What is the Smart Resume Analyzer?

It is a project that uses NLP to analyze resumes and provide recommendations for skills and courses.

Why use NLP for analyzing resumes?

NLP is used to extract and analyze text data from resumes, which helps in determining the most relevant skills and experiences.

What can the Smart Resume Analyzer detect from a resume?

It can detect the user's name, email, phone number, candidate level (e.g., fresher, intermediate, experienced), skills, and recommend further skills and courses.

How does the project recommend skills and courses?

The project matches keywords in the resume with predefined categories like data science, web development, and recommends based on matching fields.

What framework is used for the project?

The project is developed using Streamlit and Python.

What resume formats are supported?

Currently, the system supports only PDF resumes.

What software libraries are used in the project?

The project uses libraries like Streamlit, Pandas, PyPDF2, PyPDFMiner, and Pyresumeeparser.

Is the project available for public use or modification?

Yes, the code will be available on the creator’s GitHub for others to use and modify.

Resume Analyser Application using NLP Python with Code | Full Responsive Web Application

00:50:23

https://www.youtube.com/watch?v=hqu5EYMLCUw

Summary

TLDRThe video tutorial by Programmer Spidey introduces a 'Smart Resume Analyzer' project which was developed as part of a semester project. The analyzer makes use of Natural Language Processing (NLP) to assess resumes and suggest suitable skills and courses for users to improve on. It simulates the functionality of modern resume-screening software used by companies to ease the hiring process by quickly filtering through numerous applications. The video covers the full working of the project, how it parses resumes to extract essential details like personal information, skills, and experience levels, and how it provides recommendations for skill improvements and courses. The creator further details the technical components, from Python libraries used to explanation of the project code and logic, offering a comprehensive guide that viewers can replicate or modify with the source code available on GitHub.

Takeaways

🤖 The project uses NLP to analyze and enhance resume content.
📑 Only PDF resumes are currently supported for analysis.
🔍 Key phrases in the resume help determine relevant skills and fields.
🐍 The project is built using Python and Streamlit framework.
🔗 Source code will be shared on GitHub for community collaboration.
🎓 Resume assessment includes skill and course recommendations.
💼 Useful for job seekers to align resumes with industry standards.
🔍 Demonstrates modern techniques used in company resume screening.
📊 Offers a tool to compare different resume structures and content.
🔧 Encourages viewers to adapt and improve the system via GitHub.

Timeline

00:00:00 - 00:05:00
The video introduces a Smart Resume Analyzer project, which automates resume analysis using NLP to shortlist candidates, saving companies time compared to manual resume reading. The project's code will be shared on GitHub.
00:05:00 - 00:10:00
The software uses NLP to assess resumes, extracting key details like skills, and uses a sample resume to demonstrate its functionality. It’s tailored for IT resumes, and the screening is demonstrated using sample downloaded resumes.
00:10:00 - 00:15:00
The analyzer extracts key information like name, email, phone number, and assesses the resume level as intermediate. It recommends skills for Android development jobs using NLP. There are minor software bugs noted.
00:15:00 - 00:20:00
The software suggests related courses based on the resume assessment and provides tips and suggestions for enhancing the resume. It calculates a resume writing score and suggests improvements to the resume format.
00:20:00 - 00:25:00
The video explains how the backend of the system works, including PDF extraction and parsing to identify skills using defined categories. The project was divided into modules to facilitate development.
00:25:00 - 00:30:00
The project employs libraries like Pyresparser and PDFMiner to extract data from resumes and convert PDF content into text. The importance of reading documentation for libraries before use is emphasized.
00:30:00 - 00:35:00
Resume parsing involves converting resume text using PDFMiner and categorizing content into identifiable sections such as skills and experience. It temporarily stores extracted data for further analysis.
00:35:00 - 00:40:00
Machine learning models predict user skills and job matches. It recognizes keywords linked to specific fields, providing course and skill recommendations based on identified sectors like data science or web development.
00:40:00 - 00:45:00
A demonstration shows course recommendations for different job fields like Android and data science. Courses provided are predefined and output through a series of logical checks using the resume data.
00:45:00 - 00:50:23
Admin functionality includes analyzing user data, generating reports, and visualizing data in charts. The software's utility in assessing various levels of user expertise based on uploaded resumes is discussed.

Mind Map

Video Q&A

What is the Smart Resume Analyzer?
It is a project that uses NLP to analyze resumes and provide recommendations for skills and courses.
Why use NLP for analyzing resumes?
NLP is used to extract and analyze text data from resumes, which helps in determining the most relevant skills and experiences.
What can the Smart Resume Analyzer detect from a resume?
It can detect the user's name, email, phone number, candidate level (e.g., fresher, intermediate, experienced), skills, and recommend further skills and courses.
How does the project recommend skills and courses?
The project matches keywords in the resume with predefined categories like data science, web development, and recommends based on matching fields.
What framework is used for the project?
The project is developed using Streamlit and Python.
What resume formats are supported?
Currently, the system supports only PDF resumes.
What software libraries are used in the project?
The project uses libraries like Streamlit, Pandas, PyPDF2, PyPDFMiner, and Pyresumeeparser.
Is the project available for public use or modification?
Yes, the code will be available on the creator’s GitHub for others to use and modify.

