Hi Dhaval; Your video is very helpful to students who are the study of data science in rural India ; Kindly upload videos on Intermediate Stats and advanced
awesome explanation you clear each and every steps why we use this method n all..thanks alot.....the way you explain that is superb....i think no one can explain like the way you explain....
sir, why you used different approach for ------ import math median_test_score = math.floor(d['test_score(out of 10)'].mean()). instead why we cant use d.test_score(our of 10) as previously ... Please help..
If we take 0 bedroom, 0 sqft and 0 age as input, still it will give 221323 as output. Or if we take 0 bedroom, 0 SQFT and 1 years as age, it will give negative answer. Is this because, the example is hypotheical and does not cover corner cases?
Thank you and Finished:::::: import pandas as pd import numpy as nm from sklearn import linear_model import math from word2number import w2n df = pd.read_csv("hiring.csv") df['test_score(out of 10)'] = df['test_score(out of 10)'].fillna(df['test_score(out of 10)'].mean()) df.experience= df.experience.fillna('Zero') # Converting Float(Actually series) into Int df['test_score(out of 10)']=df['test_score(out of 10)'].astype(int) df.experience = df.experience.apply(w2n.word_to_num) model = linear_model.LinearRegression() model.fit(df.drop('salary($)',axis='columns'),df['salary($)']) print(model.predict([[12,10,10]])) Answer exactly like you... Learned Word2 num and how to convert series into float/Int
Sir, first of all, many warm wishes for you. You have taught in subsequent videos how to handle textual data (convert text to numbers), here exercise data set contains a text column, i-e. experience. We can convert that textual data to number. Would you please suggest here at tutorial 3 how to handle such text data.
I am using google colab and the word to number library is not working there so I used the function to convert string data into numeric in the experience column. Overall enjoyed the exercise.
sir your way of learning is very good i have doubt in above problem when i predict the value of [[3000,4,15]] i'm get the result of 602590.079 why because the answer in table is 565000
check your instruction where you have used fillna( )... because i mistakenly filled all fields with median and the answer got different then mentioned in video
HI Dhaval, We are trying to solve exercise for multivariable linear regression , have one doubt here import math median_test_score = math.floor(df['test_score(out of 10)'].mean()) median_test_score Could you please let me know why we took here mean , as in previos exercise we took median for finding NaN value..
@Subhadip : I guess we both used the median instead of mean . As, I replaced median from mean I got the actual result. [53713.86677124] & [93747.79628651].
Sir, i am converting the word to num but getting error code is df['experience']=df['experience'].apply(lambda x: x.w2n.word_to_num()) please suggest me where i am wrong
import w2n df.experience = df.experience.apply(w2n.word_to_num) I found this works after a few trials and errors. Also, did u put the w2n.oy file in that project folder. What error were u getting?
@@hemalkarambelkar9638 can u give me yr code link? or you can past here yr code I got error during w2n error: value error type of input is not string please enter a valid number word
My answers are different as your 2 yr experience, 9 test scores, 6 interview scores: 53537.10 12 yr experience, 10 test score, 10 interview score: 75641.91 Your answers are: 53713.86 and 93747.79
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Sir, because of your videos only I am able to learn the basics and concepts of ML models
Hi Dhaval;
Your video is very helpful to students who are the study of data science in rural India ;
Kindly upload videos on Intermediate Stats and advanced
Your videos are really helpful in understanding complex concepts in a simple manner and your exercises are helping me very much
I was having a difficult time understanding these algorithms before. But you have made it very clear. Really nice and easy explanation. Thank you
totally agree. great work !
Anyone want to learn machine learning implementation should watch this series, great explanation 🔥🔥
Very helpful knowledgeable and easiest way of explaining this things
awesome explanation you clear each and every steps why we use this method n all..thanks alot.....the way you explain that is superb....i think no one can explain like the way you explain....
Thanks for your kind words Preeti
UA-cam team has to make a heart ❤️ reaction so that I will give you on your every video😄😂
Best ML tutorial I found on UA-cam
Hi Sir,
Thank you so much for this video very helpful for beginners and initial stage.
sir, your way of explanation is great. thankyou sir
Thank You brother for all your help. As i Said so many times earlier and will say again that the way you teach makes perfect sense. Regards
Taran, thanks for appreciation and feedback
Really, Sir, you have great teaching skills...
really appreciated
Glad it was helpful!
I must say your videos is so well defined , sir I am big thankful to your video and your voice love ❤️
Sir ... ap nay to dill jeet liya mera ...
Lub u Sir ... :)
Stay blessed ...
Thanks Aeze for your kind words :)
Sir you are great.. after waching this linear regression video my all doubts are clear.. thanks sir.. keep teaching us
Glad to hear that
how can i convert string ('Experience Columns') into float values ??
sir, why you used different approach for ------ import math
median_test_score = math.floor(d['test_score(out of 10)'].mean()).
instead why we cant use d.test_score(our of 10) as previously ... Please help..
If we take 0 bedroom, 0 sqft and 0 age as input, still it will give 221323 as output. Or if we take 0 bedroom, 0 SQFT and 1 years as age, it will give negative answer. Is this because, the example is hypotheical and does not cover corner cases?
Thank you and Finished::::::
import pandas as pd
import numpy as nm
from sklearn import linear_model
import math
from word2number import w2n
df = pd.read_csv("hiring.csv")
df['test_score(out of 10)'] = df['test_score(out of 10)'].fillna(df['test_score(out of 10)'].mean())
df.experience= df.experience.fillna('Zero')
# Converting Float(Actually series) into Int
df['test_score(out of 10)']=df['test_score(out of 10)'].astype(int)
df.experience = df.experience.apply(w2n.word_to_num)
model = linear_model.LinearRegression()
model.fit(df.drop('salary($)',axis='columns'),df['salary($)'])
print(model.predict([[12,10,10]]))
Answer exactly like you... Learned Word2 num and how to convert series into float/Int
why you take out the mean for test_score for Nan value while in a tutorial the sir takeout median for Nan value ??
why u converted float to int?
