Trending February 2024 # Robinhood Defends Stock Block # Suggested March 2024 # Top 7 Popular

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Robinhood defends stock block – Updates on GameStop, AMC & other trades reopening

Robinhood has defended its decision to block users from buying GameStock, AMC, BlackBerry and other stock today, a controversial move that the trading platform said it plans to ease from Friday. The limit, applied earlier on Thursday, meant that Robinhood users were unable to buy more of the highly-volatile shares – which included $AMC, $KOSS, $NOK, #GME, and others – but could still sell them from their portfolio.

It followed a rollercoaster ride for a handful of otherwise uninspiring stock over the past few days. Fueled by Reddit – and the r/WallStreetBets forum specifically – investors drove the price of GameStock and other shares up exponentially. In the process, they pushed a hedge fund with a short position in the retailer nearly into bankruptcy.

Similar moves were made around Bed Bath & Beyond, American Airlines, BlackBerry, Castor Maritime, and others, with the shortlist selected primarily because they were stock hedge funds short sellers had identified. On Wednesday, most of the popular trading platforms and apps, including TD-Ameritrade, Charles Schwab, E-Trade, and Fidelty, suffered some form of downtime or system instability, with traders unable to access their accounts or complete trades.

Today, though, the situation escalated. Citing “significant market volatility,” Robinhood decided to restrict transactions for some of the targeted stock. That list included $AAL, $AMC, $BB, $BBBY, $CTRM, $EXPR, $GME, $KOSS, $NAKD, $NOK, $SNDL, $TR, and $TRVG. Individual traders found themselves unable to buy, only sell, though the bigger players in the finance industry saw no such limitations.

It was enough to catch the attention of lawmakers, with Alexandria Ocasio-Cortez among others calling for Robinhood to be investigated by the US government’s Financial Services Committee. Now, Robinhood has defended its decision.

“Amid this week’s extraordinary circumstances in the market, we made a tough decision today to temporarily limit buying for certain securities,” the company wrote in a blog post. “As a brokerage firm, we have many financial requirements, including SEC net capital obligations and clearinghouse deposits. Some of these requirements fluctuate based on volatility in the markets and can be substantial in the current environment. These requirements exist to protect investors and the markets and we take our responsibilities to comply with them seriously, including through the measures we have taken today.”

For investors hoping to continue using their Robinhood accounts to trade those particular stocks, they’ll have to wait until the market opens on Friday. “Starting tomorrow, we plan to allow limited buys of these securities,” Robinhood confirmed. “We’ll continue to monitor the situation and may make adjustments as needed.”

As for accusations that Robinhood was working on behalf of the big hedge funds, or other conspiracy theories that have unsurprisingly popped up in the aftermath of the “meme trading” phenomenon, the company denies that. “To be clear, this was a risk-management decision, and was not made on the direction of the market makers we route to,” Robinhood insists. “We’re beginning to open up trading for some of these securities in a responsible manner.”

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What Exactly Is Stock Android?

Stock Android, also called “vanilla” Android, is the most basic version of the Android operating system available. Stock Android devices run the core kernel of Android as designed and developed by Google. It’s typically distinguished by the lack of carrier-installed programs. For example, a stock Android phone on Verizon’s network will not include Verizon-installed apps. For many users removing this bloatware is a huge incentive, allowing them to regain control over their devices.

Stock Android is also typically not reskinned or redesigned by the phone’s manufacturer (OEM). For example, Sony runs a close-to-stock version of Android on most of their phones but reskins the OS to meet their design specifications. For an example of a heavily-reskinned OS, check out Samsung’s modified version of Android, called TouchWiz, or HTC’s own customized version of Android. It’s a huge departure from stock Android’s design language which may or may not be a turnoff.

Because Android is open source, OEMs and carriers can take huge liberties with core functionalities of the operating system. This is both a strength and a weakness. Android’s customization is a huge appeal for many users. Cheaply-licensed versions of the OS allow for cheap, powerful handsets. But it also means that carriers and manufacturers can make decisions that consumers don’t like and have no power to change. Stock Android frees you from all of that. You can experience Android the way it was “meant to be” without carrier or OEM interference.

