Step By Step Process In EDA And Feature Engineering In Data Science Projects

Summary

In this informative video, Krishnak delves into the crucial process of feature engineering in data science projects, emphasizing its significance in occupying about 30% of the project's timeline. He outlines the essential steps, starting from exploratory data analysis (EDA) to handling missing values, dealing with imbalanced datasets, outlier treatment, scaling, and converting categorical features into numerical ones. Krishnak also highlights the importance of feature selection to avoid the 'curse of dimensionality' and improve model performance. The video serves as a comprehensive guide for those looking to refine their feature engineering skills.

Takeaways

• 📊 Feature engineering is a crucial part of a data science project, often taking up about 30% of the total project time.
• 🔍 The first step in feature engineering is Exploratory Data Analysis (EDA), which involves analyzing raw data to understand its characteristics and issues.
• 📈 EDA includes examining numerical and categorical features, identifying missing values, and detecting outliers using visual tools like histograms and box plots.
• 📝 It's important to document EDA findings, as they inform decisions made in subsequent steps of feature engineering.
• 🔄 Handling missing values is a key step, with various methods such as mean, median, mode, or more sophisticated techniques based on feature analysis.
• 🔄 Addressing imbalanced datasets is essential for machine learning algorithms to perform accurately.
• 📉 Treating outliers is vital to ensure the quality of the data fed into machine learning models.
• 🔗 Scaling data is important to bring all features to a similar scale, using methods like standardization or normalization.
• 🔢 Converting categorical features into numerical ones is a critical step to make data suitable for machine learning algorithms.
• 🛠 After feature engineering, the 'clean' data is ready for model training, which should yield better results due to the improved data quality.
• 🔑 Feature selection follows feature engineering, focusing on choosing the most important features to avoid the 'curse of dimensionality' and improve model performance.

Q & A

What is the role of feature engineering in a data science project?

Feature engineering is the backbone of a data science project, accounting for about 30% of the entire project time. It involves cleaning the data and performing various steps to convert raw data into a format that machine learning algorithms can effectively use for making predictions.

What is the first step in the feature engineering process discussed in the video?

The first step in the feature engineering process is Exploratory Data Analysis (EDA), which is crucial for understanding the data and identifying patterns, missing values, outliers, and the nature of numerical and categorical features.

How does one begin the EDA process after obtaining raw data?

One begins the EDA process by first examining the number of numerical features, then the number of categorical features, and using diagrams like histograms and box plots to visualize the data and identify any missing values or outliers.

What are some common techniques for handling missing values in the data?

Common techniques for handling missing values include using the mean, median, or mode to fill in gaps, as well as more advanced methods like using the interquartile range (IQR) to identify and handle outliers before imputing values.

Why is it important to handle imbalanced datasets in feature engineering?

Handling imbalanced datasets is important because many machine learning algorithms do not perform well with them, which can lead to poor accuracy in predictions. Balancing the dataset can help improve the performance of the models.

What is the purpose of treating outliers in the data?

Treating outliers is important because they can significantly affect the performance of machine learning models. Outliers can skew the results, so identifying and handling them properly ensures that the model is trained on representative data.

What are some methods used for scaling data in feature engineering?

Methods used for scaling data include standardization, which transforms the data to have a mean of 0 and a standard deviation of 1, and normalization, which scales the data to a fixed range, typically 0 to 1.

Why is it necessary to convert categorical features into numerical features?

Categorical features need to be converted into numerical features because most machine learning algorithms require numerical input. This conversion allows the algorithm to process and analyze the categorical data effectively.

What is the next step after feature engineering in a data science project?

The next step after feature engineering is feature selection, where one selects only the most important features from the dataset to improve model performance and avoid the curse of dimensionality.

What is the curse of dimensionality and why is it a concern in feature selection?

The curse of dimensionality refers to the phenomenon where having a large number of features can negatively impact model performance, making it difficult to model the data accurately. Feature selection helps to mitigate this by reducing the number of features to the most relevant ones.