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Subtitles

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00:00:01
welcome back programmer spidey is back
00:00:03
with one another video in previous video
00:00:05
we have seen about a movie
00:00:06
recommendation system using streamlate
00:00:09
now in this video we are going to learn
00:00:10
about smart resume analyzer yes
00:00:13
this project was about uh
00:00:15
my seventh semester project and now
00:00:17
after the end of all the semester and
00:00:19
formalities that i'm going to explain
00:00:21
this project and reveal the code into my
00:00:23
github so don't worry guys you will find
00:00:25
the code link into the github section
00:00:28
sorry in description
00:00:30
okay so now what is this project is
00:00:32
about so this project is about a smart
00:00:35
resume analyzer guys if you are totally
00:00:37
new to my
00:00:39
uh youtube channel then just visit the
00:00:41
playlist section you will find a lots of
00:00:42
videos regarding python machine learning
00:00:44
opencv email processing nlp there are
00:00:47
lots of things available in to my
00:00:49
channel so you can just have a look if
00:00:51
you like then subscribe our machine
00:00:52
learning hub youtube channel
00:00:54
okay so now let's talk about
00:00:56
uh this project this project is about
00:00:58
smart resume analyzer so what is resume
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analyzer so if you have
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obviously if you are
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if you are like have you sent any resume
00:01:08
to any companies like
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so basically what companies are
00:01:11
currently doing they are using some
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softwares resume screening software
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basically this
00:01:16
uh
00:01:17
around five or ten years back what they
00:01:19
were doing is like
00:01:20
uh getting the 1000 resume read each and
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every person's resume by one by one it
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is time consuming process right so
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nowadays this hacker rank hacker or
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there are many companies that are
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providing resume screening softwares so
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basically they will take a 1000 resume
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now that software will analyze the best
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resume of the candidate
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but now uh just the question is how they
00:01:44
can analyze the best resume
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the answer is nlp natural language
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processing what our resume is containing
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containing our resume is containing some
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text some words and based on that words
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uh that
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smart screening software will consider
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the best resume from the thousand resume
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right so basically they will shortlist
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100 200 the best candidates right and
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based on resume
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so nowadays you're like lots of people
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telling yeah your resume resumes should
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be in proper manner yes that's the truth
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because no one is going to sing your
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resume right
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they will use the screening software
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right
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okay so now let's see how it is going to
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work okay so i'm just i have downloaded
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some of the samples because i am not
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going to include my resume or
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during that uh making of this project i
00:02:33
have tried my friend's resume and it is
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working fine on almost each and every
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resume but here now i am not going to
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use any of my friends or my resume also
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because my almost each and every resume
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have every contact details and all that
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i don't want to reveal in any video so
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that currently i'm using
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uh down you downloaded
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like sample resume from the internet
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okay
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so now basically this uh software is
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supporting only currently it is
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supporting only for the in uh it people
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like
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what it will give you just let me upload
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any resume post
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now what it will do
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it will show you the resume post
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just understand as a user your user you
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are uploading your
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resume right
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okay
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so now there are some minor books here
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also like uh so in most of the format it
00:03:24
is working fine it will show you your
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name but this is a sample resume and you
00:03:28
can see the this one is looking like a
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name and the so this is a bit confusing
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here
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so that i will explain all the things in
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later but just assume that this is this
00:03:38
will show you a name like if your name
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is crucial and you are uploading crucial
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resume it will show you hello kushal and
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the same thing it will show you here
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like hello like name email address phone
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number also it is supporting the phone
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number also
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that you have included in your resume
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now based on our nlp analysis uh it is
00:03:57
saying you are intermediate level
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now skills recommendation like then it
00:04:01
will find the skills that you have
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right the user have which kind of the
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skill so our uh our library that is
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already that i have included in project
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don't worry i will tell each in
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everything later so this will
00:04:14
automatically fetch this uh skill set
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from the this resume right
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right
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and that you can skills that you have so
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basically it is the extracted skill from
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the resume right
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now you can see on our uh like our
00:04:31
analysis say you are looking for android