Benefits of Stock Android

Running a stock or close-to-stock version of Android can accrue a number of benefits to the user.

Reclaimed storage space: stock Android removes carrier-installed apps from your device. As a result, you’ll reclaim some storage space for your own use.

Greater control over your device: removing bloatware provides greater control over what is on your device and what your device does.

Improved performance (maybe): carriers don’t always do a great job writing their apps. Poorly-written software slows your phone down and drains your battery.

Faster OS updates: most stock Android phones will get access to Android updates more quickly than their carrier counterparts. Mobile carriers have developed a well-deserved reputation for slow-rolling Android phone owners on OS updates. This often means long delays between official release of a new Android version and availability on carrier devices. Stock Android users don’t need to wait for carriers to modify and reskin Android for their phones. As a result the update can often be installed as soon as its available. This is especially true of Google’s Nexus devices and Google Play edition handsets, which are sold around the premise of immediate updates to their stock Android OS.

Consistent design: designers build stock Android to be visually consistent. When carriers reskin or modify a comportment of the operating system, it often breaks that consistent design language. This can lead to a cluttered, messy feel.

Greater customization: it’s often easier for users running stock Android to root and customize their devices. Without carrier-imposed hurdles to leap, developers and modders have a little more freedom. One of Android’s biggest appeals is deep capability for customization, especially when compared to iOS’ locked-down ecosystem. Stock Android devices allow for more of that, and ROMs and launchers can be more powerful and easier to install.

Conclusion

If you want more control over your device’s functionality and update cycle, stock Android is amazing. You might forgo some unique carrier features or designs, but on balance you’ll be getting a phone with more storage space and faster operation.

Alexander Fox

Alexander Fox is a tech and science writer based in Philadelphia, PA with one cat, three Macs and more USB cables than he could ever use.

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Stock Exchange Daily Official List (Sedol)

Stock Exchange Daily Official List (SEDOL)

A series of unique characters to each London Stock Exchange-based security that is used to identify publicly-traded stocks securely

Written by

CFI Team

Published November 12, 2023

Updated June 28, 2023

What is the Stock Exchange Daily Official List (SEDOL)?

The stock exchange daily official list (SEDOL) is a seven-digit code used to identify all securities listed and trading on the United Kingdom securities market. Companies and issuers use SEDOL to identify assets, such as investment trusts, insurance-based securities, and other common stock forms.

SEDOL is not to be confused with the CUSIP number, which is also a unique identification number assigned to all the U.S. traded stocks and registered bonds but offered by the Committee on Uniform Securities Identification Procedures. The code also serves as a global security identifier, enhances the Systematic Transfer Plan (STP) efficiencies, and reduces cross-border trade failure costs.

Summary

The stock exchange daily official list (SEDOL) is a series of unique characters to each London Stock Exchange-based security that is used to identify publicly traded stocks securely.

The U.K. and U.S.-issued securities rely on the efficiency and uniqueness of SEDOL codes to enhance seamless and correct trading.

SEDOL codes are essential in the modern global marketplace as a secure and identifiable approach to track a stock.

The Context of Assigning SEDOL Codes

Several reasons underlie the issuance of new SEDOL codes. They include reclassifications, change of a company’s name, corporate merger, assignment of new International Securities Identification Number (ISIN) numbers, and corporate headquarter changes. After the changes made on January 26, 2004, new SEDOLS codes are issued sequentially.

Structure of the Stock Exchange Daily Official List

At each character position, vowels are not used, and SEDOL codes begin with numbers followed by letters. The alphanumeric code is represented by the first six characters, while the last character is referred to as the trailing check digit. Notably, SEDOL codes are only allowed to include letters from B to Z and numerals, starting from 0 to 9 within the alphanumeric part.

Until January 26, 2004, issued SEDOL codes contained numeric characters alone. However, the codes that came after the said date are issued sequentially, beginning with B000009 for both numbers and letters. While vowels are never used to represent a character position, numbers are always ordered such that they precede letters. It means that all SEDOL codes issued after the mentioned date start with a letter. Ranges with 9 as the starting character are reserved for user allocation.

