Loan Prediction Analysis (Classification) | Machine Learning | Python

Подписаться 24 тыс.

Просмотров 50 тыс.

50% 1

⭐️ Content Description ⭐️
In this video, I have explained about loan prediction dataset and its analysis in python. We have explored various concepts like EDA, filling missing values, creating new attributes, normalization, cross validation, confusion matrix, etc.,
Text-based Tutorial: www.hackersrealm.net/post/loa...
Dataset link: www.kaggle.com/altruistdelhit...
GitHub Code Repo: bit.ly/mlcoderepo
🌐 Website: www.hackersrealm.net
🔔 Subscribe: bit.ly/hackersrealm
🗓️ 1:1 Consultation with Me: calendly.com/hackersrealm/con...
📷 Instagram: / aswintechguy
🔣 Linkedin: / aswintechguy
🎯 GitHub: github.com/aswintechguy
🎬 Share: • Loan Prediction Analys...
⚡️ Data Structures & Algorithms tutorial playlist: bit.ly/dsatutorial
😎 Hackerrank problem solving solutions playlist: bit.ly/hackerrankplaylist
🤖 ML projects tutorial playlist: bit.ly/mlprojectsplaylist
🐍 Python tutorial playlist: bit.ly/python3playlist
💻 Machine learning concepts playlist: bit.ly/mlconcepts
✍🏼 NLP concepts playlist: bit.ly/nlpconcepts
🕸️ Web scraping tutorial playlist: bit.ly/webscrapingplaylist
Make a small donation to support the channel 🙏🙏🙏:-
🆙 UPI ID: hackersrealm@apl
💲 PayPal: paypal.me/hackersrealm
🕒 Timeline
00:00 Introduction to Loan Prediction Analysis
01:52 Import modules and load data using pandas
03:19 Statistics data information
04:46 Preprocessing the loan sanction data
10:26 Exploratory Data Analysis of Loan sanction data
19:41 Creating new attribute for loan prediction
24:54 Correlation matrix for loan sanction data
28:06 Drop unnecessary columns in data frame
31:07 Label Encoding for data preprocessing
33:40 Splitting the data for training & testing
36:12 Model Training & Testing for loan prediction
44:32 Hyperparameter tuning to improve the model
46:30 Confusion matrix Analysis for the model prediction
#loanpredictionanalysis #machinelearning #hackersrealm #classification #deeplearning #datascience #model #project #artificialintelligence #beginner #analysis #python #tutorial #aswin #ai #dataanalytics #data #bigdata #programming #datascientist #technology #coding #datavisualization #computerscience #pythonprogramming #analytics #tech #dataanalysis #iot #programmer #statistics #developer #ml #business #innovation #coder #dataanalyst

Опубликовано:

30 июл 2024

Ссылка:

Скачать:

Готовим ссылку...

Добавить в:

Мой плейлист

Посмотреть позже

Комментарии : 176

@HackersRealm 3 года назад

Small correction viewers, I mentioned distribution of left and right skew graph in opposite manner. To avoid error while converting to log values add +1 to the column. I have updated the notebook in the github. Enjoy the rest of the video!!!

@Umeshwarvaithy 4 года назад

Super bro while started learning ML I found your channel and started my learning and progress doing the project thanks for your interest and effort

@HackersRealm 4 года назад

Hope the videos are useful to you!!! Thanks for watching and please share it for better reach. Thank you!!!

@vamsichittoor1974 3 года назад

I have just started learning Machine Learning and I understood every bit you explained and done one project on my own similar to this .Really great explanation. I would like to know how to master Machine Learning. I am not student of CSE I am learning this on my own interest

@HackersRealm 3 года назад

Glad it was helpful!!! kudos to you learning with your own interest. Try to pick a mini project in some domain and solve it. That's a quick way to understand and learn...

@michaelk765 2 года назад

Great explanation of your model building. Thank you!

@HackersRealm 2 года назад

Glad you liked it!!!

@MrJeffoneal Год назад

Thank you! Very insightful and thorough explanations.

@HackersRealm Год назад

Glad you liked it 😀

@avishkaravishkar1451 3 года назад

Excellent video, found it very helpful!

@HackersRealm 3 года назад

Glad it was helpful!!!

@anilsailakhinana94 3 года назад

I'm subscribed ur channel for this clear explanation 👍 it was so helpful

@HackersRealm 3 года назад

Thanks for your kind words!!!

