mar 15, and world cases.', 'duration': 92.397, 'max_score': 616.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw616497.jpg'}], 'start': 422.239, 'title': 'Covid-19 data analysis', 'summary': 'Covers displaying covid-19 datasets, extracting column names, and analyzing the data, including 1,67,449 confirmed cases, 6,440 reported deaths, and 76,034 recoveries, and predicting future cases using python and numpy arrays for the next 10 days.', 'chapters': [{'end': 518.495, 'start': 422.239, 'title': 'Displaying covid-19 datasets and extracting column names', 'summary': 'Covers the process of displaying top five rows from different covid-19 datasets and extracting column names, with emphasis on date columns and their respective values.', 'duration': 96.256, 'highlights': ['Using the .head function, the top five rows from the confirmed cases, deaths reported, and recovered cases datasets are displayed.', 'The .keys function is used to extract all the column names from the confirmed cases data frame, offering a comprehensive view of the available data.', 'Extracting date columns with information of confirmed, death, and recovered cases using the .loc function, providing a specific focus on the relevant data for analysis.']}, {'end': 708.075, 'start': 523.118, 'title': 'Covid-19 data analysis', 'summary': 'Involves analyzing covid-19 data, with total confirmed cases reaching 1,67,449, total reported deaths at 6,440, and total recoveries standing at 76,034, using python and numpy arrays to predict future cases for the next 10 days.', 'duration': 184.957, 'highlights': ['Total confirmed cases reached 1,67,449. The chapter involves analyzing COVID-19 data, with total confirmed cases reaching 1,67,449.', 'Total reported deaths at 6,440. Total reported deaths at 6,440.', 'Total recoveries standing at 76,034. Total recoveries standing at 76,034.', 'Using Python and numpy arrays to predict future cases for the next 10 days. Using Python and numpy arrays to predict future cases for the next 10 days.']}], 'duration': 285.836, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw422239.jpg', 'highlights': ['Using the .head function, the top five rows from the confirmed cases, deaths reported, and recovered cases datasets are displayed.', 'The .keys function is used to extract all the column names from the confirmed cases data frame, offering a comprehensive view of the available data.', 'Extracting date columns with information of confirmed, death, and recovered cases using the .loc function, providing a specific focus on the relevant data for analysis.', 'Total confirmed cases reached 1,67,449. The chapter involves analyzing COVID-19 data, with total confirmed cases reaching 1,67,449.', 'Total reported deaths at 6,440. Total reported deaths at 6,440.', 'Total recoveries standing at 76,034. Total recoveries standing at 76,034.', 'Using Python and numpy arrays to predict future cases for the next 10 days. Using Python and numpy arrays to predict future cases for the next 10 days.']}, {'end': 1203.939, 'segs': [{'end': 772.824, 'src': 'embed', 'start': 714.355, 'weight': 4, 'content': [{'end': 718.777, 'text': 'Next, we are converting all the integers into date-time values for better visualization.', 'start': 714.355, 'duration': 4.422}, {'end': 720.719, 'text': "So let's run it.", 'start': 719.978, 'duration': 0.741}, {'end': 726.522, 'text': 'Now, before we start building our code,', 'start': 724.501, 'duration': 2.021}, {'end': 732.345, 'text': "let's visualize the data using different charts and graphs and see what has been the impact of coronavirus so far.", 'start': 726.522, 'duration': 5.823}, {'end': 736.507, 'text': 'For visualization, we need the latest data of 15th of March.', 'start': 732.985, 'duration': 3.522}, {'end': 740.63, 'text': 'So I have extracted the last column values for all the three datasets.', 'start': 737.448, 'duration': 3.182}, {'end': 742.991, 'text': 'Now let me display these for you.', 'start': 741.57, 'duration': 1.421}, {'end': 750.257, 'text': "I'll run latest underscore confirmed.", 'start': 748.516, 'duration': 1.741}, {'end': 754.958, 'text': 'You can see it gives you all the values from the 15th of March.', 'start': 751.857, 'duration': 3.101}, {'end': 759.56, 'text': "Similarly, I'll go ahead and run the remaining.", 'start': 757.139, 'duration': 2.421}, {'end': 768.743, 'text': 'Latest deaths gives you the total deaths that were reported on 15th of March across various regions.', 'start': 761.7, 'duration': 7.043}, {'end': 772.824, 'text': 'Similarly, for the latest recoveries as well.', 'start': 770.263, 'duration': 2.561}], 'summary': 'Converting integers to date-time values for visualization, analyzing impact of coronavirus with