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app development jobs
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but how
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the android app means how they are good
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like this prediction don't worry i will
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tell you each and everything later now
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skill recommendation if you are going to
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the android uh field then there is a
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this is this is a recommendation like
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android android development flutter
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kotlin
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uh xml java kiwi git git gtc like
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everywhere sdk sqlite so these all are
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the recommended skills
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that is required by the android
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developer right
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now courses recommendation this is the
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amazing thing like it will show you
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around 10 courses you can see if i'm
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going to click on this you can see
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associate android developer course
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flutter
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android basic by google now resume tips
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and trick like awesome you added
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objective
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according to our recommendation please a
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declaration if so basically it is
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scanning all the things related to our
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resume awesome you added hobbies
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according to our recommendation please
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add achievements according to
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recommendation please add a project
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so it will automatically
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find us
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some of the resume pattern so if you
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don't know what is your resume pattern
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like then your resume should have the
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declaration hobbies
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achievements projects if it is not
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available in your project then it will
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automatically lower down your score you
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can see your resume writing score is 40.
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now you can see note this based on your
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content that you have ignore it just
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this is a simple warning message that i
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have printed now bonus video it will
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show you the bonus video also like
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resume tips for jobs and this is the
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title of the video and i just embed one
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video here regarding the resume writing
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tips
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now the second video is about bonus
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video for interview tips so this is the
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second video
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this is all i'm also embedded into my
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system
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here so this is the simple smart resume
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analyzer software it will analyze all
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whole resume and it it is for working
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fine on almost each and every format
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that i have you know gathered my lots of
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friends resume
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and almost it is working fine on each
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and every resume right
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so this is like a little bit a different
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project than another because in other
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words we are getting a 100 percent we
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are expecting a 90 95 percent 100
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accuracy but this project is about to
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like you can see you can do a little bit
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research even you can improve this
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project rather than my version because i
00:06:54
have done some minor changes here right
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minor changes means i've done so many
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things here like but you can improve the
00:07:01
this version also right i'm going going
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to give this code so you can improve it
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and you can just update me that code
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into my github so you can just give a
00:07:10
pull request if any update is there in
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my code right
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so how this all are thing working this
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is the actually we need to understand
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step by step right
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just here just i have show you the like
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how it is working
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now let's see how it is working
00:07:27
so before starting the project what i
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have done is i just divided those things
00:07:32
into the modules that always i am like
00:07:35
making like divide and conquer if you
00:07:36
know merge sort then obviously you will
00:07:38
know this term divided so basically i am
00:07:41
dividing my task and at then i'm get i
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will gather all the things in my project
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is ready
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in movement right
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so first of all task is what i need to
00:07:52
get the resume
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from the user so you you can use any uh
00:07:58
framework like you can use jungle flask
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or streamlit so i just i want to be a
00:08:03
pro head traveler so this is my collared
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project so that's why i chosen to trim
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it right
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so i am using this streamline from box
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it is bit easy to get a resume from the
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user
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now second task is what save the resume
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into system
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third task is what
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currently my system is supporting the
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only pdf resume right obviously your
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resume should have in pdf
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now that this is most important task
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pdf extracting
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so after that pdf extracting now what i
00:08:45
need to do resume parsing
