Sachin Kumar Profile picture
Apr 27 8 tweets 3 min read Twitter logo Read on Twitter
Day 44 of #100dayswithmachinelearning

Topic -- Outlier Detection using Percentile Method

A Thread 🧵
Outliers are a very important and crucial aspect of Data Analysis.

It can be treated in different ways, such as trimming, capping, discretization, or by treating them as missing values.
Percentile Method -

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%.

as outliers can significantly impact the accuracy of a model if they are not handled properly.
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. @AnalyticsVidhya

analyticsvidhya.com/blog/2021/05/f…
📷If this thread was helpful to you
1. Follow me @Sachintukumar
for daily content

2. Connect with me on Linkedin: linkedin.com/in/sachintukum…

3. RT tweet below to share it with your friend

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Sachin Kumar

Sachin Kumar Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @Sachintukumar

Apr 27
🏹SQL Interview Questions

🧵 Image
🎯Are NULL values same as that of zero or a blank space❓

🔺A NULL value is not at all same as that of zero or a blank space.
🔺NULL value represents a value which is unavailable, unknown, assigned or not applicable whereas a zero is a number and blank space is a character.
🎯What is the usage of the NVL() function❓

🔹Answer

🔺You may use NVL function to replace null values with a default value. 🔺The function returns the value of second parameter if first parameter is null.
🔺If the first parameter is anything other than null, it is left alone
Read 8 tweets
Apr 26
Day 43 of #100dayswithmachinelearning

Topic - Outlier Detection and Removal using the IQR Method

A Thread 🧵 Image
The IQR (Interquartile Range) method is a common approach for detecting and removing outliers from a dataset

IQR is the difference between 75th and 25th Quartile

we can remove the bad data from left or right skewed distribution as well for that statistics have introduced IQR Image
Finding the IQR

there are outliers that need to be removed, and for that, here is the start of the section where we will start by finding the IQR

percentile25 = df['placement_exam_marks'].quantile(0.25)
percentile75 = df['placement_exam_marks'].quantile(0.75) Image
Read 8 tweets
Apr 25
Day 42 of #100dayswithMachinelearning

Topic -- Outlier Detection & Removal using Z-score Method

A Thread 🧵 Image
The Z-score method is statistical approach used for detecting & removing outlier in dataset. An outlier is observation that lies far away from other observation in dataset. Such observations can significantly affect statistical properties of dataset & lead to erroneous conclusion Image
Approach for Outliers

- The very first step will be setting the upper and lower limit

- The first technique for dealing with outliers is trimming & this is regardless of what kind of data distribution you are working with, trimming is an applicable and proven technique for most Image
Read 7 tweets
Apr 16
" PowerBI Project For Data Analyst "

A Thread 🧵 Image
1⃣ HR Analytics Dashboard

linkedin.com/posts/sachintu…
2⃣ Finance Dashboard

linkedin.com/posts/sachintu…
Read 8 tweets
Apr 16
Day 33 of #100dayswithmachinelearning

Topic - Handling Mixed Variable in Feature Engineering 👨‍💻

A Thread 🧵 Image
Handling missing Variable is very important as many machine learning algorithms do not support data with missing values. If you have missing values in the dataset, it can cause errors and poor performance with some machine learning algorithms. Image
Variable deletion involves dropping variables (columns) with missing values on a case-by-case basis. This method makes sense when there are a lot of missing values in a variable and if the variable is of relatively less importance. Image
Read 7 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

:(