latest data of 15th march.', 'duration': 58.469, 'max_score': 714.355, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw714355.jpg'}, {'end': 845.706, 'src': 'heatmap', 'start': 796.42, 'weight': 0.839, 'content': [{'end': 801.844, 'text': "So in the next cell, I'm basically calculating the total number of confirmed cases by each country.", 'start': 796.42, 'duration': 5.424}, {'end': 807.744, 'text': 'So, I have created two empty lists, namely country confirmed cases and no cases.', 'start': 802.881, 'duration': 4.863}, {'end': 814.048, 'text': 'If the cases are greater than zero, I am appending the values to country confirmed cases.', 'start': 810.085, 'duration': 3.963}, {'end': 816.329, 'text': 'Else, I am appending it to no cases.', 'start': 814.748, 'duration': 1.581}, {'end': 827.536, 'text': 'Next, I am finding the number of cases per country or region.', 'start': 824.434, 'duration': 3.102}, {'end': 836.938, 'text': 'So you can see that China has the highest number of cases followed by Italy and Iran.', 'start': 831.533, 'duration': 5.405}, {'end': 845.706, 'text': 'Now in this cell of code, we are finding the list of unique provinces using the .', 'start': 841.302, 'duration': 4.404}], 'summary': 'Calculating total confirmed cases by country, finding china has highest cases.', 'duration': 49.286, 'max_score': 796.42, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw796420.jpg'}, {'end': 836.938, 'src': 'embed', 'start': 802.881, 'weight': 0, 'content': [{'end': 807.744, 'text': 'So, I have created two empty lists, namely country confirmed cases and no cases.', 'start': 802.881, 'duration': 4.863}, {'end': 814.048, 'text': 'If the cases are greater than zero, I am appending the values to country confirmed cases.', 'start': 810.085, 'duration': 3.963}, {'end': 816.329, 'text': 'Else, I am appending it to no cases.', 'start': 814.748, 'duration': 1.581}, {'end': 827.536, 'text': 'Next, I am finding the number of cases per country or region.', 'start': 824.434, 'duration': 3.102}, {'end': 836.938, 'text': 'So you can see that China has the highest number of cases followed by Italy and Iran.', 'start': 831.533, 'duration': 5.405}], 'summary': 'Analyzing covid-19 cases: china has most, followed by italy and iran.', 'duration': 34.057, 'max_score': 802.881, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw802881.jpg'}, {'end': 1048.92, 'src': 'embed', 'start': 1020.144, 'weight': 1, 'content': [{'end': 1025.185, 'text': 'So the total cases outside mainland China is around 86, 446, while in China it is close to 81, 000.', 'start': 1020.144, 'duration': 5.041}, {'end': 1040.478, 'text': 'And the overall cases till 15th March stand at 1,67, 449.', 'start': 1025.185, 'duration': 15.293}, {'end': 1045.739, 'text': 'Another thing I have done is to show 10 countries that have the most number of confirmed cases,', 'start': 1040.478, 'duration': 5.261}, {'end': 1048.92, 'text': 'while the rest of the countries I have grouped into a category named Others.', 'start': 1045.739, 'duration': 3.181}], 'summary': 'Global covid-19 cases: 167,449 total, with 86,446 outside china and 81,000 in china.', 'duration': 28.776, 'max_score': 1020.144, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw1020144.jpg'}, {'end': 1210.121, 'src': 'embed', 'start': 1178.467, 'weight': 2, 'content': [{'end': 1182.391, 'text': 'As you can see, Italy has the highest number of cases followed by Iran and then South Korea.', 'start': 1178.467, 'duration': 3.924}, {'end': 1190.455, 'text': 'We also have others in this category which has pretty high number of confirmed cases.', 'start': 1185.253, 'duration': 5.202}, {'end': 1199.778, 'text': "So now that we have visualized our data, let's start by building our model using a support vector machine algorithm.", 'start': 1194.296, 'duration': 5.482}, {'end': 1203.939, 'text': 'Support vector machine uses different parameters to build a model.', 'start': 1200.678, 'duration': 3.261}, {'end': 1210.121, 'text': 'These parameters are Kernel, C, Gamma, Epsilon, Shrinking and SVM Grid.', 'start': 1204.419, 'duration': 5.702}], 'summary': 'Italy has the highest cases, followed by iran and south korea. support vector machine uses various parameters to build a model.', 'duration': 31.654, 'max_score': 1178.467, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw1178467.jpg'}], 'start': 714.355, 'title': 'Covid-19 data visualization and analysis', 'summary': 'Covers converting integers into date-time values for visualization, analyzing the impact of coronavirus using charts and graphs, extracting latest data for confirmed cases, deaths, and recoveries as of 15th of march. it also includes analyzing covid-19 data per country and province, handling