00:08:47
because i need to analyze all each and
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everything now
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different skills
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like
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this uh this project is
00:08:58
means uh going to give you the like four
00:09:00
four or five recommendation like it will
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give you the data science android uix
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and web development jobs kind of the
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recommendation
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because i've just given some of the
00:09:11
limited things here
00:09:12
basically all the things are running
00:09:14
with the nlp right
00:09:16
it might be confusing at this moment but
00:09:18
end of the video you will get a 100
00:09:21
percent idea that how this project is
00:09:22
working
00:09:24
now uh define courses
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and videos
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i don't know why i'm making too much
00:09:36
spelling mistake today uh the reason is
00:09:38
that i'm currently in windows and now
00:09:41
windows is looking a bit weird because
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i'm used to with the ubuntu but because
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this project was located in my windows
00:09:47
so i just need to come to boot into this
00:09:50
windows yeah currently actually i'm
00:09:51
doing a
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job and the company is required to have
00:09:56
the ubuntu into laptop
00:09:59
okay so now the uh
00:10:01
now implement each and every step one by
00:10:04
one so basically that i am going to
00:10:05
explain this code full
00:10:07
okay so now some of the things that i am
00:10:09
going to use which is trimlet pandas
00:10:12
base64 time
00:10:14
now this is the most amazing thing which
00:10:16
is
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pi arrays person
00:10:19
and now resume a parser so what i have
00:10:22
done is i just installed one library
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called piper by resume parser you can
00:10:28
say like this
00:10:30
now if you are going to search with this
00:10:32
so this is the amazing library
00:10:36
so now uh actually at the first half i
00:10:39
think that i can create a resume parser
00:10:41
code by myself right
00:10:44
but after so many trying errors i did
00:10:46
not get that that much of the accuracy
00:10:48
so i use this reading made library which
00:10:51
is called fires parser simple resume
00:10:53
parser used for extracting information
00:10:56
so basically this will automatically
00:10:58
give you the
00:10:59
name
00:11:00
extra email mobile numbers
00:11:03
skills total experience quality name
00:11:05
degree designation all the things that
00:11:07
it will give you
00:11:09
right installation is just like peep
00:11:11
install and this library name but before
00:11:14
that you should have the download all
00:11:16
the things like nlp nlp operation you
00:11:19
should have this pc and analytical
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library installed
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right
00:11:25
now how to pass the data basically you
00:11:27
just need to pass the your pdf file here
00:11:31
and now it will give you the result like
00:11:33
this caller name company name degree so
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this is these all are the keys
00:11:38
for the dictionary email mobile number
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so guys uh before starting with any
00:11:43
library you just need to read the
00:11:44
documentation because this documentation
00:11:47
is very helpful and that with this
00:11:48
documentation i have created my project
00:11:50
right
00:11:53
right
00:11:54
so that's how this pi resume parcel is
00:11:57
working
00:11:58
now but okay so i have just uh parsed
00:12:00
the resume right
00:12:02
but now there are so many things that we
00:12:03
need to implement here
00:12:05
now i need to extract the text from the
00:12:07
resume basically this pi parcel will
00:12:09
help me to get that data right
00:12:12
but how i should get the text from the
00:12:15
resume so this is the second question so
00:12:17
basically
00:12:18
one library called pdf miner pdf miner
00:12:20
is very famous for extracting the pdf
00:12:22
using python so this is the this is the
00:12:25
library that i am using
00:12:27
to
00:12:28
get the text from the user uploaded pdf
00:12:31
now i o and random
00:12:34
and then streamlit text basically i
00:12:36
random like inbuilt library that i'm
00:12:38
using io to just
00:12:40
uh save the images which is uploaded by
00:12:42
the user
00:12:43
right
00:12:44
then extremely text that i will explain
00:12:46
you later
00:12:47
pl is always common library like python
00:12:50
image library by mysql yes i'm using
00:12:52
database into this project so make sure
00:12:55
uh your apache and mysql is running now
00:12:58
from courses basically courses dot py
00:13:00
that i have created i will explain it
00:13:02
later
00:13:03
pfe pfe is also
00:13:05
related to youtube related tasks so i
00:13:07
will explain it later totally floatless
00:13:10
for the admin module yes
00:13:12
this project have two models let me show
00:13:14
you
00:13:15
normal user and admin
00:13:17
so i admin i will show you later
00:13:21
okay so this video is might be going to
00:13:23
longer than expected
00:13:25
so guys forgive me
00:13:30
okay so now ignore all this function
00:13:32
this function is not useful at this
00:13:34
moment but i will explain each and every
00:13:36
function after that right
00:13:39
okay so now first of all i need to
00:13:41
connection make a connection with my
00:13:43
database so pi mysql dot connect
00:13:47
host is localhost my root user is root
00:13:49
password is nothing and db so make sure
00:13:52
you should create a sradb into your
00:13:54
project
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that is already i have created let me
00:13:58
show you
00:14:00
so you can see this is the sra
00:14:02
so sra database is already created right
00:14:07
now okay
00:14:09
now st dot page title config so
00:14:13
basically this is the paid title config
00:14:14
like my project name is what
00:14:16
uh smart resume analyzer so you can see
00:14:19
this is the title here on left panel
00:14:21
like smart resume analyzer an icon is
00:14:24
that i have already defined an sr logo
00:14:26
dot i say you can see this is the icon
00:14:28
so basically i'm configuring my website
00:14:31
now def run st road title first of all i
00:14:34
need to have this title in between smart
00:14:36
resume analyzer
00:14:38
activities is like two users available
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here in normal user admin
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make sure choice
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is located into sidebar so
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ht.sitebar.selectbox
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choose uh among the given option
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so already this message is printed here
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now image dot open basically in this
00:15:00
part i should have the
00:15:04
logo of my project
00:15:11
okay
00:15:16
okay sorry just i got in uh call in
00:15:18
between video recording okay so now this
00:15:21
is the that logo that i am uh
00:15:24
just displaying here now i should have
00:15:26
one image uploader
00:15:29
okay so just i'm first of all i'm just
00:15:32
showing the logo okay
00:15:39
now uh creating the database so
00:15:41
basically i i just estimate that you
00:15:44
guys are all aware with the basic sql
00:15:46
queries like create database if not
00:15:48
exist then sra will be our database
00:15:52
name right
00:15:54
so it will create a database of sra
00:15:58
uh you can see here
00:16:00
okay so database is already created
00:16:02
now it will create a one table which is
00:16:05
called user underscore data so this is
00:16:07
basically this when you are going to
00:16:09
upload the resume and then what it will
00:16:11
do it will save the it will
00:16:13
automatically save the all the basic
00:16:15
information of the user so basically we
00:16:17
can create analysis of it
00:16:20
so this is the create table query like
00:16:22
create table if it is not exit time name
00:16:24
is user data that data that i am taking
00:16:26
is id name email id resume score time
00:16:29
stamp page number i mean paid number
00:16:31
means how many pages of resume it is
00:16:34
predicted feel like recommendation that
00:16:37
is given by our system user level it is
00:16:40
pressure experience or what
00:16:42
actual skill the skills that is already
00:16:44
have by the user recommended skills that
00:16:46
is generated by our system recommended
00:16:48
course that is our generated by system
00:16:49
now primary key is id
00:16:52
right
00:16:54
now if choice is equal to normal user