@shellm1447 2 года назад

Amazing explanation

@HackersRealm 2 года назад

Glad it was helpful!!!

@rakeshnargund570 2 года назад

Hi.. well explained. i have one question ...... why you did not drop "ApplicantIncome" even though you combined with "CoapplicantIncome" and created "Totalincome"...??

@kartiksolanki9390 Год назад

Very helpful

@HackersRealm Год назад

Glad it was helpful to you!!!

@abhiavasthi624 3 года назад

i have seen that you respond to comments so i would just like to ask you, what changes do i have to make if my training and testing dataset are in different files already? for example in a kaggle project where the training and testing data are in different files, what changes in the code will i have to make?

@HackersRealm 3 года назад

For training, don't split the data, train with the whole data. After that preprocess the test data similar to train and try to predict it. You can also see the video for how to predict test data in the playlist

@abhiavasthi624 3 года назад

@@HackersRealm thanks so much man, respect your timely response. what i did is i skipped split part and simply preprocessed the test data as well and the used y_test = model.predict(x_test) for the prediction but for this case we can't check the score and all right? since i didn't see the loan_status column in the test data.

@HackersRealm 3 года назад

@@abhiavasthi624 yes, that's right, you only get the output results

@pkmisra769 2 года назад

Very nice video. Best thing is your response to people's queries (unlike others). Great Job. I have 1 suggestion. If you could also cover how to deploy this model somewhere (with fresh data coming in and how model throws output). That would be amazing. Thanks.

@HackersRealm 2 года назад

Thank you very much. In this video, I have explained the process for deployment ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-2LqrfEzuIMk.html

@mokshsharma6943 2 года назад

in Explanatory data analysis section of video, how to use for loop for sns.countplot() ?

@HackersRealm 2 года назад

You can store it in a variable and use the subplot to show multiple shots

@chiragparmar3678 3 года назад

Bro u explained much much better than edureka I swear bro thanks!

@HackersRealm 3 года назад

Thanks for your kind words!!!

@sidharth_mohanty 3 года назад

I m unable to apply correction matrix on categorical data before label encoding. How did you do that ?

@HackersRealm 3 года назад

correlation matrix can be calculated with numbers only, not with strings.

@nathanthadmalla9268 3 года назад

where can v get the main dataset the link isleading to only the train and testing dataset where can the get the first dataset tha u have entered in your video

@HackersRealm 3 года назад

that is the train data. you can use that

@SanyAnnieJohn 3 года назад

Hi Sir, Logistic regression gave the best score, then why chose Random forest for hypertuning?

@HackersRealm 3 года назад

for example purpose only

@afreen2806 2 года назад

except for logistic regression, all other models accuracy and cross-validation is changing if I run it more than once. Can u explain y?

@HackersRealm 2 года назад

you can set random state inorder to get same results for rerunning

@gautamranafounderofbexpert6539 3 года назад

Thank you , I found helpful same

@HackersRealm 3 года назад

You're welcome!!!

@iamrahul2944 3 года назад

sir, i am not able to add new column getting error as my code: data['total_income']=data['ApplicantIncome']+['CoapplicantIncome']

@HackersRealm 3 года назад

it's data['CoapplicantIncome'], please check the syntax

@diff008 Год назад

while plotting countplot keep Value Error: getting could not convert to float " Any idea why . Data set was downloaded from your kaggle link. No changes ( although looks like the file names have now changed.)

@HackersRealm Год назад

try to check the values you're plotting, that may be the issue.

@gouthamkarakavalasa4267 3 года назад

Bro, it looks like at 17:08, u applied logit for coapplicant income, but u viz graph for applicant income, ... In the co applicant income, logit function is throwing a error as it contains zeroes.. Request to pls advice on this issue.

@HackersRealm 3 года назад

you can add +1 to the data column, it will resolve the issue

@mitali3j 3 года назад

At which level does 1 needs to be added?

@HackersRealm 3 года назад

@@mitali3j you can add 1 when you see some 0 values, or you can use it generally, there won't be much change in log values

@VickyKumar-sg3jc 2 года назад

so helpful

@HackersRealm 2 года назад

Glad you liked it!!!