missing values, visualizing the data through bar and pie charts, and building a model using support vector machine algorithm.', 'chapters': [{'end': 772.824, 'start': 714.355, 'title': 'Covid-19 data visualization', 'summary': 'Focuses on converting integers into date-time values for visualization, analyzing the impact of coronavirus using charts and graphs, and extracting the latest data for confirmed cases, deaths, and recoveries as of 15th of march.', 'duration': 58.469, 'highlights': ['Converting integers into date-time values for better visualization. Converting integers into date-time values to improve data visualization.', 'Analyzing the impact of coronavirus using different charts and graphs. Focusing on visualizing the impact of coronavirus through various charts and graphs.', 'Extracting the latest data for confirmed cases, deaths, and recoveries as of 15th of March. Extracting the latest data for confirmed cases, deaths, and recoveries as of 15th of March for analysis.']}, {'end': 1203.939, 'start': 776.187, 'title': 'Covid-19 data analysis', 'summary': 'Involves analyzing covid-19 data, including the total number of confirmed cases per country and province, handling missing values, visualizing the data through bar and pie charts, and building a model using support vector machine algorithm.', 'duration': 427.752, 'highlights': ['China has the highest number of COVID-19 cases followed by Italy and Iran. China has the highest number of cases followed by Italy and Iran.', 'The total cases outside mainland China is around 86,446, while in China it is close to 81,000, and the overall cases till 15th March stand at 167,449. The total cases outside mainland China is around 86,446, while in China it is close to 81,000, and the overall cases till 15th March stand at 167,449.', 'Italy has the highest number of cases followed by Iran and South Korea, with a grouping of other countries with a high number of confirmed cases. Italy has the highest number of cases followed by Iran and South Korea, with a grouping of other countries with a high number of confirmed cases.', 'Support vector machine algorithm is used to build a model for the COVID-19 data analysis. Support vector machine algorithm is used to build a model for the COVID-19 data analysis.']}], 'duration': 489.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw714355.jpg', 'highlights': ['China has the highest number of COVID-19 cases followed by Italy and Iran.', 'The total cases outside mainland China is around 86,446, while in China it is close to 81,000, and the overall cases till 15th March stand at 167,449.', 'Italy has the highest number of cases followed by Iran and South Korea, with a grouping of other countries with a high number of confirmed cases.', 'Support vector machine algorithm is used to build a model for the COVID-19 data analysis.', 'Converting integers into date-time values for better visualization.', 'Analyzing the impact of coronavirus using different charts and graphs.', 'Extracting the latest data for confirmed cases, deaths, and recoveries as of 15th of March.']}, {'end': 1836.751, 'segs': [{'end': 1231.697, 'src': 'embed', 'start': 1204.419, 'weight': 3, 'content': [{'end': 1210.121, 'text': 'These parameters are Kernel, C, Gamma, Epsilon, Shrinking and SVM Grid.', 'start': 1204.419, 'duration': 5.702}, {'end': 1215.326, 'text': 'Kernel specifies the kernel type to be used in the algorithm.', 'start': 1211.684, 'duration': 3.642}, {'end': 1221.471, 'text': 'It must be one of linear, poly, RBF, sigmoid, precomputed, or callable.', 'start': 1215.887, 'duration': 5.584}, {'end': 1224.853, 'text': 'If nothing is given, RBF will be used.', 'start': 1222.091, 'duration': 2.762}, {'end': 1227.735, 'text': 'C is a regularization parameter.', 'start': 1225.453, 'duration': 2.282}, {'end': 1231.697, 'text': 'Gamma is the kernel coefficient of RBF, poly, and sigmoid.', 'start': 1228.235, 'duration': 3.462}], 'summary': 'Parameters include kernel, c, gamma, epsilon, shrinking, and svm grid for algorithm optimization.', 'duration': 27.278, 'max_score': 1204.419, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw1204419.jpg'}, {'end': 1288.079, 'src': 'embed', 'start': 1252.152, 'weight': 2, 'content': [{'end': 1260.458, 'text': 'Finally, we are fitting the data using svmSearch.fit function and passing xTrainConfirmed and yTrainConfirmed.', 'start': 1252.152, 'duration': 8.306}, {'end': 1262.939, 'text': 'Let me now run it.', 'start': 1262.199, 'duration': 0.74}, {'end': 1266.302, 'text': 'This might take some time.', 'start': 1265.281, 'duration': 1.021}, {'end': 1273.543, 'text': 'Since we have an epoch or an iteration defined, here we have given 40 iterations.', 'start': 