00:16:56
like if i'm
00:16:58
choicing this is a normal user then i
00:17:00
should get a one file uploader that is
00:17:02
already defined here like pdf file
00:17:04
status file uploaded choose your resume
00:17:06
and type only allowed is pdf
00:17:09
okay
00:17:10
now if i am uploading pdf like if pdf
00:17:13
file is not none so user is uploading
00:17:15
any pdf then automatically what it will
00:17:17
do
00:17:18
i just need to have one folder or full
00:17:20
folder upload resume so basically with
00:17:23
open after write video file dot get
00:17:26
buffer so basically i'm saving the user
00:17:28
uploaded pdf file here
00:17:31
right
00:17:32
so basically with this if user is
00:17:34
uploading any pdf file that so that file
00:17:37
will be saved here into this uploaded
00:17:39
resume folder
00:17:42
now
00:17:43
show pdf and i'm giving the path of the
00:17:46
full pdf right so now what is this show
00:17:48
pdf let's see so this is the one
00:17:50
function
00:17:52
so basically the what this function will
00:17:54
do
00:17:55
you can see with open file but basically
00:17:57
you should aware with the basic file
00:17:59
operation so rb rate in binary now
00:18:02
base64 pdf like base64 that's why i have
00:18:05
imported base64 dot b64 in code
00:18:09
and then f dot red record utf-8
00:18:12
okay so now what i'm doing show pdf
00:18:15
means what let me show you
00:18:18
i'm just showing the user uploaded pdf
00:18:21
into iframe tag
00:18:23
right
00:18:24
and now iframe is display here in
00:18:26
sd.markdown and allow unsafe html is
00:18:30
equal to true
00:18:31
so
00:18:32
what it will do it will get the uploaded
00:18:34
pdf from the path that is uploaded by
00:18:37
into this uploaded resume
00:18:39
now this same pdf if which is uploaded
00:18:42
by the user
00:18:44
will be display here you can see this is
00:18:46
the iframe tag and the pdf is displaying
00:18:48
here yes if user is uploading the pdf
00:18:51
then pdf should be displayed to the user
00:18:53
right
00:18:54
so that's why this pdf is only
00:18:56
you can see you can you can zoom out
00:18:58
zoom in so this is ready made like
00:19:00
iframe
00:19:05
okay
00:19:10
so this is the show pdf function
00:19:12
so that's why now that's why i like to
00:19:14
work uh create uh work we like to create
00:19:17
a small function because your work will
00:19:20
be easier right now this so pdf in one
00:19:22
line the function is already taking
00:19:24
three to four line but here i don't want
00:19:26
to miss my main code so that's why i
00:19:28
just created a function that function is
00:19:29
required
00:19:31
path of the pdf file
00:19:33
now
00:19:34
resume a parcel
00:19:36
right
00:19:38
now resume a parcel should
00:19:40
what
00:19:41
what is it actually required it required
00:19:43
the path of the pdf file
00:19:45
so basically i have stored pdf path in
00:19:48
this save image path and get extracted
00:19:51
data basically this method that we have
00:19:53
already seen here
00:19:54
okay
00:19:56
get extracted data so basically i just
00:19:58
use this documentation to complete my
00:20:00
code
00:20:01
now i get a resume data now this will be
00:20:04
you know my resume data will be same
00:20:06
like this like there will be one list
00:20:08
into one list dictionary and these all
00:20:10
are the keys
00:20:11
okay
00:20:13
okay so now this by uh by resume person
00:20:16
is working fine but what some of the
00:20:18
point
00:20:18
might be calling them maybe you will not
00:20:21
get a proper because these all are the
00:20:22
nlp things that you cannot expect of 100
00:20:25
accuracy so i am interested in not whole
00:20:27
thing like i'm not interested user
00:20:29
qualium degree
00:20:30
i'm just interested in some of the
00:20:32
things like email mobile number name
00:20:35
number of pages and skills i'm
00:20:37
interested in this thing only
00:20:39
so i will fail this thing from the
00:20:40
resume parser data now this resume
00:20:42
parser data will be stored into this
00:20:44
resume data
00:20:46
right so i'm just giving a condition if
00:20:47
resume data is not empty like if resume
00:20:49
data is not none
00:20:51
right
00:20:54
so what i need to do
00:20:56
i need to do
00:20:58
once again i need to get the pdf text
00:21:01
now why i am requiring the pdf text i
00:21:04
just got all this information from here
00:21:06
but i am getting the limited amount of
00:21:08
the information from here but i need to
00:21:11
play with some more nlp techniques so i
00:21:13
should have the full content of the user
00:21:15
speed uh resume like
00:21:18
each and every word that is containing
00:21:20
uh by the
00:21:22
pdf or you can say resume right so
00:21:24
that's why i'm just using this pdf
00:21:26
reader function here and that pdf reader
00:21:28
function
00:21:30
will be written me a text resume
00:21:32
basically it will give you the plain
00:21:34
text of the resume and every word that
00:21:35
is containing by that user and now this
00:21:37
pda function is required the original
00:21:39
path of the resume
00:21:41
now let's see what this pdf is pdf
00:21:43
reader is containing
00:21:45
and now the pdf reader is once again the
00:21:47
function
00:21:49
so i'm using pi pdf miner so resource
00:21:51
manager is equal to pdf resource manager
00:21:53
that we need to look at first first now
00:21:56
i need to get a string i o right
00:21:59
now converter basically this this code
00:22:01
is also given by the pi pdf for the
00:22:03
documentation so just i request you to
00:22:05
read the documentation
00:22:07
now basically this will convert your pdf
00:22:10
into text format all the things are
00:22:11
defined here right
00:22:13
now what it actually do it is iterating
00:22:15
to each and every pdf pages
00:22:17
and it is giving me a text of the pdf
00:22:21
any pdf not like resume it will written
00:22:23
each and everything
00:22:24
now my text will be stored in this text
00:22:26
variable now now i'm just closing all
00:22:28
the things like converter effect file
00:22:30
and error close
00:22:31
and i should get a written text
00:22:34
so if user is uploading any
00:22:36
resume then i will get a
00:22:39
basic information of the user using this
00:22:42
library called resume by parser
00:22:45
and now i will get a full text resume
00:22:48
now
00:22:49
it's time for the good looking ui okay
00:22:52
so i'm just i just done till here so you
00:22:55
can if you're going to the task you can
00:22:57
see
00:22:58
first task is completed second task is
00:23:00
completed
00:23:01
third and fourth task is completed okay
00:23:06
now what now what we just need to define
00:23:09
the some of the good things here like s
00:23:11
t dot header resume analysis line then
00:23:13
fc dot success hello
00:23:15
resume data name so now what this name
00:23:19
resume data is dictionary that is
00:23:20
returning from the
00:23:23
from the designer pi person right and
00:23:25
this is name that is already predefined
00:23:27
key if you are going to the
00:23:29
documentation then you can see
00:23:32
the name which is already defined key by
00:23:34
the hour by password resume
00:23:37
now
00:23:39
if it might be possible that each and
00:23:41
every name will cannot be detected by
00:23:43
these five parts of resume what we are
00:23:45
doing
00:23:47
we are just exception basically i don't
00:23:49
want to show this error so just i'm
00:23:51
using pass into exception
00:23:53
so if it is like it is like
00:23:56
you can see if everything is fine then
00:23:58
it should get st text like name like
00:24:01
username email the email that is
00:24:03
extracted by our
00:24:05
resume person now the text
00:24:08
and resume pages now i am uh
00:24:12
like getting the resume pages okay
00:24:15
so don't worry that i will tell you
00:24:18
okay
00:24:19
so you can see these two thing is
00:24:21
printed here for this resume but might
00:24:23
be possible other things are like uh not
00:24:27
acceptable and that means not readable
00:24:28
also because this
00:24:30
resume is taken from the internet
00:24:32
let's see for other resume
00:24:36
so might be it at this video recording
00:24:38
time i'm not uploading any my friend
00:24:39
resume otherwise it is working very fine
00:24:42
on my friend's resume even my resume
00:24:44
also it is which is you know extracting
00:24:47
each and everything like email id mobile
00:24:48
number
00:24:51
okay and now you can see contact the on
00:24:53
this it is working fine
00:24:55
the resume page is what is only one page
00:24:58
resume you can see and it is already
00:24:59
extracted here resume pages
00:25:03
i don't know which contact details it is
00:25:05
extracted okay so there as i told
00:25:07
earlier it might be wrong also so you
00:25:09
can see contact details is wrong but
00:25:11
email id
00:25:12
is fine name is actually our detector
00:25:14
which is detected by here
00:25:17
so it is wrong here but might be you can
00:25:19
consider this is a little bit error
00:25:21
right
00:25:22
still we got a good thing like good
00:25:24
amount of the extracted things here
00:25:27
now
00:25:29
i'm just given giving the candidate
00:25:31
level as a simple like i'm not defining
00:25:33
any value here
00:25:34
now i just
00:25:36
assuming that if resumed data of number
