Give this video 1000 likes then I will start a 7 days Live NLP community Sessions for everyone. Happy Learning!!

@vinayakdumbre2828

2 жыл бұрын

Wow,its should be end to end,not just basic rnn,it would be awesome

@photogenicglint239

2 жыл бұрын

Hi Krish , Collab with Sumit Mittal ( Trendytech) for big data course. He teaches in depth but offer course at high price.once he Collab with ineuron so that he can offer course in affordable price.

@vivekpandey8438

2 жыл бұрын

thanks please start NLP common file and Also Upload statistics in 1 Videos

@aryansheth7369

2 жыл бұрын

666

@faraazmohammed3693

2 жыл бұрын

992..close

Sir data analysis in sql with advance queries for portfolio project. Full length video, like this video please 🙏🏼

Thanks for this teaching Krish, your approach is simple and easy.

thanks Krish..it has been an enlighten session.. Have watched the entire 2.48hours session. Be blessed

Mind-blowing explanation bro keep it up

47:50 this is also working df[df['Aggregate rating'] == 0]['Country'].unique()

great work sir subscription done from my side

When you try to get top 3 countries percentage in pie chart, it calculates for only those three countries. But calculating over all the transactions will make sence. Percent of transactions from India means, among all the transactions what is India's percentage. But here in hour case, it allows only India, USA and UK.

you are doing excellent work sir

Very informative video. I would like to add a point regarding the UTF-8 code error i.e if you save the excel sheet as CSV UTF-8 comma delimited format then there is no need to enter the codes.

Thanks. Its really helpful

Great Sir❣️

Thanks Krish, you are the best!. A question related to the "second session" about the Product_Category_(1,2,3), I understand that you explain that in case of NaN values in categorical feature you can use the Mode to replace the NaN values. But for this particular case I think that is important to understand the data before doing that, since Product_Category_(1,2,3) indicated that the products can be part of multiples categories. For example a movie being categorized as "Drama, Action, Suspense". So for this case maybe it would be better to try to use dummies for Product_Category_(1,2,3) and then try to sum it, it would be complex to implement it but you would get the real information about your data, since you can get the info about Product_1 being a (0,1,0,0,1,0,1) if that product has 3 categories. Cheers!

My name is najiib and i from country called somaliland which is in somalia really i enjoyed this project i will hope you will upload more topics about machine learn thank you krish naik najiib from somaliland

Really helpful Sir..

Hi Krish, do you have videos of data cleaning, EDA, and feature engineering for unsupervised ML? (For both Principal Component Analysis (PCA, CA, MCA... etc) and Clustering techniques include partitioning, hierarchical, DBSCAN etc). By the way, are there differences in cleaning cleaning and feature engineering between predictive regression and inferential regression? Thank you!

thank you so much sir

Label Encoder should be used only for target labels i.e y and not on input feature. It's mentioned in sklearn Label Encoder page clearly. For nominal & ordinal variables, we should use One Hot Encoder and Ordinal Encoder respectively. These all should be done within a pipeline and column transformer for hassle free coding preferably

@rajkundra5005

Жыл бұрын

yes,same doubt

@prayashdash1815

Жыл бұрын

@@rajkundra5005 bhai link dede

Nice one. One doubt the main work of data analyst is only finding insights and done. The ML part no needed?? Is that ML job work is for Data scientist.

Thanks sir

Please post EDA video in your hindi channel also

Hi Krish , Collab with Sumit Mittal ( Trendytech) for big data course. He teaches in depth but offer course at high price.once he Collab with ineuron so that he can offer course in affordable price.

I dont understand replacing na values of product catogry_2 and product catogry_3 with mode we just manipulated the data

God-Father of Data-Science

Sir, Airline is a nominal feature and in you said that in case of nominal feature, we can do OHE or Mean encoding. Why are you using LabelEncoding ?

For data analyst work the data set is available from any data base or in form of excel or CSV ??

super

Nice

I couldn't do the part where we have to show the country names that has given 0 rating It's not showing any output

Hello Sir! Thank you. @43.00 why the observation of the maximum number of ratings is from 2.5 - 3.4?

We could have used product ID to fill product category column

Make another video in data explratoery, eda

Query for flight price prediction dataset for duration column df['Duration_hour']=df['Duration'].str.split('h').str[0].str.split('m').str[0] df['Duration_hour']= df['Duration_hour'].astype(int) It's work for me.

There are two types of variable nominal and ordinal In ordinal you can use label encoding but you can't use label encoding for nominal variable you have to use one hot encoding if you will use label encoding for nominal then machine learning model will treat nominal as ordinal so you can't use

@aishwaryapattnaik3082

2 жыл бұрын

Label Encoder should be used only for target labels i.e y and not on input feature. It's mentioned in sklearn Label Encoder page clearly. For nominal & ordinal variables, we should use One Hot Encoder and Ordinal Encoder respectively.

@shivamkumar-rn2ve

2 жыл бұрын

yeah you are right about label encoder you can only use it for target variable

feature engineering in one video

where is the blackfriday dataset

I can't find the black friday dataset on your github page

I am not finding train.csv file for the second part of video in your github

from where i can get your codes for this video ?

Sir plz turn off your notification sound!

Is this enough to mention in resume

53:15 / 2:48:54

feature engineering in 1 video

after do doinh this project can we add this resume

is this a regression problem?