1268.177, 'duration': 5.366}, {'end': 1281.872, 'text': 'Now we have successfully run that particular cell.', 'start': 1279.75, 'duration': 2.122}, {'end': 1288.079, 'text': "Now let's find the best parameters for the model.", 'start': 1285.256, 'duration': 2.823}], 'summary': 'Using svmsearch.fit function with 40 iterations to find best parameters for the model.', 'duration': 35.927, 'max_score': 1252.152, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw1252152.jpg'}, {'end': 1568.903, 'src': 'embed', 'start': 1535.614, 'weight': 0, 'content': [{'end': 1540.117, 'text': "Now, I'll go ahead and predict the cases for the next 10 days using linear regression.", 'start': 1535.614, 'duration': 4.503}, {'end': 1542.158, 'text': 'Here, you can see the predictions.', 'start': 1540.517, 'duration': 1.641}, {'end': 1548.821, 'text': 'It is different from that of the SVM model which had a higher number of cases.', 'start': 1545.379, 'duration': 3.442}, {'end': 1558.541, 'text': "Now we'll plot the total number of deaths over time.", 'start': 1555.72, 'duration': 2.821}, {'end': 1560.481, 'text': 'Let me run it.', 'start': 1559.861, 'duration': 0.62}, {'end': 1568.903, 'text': 'So as you can see from the graph, on the x-axis, we have time that is the number of days.', 'start': 1561.962, 'duration': 6.941}], 'summary': 'Using linear regression, the model predicts lower cases than the svm model, with a focus on plotting total deaths over time.', 'duration': 33.289, 'max_score': 1535.614, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw1535614.jpg'}, {'end': 1675.976, 'src': 'embed', 'start': 1650.365, 'weight': 1, 'content': [{'end': 1654.989, 'text': 'And finally, we are checking the total number of coronavirus deaths versus recoveries in a single plot.', 'start': 1650.365, 'duration': 4.624}, {'end': 1660.993, 'text': 'So here is the graph.', 'start': 1660.152, 'duration': 0.841}, {'end': 1669.76, 'text': 'The x-axis has the number of recoveries, while the y-axis has the number of deaths reported so far.', 'start': 1664.636, 'duration': 5.124}, {'end': 1675.976, 'text': 'With that, we have come to the end of the demo to analyze coronavirus outbreak globally.', 'start': 1671.994, 'duration': 3.982}], 'summary': 'Comparing coronavirus deaths to recoveries in a single plot.', 'duration': 25.611, 'max_score': 1650.365, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw1650365.jpg'}], 'start': 1204.419, 'title': 'Svm parameters and coronavirus prediction analysis', 'summary': 'Covers svm parameters like kernel, c, gamma, epsilon, shrinking, and svm grid, and model building using randomizedsearchcv function. it also demonstrates the use of svm and linear regression models to predict coronavirus cases, deaths, and recoveries, along with visualizing the data and providing safety precautions.', 'chapters': [{'end': 1251.811, 'start': 1204.419, 'title': 'Svm parameters and model building', 'summary': 'Discusses the parameters kernel, c, gamma, epsilon, shrinking and svm grid, and the process of model building using randomizedsearchcv function.', 'duration': 47.392, 'highlights': ['The parameters Kernel, C, Gamma, Epsilon, Shrinking and SVM Grid are discussed. The chapter covers the various parameters such as Kernel, C, Gamma, Epsilon, Shrinking, and SVM Grid used in building the model.', 'The process of model building using randomizedSearchCv function is explained. The model building process is elucidated, involving the use of the randomizedSearchCv function to build a model by passing the necessary parameters.']}, {'end': 1836.751, 'start': 1252.152, 'title': 'Coronavirus prediction analysis', 'summary': 'Demonstrates the use of svm and linear regression models to predict coronavirus cases, deaths, and recoveries, visualizing the data and answering related questions, concluding with important safety precautions.', 'duration': 584.599, 'highlights': ['The chapter demonstrates the use of SVM and linear regression models to predict coronavirus cases, deaths, and recoveries The chapter covers the use of SVM and linear regression models to predict coronavirus cases, deaths, and recoveries, providing insights into the predicted number of cases for the next 10 days using both models.', 'Visualizing the data and answering related questions The chapter visualizes the total number of coronavirus cases, deaths, recoveries, and mortality rate over time, and addresses questions related to coronavirus spread, protection, and the use of time series analysis and algorithms for predictions.', 'Concluding with important safety precautions The chapter concludes with important safety precautions to prevent the spread of coronavirus, emphasizing measures such as hand hygiene, social distancing, wearing face masks, and seeking medical care early if experiencing symptoms.']