In my previous article, I talked about the theoretical concepts of outliers and tried to find the answer to the question: **“When should we drop outliers and when should we keep them?”**. In this article, I will focus on outlier detection and the different ways of treating them. It is important for a data scientist to find outliers and remove them from the dataset as part of the feature engineering before training machine learning algorithms for predictive modeling. Outliers present in a classification or regression dataset can lead to lower predictive modeling performance.

I recommend you read this **article** before proceeding so that you have a clear idea about the outlier analysis in Data Science Projects. In this article we have cover topics like : outlier detection in python , outlier detection in machine learning and how to remove outlier detection in python so we mainly covering all topics and information regarding outlier detection.

**Learning Objectives**

- An Overview of outliers and why it’s important for a data scientist to identify and remove them from data.
- Undersand different techniques for outlier treatment: trimming, capping, treating as a missing value, and discretization.
- Understanding different plots and libraries for visualizing and trating ouliers in a dataset.

*This article was published as a part of the Data Science Blogathon*

Outlier is a data point that stands out significantly from the rest of the data. It can be an extremely high or low value compared to the other observations in a dataset. Outliers can be caused by measurement errors, natural variations in the data, or even unexpected discoveries.

There are 3 main types of outliers:

**Global outliers**: Stand out from the entire dataset, like a lone wolf.**Contextual outliers:** Depend on their surroundings, like a high sale at a clothing store.**Collective outliers:** Groups that deviate together, like a cluster of oddly high values.

Outlier detection is a method used to find unusual or abnormal data points in a set of information. Imagine you have a group of friends, and you’re all about the same age, but one person is much older or younger than the rest. That person would be considered an outlier because they stand out from the usual pattern. In data, outliers are points that deviate significantly from the majority, and detecting them helps identify unusual patterns or errors in the information. This method is like finding the odd one out in a group, helping us spot data points that might need special attention or investigation.

There are several ways to treat outliers in a dataset, depending on the nature of the outliers and the problem being solved. Here are some of the most common ways of treating outlier values.

It excludes the outlier values from our analysis. By applying this technique, our data becomes thin when more outliers are present in the dataset. Its main advantage is its **fastest **nature.

In this technique called “outlier detection,” we cap our data to set limits. For instance, if we decide on a specific value, any data point above or below that value is considered an outlier. The number of outliers in the dataset then gives us insight into that capping number. It’s like setting a boundary and saying, “Anything beyond this point is unusual,” and by doing so, we identify and count the outliers in our data.

For example, if you’re working on the income feature, you might find that people above a certain income level behave similarly to those with a lower income. In this case, you can cap the income value at a level that keeps that intact and accordingly treat the outliers.

**Treating outliers as a missing value: **Byassuming outliers as the missing observations, treat them accordingly, i.e., same as missing values imputation.

You can refer to the missing value article **here**.

In the method of outlier detection, we create groups and categorize the outliers into a specific group, making them follow the same behavior as the other points in that group. This approach is often referred to as Binning. Binning is a way of organizing data, especially in outlier detection, where we group similar items together, helping us identify and understand patterns more effectively.

You can learn more about discretization **here**.

- Use empirical relations of Normal distribution.
- The data points that fall below
or above*mean-3*(sigma)*are outliers, where mean and sigma are the*mean+3*(sigma)***average value**and**standard deviation**of a particular column.

Source: sphweb.bumc.bu.edu

- Use Inter-Quartile Range (IQR) proximity rule.
- The data points that fall below
or above the third quartile*Q1 – 1.5 IQR*are outliers, where Q1 and Q3 are the*Q3 + 1.5 IQR***25th**and**75th percentile**of the dataset, respectively. IQR represents the inter-quartile range and is given by Q3 – Q1.

- Use
**a**percentile-based approach. - For Example, data points that are far from the 99% percentile and less than 1 percentile are considered an outlier.

**Assumption:** The features are normally or approximately normally distributed.

**Step 1:****Importing necessary dependencies**import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns**Step 2:****Read and load the dataset**df = pd.read_csv(‘placement.csv’)

df.sample(5)**Step 3:****Plot the distribution plots for the features**import warnings

warnings.filterwarnings(‘ignore’)

plt.figure(figsize=(16,5))

plt.subplot(1,2,1)

sns.distplot(df[‘cgpa’])

plt.subplot(1,2,2)

sns.distplot(df[‘placement_exam_marks’])

plt.show()**Step 4:****Finding the boundary values**print(“Highest allowed”,df[‘cgpa’].mean() + 3*df[‘cgpa’].std())

print(“Lowest allowed”,df[‘cgpa’].mean() – 3*df[‘cgpa’].std())**Output:**

Highest allowed 8.808933625397177

Lowest allowed 5.113546374602842**Step 5:****Finding the outliers**df[(df[‘cgpa’] > 8.80) | (df[‘cgpa’] < 5.11)]

**Step 6:****Trimming of outliers**new_df = df[(df[‘cgpa’] < 8.80) & (df[‘cgpa’] > 5.11)]

new_df**Step 7:****Capping on outliers**upper_limit = df[‘cgpa’].mean() + 3*df[‘cgpa’].std()

lower_limit = df[‘cgpa’].mean() – 3*df[‘cgpa’].std()**Step 8:****Now, apply the capping**df[‘cgpa’] = np.where(

df[‘cgpa’]>upper_limit,

upper_limit,

np.where(

df[‘cgpa’]<lower_limit,

lower_limit,

df[‘cgpa’]**Step 9:****Now, see the statistics using the “Describe” function**df[‘cgpa’].describe()

**Output:**

count 1000.000000 mean 6.961499 std 0.612688 min 5.113546 25% 6.550000 50% 6.960000 75% 7.370000 max 8.808934 Name: cgpa, dtype: float64

This completes our Z-score-based technique!