Can you share file for practice

why latin-1 ?

Hi

please share blackfriday dataset ..there is no blackfriday dataset in the given link.

@kirankumar9934

2 жыл бұрын

Even I'm not able to find black_friday dataset

please explain how one can find the location of CSV or get the jupyter NB to read the file location automatically inside a folder I am getting an error while reading the file

@ManishKumar-qh2ql

2 жыл бұрын

open with path location and instead of \ use \\

@madhupincha7898

2 жыл бұрын

pwd()

@Agros92

2 жыл бұрын

You can put the csv file on the same folder of the JupyterNB file. To read it it would be - pd.read_csv("data_name.csv") -. If you put the data in another folder and that folder is located in the same folder of the JupyterNB file you can do - pd.read_csv("Folder_Name\\data_name.csv") -

first comment sir how to make sql project for portfolio please reply

@mainlykanchan8740

2 жыл бұрын

Yes..

11:00

I am getting Nan error when I try to replace F with 0 and M with 1 in Black Firday EDA ..How to resolve it?

@pavankumarjammala9262

10 ай бұрын

Once before running that particular code run all cells at a time you will get it

Find the top 10 cuisines(food) item for this for zomato dataset is this code correct final_df.Cuisines[:10].value_counts()

Can anyone explain when do we use onehotencoding and when do we use Labelencoder(ordinal encoding) since they both do the same job but in a different way, onehot creates multipe new feature while label do all the work in one feature. Like in this case wouldn't be better to use labelencoder to do encoding in Additional info feature since onhot will create multiple new sparse eatures which might increase he workload of the mode or am i missing some point here?

@sanjaysanjay862

2 жыл бұрын

One-hor encoding is used only for independent variables (feature) but label encoder is used for target variable.And they both won't do the same task one-hot encoding gives seperate columns for each catagory.As of my understanding.If wrong reply

@Abhi-qn4xv

2 жыл бұрын

@@sanjaysanjay862 well u r correct. I did some reading in this topic and found out that although label encoder can be used on independent variables too, it's usually not used. On independent variable, one hot is better than label encoder as label encoder might confuse the model into learning that feature as a rank. So instead of learning 1 as a numerical representation of a word, model will think 1 as a rank. Hope u understand my point

@sanjaysanjay862

2 жыл бұрын

@@Abhi-qn4xv Yes, I agree that

@adeshinaibrahim9641

Жыл бұрын

In simple terms use one-hot encoding when you have limited number of categories but otherwise dont.

sir how to deal with utf-8 encoding

@Coding_Hub242

6 ай бұрын

use latin=1

Hi, why did you combined the country code ?? Please explain this.

@A3dull

6 ай бұрын

The first dataset only includes the country code, while the second dataset contains both the country code and the country name. When merging them together, the country name column was populated using the information from the second dataset.

@bestofmusicc__

6 ай бұрын

@@A3dull yeah thanks man👍💪

Is necessary to hanle missing values in data analysis?

@_k_kd

7 ай бұрын

yes.

2:36:35

What are Prequesties to learn this sir?

@krishnaik06

2 жыл бұрын

python

@srirama8275

2 жыл бұрын

@@krishnaik06 Thank you sir

data[data['Aggregate rating']==0]['Country'].value_counts() , This also works

where to find the black friday dataset?

@swetamishra3580

6 ай бұрын

Did you find it?

Guys how to find top 10 Cuisines in data ? help me

@Pyrometin

3 ай бұрын

I got it, use this code. final["Cuisines"].value_counts()[:10]

im not getting zomato csv file....can anyone help????

@pavankumarjammala9262

10 ай бұрын

Yeah !! bro same prblm from my side also

# Function to convert duration to minutes def convert_to_minutes(Duration): hours = 0 minutes = 0 Duration = str(Duration) # Ensure the duration is treated as a string if 'h' in Duration: hours = int(Duration.split('h')[0]) Duration = Duration.split('h')[1] if 'm' in Duration: minutes = int(Duration.split('m')[0]) return hours * 60 + minutes # Apply the function to the 'Duration' column final_df['duration_minutes'] = final_df['Duration'].apply(convert_to_minutes) final_df.head()

How to find top 10 Cuisines final_df= final_df.replace(np.nan,'Dummy') --- Convert NaN to Dummy one_string = ','.join(final_df['Cuisines'].tolist()) -- Convert Cuisines columns to list and join one_list = one_string.replace(" ","").split(',') -- replace blank spaces by comma pd.value_counts(one_list)[:10] --- top 10 values

Everything is perfect except the pronunciation. Haha

Hindi me vedio bana digite aap

he said fucked instead of fixed 1:51:00 😆

Hindi m hota to jarur kuch Sikh pate 😓😓😓😓

Zomato Dataset Assignment: (With respect to value counts) cus_values = final_df["Cuisines"].value_counts().values cus_labels = final_df["Cuisines"].value_counts().index plt.pie(cus_values[:10],labels=cus_labels[:10],autopct='%1.2f%%') (With respect to Aggregate rating) final_df[['Aggregate rating','Cuisines']].groupby(['Aggregate rating','Cuisines']).size().reset_index().tail(10) Please correct me if i did it wrong.

How to give just zomato.csv in df line instead of giving entire path

@himanshutola3729

2 жыл бұрын

Keep the CSV and the ipynb file on same folder