What are some techniques used in feature selection to determine the importance of features?

Techniques used in feature selection include correlation analysis, k-nearest neighbors, chi-square tests, genetic algorithms, and feature importance methods like using an extra tree classifier to rank features based on their importance to the model.

Outlines

00:00

🔍 Introduction to Feature Engineering

Krishnak introduces the video by emphasizing the importance of feature engineering in a data science project, which can account for up to 30% of the project's time. He outlines the steps involved in feature engineering, starting with exploratory data analysis (EDA), and mentions the subsequent steps such as feature selection, model creation, deployment, hyperparameter tuning, and incremental learning. The focus is on the initial step of EDA, which includes analyzing numerical and categorical features, identifying missing values, and detecting outliers using visualization techniques like histograms and box plots. Krishank suggests that these observations are crucial for reporting to analytics managers and form the foundation of the feature engineering process.

05:00

📊 Steps in Feature Engineering Process

This paragraph delves deeper into the feature engineering process, detailing the steps Krishank follows after EDA. It starts with handling missing values using various techniques like mean, median, and mode, and possibly more advanced methods depending on the feature's nature. The next steps include addressing imbalanced datasets, which can affect the performance of machine learning algorithms, and treating outliers to ensure data cleanliness. Krishank also discusses scaling data using standardization or normalization to ensure all features contribute equally to the model's performance. A critical step is converting categorical features into numerical ones to make them suitable for machine learning algorithms. The paragraph concludes by stating that feature engineering is about 90% complete after these steps, highlighting the time-consuming nature of the process and the importance of following it meticulously.

10:02

🛠️ Significance of Feature Engineering and Selection

Krishank explains why feature engineering is vital, transforming raw data with multiple issues into clean data suitable for machine learning models. He discusses the transition from raw data, which may be in an improper format or have many inherent problems, to clean data that enhances model performance. The paragraph then shifts to feature selection, a process that involves choosing only the most important features from potentially thousands of available ones to avoid the 'curse of dimensionality.' Krishank outlines various techniques used in feature selection, such as correlation analysis, k-nearest neighbors, chi-square tests, genetic algorithms, and feature importance based on tree classifiers. He encourages viewers to refer to his YouTube playlists for comprehensive guides on EDA, feature engineering, and automated EDA, and ends with a reminder of the significance of these processes in a data science project.

Keywords

💡Feature Engineering

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, with the aim of improving model accuracy. In the video's context, it is emphasized as a crucial step in data science projects, taking up approximately 30% of the project's time. It involves various tasks such as data cleaning, handling missing values, and converting categorical data into a numerical format that machine learning algorithms can process.

💡Exploratory Data Analysis (EDA)

Exploratory Data Analysis is an approach to analyze and summarize the main characteristics of a dataset, often in the initial stages of dealing with data. It helps in understanding the data, discovering patterns, and generating hypotheses for further investigation. In the script, EDA is the first step in feature engineering, where the presenter discusses analyzing numerical and categorical features, identifying missing values, and detecting outliers.

💡Numerical Features

Numerical features in data science refer to the attributes of a dataset that are represented by numbers. These could be continuous (e.g., height, weight) or discrete (e.g., the number of items sold). The script mentions analyzing numerical features by drawing diagrams like histograms to understand their distribution, which is vital for feature engineering.

💡Categorical Features

Categorical features are attributes that can take on one of a limited, and usually fixed, number of possible values, giving them a categorical nature (e.g., color, gender). The video script discusses analyzing these features to determine the number of categories and their distribution, which is essential for preprocessing steps like encoding.

💡Missing Values

Missing values refer to the absence of data in a dataset. They can occur for various reasons and can affect the performance of machine learning models. The script highlights the importance of identifying and handling missing values during EDA, suggesting methods like using the mean, median, or mode for imputation.

💡Outliers

Outliers are data points that are significantly different from other observations, potentially skewing the analysis. The script describes using box plots to identify outliers and mentions the importance of treating them to avoid misleading model training. Outlier treatment is a part of the data cleaning process in feature engineering.