00:25:38
of pages is equal to one if you have one
00:25:41
page of resume that you you can consider
00:25:43
yourself as a fresher
00:25:45
that might be case right i'm not saying
00:25:47
like if you have one page resume it
00:25:48
might be possible to do a lot of
00:25:50
pressure also
00:25:52
but in this case that i am doing here
00:25:54
like candidate level is equal to fresher
00:25:55
if resume number of the page is equal to
00:25:57
two then you are at like intermittent
00:26:00
level like you have one or two year of
00:26:01
the experience right and if you are like
00:26:04
number of the pages is equal to greater
00:26:06
than three then you are at experience
00:26:07
level right
00:26:09
if you have to replace resume and then
00:26:10
obviously you have done so many things
00:26:12
you have so many years of the experience
00:26:14
so for the number of the pages that i'm
00:26:16
defining the sd dot markdown like you
00:26:18
are a
00:26:19
fresher
00:26:21
you are at intermediate level or you are
00:26:23
at experience level
00:26:26
so for that each and every different
00:26:27
like color code is different
00:26:29
so that's how that i am printing this
00:26:31
message like you are looking pressure
00:26:34
now skill recommendation
00:26:37
so now what is this skill recommendation
00:26:39
let me show you
00:26:40
so first of all i just need to
00:26:42
initialize the sd text so basically
00:26:44
streamlit doesn't have this tag model
00:26:47
inbuilt right so i just out that's why i
00:26:48
downloaded this sd text from the
00:26:51
uh
00:26:52
you can see that i have imported this ht
00:26:54
tags into import section
00:26:58
okay so now http is like skills that you
00:27:02
have basically it is giving the skills
00:27:04
that is already here by the user now how
00:27:06
we can fit the skills
00:27:08
already our resume parser has extracted
00:27:11
the skills and you can see the
00:27:12
dictionary name is skills key name is
00:27:15
skills you can see here
00:27:16
and it is returning a one list of the
00:27:18
skills
00:27:19
okay so i just need to pass this value
00:27:22
as a skill and key is equal to one it
00:27:24
should be unique
00:27:25
okay because it might be have multiple
00:27:28
sd text so key should be on unique for
00:27:30
each and every one sd text
00:27:32
okay
00:27:33
so now now i just got the user skill
00:27:35
here you can see this is also
00:27:38
extracted from the resume and skills are
00:27:41
showing here right now our
00:27:43
recommendation time
00:27:45
so some of the things some of the
00:27:47
recommendation things that i have
00:27:49
already generated okay let me explain
00:27:52
okay as i told earlier this resume
00:27:55
analysis is like is working for the id
00:27:57
resume so it is capturing the keywords
00:28:00
like data science keyword web keyword
00:28:02
weapon technology android keyword ios
00:28:04
keyword and usb skewer you can improve
00:28:07
it into more depth
00:28:09
okay so what is this ds keyword
00:28:12
tensorflow keras python so basically
00:28:14
that what i have done is i just google
00:28:17
it so many things about like what is
00:28:19
this web technology is included what is
00:28:21
this android key android technologies
00:28:23
included and i have made one list
00:28:26
of the keywords now
00:28:28
why i'm doing this
00:28:30
let me explain
00:28:31
this is the very tricky part but it is
00:28:33
very easy to understand
00:28:35
now
00:28:36
if i am going to show this thing only to
00:28:38
my like any person like this is fine
00:28:41
like that i am extracting the basic
00:28:43
information from the
00:28:45
resume but that that is not a meaningful
00:28:47
that is not you can say useful now i
00:28:49
should have the my own model here like
00:28:51
what my own nlp techniques
00:28:53
so what i need to do is
00:28:55
i need to fetch the keyword right if you
00:28:58
are going to see my resume then how you
00:29:00
can judge me like what is my working
00:29:02
field if i am including all these
00:29:04
keywords like if my resume has the
00:29:07
keyword like uh
00:29:10
you can say
00:29:13
uh tensorflow keras fight watch machine
00:29:15
learning deep learning all these
00:29:17
keywords is if it is these keywords are
00:29:19
present into my resume
00:29:22
then
00:29:22
i am working with the data science
00:29:25
if i have the keyword of the react
00:29:28
django node.js reaches flask
00:29:31
angularjs
00:29:32
it might be possible that i'm working
00:29:33
with the web technology
00:29:35
for the android you can see android
00:29:37
android development flutter hotline xml
00:29:39
key if this keyword is found in your
00:29:40
resume it might be possible that you are
00:29:42
working with android keyword
00:29:44
you can next uh you know you can create
00:29:47
a more better number of the keywords and
00:29:49
you can append into this list okay
00:29:52
but this is a this is also working fine
00:29:54
that i have already tested this uh
00:29:56
system want to like my web developer is
00:29:59
like web developer friend resume for
00:30:01
that it is working fine given that i
00:30:03
have also ui designer friend on that on
00:30:06
that resume it is working fine it is
00:30:08
predicting the same that you are
00:30:10
interested in uis job
00:30:12
now
00:30:14
this is like we are finding the
00:30:16
particular keyword from the user text
00:30:18
that we have already expected from the
00:30:20
pdf miner now recommended skills now
00:30:22
it's time to recommend the skill to the
00:30:24
user
00:30:25
you can see skill recommendation here
00:30:29
okay
00:30:30
that i need to print this line also
00:30:34
okay so this all things that i will
00:30:36
you know do it later but before that i
00:30:39
need to
00:30:40
fetch the all the things regarding the
00:30:43
user
00:30:44
and now recommended skill that i have
00:30:45
created empty list
00:30:47
recognized in field and reco course this
00:30:50
both field is currently this both value
00:30:52
is currently nothing like empty string
00:30:54
now i need to iterate the loop
00:30:57
like i need to match the skill
00:30:59
but how
00:31:01
so we have just assumed that original
00:31:02
skill and we have this keyword so
00:31:06
basically what i am doing is i am just
00:31:07
entering the with this look for and
00:31:09
resume date of skill that is already
00:31:12
skills that is you know that is already
00:31:14
have by that person
00:31:15
now if i dot labor lower that is you
00:31:18
know that will return a lowercase
00:31:19
keyword and ds keyword
00:31:21
now just assume that if user this user
00:31:24
have the uix development skill
00:31:28
you can see this all are related to you
00:31:30
adobe indies and css wordpress right so
00:31:33
you can see this guy is currently
00:31:35
working job into uiux you can assume
00:31:37
that right
00:31:39
so what it will do it will loop through
00:31:41
each and everything right if any keyword
00:31:44
of this is matching with this ui ux
00:31:46
keyword then automatically it will show
00:31:49
you
00:31:50
the
00:31:51
regarding the ui ux right
00:31:55
so let me just go through the uix here
00:31:58
you can see uix recommendation
00:32:01
like you can see it will print the here
00:32:03
so you can see print is already here
00:32:05
like prototyping
00:32:07
obviously prototyping scale is already
00:32:10
means have by the uix developer
00:32:13
now what is my recommended field so if
00:32:15
this happening this is a simple ifa if
00:32:17
this is happening then my recommended
00:32:20
field should be ux development now you
00:32:22
should get a message our analysis say
00:32:24
you are looking for the ux development
00:32:26
jobs now i have already created one list
00:32:30
recommended skill that i can recommend
00:32:32
to the user
00:32:33
like you can see now recommended skills
00:32:35
are
00:32:36
ui user experience adobe xd
00:32:39
prototyping wireframe storage like this
00:32:41
all the skill that i already googled
00:32:43
illustrator after effects you can see
00:32:45
there are so many skills available
00:32:47
that we can recommend to the user
00:32:49
recommended skill for you
00:32:51
okay so basically based on this skill we
00:32:54
are recommended this skill so that's how
00:32:56
i think you got this is very simple
00:32:58
concept now let's see for the same for
00:33:00
the data science
00:33:01
if i'm if i'm uploading any
00:33:04
uh user resume any user should have the
00:33:07
similar words like tensorflow keras then
00:33:09
it will automatically fall into this if
00:33:12
and the recommended will be data science
00:33:13
our analysis say you are looking for
00:33:15
data science job and
00:33:16
uh recommended skill will be like data
00:33:18
visualization rate analysis uh so many
00:33:21
skills that already printed here
00:33:26
now
00:33:28
this is the course recommender i think
00:33:30
you guys are got i don't need to explain
00:33:32
like when android keyword web
00:33:34
development keyboard working is working
00:33:35
is same for each and everything okay so
00:33:38
it is
00:33:39
working for the four fields okay it is
00:33:41
five
00:33:42
now
00:33:45
course recommendation you can see after
00:33:47
that it is showing me the course
00:33:48
recommendation
00:33:50
okay so now this course recommendation
00:33:52
is required what