@VickyKumar-sg3jc 2 года назад

@@HackersRealm thankyou sir for responding I am getting error on preprocessing labelencoder Typeerror:not supported between instances of str and float

@HackersRealm 2 года назад

@@VickyKumar-sg3jc I think in one column you have float and string values, Please check the type of data

@anuragupadhayay8405 11 месяцев назад

ValueError: could not convert string to float: 'Male' WHEN I AM USING THE COUNTPLOT IT KEEP SHOWING THIS

@nitisht4040 8 месяцев назад

bro did you get solution, if yes please help me out

@sakshituteja3841 4 года назад

This video is a great explanation of this project. I have just one doubt. From where I took the data set, Test data has a separate file of around 350 observations. How do I make use of that ?

@HackersRealm 4 года назад

Glad you liked this video!!! You can use the test data to predict the output and submit it, if there is a competition. For practice, there won't be much use to it.

@PravinKumar-zc2eq 3 года назад

@@HackersRealm how to do it??

@brit_indi1930 2 года назад

U JUST EARNED THE SUB

@HackersRealm 2 года назад

Thanks man!!!

@user-pv8lj9jf2n 7 месяцев назад

Hello Sir I followed your codes, arrival at section ' Exploratory Data'. I replaced the missing values ' df['Gender']=df['Gender'].fillna(df['Gender'].mode()[0]) the line of codes below sns.countplot(df['Gender']) the result ValueError: could not convert string to float: 'Male' could you please advise me, to correct the codes. Thank you

@HackersRealm 7 месяцев назад

try this, sns.countplot(x='Gender', data=df)... It's due to update in seaborn package.

@muhammedfaizals4427 Год назад

for i in ['LoanAmount','Loan_Amount_Term','Credit_History']: tr_data[i] = tr_data[i].fillna(tr_data[i].mean()) we can use this instead filling everything seperately

@HackersRealm Год назад

yes, we could do that!!!

@LoneWolfff07 3 года назад

bro how can i get accuracy more than 80.42 which algorithm should i use

@HackersRealm 3 года назад

It depends on every factor, not only algorithm, Check out other projects in the tutorial series, so you can get additional insights on increasing accuracy.

@SanyAnnieJohn 3 года назад

Hi Sir, When I am plotting for Gender, why my x axis not giving the labels, as Male and Female. Instead it is displaying 0 and 1

@HackersRealm 3 года назад

If you have done some transformation on that column, it will show like that

@SanyAnnieJohn 3 года назад

@@HackersRealm Thanku, got it....

@dr.mahaboobbasha1074 Год назад

Sir..we normalised data of income of applicants and coapplicant and where it is impacting on analysis

@HackersRealm Год назад

It will impact on the model training and testing... but those comparison is not covered in the video

@rameshkannan1075 3 года назад

Can u explain the credit history in data mentioned 0 and 1. Can u post video or tutorial link how cibil data are analysed to get credit history values

@HackersRealm 3 года назад

If the person has credit history, it's 1 or else its 0. I will try analysing cibil data if possible

@rameshkannan1075 3 года назад

@@HackersRealm I need to know there will be n no of customers. These customers cibil how to extract to single excel file. Then based on past repayment we can decide the probability of default.

@siddharthlasiyal4037 3 года назад

Thank uuuuu boss

@tusharpandey3566 3 года назад

Very helpful

@HackersRealm 3 года назад

Glad it was helpful!!!

@oushnik 3 года назад

Can I segregate and train the model instead of using log function? Or else It's necessary to use Log function in this whole project. And 1 more confusion as I'm new so what is the agenda of this whole project? I know it sounds like silly but please explain me.

@HackersRealm 3 года назад

We are trying to predict whether a person can get loan or not from the bank. And log transformation is not compulsory, you can use other methods

@oushnik 3 года назад

@@HackersRealm hmm so I used the same as previous then it's ok...another thing why feature scaling is not working here??? I'm getting error like this "TypeError: float() argument must be a string or a number, not 'StandardScaler'"

@akshaykrishnan7985 3 года назад

Hi Ashwin. Could you please upload videos on model deployment with flask using heroku?

@HackersRealm 3 года назад

Hello, deployment of models, I will cover in later videos for sure, now just covering the basic concepts for better understanding!!!