💡Imbalance Data Set

An imbalanced data set occurs when the classes in a classification problem are not equally represented. This can lead to poor model performance as the model may become biased towards the majority class. The script points out handling imbalanced data sets as an important step in feature engineering to ensure fair representation of all classes.

💡Scaling

Scaling is the process of changing the scale or range of the data to make it suitable for analysis, often required by certain machine learning algorithms. The script mentions techniques like standardization and normalization as part of feature engineering to ensure that all features contribute equally to the model's performance.

💡Curse of Dimensionality

The curse of dimensionality refers to the phenomenon where the volume of the feature space increases so fast with the addition of dimensions that the available data become sparse. This can negatively impact machine learning model performance. The script briefly touches on this concept, emphasizing the importance of feature selection to avoid it.

💡Feature Selection

Feature selection is the process of choosing a subset of relevant features for model construction. It is used to reduce overfitting and improve model performance. The script describes feature selection as a step following feature engineering, where techniques like correlation, chi-square, and feature importance are used to select the most informative features.

Highlights

Feature engineering takes approximately 30% of the entire data science project time, emphasizing its importance.

The lifecycle of a data science project begins with feature engineering, followed by feature selection, model creation, deployment, hyperparameter tuning, and incremental learning.

Exploratory Data Analysis (EDA) is the first step in feature engineering and involves various steps beyond basic data analysis.

Understanding the number of numerical and categorical features is crucial as a part of EDA.

Visualizing data with histograms and box plots is essential for identifying missing values and outliers.

Communicating findings from EDA to analytics managers is important for project alignment.

Handling missing values is a critical step, with various techniques such as mean, median, and mode.

Imbalance datasets can negatively impact machine learning algorithms' performance and need to be addressed.

Outlier treatment is important, with methods including box plots and the Interquartile Range (IQR).

Scaling data using techniques like standardization and normalization is part of feature engineering.

Converting categorical features into numerical features is a key step for machine learning algorithms.

Feature engineering is a time-consuming process, especially with large datasets, and requires careful attention to detail.

Feature selection follows feature engineering, focusing on choosing important features to avoid the curse of dimensionality.

Various techniques such as correlation, k-neighbors, chi-square, and genetic algorithms are used in feature selection.

Feature importance derived from ensemble methods like Extra Trees Classifier is vital for selecting the best features.

The presenter provides dedicated playlists on EDA and feature engineering for further learning and understanding.

Automated EDA tools are mentioned as a part of the playlist, offering a more efficient approach to EDA.

The importance of feature engineering is reiterated, as it transforms raw data into a format suitable for machine learning models.

