# This is the solution of exercise given by sir, # hope it helps # sample input from gender to Property_Area = 1,1,4,1,0,3036,2504.0,158.0,360.0,0.0,1 Input_data = (1,1,4,1,0,3036,2504.0,158.0,360.0,0.0,1) Input_data_as_numpy_array = np.asarray(Input_data) # reshaping the data as we are pridicting for one instance input_reshaping = Input_data_as_numpy_array.reshape(1,-1) x = classifier.predict(input_reshaping) print(x)
Thankyou Mr. Siddhardhan I would like to tell all that we can use logistic regression for the same as it gives same accuracy like SVM and logistic is faster in computing
Hi @siddhardhan, nice explanation., But it will be great if you do more EDA part like krish naik do by taking some difficult data set ,like how t to find outlier, how to remove, box plot, null value treatment , most of the project your approach is same , after practicing also from your video ,when we go to solve real world problem we are facing difficulties
I wrote a prediction system: #making a predictive system #Convert the dataframe to an array X_new = X_test.iloc[39].to_numpy() #reshape array X_predict_reshaped=X_new.reshape(1,-1) prediction=classifier.predict(X_predict_reshaped) print(prediction) if (prediction[0]==0): print('Not eligible for loan') else: print('Eligible for loan')
#check the actual value Y_new = Y_test.to_numpy() print(Y_new[39])
There are 614 data points in this example. Dropping all rows with NaN values leaves only 480 data points. Thus, a significant proportion of data points is dropped. Shouldn't we try another method like filling mean values in numerical columns, etc.?
You can treat the Credit_History NaN values as third category. Use ordinal encoding here. 0 for bad (0.0), 1 for nan, 2 for good (1.0) NaN values here indicates that the applicant is applying for the loan for very first time. For other features, all of them have like about 5% missing data. So that won't really affect the model as much as you might think.
sir, while making model as a input when i am feeding data to predict its having textual data, so i just want to know that do we have to import libraries like Tfidfvectorizer or something?
Thank you for the video. Great lesson. However, I do have a question. Why was feature scaling not done on the dataset considering that some features like ApplicantIncome, CoapplicantIncome, LoanAmount, etc have higher values (weight) than the rest? I also know that the performance of the SVM model is affected by feature scaling.
Scaling may make the algorithms works better. The vid contains just the most important or essencial things in order to make the models works. It is an educational vid and scaling is an advance thing but obviously recommended when necessary.
I guess you should have used StandardScaler(), coz some of the columns values are 1, 0 and some of columns contains above 3000 or 4000 values. Let me know if I'm wrong..
You guys are right! @samuelpaul5923 and @vanshdoshi3283, you can surely try to use the StandardScaler. You basically check the spread of every feature and scale the one with pretty high spread. Here, as much as I could see, there is not a LOT of spread in the numeric data. Hence, the scaling won't contribute a lot as you may think. Still give it a try and let us know.
1) why are we not doing feature scaling in this dataset given features are of different measures. 2) in case we have another column here lets say profession which contains more than 20 different professions, how would we convert that into numerical? Thanks for the video though Siddhardhan
X_new = X_test[0] # Reshape X_new into a 2D array X_new = X_new.reshape(1, -1) prediction = classifier.predict(X_new) print(prediction) if (prediction[0]==1): print('Loan Approved') else: print('Loan Denied') prediction system for Loan Status
Surely. Try Logistic Regression for example, it shall work as good as what you have seen here as it is a binary classification problem and dataset is also small.
My code is working absolutely fine but I’m unable to understand the reasoning behind the “Train Test split” why exactly are we doing that? And why did we do those four different variables, xtrain test, train, test? Could you please elaborate why we do that?
Sir pls I need a quick answer. Am having a problem with the prediction system. Am not having an accurate results it's seem the model is not learning from the data. The model is predicting '1' for all input data even when it's supposed to be '0' ie for loan rejected
Thank you for also showing the data visualizations in this ML Project. However, I have 1 question, do we need to do these Visualizations every time while we need to do the ML, or is it just an optional thing just to show the relationship of the data?
Hello sir. This video is very much helpful for me as I'm doing my final project based on the same concept.. But the problem is, my faculty asked me that in the verification process cibil score is checked and why are these required.. can you help me answer that.. expecting your reply...
credit history has a direct impact on cibil score better the credit history better the cibil score so the credit history feature shall do that task for you.
why every feature is selected for building the model..is there a process called feature selection..I am a student doing a mini project in ML. so please sombody answer this.