A common method to ascertain the SEDOL code is to determine if the weighted sum of all the characters is a multiple of 10. The code includes a check digit that verifies its correctness. For the verification process, different numbers are assigned to letters. Each letter is equivalent to the number that matches its position in the alphabet, plus nine. For example, K would be equivalent to 20 (9+11).

Impacts of SEDOL

The London Stock Exchange (LSE) recognizes SEDOL as an important market-level security identifier and a global security tool. As a result, it minimizes the costs sustained during the cross-border trade failure and enhances trade efficiency and security transactions. Through SEDOL codes, the United Kingdom’s exchanges offer high service levels by curbing such failures and streamlining the transaction processing cycles.

Particular to such regard are the features of SEDOL codes. First, they are unique, and the identification of stocks becomes seamless through the assigned country-level numbers. Each country is usually assigned one number. In the same vein, SEDOL codes are prompt, abridging the issuance processing time frames.

Another feature of SEDOL codes that enhances the trade process is commonality. SEDOL codes are extended to every asset class, and every listed or unlisted security in a certain country is allocated codes, reducing the need for multiple identifiers.

Examples of SEDOL Codes

Traders can determine whether the assigned SEDOL code is correct by simply multiplying each digit by its assigned weight and summing them up. In such a case, (0) + (5×3) + (4×1) + (0) + (5×3) + (2×9) +(1×8) = 60. The SEDOL code is correct because 60 is a multiple of 10.

Special Considerations

In today’s global marketplace, trading assets need a secure and identifiable method to keep track. It makes SEDOL codes relevant for stocks, bonds, mutual funds, and hedge funds. The U.K. and U.S.-based securities contain SEDOL codes based on their efficiency and uniqueness in trailing assets, not to mention their ability to reduce confusion among investors and ensure they purchase the right stocks.

The SEDOL Masterfile service is an example of a service company that provides traders with information regarding securities and financial instruments. Various organizations use comprehensive and global reference data to identify business processes such as price feeds processing, portfolio valuation, and trade execution.

Additional Resources

Bajaj Finance Stock Price Prediction In Python

Introduction

The motivation behind This article comes from the combination of passion(about stock markets) and a love for algorithms. Who doesn’t love to make money and if you know the algorithms that you have learned will or can help you make money(not always), why not explore it.

Business Usage: The particular problem pertains to forecasting, forecasting can be of sales, stocks, profits, and demand for new products. Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Businesses utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time.

Forecasting has got its own set of challenges like which variables affect a certain prediction, can there be an unforeseen circumstance(like COVID) that will render my predictions inaccurate. So forecasting is easier said than done, there is less than a % chance that the observed value will be equal to the predicted value. In that case, we try to minimize the difference between actual and predicted values.

The Data

I have downloaded the data of Bajaj Finance stock price online. I have taken the data from 1st Jan 2024 to 31st Dec 2023.1st Jan 2023 to 31st Dec 2023, these dates have been taken for prediction/forecasting. 4 years of data have been taken as training data and 1 year as test data. I have taken an open price for prediction.

EDA :

The only EDA that I have done is known as seasonal decompose, to break it up into Observed, trend, seasonality, and residual components

The stock price always tends to show an additive trend because seasonality does not increase with time.

Now when we decompose a time series, We get:

Observed: The average value in the series.

Trend: The increasing or decreasing value in the series, the trend is usually a long term pattern, it spans for over more than a year.

Seasonality: The repeating short-term cycle in the series. In time-series data, seasonality is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. …Seasonal fluctuations in a time series can be contrasted with cyclical patterns.

Residual: The component which is left after level, trend, and seasonality has been taken into consideration.

Model Building:

ARIMA(AutoRegressive Integrated Moving Average): This is one of the easiest and effective machine learning algorithms for performing time series forecasting. ARIMA consists of 3 parts, Auto-Regressive(p), Integrated or differencing(d), and Moving Average(q).