Transcripts

hello all my name is krishnak and

welcome to my youtube channel so guys

today in this particular video we are

going to discuss what all steps we

actually perform in what kind of order

to complete the feature engineering

process

now in a data science project guys if i

just consider feature engineering it

takes somewhere around

30 percentage of the entire project time

right 30 so it is very very huge you

know and there are many many people who

have asked me questions like krish what

is the exact order see after i get the

data the raw data what should i first do

you know and if you remember in the life

cycle of a data science project the

first module that actually comes is

feature engineering and then after that

feature selection then you have model

creation then you have model deployment

then you have hyper parameter tuning

before model deployment you have hyper

parameter tuning then you also have

incremental learning and there are many

more steps as such but the most

important the crux the backbone of the

entire data science project is feature

engineering because you will be cleaning

the data you will be doing a lot of

steps so let me talk about every step

that you may be performing in future

engineering step by step okay so the

first step that i want to discuss about

is i'm just going to write it down the

stop one is basically eda

now eda is nothing but

exploratory data analysis

exploratory data analysis

now this is a very very important thing

and remember guys in my youtube channel

i have created dedicated playlist on

feature engineering on eda everything so

i'll also be giving those entire link at

the last okay so first step step one is

basically eda that is exploratory data

analysis

now you may be thinking okay fine

exploratory data analysis is it only

about data analysis no there are many

many steps that we actually perform in

this so let me write it down one by one

in eda as soon as we get the raw

data

okay as soon as we get the raw data

because

the entire feature engineering is

actually done on the raw data itself

right so as soon as we get the raw data

what do we do we start doing the

analysis now what kind of analysis we do

first of all i i'll just give you one

example first of all what i actually

follow as soon as i get the data i

basically see that how many numerical

features may be there

okay how many numerical features

may be there

right

then i may go up with how many

categorical or discrete categorical

features may be there

categorical features

i may i may try to see this numerical

features i'll try to dis

define or draw different different

diagrams like histogram

right like pdf function

right and obviously you know all these

things you can use libraries like c bond

right i hope everybody's familiar with c

bond we use c bond you know you can also

use matplotlib to see all this kind of

diagrams right and then in the category

features you'll try to analyze the

category features like how many category

features may be there you know in those

features how many categories maybe there

is there multiple categories see all

this observation is actually necessary

you know

all this observation is basically very

very much necessary okay now coming to

the third step that i will definitely

follow i'll just try to see whether

there is any missing values

i will just try to clearly draw

clearly draw

visual and i'll just say i'll try to

visualize all these graphs

visualize all these graphs

you know with the help of missing values

also if there is any missing values i'll

try to see you know probably uh i may go

with my fourth step i'll try to see

whether there are outliers and how do

you draw an outlier simple box plot

right box plot i'll go with box plot

i'll try to see whether there is any

outliers now these observations are very

much necessary because whatever diagrams

you are actually drawing this all needs

to be sent to your manager

to your analytics manager

because that is what you have done in

the eda right and this is just a

the first step in the entire feature

engineering and trust me there are many

more steps which i will be telling you

in just a while right outliers missing

values category features numerical

features and you know there are various

there are three to four different types

of uh handling missing values missing

values will be because of different

different reasons and based on that you

have to act accordingly right so you

have outliers probably you know you'll

try to see

whether the raw data needs cleaning also

or not

cleaning or not right so this this is

also very important step the raw data

may have many information in just one

feature and out of that if you wrote if

you require all those information or not

right but again understand the main step

over here what we are trying to do we

are trying to convert

the raw

data into useful data

into useful data

so that

our ml algorithms will be able to

ingest them properly

ingest them

for

giving amazing predictions right so in

the eda part we see all these things

right

now let's come to the second step very

very simple now in the second step what

i always do

is that

i start handling the missing values

i start handling the missing values very

very important

there are various ways of handling the

missing values you may be saying okay

krish we may use mean

median

more right all these things

right

not only this guys not only mean medium

mode i'll try to analyze those features

i'll try to see whether there is an

outlier in that particular feature this

three just one some of the three steps

and we have lot of various modes

a lot of various ways to handle the

missing values right mean median mode

are one of them you know i may replace

some of the features by considering some

different different techniques also and

the entire details is mentioned in my

feature engineering playlist again

feature engineering playlist

you know i may analyze it i may i may

create a lot of box plots to see okay if

i'm utilizing iqr in removing you know

if you remember there is a formula with

respect to iqr also to remove the

outliers and after handling the outliers

what i'll do i'll try to handle the

missing values by median in short if you

don't want the impact of

the outliers you can directly use median

or mode right

so this is basically about the second

step the third step what i do is that

you know

step step three

so in the step three what i can actually

do is that handling imbalance data set