Hi! I wasn't focusing much on data analysis and feature engineering part as this is a machine learning course. Those topics will be taught in the data science course. The idea here is to show the implementation of ML models. But I would advise u to do all the feature engineering part if u r working on an end to end project.
Hi sir, Thanks for the great effort.Can u plz explain which model (linear or logisticregression or svm or random forest )has to be chosen at what criteria for the data,as i m confused to chose which model.
It depends on the data, I guess there isn't any hard and fast rule which works everywhere, as this is a classification problem so it rules out linear regression. You will need to use several models and then you will observe which model is performing better than the others. Keep watching other projects, every video will hit a new concept and by the end of the playlist, you will not have any such doubts.
Logistic should be pretty fine to work with as its a small dataset + binary classification. If this doesn't work... then you can train multiple classification algorithms and compare which one is best and then fine tune it.
Brother can you tell me what I have done wrong here? My program always shows that , the person won't receive any kind of loan no matter whichever parameter I feed it. input_data = (1,1,1,1,0,4583,1508,128,360,1,0) # changing the input_data to numpy array input_data_as_numpy_array = np.asarray(input_data) # reshape the array as we are predicting for one instance input_data_reshaped = input_data_as_numpy_array.reshape(1,-1) # standardize the input data std_data = scaler.transform(input_data_reshaped) print(std_data) prediction = classifier.predict(std_data) print(prediction) if (prediction[0] == 0): print('The person will receive loan') else: print('The person will not receive loan')
In my case, i have not used standard scaler but the code is till having some problems since input_data = (1,1,1,1,0,4583,1508,128,360,1,0) shows person won't receive loan
hi! I cannot make interview questions specifically for this project. today evening, I'll upload a video on interview questions for Machine Learning. it will be a general topic.
@@Siddhardhan k sir can u tell me if interviewer ask me out of logistics regression and support vector machine which is better and you didn't tryed with decision tree... And other algorithms?? How to answer
Hi, one question: The semi_urban showed high chances of getting loan approved than other two, so was it more appropriate to give it the weightage with value=2 when you replaced the category. Thanks in advance.
Hello sir, I have the same dataset with more columns and around 30000 rows and need to predict the loan amount where I am creating a Regression model. But the problem is handling the missing values. I am a bit confused about whether to replace it with mean, mode, or median in place of missing values. Sir can you help me with this as stuck with this problem.
Hey, when you are converting the Y & N to 1,0 you will get the type error (Cannot compare types 'ndarray(dtype=int64)' and 'str'). To fix this issue you need to change the type of Loan_Status column to string or object.
Hi! You can join this Telegram group for regular updates about my videos: t.me/siddhardhan
Thank you!
Can I have data set of this video?
You can find it in the description of the video
can you share the ppt
I am sorry... I cannot share the ppt
# This is the solution of exercise given by sir,
# hope it helps
# sample input from gender to Property_Area = 1,1,4,1,0,3036,2504.0,158.0,360.0,0.0,1
Input_data = (1,1,4,1,0,3036,2504.0,158.0,360.0,0.0,1)
Input_data_as_numpy_array = np.asarray(Input_data)
# reshaping the data as we are pridicting for one instance
input_reshaping = Input_data_as_numpy_array.reshape(1,-1)
x = classifier.predict(input_reshaping)
print(x)
Good
That's really great, Nilesh! All the best 👍
hi is it working fine for u?
@@srikanthreddymanneti7116 bro did you find any solutions?
@@fahaamshawl9335 nope have u found?
i can say this person is one of the besttttttttttttttttttt person everyone needs to get to learn data science!!!!!!!!!!!
Thankyou Mr. Siddhardhan I would like to tell all that we can use logistic regression for the same as it gives same accuracy like SVM and logistic is faster in computing
God bless you for distinct explanation...
I had a total understanding of every step
Thanks brother
Amen
Your videos are very helpful.
can you tell me why logistic regression model wasn't used in this project?😕
Hello Sir the predictive system isn't working since we have the attributes as text,cat as well.Please help with the predictive system for this example
Convert the text manually to the encoded values after you get input from the user.