Used when our data distribution is skewed.

```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```

```
df = pd.read_csv('placement.csv')
df.head()
```

```
plt.figure(figsize=(16,5))
plt.subplot(1,2,1)
sns.distplot(df['cgpa'])
plt.subplot(1,2,2)
sns.distplot(df['placement_exam_marks'])
plt.show()
```

`sns.boxplot(df['placement_exam_marks'])`

```
percentile25 = df['placement_exam_marks'].quantile(0.25)
percentile75 = df['placement_exam_marks'].quantile(0.75)
```

```
upper_limit = percentile75 + 1.5 * iqr
lower_limit = percentile25 - 1.5 * iqr
```

```
df[df['placement_exam_marks'] > upper_limit]
df[df['placement_exam_marks'] < lower_limit]
```

```
new_df = df[df['placement_exam_marks'] < upper_limit]
new_df.shape
```

```
plt.figure(figsize=(16,8))
plt.subplot(2,2,1)
sns.distplot(df['placement_exam_marks'])
plt.subplot(2,2,2)
sns.boxplot(df['placement_exam_marks'])
plt.subplot(2,2,3)
sns.distplot(new_df['placement_exam_marks'])
plt.subplot(2,2,4)
sns.boxplot(new_df['placement_exam_marks'])
plt.show()
```

```
new_df_cap = df.copy()
new_df_cap['placement_exam_marks'] = np.where(
new_df_cap['placement_exam_marks'] > upper_limit,
upper_limit,
np.where(
new_df_cap['placement_exam_marks'] < lower_limit,
lower_limit,
new_df_cap['placement_exam_marks']
```

```
plt.figure(figsize=(16,8))
plt.subplot(2,2,1)
sns.distplot(df['placement_exam_marks'])
plt.subplot(2,2,2)
sns.boxplot(df['placement_exam_marks'])
plt.subplot(2,2,3)
sns.distplot(new_df_cap['placement_exam_marks'])
plt.subplot(2,2,4)
sns.boxplot(new_df_cap['placement_exam_marks'])
plt.show()
```

This completes our IQR-based technique!

- This technique works by setting a particular threshold value, which is decided based on our problem statement.
- While we remove the outliers using capping, then that particular method is known as
**Winsorization**. - Here, we always maintain
**symmetry**on both sides, meaning if we remove 1% from the right, the left will also drop by 1%.

Steps to follow for the percentile method:

```
import numpy as np
import pandas as pd
```

```
df = pd.read_csv('weight-height.csv')
df.sample(5)
```

`sns.distplot(df['Height'])`

`sns.boxplot(df['Height'])`

```
upper_limit = df['Height'].quantile(0.99)
lower_limit = df['Height'].quantile(0.01)
```

`new_df = df[(df['Height'] <= 74.78) & (df['Height'] >= 58.13)]`

```
sns.distplot(new_df['Height'])
sns.boxplot(new_df['Height'])
```

**Winsorization**

```
df['Height'] = np.where(df['Height'] >= upper_limit,
upper_limit,
np.where(df['Height'] <= lower_limit,
lower_limit,
df['Height']))
```

```
sns.distplot(df['Height'])
sns.boxplot(df['Height'])
```

This completes our percentile-based technique!

Outlier detection and removal is a crucial data analysis step for a machine learning model, as outliers can significantly impact the accuracy of a model if they are not handled properly. The techniques discussed in this article, such as Z-score and Interquartile Range (IQR), are some of the most popular methods used in outlier detection. The technique to be used depends on the specific characteristics of the data, such as the distribution and number of variables, as well as the required outcome.

Hope in this you get a clear information for outlier detection , whether it is outlier detection method, outlier detection python , and how to remove outlier detection in python. These required information we have covered.

**Key Takeaways**

- Outliers can be treated in different ways, such as trimming, capping, discretization, or by treating them as missing values.
- Emperical relations are used to detect outliers in normal distributions, and Inter-Quartile Range (IQR) is used to do so in skewed distributions. For all other distributions, we use the percentile-based approach.
- Z-score treatment is implemented in Python by importing the necessary dependencies, reading and loading the dataset, plotting the distribution plots, finding the boundary values, finding the outliers, trimming, and then capping them.

A. Most popular outlier detection methods are Z-Score, IQR (Interquartile Range), Mahalanobis Distance, DBSCAN (Density-Based Spatial Clustering of Applications with Noise, Local Outlier Factor (LOF), and One-Class SVM (Support Vector Machine).

A. Libraries like SciPy and NumPy can be used to identify outliers. Also, plots like Box plot, Scatter plot, and Histogram are useful in visualizing the data and its distribution to identify outliers based on the values that fall outside the normal range.

A. The benefit of removing outliers is to enhance the accuracy and stability of statistical models and ML algorithms by reducing their impact on results. Outliers can distort statistical analyses and skew results as they are extreme values that differ from the rest of the data. Removing outliers makes the results more robust and accurate by eliminating their influence. It reduces overfitting in ML algorithms by avoiding fitting to extreme values instead of the underlying data pattern.

The best algorithm depends on your data (distribution, size) and goals (how you define outliers, efficiency needs). Consider statistical methods (z-score), distance-based (LOF), nearest neighbor (Isolation Forest), or clustering (DBSCAN) approaches.

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Hello, thanks a lot for the article ! Is there a link to download the data: placement.csv file ? Thanks again. Best regards.

thank you so much. this article is well decorated and helpful must say. how can I get the dataset that is used in his article, please?

I wish you guys would provide links to the datasets. That way people like me who trying to learn could do the work as we read the article.

Am requesting for a link so that I can learn more on how to detect and remove outliers as I go along ...Thanks CHIRAG GOYAL for introducing us to this..Personally impressed with the article.