you know this is also a very very

important step

because not all the machine learning

algorithms works well with an imbalanced

data set right you may get a very bad

accuracy and you may be thinking that

okay you have got amazing accuracy but

because of the imbalanced data set you

may get a very bad one right now the

fourth one that i would like to do is

that treating the outliers

right

this is also very much important step

okay there are various there are two to

three ways to handle the outliers also

which you should definitely explore i'm

just telling you step by step whatever i

do i'll basically use this and before

all the uh one more step that i can

actually do is scaling the data

right scaling the data

in the same scale we use different

different process like standardization

right standardization

normalization

right all these techniques we actually

used in feature engineering right coming

to the sixth step uh this is very very

much important that is converting your

categorical features

converting the categorical features

categorical features

into numerical features right

this is the most important step

numerical features one example i'll tell

you suppose you have an example like pin

code in pin code you have different

different different values right and

here you have so many features so many

unique categories so what technique you

may probably use in order to convert

this categorical features into numerical

features and probably you have to

actually use this right now coming to

the uh next step let's see all these

things what what by by this all these

things what we are actually doing see uh

if i go from step one eda step two

handling the missing values handling

imbalance data set treating the outliers

scaling the data scaling down the data

just write it scaling down the data

right

and then converting the category

features into numerical features once i

perform all the steps what i think is

that yes

feature engineering is about 90

completed and don't think that you'll

just be able to do it this in one day or

two day if you have a small data set

obviously i'll say that you will be able

to do it in three to four hours but

understand i have worked with data set

where you have one million records

right and for doing all these things it

takes time right always make sure that

you follow this process you always

remember the steps okay this is very

very much important let me just check

out if i have missed any um

scaling down category for outlier

treatment everything is mentioned over

here very clearly okay so these are most

of the steps that we do in the future

engineering and till here right from

here to here

so what has happened now see why feature

engineering is important let me talk

about it

the raw data

the raw data in this raw data you'll

have so many problems you'll be having

right it'll probably the json format

probably it may be not having uh proper

features it may be

not in the proper format you know

there may be many things right

this raw data

after this entire process of feature

engineering

you will be having this clean data

and this d clean data will now be given

to your

ml models

for the further

training purpose

now when you have the clean data and

you're giving your model to for the

training purpose obviously your model is

going to give you better results there

is one more step after feature

engineering which is called as feature

selection feature selection is pretty

much simple guys in future selection

what we do

we

select only those features that are

important now let me tell you that if

there are thousand features in your data

set

right and out of this entire feature out

of all these thousand features it is not

necessary that all the thousand features

are required

you know and if you have that many

number of features there is also a term

which is called as curse of

dimensionality

right and this usually happen when you

have many many features

it is also a curse so we should take

those features that are very important

and in future selection what are steps

we do let me write it down over here

in future selection what are steps we

actually do

in feature selection

in feature selection we perform various

steps right if you remember

right you have correlation

right you have

um one step is basically correlation

and if i talk about more uh you also

have k neighbors you can use k neighbors

for the future selection purpose you

have chi square

right

you you have chi square you have genetic

algorithms

for doing this right genetic algorithms

for doing this uh you have something

called as feature importance

right dissolved techniques are there

feature importance internally uses extra

tree classifier here specifically you

use something called as extractory

classifier

right all these steps

and i've uploaded videos on this also

right so all these steps is basically

used for selecting selecting the best

features

right selecting the best features

okay

now see

this is the most important step and

again if you are

having any confusion with respect to

anything what i'll do is that just just

open the youtube channel okay go to this

two playlist one is chris eda

okay so here is your exploratory data

analysis playlist you can go and have a

look on to this here in the same steps i

have explained everything if you go and

check out this entire playlist right in

the same step eda

then feature engineering and then

feature selection

in the same step i've actually explained

everything and here i've also explained

the automated eda part okay so it will

be very much easy this is the one

playlist and the other playlist is

basically about the feature engineering

so this too

is must trust me because 30 percentage

of the time and here all the other

different different types of feature

engineering how do we handle category

features how do we handle missing values

see three days four days on handling

missing values only has been explained

you know how to handle category features

everything is being explained what is

standardization transformation

everything is explained in this handling

missing data and even outlines all these

things has been explained right so my

suggestion would be that go ahead have a

look onto this and yes uh if you like

this particular video please do make

sure that you subscribe the channel

press the bell notification icon but

understand feature engineering is a very

important step all together i'll see you

all in the next video have a great day

thank you and all bye