Sir, first of all, many warm wishes for you. You have taught in subsequent videos how to handle textual data (convert text to numbers), here exercise data set contains a text column, i-e. experience. We can convert that textual data to number. Would you please suggest here at tutorial 3 how to handle such text data.
abi dear sir muje lagta hai mai data scientist banwo ga thanks for a such a nice content thanks
I am using google colab and the word to number library is not working there so I used the function to convert string data into numeric in the experience column. Overall enjoyed the exercise.
Please explain the use case when you need to develop a model which has categorical variables and how you handled it.
Thank you so much sir.
great explanation
Good explaination and understanding of ml thanks you sir
Hi Sir,
which condition we apply median or mean numbers.
Good explanation sir
Could you please provide a dataset of engineering problem statement like to predict the pressure temperature or selection of sensors
in assesment file how can i change string value to floatvalue with machine learning
sir, i am facing this problem[TypeError: float() argument must be a string or a number, not 'method'] when you removing NAN from table 5:53
Thank you
lajab bemisal excellent awesom
Awesome .. good job
Really great videos 👌❣️👌
Thanks ✌️
Sir, if my area is zero then model why predicting a value
sir your way of learning is very good
i have doubt in above problem when i predict the value of [[3000,4,15]] i'm get the result of 602590.079 why because
the answer in table is 565000
check your instruction where you have used fillna( )... because i mistakenly filled all fields with median and the answer got different then mentioned in video
salary prediction for 2yrs experience + 9 test score + 6 interview score is : 47056.00 salary
Exercise :- 2year experience, 9 test score, 6 interview
My Output is :- 49789. 5915 (but it is not match your answer 😕
how you convert expiriance column into float??
HI Dhaval, We are trying to solve exercise for multivariable linear regression , have one doubt here
import math
median_test_score = math.floor(df['test_score(out of 10)'].mean())
median_test_score
Could you please let me know why we took here mean , as in previos exercise we took median for finding NaN value..
In exercise questions, it is coming 47056.91 and 88227.64 respectively.
Yes! I got the same result. [47056.91056911], [88227.64227642]. Can anyone please affirm the actual answer?
@Subhadip : I guess we both used the median instead of mean . As, I replaced median from mean I got the actual result. [53713.86677124] & [93747.79628651].
In exercise, did you implement word2num module for experience, or wrote the code , please help. Thanks
what we need to take in place of NA? mean or median?
@@DurgeshMishrablog same result same as u
Thank you!!
Thank you, Sir !
sir please provide whole cousre in website in hindi language
sir excercise me experience words me he use numeric me change karna nhi aa rha,
Neetu Try Loading word2number package.
@@taranoberoi ye kya bola vhai tm n?????
@@farhazyounis5171 I did not get u bhai.. i just said try loading package which will help converting word to numbers...
@@taranoberoi yes done bro
@@taranoberoi is this available with jupyter?
Sir, i am converting the word to num but getting error
code is
df['experience']=df['experience'].apply(lambda x: x.w2n.word_to_num())
please suggest me where i am wrong
import w2n
df.experience = df.experience.apply(w2n.word_to_num)
I found this works after a few trials and errors. Also, did u put the w2n.oy file in that project folder. What error were u getting?
@@hemalkarambelkar9638 can u give me yr code link? or you can past here yr code
I got error during w2n error: value error type of input is not string please enter a valid number word
Why find median not mean ?
take big data sir and buil linear regression ..
csv file is not downloaded please help me
Supper sir
Keep watching
How we sure our prediction is correct
Line 6 is giving error ...plz tell me
Sir the coefficient is calculated how?
watch 3blue1brown how neural network works series
Awesome
sir ji plz help ...results are not matched
use mean instead of median to fill NAN values in test_score(out of 10)
sir how to write these codes
Please download the library module word 2 numbers from the link below for the program to execute.
pypi.org/project/word2number/
Thank you
Mere kuch smjh nhi a rha how do work ..????
Sir my answer is 53746.90 and 96343.08 and test score is 0.72
That’s the way to go hrushi, good job working on that exercise
i got 53537.10 and 75641.91487169 respectively. can you share your code
excercise answer 53713.86677124 and93747.79628651
My answers are different as your
2 yr experience, 9 test scores, 6 interview scores: 53537.10
12 yr experience, 10 test score, 10 interview score: 75641.91
Your answers are: 53713.86 and 93747.79
i got the same answer
time wast
import pandas as pd
import numpy as np
from sklearn import linear_model
from word2number import w2n
xlsx = pd.ExcelFile('hiring.xlsx')
df1 = pd.read_excel(xlsx, 'Sheet1')
df2 = pd.read_excel(xlsx, 'Sheet2')
df2
df2.experience = df2.experience.fillna('zero')
df2
df2.experience = df2.experience.apply(w2n.word_to_num)
df2
import math
mid_score = math.floor(df2['test_score(out of 10)'].mean())
mid_score
df2['test_score(out of 10)'] = df2['test_score(out of 10)'].fillna(mid_score)
df2
model = linear_model.LinearRegression()
model.fit(df2.drop('salary($)', axis='columns'),df2['salary($)'])
model.predict([[2,9,6]])
array([53713.86677124])
model.predict([[12,10,10]])
array([93747.79628651])
51369.90760877 & 81881.42026808.. not matched ..sorry
Thank you so much sir