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Sachin Kumar Profile picture
@Sachintukumar
Apr 26 • 8 tweets • 3 min read Twitter logo Read on Twitter
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
Finding Outliers

filter conditions on top of the upper and lower limits so that we will get those rows/tuples which are considered to be bad data points
Trimming removes all the bad data from the dataset just to ensure the quantity of data is not much; otherwise, for analysis

Capping is a second way to impute the outliers with some other values. There can be mean, median or mode or any constant value also
@github Notebook Link

github.com/sachinkumar160…
Blog by @AnalyticsVidhya

analyticsvidhya.com/blog/2022/09/d…
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More from @Sachintukumar

Sachin Kumar Profile picture
Apr 27
Netflix Data Analysis Project

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@ProjectJupyter NoteBook Link --

github.com/sachinkumar160…
@github Link --

github.com/sachinkumar160…
Read 4 tweets
Sachin Kumar Profile picture
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
Sachin Kumar Profile picture
Apr 27
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.
Read 8 tweets
Sachin Kumar Profile picture
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
Sachin Kumar Profile picture
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
Sachin Kumar Profile picture
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

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