Auto-Regressive: This part deals with the fact that the current value of the time series is dependent on its previous lagged values or we can say that the current value of the time series is a weighted average of its lagged value. It is denoted by p, so if p=2, it means the current value is dependent upon the previous two of its lagged values. Order p is the lag value after which the PACF plot crosses the upper confidence interval for the first time. We use the PACF(Partial autocorrelation function)to find the p values. The reason we use the PACF plot is that it only shows residuals of components that are not explained by earlier lags. If we use ACF in place of PACF, it shows a correlation with lags that are far in fast, hence we will use the PACF plot.

Integrated(d): One of the important features of the ARIMA model is that the time series used for modeling should be stationary. By stationarity I mean, the statistical property of time series should remain constant over time, meaning it should have a constant mean and variance. The trend and seasonality will affect the value of the time series at different times.

How to check for stationarity?

One simple technique is to plot and check.

We have statistical tests like ADF tests(Augmented Dickey-Fuller Tests).ADF tests the null hypothesis that a unit root is present in the sample. The alternative hypothesis is different depending on which version of the test is used but is usually stationarity or trend-stationarity. It is an augmented version of the Dickey-Fuller test for a larger and more complicated set of time series models. The augmented Dickey-Fuller (ADF) statistic, used in the test, is a negative number. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at some level of chúng tôi if value p value<alpha(significance) ,we will reject the null hypothesis,i.e presence of unit root.

Stationarity test

How to make time-series stationarity?

Differencing: The order of differencing (q) refers to the no of times you difference the time series to make it stationary. By difference, I mean you subtract the current value from the previous value. After Differencing you again perform the ADF test to check whether the time series has become stationary or you can plot and check.

Log Transformation: We can make the time series stationary by doing a log transformation of the variables. We can use this if the time series is diverging.

Moving Average(q)

In moving average the current value of time series is a linear combination of past errors. We assume the errors to be independently distributed with the normal distribution. Order q of the MA process is obtained from the ACF plot, this is the lag after which ACF crosses the upper confidence interval for the first time, As we know PACF captures correlations of residuals and the time series lags, we might get good correlations for nearest lags as well as for past lags. Why would that be?

Since our series is a linear combination of the residuals and none of the time series own lag can directly explain its presence (since it’s not an AR), which is the essence of the PACF plot as it subtracts variations already explained by earlier lags, its kind of PACF losing its power here! On the other hand, being a MA process, it doesn’t have the seasonal or trend components so the ACF plot will capture the correlations due to residual components only.

Model Building:

I had used auto Arima to build the model. Auto ARIMA is available in pmdarima. After fitting the test set I got an output of ARIMA(0,0,0) which is commonly known as white noise. White noise means that all variables have the same variance (sigma²) and each value has a zero correlation with all other values in the series.

After fitting the Arima model, I printed the Summary and got the below.

Log-Likelihood

The log-likelihood value is a simpler representation of the maximum likelihood estimation. It is created by taking logs of the previous value. This value on its own is quite meaningless, but it can be helpful if you compare multiple models to each other. Generally speaking, the higher the log-likelihood, the better. However, it should not be the only guiding metric for comparing your models!

AIC

AIC stands for Akaike’s Information Criterion. It is a metric that helps you evaluate the strength of your model. It takes in the results of your maximum likelihood as well as the total number of your parameters. Since adding more parameters to your model will always increase your value of the maximum likelihood, the AIC balances this by penalizing for the number of parameters, hence searching for models with few parameters but fitting the data well. Looking at the models with the lowest AIC is a good way to select to best one! The lower this value is, the better the model is performing.

BIC

BIC (Bayesian Information Criterion) is very similar to AIC, but also considers the number of rows in your dataset. Again, the lower your BIC, the better your model works. BIC induces a higher penalization for models with complicated parameters compared to AIC.

Both BIC and AIC are great values to use for feature selection, as they help you find the simplest version with the most reliable results at the same time.

Ljung Box

The Ljung–Box test is a type of statistical test of whether any of a group of autocorrelations of a time series are different from zero. Instead of testing randomness at each distinct lag, it tests the “overall” randomness based on a number of lags and is, therefore, a portmanteau test.

Ho: The model shows the goodness of fit(The autocorrelation is zero)

Ha: The model shows a lack of fit(The autocorrelation is different from zero)

My model here satisfies the goodness of fit condition because Probability(Q)=1.

