Full Text: 1 Introduction The novel coronavirus is responsible for the pandemic that started in December 2019 form Wuhan, which is the capital city of Hubei province in China and has rapidly prevailed over the world (Chimmula & Zhang, 2020). The World Health Organization (WHO) declared coronavirus as a global pandemic on 11 March 2020. Many countries around the world are trying to restrict the further spreading of this virus (Tomar & Gupta, 2020). COVID-19 is a third Coronavirus outbreak globally and earned the 3^rd highest death rate in the history of pandemics (Bilal et al., 2020). The world's economy has stood on the verge of destruction; even the world's largest economies could not control the virus at this time. So far, there has been no treatment of the disease.  The COVID-19 case in Pakistan was reported in Karachi, Sindh province, on 26^th February 2020 and was confirmed by the country’s Ministry of Health (Waris et al., 2020). Multiple linear regression (Rath et al., 2020), linear regression (Ghosal et al., 2020), advance integrated moving average (Singh et al., 2020), autoregressive integrated moving average (ARIMA)(Ilie et al., 2020), data-based forecasting (Anastassopoulou et al., 2020), and short-term and long-term prediction models are successfully utilized for gauging the impact of COVID-19 spread in different countries of the world (Ahmar & del Val, 2020; Seyed Mohammad Ayyoubzadeh et al. 2020). The common purpose is to identify and plan the peak time, scale of spread, and allocate resources for prevention, control, and cure of COVID-19. Some prediction tools may help handle the crisis in the best administrative way. As recently, we have used the SIR model to predict the expected number of infected cases and deaths (Ali Shah et al., 2020). Here, we propose a linear regression method for the prediction of the number of COVID-19 cases during the upcoming days. Based on previous data, Shah et al. (2020) have used the Regression model to predict COVID-19 in Pakistan by using the said model. Here, we will predict the number of infected and diagnosed cases and deaths in September 2020. A different Beta and Gamma values are used to predict the COVID-19. 1.1. Related work Waris et al. (2020) have compared Pakistan with other countries having a strong economy. They analyzed the number of available hospitals for COVID-19, inadequate health facilities in Pakistan, and also discussed quarantine facilities in the country. The authors have discussed the complete scenario of Pakistan’s hospital capability and economic condition. Yang et al. (2020) used artificial intelligence by using the trained SARS 2003 data to predict current COVID-19. The authors have also used the susceptible, exposed, infected, and recovered (SEIR) model to derive the COVID-19 curve and to find peaks and magnitude of the spread of COVID-19. The study has predicted that the peak of COVID-19 in China will be in late February and a gradual decline by the end of April (Yang et al., 2020). A data-driven long-short term memory (LSTM) has been used to predict COVID-19 in India for the next month (May-2020).The number of positive cases and recovery rates per day is estimated, utilizing LSTM methods. The study evaluated the increase and deceased of cases and predicts that the spread of the virus could be reduced if preventative measures, like lockdown and social distance, be exercised (Tomar & Gupta, 2020). Chimmula & Zhang (2020) used LSTM by employing available data and to build a time series, sequential model, and to predict and find peak COVID-19 in Canada. The study also predicted that the current pandemic in Canada would end within three months (first week of August 2020), and a small number of infection clusters may appear until the end of 2020. The study also has compared the transmission rate in Canada with other countries, like Italy and America (Chimmula & Zhang, 2020). Ayyoubzadeh et al. (2020) collected data from Google Trend and used LSTM and linear regression to estimate the number of positive cases of COVID-19 in Iran. The authors have claimed that the prediction could be very helpful to policymakers and the health care department to allocate health resources accordingly. Bandyopadhyay & Dutta (2020) have utilized deep learning and neural networks long short-term memory and gated recurrent unit (GRU) to train the model of neural network (NN). The combination of n of LSTM and GRU provides better prediction of COVID-19. Supplementary rechecking of frontline medical workers could be a promising strategy. It is crucial to improve the quality of results by implementing the accurately designed and optimized detection process (Bandyopadhyay & Dutta, 2020). 