Outlier detection is an important ingredient to flag inconsistencies or anomalies existing within a dataset. Outliers are pointers which presents you with the anomalies in a data filled with values. Nowadays, data is everywhere and to ensure its consistency and integrity. Outlier detection finds these anomalies and remove it on time to ensure accurate results.
Here, we will understand what is outliers and the methods to find these outliers in dataset.
What Are Outliers?
Outliers are anomalies that exist in a dataset, significantly different from the majority of the data in a dataset. They either fall much higher or much lower than the other values in the dataset.
Outliers can have a major impact on the overall data, resulting in deviated insights, averages, and more. There are methods that can detect outliers in a dataset. Outliers can reveal potential errors in data collection.
For example, in a dataset of monthly course sales for different months, you notice extensive high sales in the current month. This might be flagged as abnormal and would be considered an outlier.
- Outliers are points in data that deviate significantly from other observations in a dataset.
- The early stage of these anomalies might become visible with surprisingly high or low values, disturbing the normal distribution of data.
- Outliers can result in deviation from the expected insights and results from the given set of data; hence, it becomes necessary to remove outliers.
- Outlier detections is important for data cleaning, anomaly detection, and maintaining the quality of data using different methods like machine learning algorithms, statistical techniques, tools, and more.
- Outlier detection can help increase the accuracy of a model and avoid incorrect output.
- Outliers need not be invalid in all circumstances, it can be a valid observation or errors in data entry, measurement or processing.
Why Is Removing Outliers Important?
As dependence on data is increasing day by day, understanding the concept of outliers and taking necessary steps to recognise them and remove them from the dataset is way more important to get the desired result. Outliers can deviate from the expected result of a given dataset.
Outliers will have an inappropriate influence on the statistical measures in a given dataset, leading to misguided results.
When steps are taken to remove outliers, it helps ensure the analysis is aligned with the available set of information. Outliers can have an effect on the reliability of data. Identifying and removing outliers is important to ensure the integrity of data.
What Is Outlier Detection?
Outlier detection is a method of identifying data points that are different from the rest of data in the dataset. This is important to ensure data consistency, integrity and valid results. Outlier detection and removal help in maintaining the quality of data, ensure accuracy and avoid incorrect results.
For instance, think of your car has a burst tire and you notice that it is not running fine. What will you do in this condition? Probably the best thing to do is to find the root cause of the problem. Similarly, outliers might be the cause of inaccuracy and deviation of the actual results of a dataset which must be fixed.
Consider the following dataset, which include the scores of a student in different subjects.
| [90, 93, 87, 89, 42, 77] |
The score 45 here can be considered as an outlier as it is much lower than the other score.
Outliers need not be invalid in all circumstances, it can be a valid observation or errors in data entry, measurement or processing.
Types of Outliers
There are three major types of outliers based on how they are arranged in a dataset.
- Global Outliers: They are also known as point anomalies in data where these data points significantly different from the rest of the data in the dataset.
- Contextual Outliers: It is also known as conditional anomalies. These are data points that are considered outliers in certain conditions or a period of time. For example, A rainfall of 50mm a day during monsoon is normal while the same amount during the dry season might be an outlier.
- Collective Outliers: It is also known as group anomalies where a sudden change in group of data creates deviation from the rest of the dataset. For example, a sudden spike cluster orange labeled might be tagged as an outlier as it is an unusual traffic burst.
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How to Detect Outliers In A Dataset?
There are many methods which you can use to detect outliers in a dataset. Check below.
1. Standard Deviation Method
The standard deviation method works based on assumptions in a normal distribution. When you get data points outside the three standard deviations from the mean, it can be considered as an outlier.
You have to calculate the standard deviation of the dataset, define the lower and upper bounds for the outliers and find out the outliers that lies outside these bounds.
For example consider a given dataset [4, 5, 6, 5, 4, 6, 20]. Follow the step one by one to get to the desired result.
1. Calculate the Mean value (μ)
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2. Calculate the Standard Deviation (s)
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3. Calculate the Lower and Upper Bound
Lower Bound= μ−2s = 7.14−10.6 = −3.46
Upper Bound = μ+2s= 7.14+10.6 = 17.74
4. Mark the Outlier
Here, in this dataset 20 can be considered as an outlier as 20 > 17.74, the upper bound limit for this dataset.
2. IQR Method
The IQR method or Interquartile Range Method works by focusing on the spread of 50% of the data. It focus on calculating quartiles which includes.
- First Quartile (Q1): This is the 25th percentile where the value below which 25% lies.
- Third Quartile (Q3): This is the 75th percentile, the value below which 75% of the data lies.
- You can also find the lower bound and upper bound using these quartiles to locate the outliers.