Heteroscedasticity

Heteroscedasticity means unequal scatter. In regression analysis, we talk about heteroscedasticity in the context of the residuals or error term. Specifically, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values.

My residuals are heteroscedastic in nature since Probability(Heteroskadisticy)=0.

Tests For Heteroscedasticity Brusche Pagan test:

The Breusch-Pagan-Godfrey Test (sometimes shortened to the Breusch-Pagan test) is a test for heteroscedasticity of errors in regression. Heteroscedasticity means “differently scattered”; this is opposite to homoscedastic, which means “same scatter.” Homoscedasticity in regression is an important assumption; if the assumption is violated, you won’t be able to use regression analysis.

Ho: Residuals are homoscedastic

Ha: Residuals are heteroscedastic in nature.

Goldfeld Quandt Test:

The Goldfeld Quandt Test is a test used in regression analysis to test for homoscedasticity. It compares variances of two subgroups; one set of high values and one set of low values. If the variance differs, the test rejects the null hypothesis that the variances of the errors are not constant.

Although Goldfeld and Quandt described two types of tests in their paper (parametric and non-parametric), the term “Quandt Goldfeld test” usually means the parametric test. The assumption for the test is that the data is normally distributed.

The test statistic for this test is the ratio of mean square residual errors for the regressions on the two subsets of data. This corresponds to the F-test for equality of variances. Both the one-tailed and two-tailed tests can be used.

Forecasting Using Arima:

The metrics we use to see the accuracy of the model are RMSE.

RMSE

Let us see the forecasting Using ARIMA.

Here the forecast values are constant because it’s an ARIMA(0,0,0) model.

ACF Plot(q value)

PACF plot (p-value)

Prediction Using Simple Exponential Smoothing

The simplest of the exponentially smoothing methods are naturally called simple exponential smoothing. This method is suitable for forecasting data with no clear trend or seasonal pattern.

Using the naïve method, all forecasts for the future are equal to the last observed value of the series. Hence, the naïve method assumes that the most recent observation is the only important one, and all previous observations provide no information for the future. This can be thought of as a weighted average where all of the weight is given to the last observation.

Using the average method, all future forecasts are equal to a simple average of the observed data. Hence, the average method assumes that all observations are of equal importance, and gives them equal weights when generating forecasts.

We often want something between these two extremes. For example, it may be sensible to attach larger weights to more recent observations than to observations from the distant past. This is exactly the concept behind simple exponential smoothing. Forecasts are calculated using weighted averages, where the weights decrease exponentially as observations come from further in the past — the smallest weights are associated with the oldest observations.

So large value of alpha(alpha denotes smoothing parameter)denotes that recent observations are given higher weight and a lower value of alpha denoted that more weightage is given to distant past values.

Modelling Using Simple Exponential Smoothing:

By modeling Using Simple Exponential Smoothing, we have taken 3 cases.

In fit1, we explicitly provide the model with the smoothing parameter α=0.2

In fit2, we choose an α=0.6

In fit3, we use the auto-optimization that allows statsmodels to automatically find an optimized value for us. This is the recommended approach.

Fitted values for Simple Exponential Smoothing.

Simple Exponential Smoothing Predictions

The best output is given when alpha=1, indicating recent observations are given the highest weight.

Holt’s Model

Holt extended simple exponential smoothing to allow the forecasting of data with a trend. (alpha for level and beta * for trend). The forecasts generated by Holt’s linear method display a constant trend(either upward or downward). Due to this, we tend to over forecast-hence we use a concept of damped trend. It dampens the trend to a flat line in the near future.

Modeling Using Holt’s Model:

Under this, we took three cases

1.In fit1, we explicitly provide the model with the smoothing parameter α=0.8, β*=0.2.

2.In fit2, we use an exponential model rather than Holt’s additive model(which is the default).

3.In fit3, we use a damped version of the Holt’s additive model but allow the dampening parameter ϕ to be optimized while fixing the values for α=0.8, β*=0.2.

Holt’s Model.

Holt’s model fitted values.