2 Materials and Methods All the global data is taken from the WHO daily situation report (WHO 2020), and Pakistani data is obtained from the official page of the government of Pakistan for COVID-19 (Government of Pakistan 2020). The data are downloaded in the CVS file and Jupiter notebook and analyzed with the Python 3.5 software uploaded. To obtain information related to infections and death cases by country, many researchers often use linear regression to estimate the precise average numerical values Y for a known value of X by the application of a regression line. If the y-intercept and slope of the regression line given, the value of X can be driven. Through these x values, values of Y can be estimated by calculation, which can be considered as estimated values. Linear regression analyses were used to establish a linear regression model that could successfully facilitate the average numerical values prediction of COVID-19 future in Pakistan. The cumulative figure of COVID-19 cases in Pakistan as the dependent variable and plotting the regional distribution of the number of cases. 3 Results and Discussion Figure 1 explains the machine learning classification. Machine learning has three main types: reinforcement learning, unsupervised learning, and supervised learning. The proposed regression analysis in current manuscript falls into the category of supervised learning, which is an approach of creating artificial intelligence, where the program is given labelled input data and the expected output results. Supervised learning allows you to collect data or produce a data output from the previous experience, and the linear regression model predicts a single output value using training data. Linear Regression is not just a machine learning algorithm; it plays a considerable role in statistics. In supervised learning, each input is associated with a target label. The task of the model is to understand the pattern and find the best fit line that covers each input target pair. Through the model, the value of y is calculated by the calculations through linear combinations of input values of variables x. This hypothesis is written as the following equation. hθ (x)=θ0α0+θ1α1+θ2α2+…+θnαn (1) Where h is calculated value obtained from the dependent variable for the known value of the independent variable (x),θ0  is the intercept, the predicted value of h when the α1  is 0. Θ  here store the coefficients/weights of the input features x  and is of the same dimensionality as x. Note that to add support for a constant term in our model, we prefix the vector x  with 1. With single input and output variable, the method is called simple linear regression Figure 2 showed the number of deaths of top countries; the number of dead is very high in the USA, Brazil, and India as compared to other countries. Figure 3 shows cases and prediction of COVID-19 for 14 days in Pakistan by using Linear Regression; regression means output is a continuous variable. This Figure showed that COVID-19 cases decreasing after hit peak level next two weeks’ cases of COVID-19 reducing in Pakistan. 3.1 Confirmed Cases Table 1 shows the number of COVID-19 confirmed cases in the top 30 countries. Confirmed cases in Pakistan are gradually decreasing day by day, but cases still increased in the USA, Brazil, and India in the top 30 countries confirmed cases, Pakistan is in 17^th number  Table 2 exhibits the number of death due to COVID-19 in the top 30 countries. United State, Brazil, and Mexico are the top three countries where the number of death due to COVID-19 is very high. In Pakistan, more than six thousand deaths due to COVID-19 are reported.         