@akshaykrishnan7985 3 года назад

Thanks a lot 😊

@sodiqrafiu9072 3 года назад

Please, come up with more projects

@HackersRealm 3 года назад

working on it

@rodsdesignestudio Год назад

hi, thanks for the vids but i want ask: why u did use LabelEncoder to the input values (['Gender',"Married","Education",'Self_Employed',"Property_Area","Loan_Status","Dependents"])? thx

@HackersRealm Год назад

we have to convert string to numeric values so model can accept the input. label encoder is one of the technique

@afserali450 Год назад

@@HackersRealm how to convert male in gender column to float

@HackersRealm Год назад

@@afserali450 In video, I used label encoder or one hot encoder to do that.. You can use whichever method that is feasible

@snehamagadum1342 2 года назад

Sor I did not get the conclusion of this project, After the heat map , How can we tell the loan is approved or not?

@HackersRealm 2 года назад

the model training and results, section you're asking?

@shellm1447 2 года назад

Have you also covered hmeq dataset for loan default prediction

@HackersRealm 2 года назад

No not yet!!!

@Kalyan1143 5 месяцев назад

Finally the final output is wt? I mean loan eligible yes or no?

@HackersRealm 5 месяцев назад

for the test data, we are predicting from the model and calculating the score of how well it's predicting

@rahulgaddam7110 4 года назад

how to remove -inf total income coapplicantincome i was tried but not couldn't resolve it.pls help

@HackersRealm 4 года назад

If you are using log transformation, try like this - np.log(1+df['name']), it will solve the problem

@akhilkrishna8521 4 года назад

np.seterr(divide = 'ignore') train['CoapplicantIncomeLog'] = np.where(train['CoapplicantIncome']>0, np.log(train['CoapplicantIncome']), 0) this will solve your problem

@mitali3j 3 года назад

But after adding 1 then in the graph generated, I can see 2 bell curves.... What does that mean?

@snrmedia8965 3 года назад

How you directly fill with mean in loan amount why not check outlier

@HackersRealm 3 года назад

To handle outlier, used log transformation

@dr.mahaboobbasha1074 Год назад

Sir..will it possible to get the python code..of this and other videos

@HackersRealm Год назад

It's available in the github repo, link in the description

@nandinijain4461 3 года назад

From where we can download the dataset can you provide link or dataset in zip format

@HackersRealm 3 года назад

links are in the description

@funnybunnies3985 2 года назад

why are you using log transformation? you can normalise the data?

@HackersRealm 2 года назад

you can use any preprocessing approach. It's no issue, try to test & see how it works

@MEGAMINDLIVE 3 года назад

14:38 you are saying distribution is left skewed but its right skewed.

@HackersRealm 3 года назад

Sorry, I mispronounced the skewed data

@DhirajKrGupta-ke7xn 2 года назад

What tech skills you learnt from the project • Why did you pick that domain? • Where can we use your tech skills / software’s learnt during project • Reason for working on that project Sir Please Help me for Interview preparation

@kumarsanjibray9415 2 года назад

sns.distplot is working but not showing the graph properly ..could u tell me what to do??

@HackersRealm 2 года назад

try specifying the x, y values properly

@kumarsanjibray9415 2 года назад

@@HackersRealm How to specify them ??...Tell me If u can

@HackersRealm 2 года назад

@@kumarsanjibray9415 seaborn.pydata.org/generated/seaborn.distplot.html try this documentation

@oushnik 3 года назад

Another question...why feature scaling is not working here?

@HackersRealm 3 года назад

we can use feature scaling too. There are various preprocessing methods to use and get insights.

@varunrokade1617 3 года назад

can some one tell me what is the currency of applicant income and the other amount (currency) in this data set

@PravinKumar-zc2eq 3 года назад

It's in dollars

@varunrokade1617 3 года назад

@@PravinKumar-zc2eq thank you !!

@be_it_b_76_saurabhyadav36 3 года назад

@@PravinKumar-zc2eq Is it in dollars after log transformation? because before log transformation for example in 1st row applicant income was 5489 then it became 8.67. What if i want income like it was in original dataset? im guessing it was in rupees before log. kindly help if u know praveen.

@HackersRealm 3 года назад

No, it's in dollars all the time, I have done some data preprocessing on that, that's why the values are small after that. That will be helpful in getting good results

@ranjangowda9878 3 года назад

Hello, Can use this project as my mini project.??