}], 'duration': 632.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/sHWKN5dakPw/pics/sHWKN5dakPw1204419.jpg', 'highlights': ['The chapter covers the use of SVM and linear regression models to predict coronavirus cases, deaths, and recoveries, providing insights into the predicted number of cases for the next 10 days using both models.', 'The chapter visualizes the total number of coronavirus cases, deaths, recoveries, and mortality rate over time, and addresses questions related to coronavirus spread, protection, and the use of time series analysis and algorithms for predictions.', 'The process of model building using randomizedSearchCv function is explained, involving the use of the randomizedSearchCv function to build a model by passing the necessary parameters.', 'The parameters Kernel, C, Gamma, Epsilon, Shrinking and SVM Grid are discussed, covering the various parameters used in building the model.']}], 'highlights': ['Utilizes machine learning algorithms to predict upcoming confirmed cases for the next 10 days.', 'Covers the total cases, deaths, and recoveries globally.', 'Emphasizes the importance of safety measures and precautions to stay safe from Coronavirus.', 'Provides an overview of what Coronavirus is, its emergence, symptoms, and the outbreak analysis across different regions.', 'The city of Wuhan in Hubei province, China, with 11 million residents, became the center of a pneumonia outbreak of unknown cause in December 2019.', 'The total deaths reported globally stood at 5,735, while more than 100,000 people were infected worldwide as of March 15, 2021, according to the World Health Organization.', 'The chapter discusses the use of algorithms such as linear regression and support vector machines in Python to predict the upcoming cases for the next 10 days, utilizing data from January 22 to March 15, 2021.', 'Older people and people with other medical conditions such as asthma, diabetes, or heart disease may be more vulnerable to becoming severely ill from COVID-19.', 'The analysis utilizes three CSV files containing data related to confirmed cases, deaths, and recovered cases, and describes the implementation of code on Jupyter Notebook using libraries like numpy, pandas, matplotlib, and sklearn.', 'Using the .head function, the top five rows from the confirmed cases, deaths reported, and recovered cases datasets are displayed.', 'The .keys function is used to extract all the column names from the confirmed cases data frame, offering a comprehensive view of the available data.', 'Extracting date columns with information of confirmed, death, and recovered cases using the .loc function, providing a specific focus on the relevant data for analysis.', 'Total confirmed cases reached 1,67,449. The chapter involves analyzing COVID-19 data, with total confirmed cases reaching 1,67,449.', 'Total reported deaths at 6,440. Total reported deaths at 6,440.', 'Total recoveries standing at 76,034. Total recoveries standing at 76,034.', 'Using Python and numpy arrays to predict future cases for the next 10 days. Using Python and numpy arrays to predict future cases for the next 10 days.', 'China has the highest number of COVID-19 cases followed by Italy and Iran.', 'The total cases outside mainland China is around 86,446, while in China it is close to 81,000, and the overall cases till 15th March stand at 167,449.', 'Italy has the highest number of cases followed by Iran and South Korea, with a grouping of other countries with a high number of confirmed cases.', 'Support vector machine algorithm is used to build a model for the COVID-19 data analysis.', 'Converting integers into date-time values for better visualization.', 'Analyzing the impact of coronavirus using different charts and graphs.', 'Extracting the latest data for confirmed cases, deaths, and recoveries as of 15th of March.', 'The chapter covers the use of SVM and linear regression models to predict coronavirus cases, deaths, and recoveries, providing insights into the predicted number of cases for the next 10 days using both models.', 'The chapter visualizes the total number of coronavirus cases, deaths, recoveries, and mortality rate over time, and addresses questions related to coronavirus spread, protection, and the use of time series analysis and algorithms for predictions.', 'The process of model building using randomizedSearchCv function is explained, involving the use of the randomizedSearchCv function to build a model by passing the necessary parameters.', 'The parameters Kernel, C, Gamma, Epsilon, Shrinking and SVM Grid are discussed, covering the various parameters used in building the model.']}