This is one of the most practical AI projects because it solves a real business problem used by HR teams and recruiters. Large companies receive thousands of resumes for a single job opening. Manually reviewing every resume is slow and expensive.
AI helps by automatically matching resumes with job descriptions and ranking the best candidates.
🎯 Project Goal
Build an AI-powered Resume Screening System that can:
✅ Upload resumes
✅ Parse resume content
✅ Analyze skills
✅ Compare with job descriptions
✅ Calculate ATS score
✅ Rank candidates automatically
🧠 Skills You'll Learn
NLP
Text Preprocessing
Resume Parsing
Keyword Extraction
Text Similarity
Machine Learning
TF-IDF
Cosine Similarity
Embeddings
Generative AI
LLM-based Resume Analysis
Candidate Feedback Generation
Deployment
Streamlit
FastAPI
📌 Real-World Workflow
Resume
Text Extraction
NLP Processing
Skill Extraction
Compare with Job Description
ATS Score
Candidate Ranking
📂 Step 1: Install Required Libraries
pip install pandas
pip install nltk
pip install scikit-learn
pip install pdfplumber
pip install streamlit
📄 Step 2: Extract Text From Resume PDF
Using PDF processing:
import pdfplumber
with pdfplumber.open("resume.pdf") as pdf:
text = ""
for page in pdf.pages:
text += page.extract_text()
Now the resume content becomes machine-readable text.
🔍 Step 3: Extract Important Skills
Example Resume:
Skills: Python SQL Power BI Excel Tableau
Create skill list:
skills = ["python", "sql", "power bi", "excel", "tableau"]
found_skills = []
for skill in skills:
if skill in text.lower():
found_skills.append(skill)
📋 Step 4: Process Job Description
Example Job Description:
Looking for a Data Analyst with Python, SQL, Power BI, Communication Skills
Store as text:
job_description = """Python SQL Power BI Communication Skills"""
🧹 Step 5: Text Preprocessing
Clean resume and job description:
import re
text = re.sub(r"[^a-zA-Z ]", "", text.lower())
This removes:
✅ Numbers
✅ Symbols
✅ Special characters
🔤 Step 6: Convert Text Into Vectors
Using TF-IDF:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform([resume_text, job_description])
📊 Step 7: Calculate Similarity Score
Using Cosine Similarity:
from sklearn.metrics.pairwise import cosine_similarity
score = cosine_similarity(vectors[0], vectors[1])
print(score)
Example Output 0.87
Meaning: 87% match between resume and job description.
🏆 Step 8: ATS Score Generation
Example Formula:
ats_score = similarity_score * 100Output: ATS Score: 87%
ATS Score Interpretation
90-100 Excellent Match
80-89 Strong Match
70-79 Good Match
Below 70 Needs Improvement
🤖 Step 9: Add LLM-Based Feedback
Instead of showing only score: Ask AI: Analyze this resume against the job description and suggest improvements.
Example Output
Strengths: Strong SQL skills, Relevant Power BI experience
Missing Skills: Communication Skills, Data Modeling
Suggestions: Add project details, Highlight business impact
This makes the project much more impressive.
🎨 Step 10: Build Streamlit Interface
import streamlit as st
resume = st.file_uploader("Upload Resume")
jd = st.text_area("Paste Job Description")
if st.button("Analyze"):
score = calculate_score()
st.success(f"ATS Score: {score}%")
📈 Step 11: Candidate Ranking
Suppose:
Candidate A 95
Candidate B 87
Candidate C 75
Sort candidates:
df.sort_values("score", ascending=False)
Recruiters instantly see the best candidates.
🚀 Step 12: Deploy Application
Deployment Options:
Render
Railway
Hugging Face Spaces
⭐ Features to Add
Beginner
✅ Resume Upload
✅ ATS Score
✅ Skill Matching
Intermediate
✅ Multiple Resume Comparison
✅ Candidate Ranking
✅ Missing Skill Detection