Like if user specifies male, change the gender variable to 1.
do we need to standardize the data before prediction? pls answer
Hi @siddhardhan, nice explanation.,
But it will be great if you do more EDA part like krish naik do by taking some difficult data set ,like how t to find outlier, how to remove, box plot, null value treatment , most of the project your approach is same , after practicing also from your video ,when we go to solve real world problem we are facing difficulties
on what basis you have selected the SVR model ?
please specify
I wrote a prediction system:
#making a predictive system
#Convert the dataframe to an array
X_new = X_test.iloc[39].to_numpy()
#reshape array
X_predict_reshaped=X_new.reshape(1,-1)
prediction=classifier.predict(X_predict_reshaped)
print(prediction)
if (prediction[0]==0):
print('Not eligible for loan')
else:
print('Eligible for loan')
#check the actual value
Y_new = Y_test.to_numpy()
print(Y_new[39])
Thanks
Thanks
There are 614 data points in this example. Dropping all rows with NaN values leaves only 480 data points. Thus, a significant proportion of data points is dropped. Shouldn't we try another method like filling mean values in numerical columns, etc.?
I tried this and it didn’t change the accuracy score that much
You can treat the Credit_History NaN values as third category.
Use ordinal encoding here. 0 for bad (0.0), 1 for nan, 2 for good (1.0)
NaN values here indicates that the applicant is applying for the loan for very first time.
For other features, all of them have like about 5% missing data. So that won't really affect the model as much as you might think.
Clear explanation.. keep up the good work!..
thank you so much 😇
sir, while making model as a input when i am feeding data to predict its having textual data, so i just want to know that do we have to import libraries like Tfidfvectorizer or something?
Why did we separated labels with data?
To train the model correctly, as we want to predict the label & not just feed the label to model...
in this project could we use logistic regression ???
when replacing dependent values, it's assigned to new variable, 28:45 but before when replacing loan status no assigning,, why ?
Thank you for the video. Great lesson.
However, I do have a question.
Why was feature scaling not done on the dataset considering that some features like ApplicantIncome, CoapplicantIncome, LoanAmount, etc have higher values (weight) than the rest?
I also know that the performance of the SVM model is affected by feature scaling.
Scaling may make the algorithms works better. The vid contains just the most important or essencial things in order to make the models works. It is an educational vid and scaling is an advance thing but obviously recommended when necessary.
How do we understand this Loan project will work best on SVM and not Logistic Regression?
He just took that as an example. Logistic Regression will work as good!
Hi Siddhardhan, why didn't we use Logistic Regression here pls ?
Awesome video, keep up the incredible work! :)
Thanks 😇
I guess you should have used StandardScaler(), coz some of the columns values are 1, 0 and some of columns contains above 3000 or 4000 values. Let me know if I'm wrong..
same thought... @siddhardhan pls tell its good to use standardscalar here or not?
You guys are right! @samuelpaul5923 and @vanshdoshi3283, you can surely try to use the StandardScaler.
You basically check the spread of every feature and scale the one with pretty high spread.
Here, as much as I could see, there is not a LOT of spread in the numeric data. Hence, the scaling won't contribute a lot as you may think. Still give it a try and let us know.
@@rohanshah8129 I tried scaling column that is income but the accuracy decreased
Sir I got error that couldn't convert string to float: 'semiurban'
What is the problem
I followed all the steps in ur vedio sir
1) why are we not doing feature scaling in this dataset given features are of different measures.
2) in case we have another column here lets say profession which contains more than 20 different professions, how would we convert that into numerical?
Thanks for the video though Siddhardhan
for the second one we'll do one hot encoding i guess
predictive system
input_data=(1,1,4,1,0,3036,2504,158,360,0,1)
#changing th array to numpy array
input_data_as_numpy_array=np.asarray(input_data)
prediction=classifier.predict(input_data_as_numpy_array.reshape(1,-1))
print(prediction)
if (prediction[0]==0):
print('not approved')
else:
print('approved')
Bro if u r from Itahari International College then plz skip this project bcz i have already copied it ;))
X_new = X_test[0]
# Reshape X_new into a 2D array
X_new = X_new.reshape(1, -1)
prediction = classifier.predict(X_new)
print(prediction)
if (prediction[0]==1):
print('Loan Approved')
else:
print('Loan Denied')
prediction system for Loan Status
Hello
Why didn't you use another classification algorithm? Can we use any other classification algorithms as well?
Surely. Try Logistic Regression for example, it shall work as good as what you have seen here as it is a binary classification problem and dataset is also small.
is it possible to SVM algorithm plot into the graph..
hi! go through this documentation
Sir ur teaching is awesome
But why can we do like this to get accuracy
classifier.fit(x_train,y_train)
classifier.score(x_test,y_test)
Hi! you can do this as well.