00:33:54
this web course
00:33:59
okay i will a web course is basically is
00:34:02
the kind of the list okay let me show
00:34:04
you
00:34:05
ds course web course here you can see in
00:34:07
parameters
00:34:08
so what i have done is
00:34:11
first let me show you the course
00:34:12
recommended so basically this course
00:34:14
recommender will function will create a
00:34:16
one thing like course recommended scores
00:34:18
and certificate recommendation
00:34:20
it will count the number recommended
00:34:22
course list
00:34:24
it will be empty now st dot slider i am
00:34:26
getting a 1 to 10 slider value from the
00:34:28
user like how many courses
00:34:29
recommendation you want like
00:34:31
1 2 3 4 five based on that
00:34:35
what i am doing for c name and c link so
00:34:37
basically this course list will be
00:34:39
dictionary now what is this course list
00:34:43
so course list will be given by the
00:34:46
user okay
00:34:49
now this counter will be increase and s
00:34:51
t dot markdown course name and course
00:34:53
link will be generated and recommended
00:34:56
course will be
00:34:57
you know appended into the
00:35:00
this list which is a recommended course
00:35:02
if c is equal to number of the responses
00:35:04
like c is the counter and number of the
00:35:07
uh recommendation that is defined by
00:35:09
user so if
00:35:10
if user want five any five course is
00:35:13
already shown then counter will be five
00:35:15
is equal to five then loop will be break
00:35:16
and it will give you the recommended
00:35:18
course
00:35:19
right
00:35:21
so i'm returning this recommended course
00:35:22
because i want to store all this thing
00:35:24
into the database so that's why i'm
00:35:26
returning the recommended
00:35:28
courses
00:35:29
okay so now this how this course list is
00:35:32
coming
00:35:33
okay
00:35:35
so let me show you the this thing once
00:35:37
again
00:35:39
so this course list is coming from this
00:35:41
ds course
00:35:42
so what i have done is very it is very
00:35:44
statically is type you can see there is
00:35:47
one
00:35:47
file dot courses dot by
00:35:50
ds course web course i have defined some
00:35:52
of the predefined courses
00:35:56
like android course ios course
00:35:59
uix course now this
00:36:01
you know this course is containing two
00:36:03
things list into list
00:36:05
like first member is about our course
00:36:06
name second member is about to
00:36:09
course link
00:36:10
so this is the top most code that i have
00:36:12
taken from the google that i'm already
00:36:14
showing to the user
00:36:16
right
00:36:18
so that's why i have defined this two
00:36:20
thing into for loop it might be possible
00:36:22
that you get amazed like what i'm
00:36:24
unpacking two values into one list so
00:36:26
basically it is returning this two thing
00:36:28
course name and course link
00:36:32
okay
00:36:34
the same word that i have already
00:36:35
defined in the sum of the videos okay so
00:36:36
that thing that i will explain in later
00:36:42
okay so my course recommendation is also
00:36:44
done here
00:36:45
okay
00:36:47
so let me just
00:36:48
guide through this now resume tips and
00:36:51
ideas
00:36:53
okay so before starting all these things
00:36:55
just i am inserting all this thing in
00:36:56
root table like current time stem when
00:36:59
the user is uploading this resume
00:37:02
okay
00:37:05
now
00:37:06
resume writing recommendation now i just
00:37:09
given the courses and recommendation now
00:37:12
some of the things that might be present
00:37:14
in resume or not so what this is once
00:37:16
again very simple resume text i got the
00:37:19
resume text from the pdf miner basically
00:37:22
that from that function that i am
00:37:23
returning the resume text
00:37:25
now if there is a simple thing you are
00:37:27
getting 1000 but obviously you can find
00:37:30
like if objective is not present into
00:37:32
resume tags then obviously you should ah
00:37:36
get the user should get message right
00:37:39
so i am just defining the condition if
00:37:40
objective is in resume text then resume
00:37:43
score will be plus 20
00:37:45
now resume score that i have initialized
00:37:47
zero now st dot markdown user should get
00:37:50
message like awesome you added objective
00:37:53
if our code means uh
00:37:55
objective what that found into user
00:37:57
uploaded resume then automatically score
00:37:59
will be increased by 20 otherwise goal
00:38:02
will be not increase
00:38:03
like you should get a message like this
00:38:06
according to our recommendation please
00:38:07
add career objective this message should
00:38:09
be in the same for the declaration
00:38:13
declaration should be present into
00:38:16
resume text so now you you can think of
00:38:18
like this if objective is and
00:38:20
declaration will be present in the small
00:38:22
letter
00:38:23
okay
00:38:24
like first letter is small but it is not
00:38:26
possible and the reason is that if you
00:38:28
are creating any resume that says this
00:38:30
thing this what call as a section
00:38:32
instruction name always first letter of
00:38:34
the capital in most of the resume
00:38:37
the same thing for the hobbies and or
00:38:39
interest so now just think of it in some
00:38:42
of the resume it might be possible that
00:38:44
hobbies or interest maybe two key
00:38:46
different keywords one that i'm having
00:38:49
these two keywords like i'm using these
00:38:50
two k words if achievements is not
00:38:52
present in resume the same thing i'm
00:38:54
increasing this score if it is present
00:38:56
otherwise it is like it will not
00:38:58
increase the score the same for the
00:39:00
project if projects were into my list
00:39:03
then automatically it will increase the
00:39:04
score and give yes congratulations you
00:39:06
added projects otherwise it will show
00:39:09
like this according to our
00:39:10
recommendation please add the projects
00:39:14
now score
00:39:16
now i need to pass the resume score to
00:39:18
the user like st dot sub header uh
00:39:21
resume score here you can see after that
00:39:24
your writing score algorithm score so
00:39:27
basically i am initializing com progress
00:39:29
bar score is equal to zero now four
00:39:31
percent complete in range regime score
00:39:33
that resume score will be total like if
00:39:35
my resume score is 40 50 60
00:39:38
it will iterate to this
00:39:40
range for loop obviously and then score
00:39:42
will be increased by one and time dot
00:39:44
sleep
00:39:45
so let me refresh it once again once
00:39:47
again
00:39:49
i'm just showing i just want to show you
00:39:51
the progress bar
00:39:54
you know you can see this progress bar
00:39:55
is increasing by a step
00:39:58
your resume score is 20 because this
00:40:00
resume doesn't have anything
00:40:02
like it uh obviously you can see only it
00:40:04
is have obvious or 20 score is plus for
00:40:07
that
00:40:09
i'm just i'm printing this
00:40:10
warning this score is calculated based
00:40:12
on the content that you have added in
00:40:13
your resume
00:40:14
okay
00:40:15
so time dot slip basically that
00:40:18
my interpreter will be sleep for a
00:40:20
fraction of seconds after each and every
00:40:22
iteration
00:40:24
okay so that's why it is you know very
00:40:26
slowly slowly increasing otherwise you
00:40:28
know it will give you the full
00:40:29
loaded progress but i don't what i just
00:40:32
want small amount of the animation here
00:40:34
so that's why what i'm doing here
00:40:36
okay now at the end we everything fine
00:40:38
then it will show you the sum of the
00:40:39
balloons that is already provided by the
00:40:41
streamlit
00:40:43
okay
00:40:44
now
00:40:45
at the end of all this thing i'm
00:40:47
inserting all this data into the
00:40:49
our database
00:40:51
this insert data is function about to
00:40:53
inserting the data it is generating you
00:40:55
know insert query
00:40:57
it is creating the tuple of all the
00:40:59
things okay so the things that i'm
00:41:01
including into database like name email
00:41:03
that already have explained and cursor
00:41:05
dot execute connection dot commit
00:41:08
okay
00:41:09
so most of ninety percent okay eighty
00:41:11
percent project is going means explained
00:41:13
by me
00:41:14
now resume writing video and interview
00:41:18
tips so the same thing here
00:41:21
so i have already created the
00:41:24
resume video
00:41:28
okay fetch yt video okay bonus video for
00:41:31
resume writing tips
00:41:32
random dot choice so this is the resume
00:41:35
videos
00:41:37
that function already that list that i
00:41:39
have already created into this course is
00:41:41
not by receiving interview videos
00:41:43
okay so i have almost extracted around
00:41:46
four four eight eight videos
00:41:48
for the resume preparation and eight
00:41:50
videos for the interview preparation
00:41:52
now what i'm doing is i'm just picking
00:41:54
picking up a random video like random
00:41:56
choice
00:41:57
now
00:41:58
the choice video should be display here
00:42:00
too is so nice i have created one
00:42:02
function fetch youtube video
00:42:06
now
00:42:06
this fast video youtube like it is
00:42:08
nothing just it is required one link
00:42:11
i'm using puffy to get the pfe dot news
00:42:14
