Python treatment for outliers in data science

August 12, 2022

Finding and dealing with outliers is one of the most crucial processes in data preparation since they can have a detrimental impact on statistical analysis and the training of a machine learning or AI algorithm, leading to reduced accuracy.

In this blog, we will brief you on what an outlier is, how it is created, detecting techniques for an outlier, and what are the treatment techniques that you can use to handle an outlier.`

What is an Outlier?

An observation that differs significantly from the other observations in a dataset is considered an outlier. An outlier is therefore considerably larger or smaller than the other values in the collection. 

An outlier may appear as a result of experimental error, human mistake, data variability, or all three. A dataset of class 7 students, for instance, shows that each student is between the ages of 13 and 15, but accidentally includes information about a student from the 12th grade who is actually 19 years old. The student who is 19 years old is, therefore, an outlier for this batch of data.

How an Outlier is created in the Data?

Incorrect data input or a processing mistake value gaps in a dataset. The targeted sample did not yield the expected data. During experiments, mistakes happen. It wouldn't be a mistake, but it wouldn't be like the original. an abnormally extreme distribution.

Detecting Outliers using Python

If our dataset is tiny, we can find the outlier by simply scanning it. But what if our dataset is very large? How would we recognize the outliers in that case? We must employ quantitative and visual methods for detecting outliers. 

  1. Visualization

The most common and easy visualization technique to detect outliers in any data set is using the Box plots and Scatter plots. With just a box and a few whiskers, Box plots effectively and efficiently capture the data summary. Boxplot uses 25th, 50th, and 75th percentiles to summarise sample data. Simply by glancing at the dataset's boxplot, one may gain insights (quartiles, the median, and outliers) about the data. Whereas Scatter plots are used when you have paired numerical data when your dependent variable contains numerous values for each reading independent variable when you're attempting to establish a link between the two variables, or in any other of these situations.

Code for Box Plot

import seaborn as sns

sns.boxplot(dadata_frame['col_name'])

Code for Scatter Plot

fig, plot = plt.subplots(figsize = (18,10))

plot.scatter(data_frame['X_axis'], data_frame['Y_axis'])

  1. Z Score

This number or score aids in determining how far the data point deviates from the mean. After establishing a threshold value, one may use the z scores of the data points to identify outliers.

Zscore = (data_point - mean) / std. Deviation

from scipy import stats

import numpy as np

 

outlier = np.abs(stats.zscore(data_frame['col_name']))

 

  1. Inter Quartile Range (IQR)

By splitting a data set into quartiles, the IQR is used to quantify variability. The information is divided into 4 equal portions and arranged in ascending order. The values that divide the four equal halves are known as the first, second, and third quartiles, or Q1, Q2, and Q3, respectively. The interquartile range, or IQR, is the space between the first and third quartiles, or Q1 and Q3 i.e. IQR = Q3 - Q1.

 

Q1 = np.percentile(data_frame['col_name'], 25,

                   interpolation = 'midpoint')

 

Q3 = np.percentile(data_frame['col_name'], 75,

                   interpolation = 'midpoint')

IQR = Q3 - Q1

 

Defining the upper and lower boundaries (1.5*IQR value is taken into consideration) can help you determine the outlier's base value, which is specified above and below the dataset's typical range:

 

Upper = 1.5*IQR + Q3 

Lower = Q1 - 1.5*IQR

upper = data_frame['col_name'] >= (Q3+1.5*IQR)

lower = data_frame['col_name'] <= (Q1-1.5*IQR)

 

Outliers are data points that are either higher than the upper or lower than the lower limits.

 

print(np.where(upper))

print(np.where(lower))

Treatment for outliers

Once we have figured out what are the outliers in our data set, the next question is what to do with them.

Here are a few approaches to handling outliers. 

  1. Deleting or Trimming the outlier

We eliminate the outliers from the dataset using this method. First, from the visualization, we can have an estimation of the range where data outliers might lie and on the basis of that, we can drop all the outliers from the data set. 

 

A good example of this is the ‘Age’ variable, which ranged from 0 to 100. An index is created for all the data points when the age takes these two values in the first line of code below. And after that, the second line of the code below drops all the outliers from our dataset.

index=data_frame[(data_frame['Age']>=100)|(data_frame['Age']<=18)].index 

df.drop(index, inplace=True)

  1. Flooring and capping based on quantiles

With this method, we will floor the lower values at, say, the 10th percentile, and cap the higher values at, say, the 90th percentile. The lines of code following output the variable "Income10th "'s and 90th percentiles, respectively. The quantile-based flooring and capping will be done using these values.

# Computing 10th, and 90th percentiles and replacing the outliers 

import numpy as np

tenth_percentile = np.percentile(data, 10) 

ninetieth_percentile = np.percentile(data, 90) 

treated_data=np.where(data<tenth_percentile,tenth_percentile, treated_data) 

treated_data =np.where(data>ninetieth_percentile, ninetieth_percentile, treated_data) 

Now the dataset treated_data contains the data without the outliers, using the flooring and capping method outliers are treated here. 

  1. Median treatment

With this method, the extreme numbers are swapped out for the mean or median values. It is cautioned against using mean values because the mean value is highly influenced by the outliers. So it is better to rely on media value for treating the outliers. The median value is used to replace any values in the ‘data_frame’ variable that are higher than the 95th percentile. 

median = np.median(data) 

for i in sample_outliers: 

c = np.where(data==i, 14, data)

Conclusion

In this blog, we learned about handling outliers, a crucial step in data preparation. We currently have a variety of techniques for identifying and managing outliers. But since there is no mathematically correct or incorrect answer, handling outliers is a highly subjective endeavor. Treatment choices may be made more easily with the use of qualitative knowledge, such as understanding the origin or impact of an anomaly.

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Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

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What is a Strong Dollar?

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Increase in Cloud Costs Due to Strong Dollar

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Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

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  1. A Coordinated Effort

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The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.

Conclusion

In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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