My code is working absolutely fine but I’m unable to understand the reasoning behind the “Train Test split” why exactly are we doing that? And why did we do those four different variables, xtrain test, train, test? Could you please elaborate why we do that?
finished watching
Can we import Label Encoder from Sklearn and do the encoding?
How can we know what is labeled what by the label encoder?
Use the value_counts() to see the unique categories after encoding is done
Hi can we use LogisticRegression model for thie same
yes
Predictive system:
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.2,stratify=y,random_state=2)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train) # Fit and transform the training data
X_test_scaled = scaler.transform(X_test)
classifier = svm.SVC(kernel='linear', class_weight='balanced')
classifier.fit(X_train_scaled, y_train)
input_data=(0,0,0,1,0,3510,0,76,360,0,1) # expected prediction 0
input_data_array=np.asarray(input_data)
input_data_reshaped=input_data_array.reshape(1,-1)
input_data_scaled = scaler.transform(input_data_reshaped)
prediction = classifier.predict(input_data_scaled)
print(f'predicted value is :{prediction[0]}')
if prediction[0]==0:
print('loan rejected')
else:
print('loan approved')
X is not defined please help
Hi Siddhardhan, this is great. May i know how to change the code if i need to read a pdf file and excel file and then load into dataset?
You can use pd.read_excel(excel_path)
For pdf, it will be a bit long to explain here.
can you send the link for the application test data
Sir pls I need a quick answer. Am having a problem with the prediction system. Am not having an accurate results it's seem the model is not learning from the data. The model is predicting '1' for all input data even when it's supposed to be '0' ie for loan rejected
Some issue with your procedure
Thank you for also showing the data visualizations in this ML Project. However, I have 1 question, do we need to do these Visualizations every time while we need to do the ML, or is it just an optional thing just to show the relationship of the data?
Data Analysis helps you in preparing a good Data Preprocessing Pipeline.
Great work 👏
Thank you so much 😇
Sir why we use linear regression ,why we don't use logistics regression can you plz tell or guide
Hey can you please give an example of input data for predictive system. Any one example.. plz
hi! please refer diabetes prediction and other project videos. it's the same procedure.
Hello sir. This video is very much helpful for me as I'm doing my final project based on the same concept.. But the problem is, my faculty asked me that in the verification process cibil score is checked and why are these required.. can you help me answer that..
expecting your reply...
Hellooo am doing same project help me
credit history has a direct impact on cibil score
better the credit history better the cibil score
so the credit history feature shall do that task for you.
@@rohanshah8129 thank you
Sir I faced problem when I fit the data.... there is a value error: could not convert string to float: 'LP001431'
PLEASE RESOLVE THE QUERY
Drop this column of LOAN ID
Sir please provide how to implement logistic regression for loan approval prediction
hi! it's the same procedure. watch my other videos to learn how to implement logistic regression
Just change the SVM with LogisticRegression() and it should work just fine.
In input_data , we should not add loan id and loan status right??
BTW love your video :)
Yes, correct. and thanks 😇
giving error please help
how do we approach if we were given with traindata and test data separately, instead of the one dataset??
Split would not be required in that case and the same Feature Engineering Steps must be applied on the test dataset too before the prediction.
Which software you are using for making the slides ??
I am just using MS PowerPoint.
@@Siddhardhan all right thanks and your English is quite good I also speak English but hesitates a little bit
Thanks for your appreciation 😇 practice more... Eventually you will be good at it. All the best!
@@Siddhardhan yes sure
why every feature is selected for building the model..is there a process called feature selection..I am a student doing a mini project in ML. so please sombody answer this.
Hi! I wasn't focusing much on data analysis and feature engineering part as this is a machine learning course. Those topics will be taught in the data science course. The idea here is to show the implementation of ML models. But I would advise u to do all the feature engineering part if u r working on an end to end project.
@@Siddhardhan thanks fot the reply
Hi sir, Thanks for the great effort.Can u plz explain which model (linear or logisticregression or svm or random forest )has to be chosen at what criteria for the data,as i m confused to chose which model.
It depends on the data, I guess there isn't any hard and fast rule which works everywhere, as this is a classification problem so it rules out linear regression. You will need to use several models and then you will observe which model is performing better than the others. Keep watching other projects, every video will hit a new concept and by the end of the playlist, you will not have any such doubts.