Prediction Using Holt’s model

The lowest value of RMSE is when alpha=0.8 and smoothing-slope=0.2 when the model is the exponential model in nature.

Holt’s Winter Model

Holt and Winters extended Holt’s method to capture seasonality. The Holt-Winters seasonal method comprises the forecast equation and three smoothing equations. It has three parameters alpha which is the level, Beta* which is the trend, and gamma which is the seasonality. The additive method is preferred when the seasonal variations are roughly constant through the series, while the multiplicative method is preferred when the seasonal variations are changing proportionally to the level of the series.

Modeling Using Holt’s Winter Model

1.In fit1, we use additive trend, additive seasonal of period season_length=4, and a Box-Cox transformation.

2.In fit2, we use additive trend, multiplicative seasonal of period season_length=4, and a Box-Cox transformation.

3.In fit3, we use additive damped trend, additive seasonal of period season_length=4, and a Box-Cox transformation.

4.In fit4, we use multiplicative damped trend, multiplicative seasonal of period season_length=4, and a Box-Cox transformation.

Box-Cox Transformation: A Box-Cox transformation is a transformation of a non-normal dependent variable into a normal shape. Normality is an important assumption for many statistical techniques; if your data isn’t normal, applying a Box-Cox means that you are able to run a broader number of tests.

Case1:

In fit1, we use additive trend, additive seasonal of period season_length=4, and a Box-Cox transformation.

Case 2:

In fit2, we use additive trend, multiplicative seasonal of period season_length=4, and a Box-Cox transformation.

Case 3:

In fit3, we use additive damped trend, additive seasonal of period season_length=4, and a Box-Cox transformation.

Case 4:

In fit4, we use multiplicative damped trend, multiplicative seasonal of period season_length=4, and a Box-Cox transformation.

Best Model:

The holt winter is giving me the lowest RMSE(281.91) when trend and seasonality are additive.

Linear Regression

Up Next I applied the Linear Regression Model with Open Price as my dependent variable.Steps involved in Linear Regression

Check for missing Values.

Check for multicollinearity, if there is high multicollinearity -calculate VIF, the variable with the highest VIF, drop that variable. Repeat the process until all the variables are not affected by multicollinearity.

Correlation and calculation of VIF

Fig 1

In the above (fig 1) we see that adj_close and close both have very high VIF then we saw that Close has more VIF then we will drop it. Post that we will again check the correlation and we see that Adj Close and High have a high correlation.

Fig 2

In the above fig(fig 2) we see that Adj close has a higher vif value than High, So we will drop that variable.

Fig 3

Now we can perform the modeling part. Let us look at the OLS regression.

OLS OUTPUT(1)

OLS Analysis:

Significant Variables: As per the p values, significant variables are Low, Volume.

Omnibus: Omnibus is a test for skewness and kurtosis. In this case, the omnibus is high and the probability of omnibus is zero, indicating residuals are not normally distributed.

Durbin Watson: It is a test for autocorrelation at AR(1) lag.

(For a first-order correlation, the lag is a one-time unit).

In this case, autocorrelation is 2.1 indicating negative autocorrelation.

Heteroskadistic Tests:

Brusche Pagan Test.

As per the above test, our residuals or errors are heteroskedastic in nature. Heteroskedasticity is a problem because it makes our model less inefficient because there will be some unexplained variance that cannot be explained by any other model.

RMSE:

RMSE

Error: 4.4%, which is quite acceptable.

Random Forest:

Next, I build a model using Random Forest Regressor. Firstly I built a model with the given hyperparameters.

Error rate:26% which is quite high and RMSE is 879.612

Next, we tuned our hyperparameters using Grid search.

Grid Search Output

So After tuning the hyperparameters, I saw that RMSE scored had decreased and error has become 24% (which is still very high ) as compared to linear regression.

Support Vector Regressor Linear Kernel:

In Svm Scaling is a necessary condition otherwise it takes a lot of time to converge. It is necessary to scale both the independent and dependent variables. Post prediction we need to do reverse scaling, otherwise, the scale of predicted variables and the test set will be different.

After building the model we see that RMSE score is 159.24 and the error is 4.7% which is quite close to Linear Regression.