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| IQR = Q3 – Q1 |
Let us take an example to understand the complete IQR method of outlier detection.
| import numpy as np
data = [10, 12, 14, 15, 18, 19, 20, 21, 22, 90] Q1 = np.percentile(data, 25) Q3 = np.percentile(data, 75) IQR = Q3 – Q1 lower_bound = Q1 – 1.5 * IQR upper_bound = Q3 + 1.5 * IQR outliers = [x for x in data if x < lower_bound or x > upper_bound] print(“Q1:”, Q1) print(“Q3:”, Q3) print(“IQR:”, IQR) print(“Outliers:”, outliers) |
Output
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3. Z – Score Method
The Z -score method is also known as standard score method outlier detection which tells you how many standard deviations a data point is from the mean value. It measures how “far” a vaule is from the average compared to the spread of the data.
 |
Where,
- X = data point
- μ = mean of the dataset
- σ = standard deviation of the dataset
When the value of |Z| > 3 then the point is considered as an outlier in the dataset. Let us take a simple example to understand how to calculate Z-score for a dataset using Python programming.
| import numpy as np
data = [10, 12, 14, 15, 18, 19, 20, 21, 22, 90] mean = np.mean(data) std = np.std(data) z_scores = [(x – mean) / std for x in data] outliers = [x for x, z in zip(data, z_scores) if abs(z) > 3] print(“Mean:”, mean) print(“Standard Deviation:”, std) print(“Z-scores:”, z_scores) print(“Outliers:”, outliers) |
Output
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4. Visual Method
The visual method is one of the preliminary method of finding outliers in a dataset. Here, you can easily notice the inconsistencies in the data. You can use histogram, bar graphs, or more to locate the outliers in data.
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Applications of Outlier Detection
Outlier detection is a important practice for organisations dependent on plenty of data manipulation and more.
1. Fraud Detection
Outlier detection can help you identify fraudulent transactions in critical transactional sectors like banking, finance, and e-commerce. The outliers involve activities that often show unusual spending patterns or transaction amounts that differ significantly from normal behavior.
For instance, if a credit card suddenly shows a transaction 10 times higher than the user’s usual spending, it’s flagged as an outlier. Machine learning models trained with outlier detection can automatically detect and alert such anomalies in real-time. This can be a big thing in preventing financial losses.
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2. Network
In Cybersecurity, outlier detection help identify suspicious network traffic, intrusions, or data breaches. There are many patterns such as sudden spikes in data request, transfer, unusual login attempts, and more.
Regular monitoring network logs can help you catch these anomalies and prevent your site from criticial danger.
3. Healthcare
In healthcare, outlier detection is used to identify abnormal medical readings that may indicate diseases or emergencies.
For example, unusual heart rate, blood pressure, or oxygen levels in patient monitoring systems can be flagged as outliers. This help doctors or concerned authorities to take timely actions to prevent further fatal consequences.
4. Quality Control
In industries like manufacturing outlier detection can help ensure product is stable and passes the quallity check. When the Q&A agent notices something unusual such as unusual readings, it might signal an outlier that needs to be noticed and taken care of before moving it to the passed stage.
This help eliminate security risks and product quality before it reaches the customer and leads to dissatisfaction.
5. Sensor Data Analysis
Outlier detection is widely used to identify any fault in sensors, unusual environmental conditions, and much more. They are installed or placed at weather stations, pollution monitoring stations, and more.
When the reading deviates from the normal readings, it gets highlighted as a potential outlier, signaling authorities on time.
6. Business Intelligence & Analytics
Outliers detection in business analytics identify market trends or customer behavior. This might get tagged by noticing a sudden change in monthly sales, customer purchase frequency, or more.
When these anomalies are catched at an early stage it enables companies to take data driven decisions and steps to improve customer satisfaction and retention.
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Outlier Detection FAQs
Q1. What is an outlier?
Ans: Outliers are anomalies that exist in a dataset, significantly different from the majority of the data in a dataset. They either fall much higher or much lower than the other values in the dataset.
Q2. What is outlier detection?
Ans: Outlier detection is a method of identifying data points that are different from the rest of data in the dataset. This is important to ensure data consistency, integrity and valid results.
Q3. Is outlier detection important?
Ans: Outlier detection can help you find data that deviates an insights from accurate results. It help organisations find the anomaly that is blocking the expected results. Once identified they can be fixed to gain accurate insights.
Q4. What are types of outlier detection?
Ans: Global outliers, contextual outliers, and collective outliers are three major types of outliers.
Q5. What methods are used to identify outliers?
Ans: Outliers can be identified using Standard deviation method, Z-square method, IQR method, visual methods and more.