Table 3 illustrates the state of daily confirmed cases in Pakistan. From this table, it can be seen that in the beginning period, there were no cases reported in Pakistan. In Pakistan, the first case was registered on 26 February 2020, and then gradually, it increased, and then the peak of Covid-19 was observed in mid of July month. Whereas, from August 2020, a gradually decreasing trend in cases was recorded. 3.3 Coefficients of regression polynomials Regression coefficients are estimated values of undetermined parameters, and they express the relationship of predictor variable with a response. For linear regression, values that multiply the predictor are known as coefficients. For example, in equation y= 5X+3, the coefficient is +5   predictor is X while 3 is constant. There is another form of linear regression known as polynomial regression, in which the relationship between dependent and independent variables is reproduced as the specific degree polynomial like nth degree. The relationship adjusted by polynomial regression between dependents variable y and independent variables x is adjusted in a nonlinear relationship. [0.00000000e+00 -2.96412582e+00 -5.40109773e+01 2.70260730e+00 -5.11969748e-02 4.53852559e-04 -1.85262628e-06 2.81785306e-09] Results of current study indicate variance percentages of the dependent variable that the independent variables explain collectively. R² measures the strength of the relationship between the models and the dependent variable on a convenient 0 – 100% scale. The value of R² = 0.9987181307458601 in this case. 4 Conclusion and future work In this research analysis, the linear regression model is commonly used to has an estimation of the expected number of patients and predict the expected peaks of diseases. Similarly, we have used the linear regression model for the prediction of the number of COVID-19 cases in Pakistan. If the same situation prevails, according to the     Table 3 The data of confirmed cases in Pakistan from 22^nd January 202 till 04^th September 2020   Date Cases Date Cases Date Cases Date Cases 22-Jan 0 21-Mar 776 19-May 45898 17-Jul 261917 23-Jan 0 22-Mar 875 20-May 48091 18-Jul 263496 24-Jan 0 23-Mar 972 21-May 50694 19-Jul 265083 25-Jan 0 24-Mar 1063 22-May 52437 20-Jul 266096 26-Jan 0 25-Mar 1201 23-May 54601 21-Jul 267428 27-Jan 0 26-Mar 1373 24-May 56349 22-Jul 269191 28-Jan 0 27-Mar 1495 25-May 57705 23-Jul 270400 29-Jan 0 28-Mar 1597 26-May 59151 24-Jul 271887 30-Jan 0 29-Mar 1717 27-May 61227 25-Jul 273113 31-Jan 0 30-Mar 1938 28-May 64028 26-Jul 273113 01-Feb 0 31-Mar 2118 29-May 66457 27-Jul 274289 02-Feb 0 01-Apr 2421 30-May 69496 28-Jul 275225 03-Feb 0 02-Apr 2686 31-May 72460 29-Jul 276288 04-Feb 0 03-Apr 2818 01-Jun 76398 30-Jul 277402 05-Feb 0 04-Apr 3157 02-Jun 80463 31-Jul 278305 06-Feb 0 05-Apr 3766 03-Jun 85264 01-Aug 278305 07-Feb 0 06-Apr 4035 04-Jun 89249 02-Aug 279699 08-Feb 0 07-Apr 4263 05-Jun 93983 03-Aug 280461 09-Feb 0 08-Apr 4489 06-Jun 98943 04-Aug 280461 10-Feb 0 09-Apr 4695 07-Jun 103671 05-Aug 281136 11-Feb 0 10-Apr 5011 08-Jun 108317 06-Aug 281863 12-Feb 0 11-Apr 5230 09-Jun 113702 07-Aug 282645 13-Feb 0 12-Apr 5496 10-Jun 119536 08-Aug 283487 14-Feb 0 13-Apr 5837 11-Jun 125933 09-Aug 284121 15-Feb 0 14-Apr 6383 12-Jun 125933 10-Aug 284660 16-Feb 0 15-Apr 6919 13-Jun 132405 11-Aug 285191 17-Feb 0 16-Apr 7025 14-Jun 144478 12-Aug 285921 18-Feb 0 17-Apr 7638 15-Jun 148921 13-Aug 286674 19-Feb 0 18-Apr 8348 16-Jun 154760 14-Aug 287300 20-Feb 0 19-Apr 8418 17-Jun 160118 15-Aug 288047 21-Feb 0 20-Apr 9565 18-Jun 165062 16-Aug 289215 22-Feb 0 21-Apr 10076 19-Jun 171666 17-Aug 289515 23-Feb 0 22-Apr 11155 20-Jun 176617 18-Aug 289832 24-Feb 0 23-Apr 11940 21-Jun 181088 19-Aug 290445 25-Feb 0 24-Apr 12723 22-Jun 185034 20-Aug 290958 26-Feb 2 25-Apr 13328 23-Jun 188926 21-Aug 291588 27-Feb 2 26-Apr 13915 24-Jun 192970 22-Aug 292174 28-Feb 4 27-Apr 14612 25-Jun 195745 23-Aug 293261 29-Feb 4 28-Apr 15525 26-Jun 198883 24-Aug 293461