Detect and Remove Outliers Using Z-Score and Standard Deviation

Table of Contents:

1. Introduction
2. Understanding Z-score and Standard Deviation
3. Using Z-score and Standard Deviation to Remove Outliers
4. Loading the Dataset
5. Plotting the Histogram
6. Understanding Normal Distribution
7. Plotting the Bell Curve
8. Calculating Mean and Standard Deviation
9. Removing Outliers Using Three Standard Deviations
10. Using Z-score to Remove Outliers
11. Exercise: Removing Outliers from Bangalore Property Prices Dataset

Introduction:

In this article, we will explore how we can use Z-score and standard deviation to remove outliers from a dataset. We will be using a real dataset from Cagle comm and remove outliers using Z-score and three standard deviations. We will also walk through an exercise to practice the techniques learned.

Understanding Z-score and Standard Deviation:

Before we dive into removing outliers, let's first understand what Z-score and standard deviation are. A Z-score tells us how many standard deviations a data point is away from the mean. Standard deviation, on the other HAND, measures the dispersion or spread of data points from the mean.

Using Z-score and Standard Deviation to Remove Outliers:

To remove outliers from a dataset, we can use either Z-score or standard deviation. Z-score is commonly used in the industry and provides a threshold to identify outliers. By considering a certain range of Z-scores, we can filter out the outliers and get a cleaner dataset.

Loading the Dataset:

To demonstrate the process of removing outliers, we will use the weight and Height dataset from Giggle. After importing the necessary modules and loading the dataset into a pandas dataframe, we will proceed with our analysis.

Plotting the Histogram:

Before identifying outliers, it's important to understand the distribution of the data. We can do this by plotting a histogram, which visualizes the frequency of different values in the dataset. By analyzing the histogram, we can determine if the data follows a normal distribution.

Understanding Normal Distribution:

A normal distribution is a common type of distribution where values are centered around the mean, creating a bell-shaped curve. In a normal distribution, the majority of values are concentrated near the mean, while fewer values are found as you move away from the mean. Examples of data that often follow a normal distribution include heights of people, sizes of objects produced by machines, blood pressure readings, and test scores.

Plotting the Bell Curve:

To further Visualize the normal distribution, we can plot a bell curve alongside the histogram. This curve represents the expected distribution of values if they were following a perfect normal distribution. By comparing the histogram to the bell curve, we can validate if the data indeed follows a normal distribution.

Calculating Mean and Standard Deviation:

To remove outliers, we need to calculate the mean and standard deviation of the dataset. The mean represents the average value, while the standard deviation measures the spread or dispersion of values from the mean. Both these values are essential in determining the range within which data points can be considered normal.

Removing Outliers Using Three Standard Deviations:

One common approach to remove outliers is to use a specified number of standard deviations. In this case, we will consider three standard deviations from the mean as the threshold for outliers. Any value beyond this threshold will be deemed an outlier and removed from the dataset.

Using Z-score to Remove Outliers:

Alternatively, we can use Z-score to identify outliers. Z-score calculates the number of standard deviations a data point is away from the mean. By using a predefined threshold for Z-score, we can remove outliers from the dataset. In this case, a Z-score of three will be used as the threshold for outliers.

Exercise: Removing Outliers from Bangalore Property Prices Dataset:

To practice the techniques learned, we have an exercise for you. We will be working with the Bangalore property prices dataset. Your task is to first remove outliers using the percentile technique, then use four standard deviations to remove outliers, plot a histogram, and finally, use a Z-score of four to remove outliers. Instructions and the dataset can be found in the exercise folder provided.

By following these steps, you will gain a deeper understanding of how to use Z-score and standard deviation to remove outliers from a dataset. Remember to practice and experiment with different thresholds to achieve the desired results.

faq:

Q: What is the purpose of removing outliers from a dataset? A: Removing outliers helps in creating a cleaner dataset, reducing the impact of extreme values on statistical analysis, and improving the performance of machine learning models.

Q: How can I determine if a dataset follows a normal distribution? A: One way to determine if a dataset follows a normal distribution is by plotting a histogram and visually analyzing the shape of the distribution. Additionally, statistical tests like the Shapiro-Wilk test can be used for a more quantitative assessment.

Q: Can outliers be valid data points? A: Yes, outliers can be valid data points. However, depending on the context and analysis goals, they can be removed to improve the accuracy and reliability of statistical analysis or machine learning models.

Q: What other techniques can I use to remove outliers? A: In addition to Z-score and standard deviation, other techniques to remove outliers include using percentiles, interquartile range (IQR), Tukey's fences, and box plots.

Q: How can I determine the threshold for removing outliers? A: The threshold for removing outliers depends on the specific dataset, analysis goals, and domain knowledge. It is often determined using a combination of statistical methods, visualization, and subject matter expertise.

Q: Is it necessary to remove outliers from every dataset? A: Removing outliers is not always necessary and depends on the specific analysis goals and context. In some cases, outliers may carry meaningful information or represent rare events that need to be studied separately.