Step By Step Process In EDA And Feature Engineering In Data Science Projects
Summary
TLDRIn 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
🔍 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.
📊 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.
🛠️ 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.
Mindmap
Keywords
💡Feature Engineering
💡Exploratory Data Analysis (EDA)
💡Numerical Features
💡Categorical Features
💡Missing Values
💡Outliers
💡Imbalance Data Set
💡Scaling
💡Curse of Dimensionality
💡Feature Selection
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
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