Live Day 1-Live Session On EDA And Feature Engineering- Zomato Dataset
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- Опубліковано 22 сер 2024
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so just to clarify, UTF8 and Latin1 encoded means
UTF8 is used for electronic communication, like the data we process we convert this alpha numeric data into machine language using UTF8 encoding .. and we all know at granular level the machine language is in 0s and 1s, so we need some sort of encoding to convert the incoming English language alphabets into machine language for a machine to process the data ...
but when the incoming language has some characters which are not defined in English language then machine will not able to convert it into 0s and 1s cos for it its a foreign word/character not listed in given reference directory which is UTF8 in this case, so we need to provide appropriate reference so in case of incoming data has Japanese character then you can use JIS encoding, in case of incoming data has Latin characters then latin-1 etc ...
That was very informative brother. Thanks a lot for the explanation 🤗
thanks a lot, it really helped!
thsnk you m an
That's informative thanks you
But how to find out that our data has japanese or latin or any other foreign characters in it??
Enjoyed going through this EDA session. Sharing my new learnings from my first EDA session as my thanksgiving.
1) Learnt about encoding errors.
2) Learnt about matplotlib figure sizing use rcParams
3) Learnt about why hue did not work in Seaborn as intended.
4) Learnt to understand the difference between value_counts and groupby().size
5) Loved to see how reset_index was being used time and again.
58:08
We use hue when we wish to differentiate the categories. But remember the colours of the bar graph were already in different colours. Why was that? In Seaborn library, the default palette is to show a mix of colours for each bar, which we subsequently changed to the colours we wanted. So the hue parameter did not do anything additional in this context. But the hue parameter did give us a legend.
1:18:45
We used groupby().size plenty of times. I was wondering when to use value_counts and when to use groupby().size
df[condition] returns a series ==> Here we can use value_counts(). We cant use groupby when it is a series.
df[df[condition]] returns a dataframe ==> We can use groupby().size here.
Also size gives total count of all elements. If used with a groupby, it gives count of all elements for each group, including null values. But if we use value_counts, we get the element-wise total excluding null values.
Is really helpful that someone with real knowledge and experience teaches this kind of hands on real example stuff! so many thanks Krish
Just completed this session. Now everything seems so relatable and understandable. THANK YOU
hi
pls help me to understand 😭🙏🏽
Placement hue aapki kahi
In real project we do not import CSV file, we pull data from mongo or from SQL db, can you please create video on importing data frame from Database.
I cannot open this CSV to read in pandas can you please help me to read it.
I'm getting a permission error
@@iakhileshgupta3553 save the csv file in local folder ... then assign the path example
Data= pd.read_csv(C:\\program file \\user\\folder\\'file_name.csv')
Then print data.head() ... First 5 columns & rows come into picture ...
@@sidindian1982 permissionerror: [errno 13] permission denied: 'c:\\users\\hp\\desktop' this is the error message
@@iakhileshgupta3553 recheck CMD prompt ... whether pandas is installed or not ...if not then ...
Pip install Numpy ...
Pip install pandas ....both in cmd propmt
In Jupyter notebook .. .import
Import pandas as pd
Import Numpy as np ..
THEN RUN IT .. CLICK ON RUN BUTTON ( it has to reset always whenever you open Jupyter notebook ....
Then type ... Df= read_csv('dats.csv')
df.head ()
Sir @@sidindian1982 i have pandas & numpy installed i have checked on cmd as pip list it is there & also done what you suggested it says already installed but it won't read the file & gives the above permission error in the terminal
Thank you sir for this! I think, you should conduct more such sessions where you assign us such questions related to Data Analytics and then discuss answers
Awesome session , much better than paid session, please keep posting sir.. thanks lot
Thanks!
final_df['Cuisines'].value_counts(sort=True, ascending=False)[:10]
on which basis you define the top 10 cuisines..... and one more thing I tried a code
final_df[final_df['Rating text']=='Excellent']['Cuisines'].head(10) so I found this code more efficient on the basis of info.
which code is more appropricate??
final_df['Cuisines'].value_counts().head(10)
@@ankitraj3180 yepp, correct way
Thank you sir. This is a great session. It is very helpful and everything seems very easy to understand.
Best teaching I am from non tech background even I understand ur teaching 😊 god of data science krish naik
Wonderful session sir thank you very much
we are catching up please don't get disheartened by viewer counts
Superb sir, we are lucky to have a mentor like you
Thank you sir for this amazing session. Sir please do live sessions on deep learning and NLP
Thanks for this video. I spent my Sunday morning in a very useful way in brushing up my Python skills.
You make it very easy sir..the way you teach is fabulous....
I am from Pakistan and I have seen a lot of videos about EDA, But this one is just amazing. It is really easily understandable for begginers.
Respect to you Sir!!!
do you work as a data analyst?
@@cyberpro151 yes
Bruh Watching this legend and honestly man today i miss the superchat faeture must be added in youtubePk
Good live class ,understandable,thank you for the session
for assignment 1:26:16
final_df['Cuisines'].value_counts().reset_index().head(10)
I tried this
`df.Cuisines.value_counts()[:10].index`
But both are basically the same thing I guess.
This is convenient data set, no strings in numerical columns, or extra characters. That's a challenge.
Thank you sir for this amazing session. Sir please do live sessions on NLP and Deep Learning
Great session... Thank you
Prefect explanation! the visualization, the sub-setting , everything, the queries and the observations! GREAT thanks so much... (i just sttruggle a little bit about when to use groupby)
Awesome, thank you Krish!
For the last Assingment which he has given as homework
you can replace final_df as what you've coded in your book
final_df[["Cuisines"]].groupby(["Cuisines"]).size().reset_index().sort_values(by=0,ascending=False).head(10)
Can you please explain??