Support Vector Regression(RBF KERNEL)

In the above snippet, we actually see that when we use RBF kernel our RMSE score increased drastically to 1307, and error increased to 39.3%. So RBF kernel is not suitable for this model.

Support Vector Regression(Polynomial KERNEL)

From the above output, it is clear polynomial kernel is not suitable for this dataset because RMSE(5048.50) is very high and the error is 151.8%. So this gives us a clear picture that data is linear in nature.

Analysis

Out of all the models, we have applied, the best model is Linear Regression(lowest RMSE score of 146.79) and the error is 4.45%

About the Author

Neel Roy

As an executive with over 4 years of experience in the BFSI Industry, I offer a record of success as a key contributor in the process & sales operations management that solved business problems in consumer targeting, market prioritization, and business analytics. My background as a marketer, export & import/ LC process expert, combined with my technical acumen has positioned me as a valuable resource in delivering and enhancing solutions.

I have successfully completed Data Science Certification from Jigsaw Academy, 1st level of Certification from IIBA (International Institute of Business Analysis), and currently pursuing PGP Data Science from Praxis Business School, seeking an assignment to utilize my skills and abilities in the field of Data Science. I have good knowledge of Python, SQL, R & Tableau, and Data Processing.

I am equally comfortable in the business and technical realms, communicating effortlessly with clients and key stakeholders, I leverage skills in today’s technologies spanning data analytics & data mining, primary research, consumer segmentation, and market analysis. I engage my passion, creativity, and analytical skills to play a vital role in facilitating the company’s success and helping to shape the organization’s future.

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How To Block Emails On Gmail

Alongside personal use, Gmail has proved to be beneficial for many businesses. From sending mass emails to periodic newsletters, it isn’t unlikely for users to get spammed with an inbox full of emails. Gmail provides limited storage space of 15GB, so you would want to block senders that send emails you do not wish to view.

If you wish to block emails on Gmail, allow this article to guide you. In this article, we have mentioned the ways to block emails on Gmail. Later in this article, we have also included alternate routes to stop seeing emails from a user on Gmail.

There could be many reasons why you would want to block emails on Gmail. Asides from personal reasons, you would like to block them for security reasons. You must consider blocking the sender if you constantly receive malicious emails from the same sender that looks like spam or phishing.

We have gathered two ways you can block emails on Gmail. You could select the sender’s email from your Gmail inbox or manually search them up and then block them. When you block a user on Gmail, all emails you receive from them in the future will be sent to the Spam folder.

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If you wish to create such filters to block particular emails, follow these steps:

If you do not wish to block the sender but still want to stop viewing the emails from the sender, you can unsubscribe to mass emails or manually send the email to the spam folder.

When you choose to unsubscribe, you will still receive the email from the sender but not from the email chain. If you wish to stop viewing a mass email, for instance, a newsletter, you can unsubscribe from the email following these steps:

Marking an email as spam will move your email from the inbox to the spam folder. This will clear your inbox of emails you do not wish to view and contribute to productivity. Here are the steps you can follow to mark an email as spam on Gmail:

How To Block Someone On Instagram

If you don’t fee like changing the privacy settings on your Instagram account, but you also don’t want a certain user to access your pictures, you always have the option of blocking that person. The following guide will show you how easy it is, and in no time that annoying user will be a thing of the past.

To block any user on Instagram, you will need to launch the Instagram app and go to the profile of the person you want to block. (You can do this as well if you wish to block a regular user or any organization).

How to Unblock Someone on Instagram

Have you forgiven that user for being so annoying and want to give him or her a second chance? If you do, unblocking someone is as easy as blocking them. To unblock someone, you just need to follow the same steps you did when you blocked them in the first place, but “Block” will now be replaced by “Unblock.” Just tap on the wanted action and you are good to go. Remember, you can only block and unblock users from the official Instagram app and not from the web app.

What Happens After you Block Someone on Instagram Conclusion

Judy Sanhz

Judy Sanhz is a tech addict that always needs to have a device in her hands. She loves reading about Android, Softwares, Web Apps and anything tech chúng tôi hopes to take over the world one day by simply using her Android smartphone!

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