@HackersRealm 3 года назад

yes, you can

@snehacookie4138 2 года назад

Is this project can be done for final year project is this good topic to do

@HackersRealm 2 года назад

yeah many people have done this as final year project

@snehacookie4138 2 года назад

@@HackersRealm tq u Like this itself we can present ryt

@HackersRealm 2 года назад

@@snehacookie4138 yes

@snehacookie4138 2 года назад

@@HackersRealm bro is this project good for jobs when u put in resume is this good for getting selected in a company pls say bro

@HackersRealm 2 года назад

@@snehacookie4138 Well that completely depends on the recruiter, but students said they used for resume

@PravinKumar-zc2eq 3 года назад

Can u tell how to train LogisticRegression model??🙏

@HackersRealm 3 года назад

i think i have explained how to train logistic regression also, could you please check the video again.

@PravinKumar-zc2eq 3 года назад

@@HackersRealm sorry I mean to say that how to tune the LogisticRegression model

@HackersRealm 3 года назад

ok, i didn't cover hyperparameter tuning, it will take a complete video for that. I will try to post the videos for that in future

@vedgadge8659 2 года назад

At 40:36 dependents is already in numeric form why does it require label encoding?

@HackersRealm 2 года назад

yes, we don't need to include that

@vedgadge8659 2 года назад

@@HackersRealm hey man I tried that but if we don't include dependents it gives and error while classifying. It is the same error as in the video ValueError: could not convert string to float:'3+'. I'm not understanding this

@HackersRealm 2 года назад

@@vedgadge8659 Oh yeah, i forgot that, it represents as string, that's y i used label encoder. but you can remove that + and convert that string to integer

@vedgadge8659 2 года назад

@@HackersRealm okay sure I'll try thanks man

@saisudhir5005 3 года назад

How to increase accuracy?

@HackersRealm 3 года назад

using different models, hyperparameter tuning, etc., watch other projects of mine to learn more techniques

@lalithkishorep2618 4 месяца назад

How u say that imputed with mean??

@HackersRealm 4 месяца назад

which part you're referring?

@zainabkhalil268 2 года назад

is there any way of connecting with you via email etc?

@HackersRealm 2 года назад

you can reach me via linkedin or instagram, links are in the description

@naveengodara6777 3 года назад

hi...needed some help for loan prediction workshop...could you please help

@HackersRealm 3 года назад

please reach me via insta or linkedin

@naveengodara6777 3 года назад

@@HackersRealm texted on instagram...please have a look

@mohmmedshahrukh8450 3 года назад

but in your result doesnt shown any where who are eligble or not

@HackersRealm 3 года назад

If you check the y label, it will be there

@niklausmikealson3115 3 года назад

How to check y label

@HossainRabin 3 года назад

Excellent tutorial but you mispronounced left-skewed and right-skewed data. Appreciate your effort.

@HackersRealm 3 года назад

Yes, you are right. I will correct it next time. Thanks for watching the video

@lumdevsawarkar4497 Год назад

outliers detection

@HackersRealm Год назад

There is a separate video in ml concepts playlist, You can check that out!!!

@niklausmikealson3115 3 года назад

I didn't understand where it's shown how many people are approved for loan and already

@HackersRealm 3 года назад

In the dataset itself, it is clearly mentioned, please use head function to see the labels

@niklausmikealson3115 3 года назад

What is the goal pls tell

@HackersRealm 3 года назад

@@niklausmikealson3115 based on the attributes of the person, we need to find whether they are eligible for loan

@quincykao749 2 года назад

Is it possible if you can add subtitles

@HackersRealm 2 года назад

It may automatically generated by youtube

@quincykao749 2 года назад

@@HackersRealm it is not avalible for some reason

@akhilkrishna8521 4 года назад

at line number 23 u havent done sns.distplot for coaplicant so u have done wrong ??

@HackersRealm 4 года назад

I have done for coapplicant income, check 16th minute of video. But mistakenly plotted applicant income, sry for that.

@binduskumar3201 4 года назад

Hi, You are doing a good job....thanks for the video.... there is a mistake while plotting the distplot of 'CoapplicantIncome' Instead of 'CoaaplicantIncome' you have choosen 'ApplicantIncome'....

@binduskumar3201 4 года назад

And one more thing, we cannot apply log function to 'CoapplicantIncome' since it contains zero value....

@HackersRealm 4 года назад

If you are using log transformation, try like this - np.log(1+df['name']), it will solve the problem

@HackersRealm 4 года назад

Yes, my mistake. Sorry for the error