Damn man that's a long code.
Or you could simply write : final_df['Cuisines'].value_counts().head(10)
cuisine_names=final_df.Cuisines.value_counts()
cuisine_names[:10]
This was sooo helpful krish sir! U made it like butter for us. I liked everything thank you u keep inspiring us
thank you for giving such an amazing things that from your channel....
Thank you so much Sir for the great video
In missing values part the heat map plot is not visible instead if we resize the scale of the graph we will be able to actuallyplot the point.
plt.figure(figsize = (15,8))
sns.heatmap(df.isnull(), yticklabels = False, cbar = True, cmap = 'viridis')
the figure method will help us to resize the heat map accordingly.
achaaa
Hi Krish.
Please do an example for the difference between using LSTM for classification and LSTM for regression.
Explain the difference between using LSTM for the two. Especially for multivariate.
You have always been my teacher. I learned machine learning and deep learning from you. No other bootcamp, I didn't do any computer science course in University. Just your UA-cam videos.
Thank you so much.
Thank you so much sir. You are like a way to build confidence in me to start data science
Really appreciate your work
final_df[['Cuisines']].groupby('Cuisines').size().reset_index().rename(columns={0:'Count'}).head(10).sort_values(by='Count',ascending=False)
Hello the explanation is great and i have done the assignment it was super easy:
# Finding top 10 cuisines
cuisine_counts = df.groupby('Cuisines').size().reset_index()
top_10_Cuisines = cuisine_counts.head(10)
Wonderful Krish. Just love it.
The most helpful video sir thank you so much for your valuable efforts ❤❤
assignment: df_combined['Cuisines'].value_counts().reset_index().rename(columns = {'index' : 'Food'}).iloc[:11,:]
Great session. Wish I could have joined online
You are the best!
In cell no 10 you added so many features it is important pls reply nice explanation 👍
final_df[final_df['Aggregate rating'] == 0][['Aggregate rating','Country']].value_counts()
this also workes at 1:11:19
Thank you for the amazing session
great session sir love u sir 🥰😍
Hello sir, your contributions are very much helpful to many aspirants. one question why have stopped linear algebra tutorial ?
great session thank you I learnt so much
you are just amazing!
Pronunciation of Cuisines was hilarious 1:26:20 HHAHAHAHAHHHAHAHAHAHAHAHAHAHAHAHAHA. It was an Amazing Video Kudos
Great session
Wonderful Content
Thank you amazing video
Thank you Krish.
Very helpful content brother
Hi Krish Amazing video. Thanks alot for all the videos, keep it up 👍
Have a doubt @1.02, why max no of ratings is between 2.5 to 3.4? And not 2.5 to 3.9?
nice section very informative.....
You are a source of motivation... Keep up the good work, Krish! May Allah bless you and all your beloved!
#find the top 10 cuisines
final_df['Cuisines'].value_counts().head(10).reset_index()
# Find the top 10 cuisines
final_df['Cuisines'].value_counts().sort_values(ascending = False).head(10)
Really Helpful.
really a great session .
Thanks sir
One issue is Krish sir moves his screen rapidly, it is difficult to code along with him,
In UA-cam we can pause video and look for that section, but in live class difficult.
💖💖💖AMAZING💖💖💖
awesome work,
Nice sir
Thank you sir
we can use
import matplotlib.pyplot as plt
plt.figure(figsize=(10,4)) for figure size in visualization
yaa its just for background of ur chart
Good learning
Mzza hi a gya
Please make more vedios on different use cases
Very helpful..
final_df['Country'][final_df['Rating text'] == 'Not rated'].value_counts()
Amazing session Krish😁
Osm session ❤👍
Nice session thanku sir
In jupyter how we execute all cell in one command
How to change the background color of seaborn plot to black??
sns.set(rc={'axes.facecolor':'dark', 'figure.facecolor':'dark'})
Using concat instead of merge will result in NaN values perhaps
How to create website video. thank you
sir please do EDA and ML project.
Thankyou sir..
pl upload EDA videos in community group
sir i am unable to open this dataset on my jupyter notebook
i m getting error in barplot where we plotting aggregate rating vs rating count
(the error showing value error not able to interpret the rating count) at 55.20 mins
Sir, Django playlist has been left incomplete, I believe. Please upload more videos on django
About error we can ask chatgpt direct
what are the pre-requisites for this series?
Pata chala
@@silverstone9952playlist ka pehla vid dekho dost
Hi sir can you explain how to aggregrate multiple columns in group by
1.11.00 sir aapne kya paadha hai us time😆😆😆
Thank you sir, Where to do we get the notepad which you have worked? is it saved somewhere. please inform
finished coding
please help me how to find top 10 cuisines
final_df[final_df['Rating text'] == 'Excellent'][final_df['Aggregate rating'] == final_df['Aggregate rating']]['Cuisines'].value_counts()
1:10:58 I Think The last record shouldn't come sir. Because, it has 1.8 rating and we require only 0 rating countries.
Yes we should use
country_rating= final_df.groupby(['Aggregate rating','Country']).size().reset_index().rename(columns={0:'Rating count'})
country_rating[country_rating['Aggregate rating']==0]
in case of skewed data, do we need to perform yeo-johnson power transformation and then standard scaling for the SVM model?
df["Cuisines"][0:10]
ty sir
put time stamp
Sir I want to extract features from a text .CSV file, by using TFIDF and Ngram model, and I want the output in sparse matrix, Do you have any tutorial on that plz guide.
Naye bottle chamka rahae ho Kris naik
Wonderful, I want to interact with you in regarding of Data science?
Hi sir, while merging two different data sets. either data shape should be same or different?
so it is a regression dataset