@@symbiontacademy9232 Thanks for the reply
Logistic should be pretty fine to work with as its a small dataset + binary classification.
If this doesn't work... then you can train multiple classification algorithms and compare which one is best and then fine tune it.
Y u r using some model can use decision tree random forest any particular reason
hi! each model has its pros & cons. refer my video on model selection for more information.
Brother can you tell me what I have done wrong here? My program always shows that , the person won't receive any kind of loan no matter whichever parameter I feed it.
input_data = (1,1,1,1,0,4583,1508,128,360,1,0)
# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)
# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
# standardize the input data
std_data = scaler.transform(input_data_reshaped)
print(std_data)
prediction = classifier.predict(std_data)
print(prediction)
if (prediction[0] == 0):
print('The person will receive loan')
else:
print('The person will not receive loan')
hi! you shouldn't use standard scaler as we have lot of categorical columns in our data. do the code without standardizing the data.
Were u able to get this ? I am a bit confused
In my case, i have not used standard scaler but the code is till having some problems since input_data = (1,1,1,1,0,4583,1508,128,360,1,0) shows person won't receive loan
Did you find any solutions for input data? Having issues with the inputs to predict
use sclaer only on numerical features
Sir u said I will do videos on interview questions in loan prediction but u didn't started yes sir makeee I have no time
hi! I cannot make interview questions specifically for this project. today evening, I'll upload a video on interview questions for Machine Learning. it will be a general topic.
@@Siddhardhan k sir can u tell me if interviewer ask me out of logistics regression and support vector machine which is better and you didn't tryed with decision tree...
And other algorithms??
How to answer
Hi, one question: The semi_urban showed high chances of getting loan approved than other two, so was it more appropriate to give it the weightage with value=2 when you replaced the category. Thanks in advance.
hi! that's a really good insight. we can do that.
Sir can you help us with some doubts in making predictive system for loan status prediction using ML ?
Hello sir, I have the same dataset with more columns and around 30000 rows and need to predict the loan amount where I am creating a Regression model. But the problem is handling the missing values. I am a bit confused about whether to replace it with mean, mode, or median in place of missing values. Sir can you help me with this as stuck with this problem.
hi! watch my video on handling Missing Values
@@Siddhardhan sir I cannot find exact video for it on your playlist. Can you give me the link please.
th-cam.com/video/GzZmfe030PU/w-d-xo.html
if we encode categorical variables with numbers like 1, 2 wont the model find pattern in it?
hi! if we 2 or 3 categories, then it is not a problem. if we have more categories like 6 or more than that, we can use "One Hote Encoding".
Tell how to make the predictive system for this model the last part
Pls post in soon are send any link
i have a doubt, why feature scaling not done
the spread of data is not huge, hence scaling won't really help our model here. However, if you try it there should not be any issues.
Hey, when you are converting the Y & N to 1,0 you will get the type error (Cannot compare types 'ndarray(dtype=int64)' and 'str'). To fix this issue you need to change the type of Loan_Status column to string or object.
Can you please provide the code for it ? How do we change the type?
@@kumarishikha8490 you can use this as model. df['column_name'] = df['column_name'].apply(lambda x: '1' if x == 'Y or N up to you' else '0')
@@felipemaldonado1057 Thanks
Bro is dng this project as final project is good or not bro can u pls say
Good video
thanks 😊
Sir please tell us the code for making a predictive system for this case
😢
i am not able to make predictive system pls help to create
Sir can you plz share ppt on it
any one here can share the code of the predictive system as well?
Did you get the code? @aquasaif8847
Thanks a lot !
where is the dataset?
in spite of using replace we could have simply gone for get_dummies ...isnt it ?
yes, you can try it.
@@Siddhardhan I just finished up this project and accuracy certainly dropped to 71.27 to yours 80 ...
Drop correct predictive system please
sir can i get ppt you made so i can understand plz sir
hi! I am sorry. I cannot share the ppt.
Whu u not fill the missing value
big thanks
Did anyone found out how to make predictive system?
hyyy anyone please help with making predictive system provide the code
Can you please provide PPT of this
Need help for same project
hi! mail to siddhardhselvam2317@gmail.com
Sir i want pdf file sir, to learn..
Why used svm here
is this project complete
complete in the sense?
Need your help for such projectd
Hi! You can contact me in social medias.
finished coding
👍
😢😢
gender can"t be segregated into 0's and 1's 😂
what if the person is transgender lol
haha
then assign 2 for transgender