so basically pfe will open the youtube
00:42:15
video and it automatically it will fade
00:42:17
the title for you like what is the video
00:42:19
title
00:42:21
okay so i just need to get the video
00:42:23
title
00:42:24
for the you know
00:42:25
uh video now sd dot sub header then like
00:42:29
and then i just need to print the video
00:42:31
title that i just got from this function
00:42:33
you can see how to write a
00:42:35
resume software engineer resume tips for
00:42:37
fresher
00:42:39
and experience and you can see the title
00:42:40
is same here also
00:42:42
so that's why i actually extracted the
00:42:44
title from the
00:42:46
video link okay now st dot video
00:42:49
basically this is in build model of the
00:42:51
you know displaying the youtube video
00:42:53
like embedding youtube video into
00:42:54
webpage so nothing to do with just you
00:42:57
need to pass the link of the video that
00:42:59
is already random generated so i'm
00:43:01
displaying two video resume writing tips
00:43:03
and interview preparation then at the
00:43:05
end i'm connecting the connection dot
00:43:06
commit
00:43:08
because i'm using database now if
00:43:10
anything red white will give you the
00:43:11
warning hd dot error something went
00:43:13
wrong and now my code is around 300 line
00:43:16
now user side is completed now adam
00:43:18
inside
00:43:19
okay
00:43:21
now admin side is what
00:43:24
uh it is you know asking for the user id
00:43:26
password so spidey don't decide crucial
00:43:28
one two three is the password so same
00:43:30
thing same thing here
00:43:32
now i should get uh analysis of admin
00:43:36
set like
00:43:37
okay let me show you you know it is the
00:43:39
most interesting thing that it will show
00:43:40
you the some of the visualization if std
00:43:43
button dot login
00:43:45
right
00:43:45
if this admin is pressing any login
00:43:47
button here and if user password and
00:43:49
this thing are match
00:43:51
don't mind guys that for that particular
00:43:54
thing that i am not going to create
00:43:55
database it is statically that however
00:43:57
given
00:43:58
so you can see you will like welcome
00:44:00
kushal users data data that is already
00:44:02
extracted by
00:44:04
our database so when you are uploading
00:44:06
any resume you can see this thing will
00:44:08
be stored into my database table right
00:44:11
here
00:44:12
in this table
00:44:14
now what what i need to do with this
00:44:17
data okay so that this is the data that
00:44:20
i'm storing like recommended skill
00:44:22
recommended course name actual skill
00:44:25
user level predicted field total number
00:44:28
of page resume time of uploading resume
00:44:30
score email
00:44:32
even i can download this report also if
00:44:34
i'm going to download this report then
00:44:36
you know
00:44:37
uh it will be have into the csc but i'm
00:44:40
not going to open
00:44:44
okay i think i just got error because i
00:44:46
just closed unexpectedly
00:44:48
now pie charts for the predictor field
00:44:50
recommendation now i'm just defining the
00:44:52
sum of the things like regarding the pie
00:44:54
chart so most of the resume
00:44:56
are base is a data science
00:45:00
means my system have most of the
00:45:02
recommended values data science because
00:45:04
that i am uploading many datas and
00:45:05
resume into the system now some of the
00:45:07
ios some of the recommendation has two
00:45:10
like value two then uix and most of
00:45:12
their web development
00:45:14
now experience level like in my system
00:45:17
if i have 1000 resume uploaded into the
00:45:19
system then i should get knowledge like
00:45:21
how many people how many percent of
00:45:23
experience and how many percent of the
00:45:25
fresher you can see
00:45:26
like intermediate is 23 and fresher is
00:45:29
only 16
00:45:31
and this is the end of the this our
00:45:33
admin panel when i can display more
00:45:35
graphs here but uh
00:45:38
okay just i need to close this excel
00:45:40
warnings
00:45:43
okay
00:45:44
i don't know after the you know using
00:45:47
this windows so many time
00:45:51
it is very difficult
00:45:53
okay
00:45:55
so that i am just showing you how you
00:45:56
can display these things
00:45:59
now i need to get the uh you know data
00:46:02
from the database so there is one thing
00:46:04
like cursor.execute you know this query
00:46:06
select star from the user data and this
00:46:08
data means will fetch the
00:46:11
this patch all function will fade to all
00:46:13
the data it will give you the list now i
00:46:15
just need to create a data frame and
00:46:16
data frame should have the
00:46:18
like column name right
00:46:21
now i just need to display the data from
00:46:23
so there is streamlined sql data frame
00:46:25
will display this
00:46:27
beautiful table here you can see this is
00:46:29
the table
00:46:30
right
00:46:31
now
00:46:33
admin the this thing is already
00:46:36
uh you know the admin side data that i
00:46:38
already displayed here right
00:46:41
now the thing is that how i can display
00:46:43
the recommendation chart so first of all
00:46:46
from the plot data what is this close
00:46:48
data it is like select star from the
00:46:50
user data
00:46:52
once again i'm loading this query and
00:46:53
i'm storing into the plot data variable
00:46:56
now plot data dot predictor field dot
00:46:58
unique
00:46:59
so i'm just what i'm doing i'm getting
00:47:01
the unique labels that is already like
00:47:03
data science job or android app web
00:47:06
development job
00:47:08
now what i'm doing i'm just getting the
00:47:10
value counts value counts whatever
00:47:12
return like data science over 16 values
00:47:14
like 16 recommendation a web app have
00:47:17
five recommendation
00:47:19
ui six recommendation
00:47:21
and now i'm just by making a pie chart
00:47:24
px dot pi i'm not using metro lab i'm
00:47:27
using the plotly express
00:47:29
okay so plotter will give you the
00:47:31
beautiful you know visualization and
00:47:33
just i'm making the plural chart here
00:47:37
passing the values labels and title like
00:47:40
predictor
00:47:41
field according to the skill and the
00:47:43
same for the pie chart
00:47:45
the pie chart like once again i'm making
00:47:47
the display chart and i'm passing the
00:47:49
user level user level is like experience
00:47:51
or not experience intermediate and the
00:47:54
things are
00:47:55
at the end this time making chat once
00:47:57
again if anything gets wrong it will
00:47:59
show you the wrong id password provided
00:48:02
okay so almost i explained uh 3 360 line
00:48:07
of each and everything and i think i
00:48:09
have explained all the function okay now
00:48:11
i have already mistakenly you know
00:48:14
forget this function download report so
00:48:16
basically just what i am doing is this
00:48:18
function is required creating like data
00:48:20
frame file name and text
00:48:22
text means you just want to download it
00:48:24
means
00:48:25
this download report like text name of
00:48:28
this link okay so csv df2 csv index is
00:48:31
equal to false but before that i should
00:48:34
get like basically for bm encode so
00:48:36
basically what it will do it will encode
00:48:38
the data frame data
00:48:40
and you know it will show you the means
00:48:42
it will generate the download link of it
00:48:46
okay and it will written the href hrdf
00:48:49
means what this link because so
00:48:51
basically in this function i just need
00:48:53
to pass the data from file name and text
00:48:55
you can see here
00:49:00
you can see ht dot markdown get download
00:49:02
link data frame
00:49:04
user data dot cs in my files name you
00:49:07
can see user data dot css download
00:49:09
report allow on sub html is equal to
00:49:11
true and my report is downloaded okay
00:49:14
so i think i have explained each and
00:49:16
every line regarding the project
00:49:18
you can see insert data function is
00:49:19
nothing just inserting the data
00:49:21
course recommender that i explained show
00:49:24
pdf pdf reader okay so all the things
00:49:27
that i have explained and i don't think
00:49:29
but this video is around 14 9 minutes
00:49:31
longer and i think i just need to
00:49:34
get one hour for this rendering the
00:49:36
video so guys uh that i have already
00:49:38
explained the full thing if you did not
00:49:40
get anything or if you are having any
00:49:42
doubt with this video you can just
00:49:43
comment down below code that i will
00:49:45
share with to you guys okay so don't
00:49:48
worry about the code but guys don't make
00:49:50
the full copy paste of that code if you
00:49:52
if you are understanding each and
00:49:53
everything then you can make a copy
00:49:55
paste right
00:49:56
even if you want to add anything like
00:49:58
anything here into this code just give
00:50:00
the pull request into the github if it
00:50:02
is appropriate then i will merge into it
00:50:04
okay
00:50:06
so guys thank you for the listening me
00:50:08
and like and share this video and don't
00:50:09
forget to subscribe the youtube channel
00:50:11
of machine learning hub you know it will
00:50:13
motivate me to create more content like
00:50:15
this so guys see you in the next one and
00:50:17
thank you for the listening me
00:50:19
so guys thank you bye have a good day