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SIDDHARTH PATIL
SIDDHARTH PATIL

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praj - tatya vinchu

minimum study, maximum confidence.
What follows is exactly enough to survive resume shortlisting and answer fresher-level interview questions—nothing extra, nothing scary.

No fluff. No theory overload. This is mug-up friendly but still honest.

PROJECT 1 STUDY MATERIAL

AI-Based Resume Screening & Skill Matching (Python)

What problem does it solve? (memorize)

Manual resume screening is slow and inconsistent. This project automates the initial screening by matching candidate skills with job requirements.

Very Simple Implementation Idea

(No ML. No libraries headache.)

Files

  • resume.txt
  • job_description.txt
  • resume_screening.py

Core Steps (say this in interview)

  1. Read resume and job description text files
  2. Clean the text (lowercase, remove special characters)
  3. Extract skills using a predefined skill list
  4. Compare resume skills with JD skills
  5. Calculate match percentage

Ultra-Simple Code Logic (understand this, not memorize) 

skills = ["python", "java", "sql", "html", "css"]

resume_text = open("resume.txt").read().lower()
jd_text = open("job_description.txt").read().lower()

resume_skills = [s for s in skills if s in resume_text]
jd_skills = [s for s in skills if s in jd_text]

match_score = (len(resume_skills) / len(jd_skills)) * 100
print("Match Score:", match_score)
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That’s it. This alone is enough.

Keywords to memorize

  • Resume parsing
  • Rule-based NLP
  • Skill extraction
  • Match scoring
  • Automation

Interview Q&A (ONLY THESE)

Q: Is this machine learning?

No, it is a rule-based NLP system designed for basic automation.

Q: Why not ML?

For initial screening, rule-based systems are faster, simpler, and cost-effective.

Q: How can this be improved?

It can be extended using ML models, databases, and web interfaces.

PROJECT 2 STUDY MATERIAL

Automated Employee Attendance Tracking System (Python)

What problem does it solve? (memorize)

Manual attendance tracking is error-prone and time-consuming. This system automates attendance recording and report generation.

Very Simple Implementation Idea

Files

  • attendance.csv
  • attendance_system.py

Core Steps

  1. Take employee ID and check-in/check-out time
  2. Store data in CSV file
  3. Calculate working hours
  4. Mark attendance status
  5. Generate report

Ultra-Simple Code Logic 

from datetime import datetime
import csv

check_in = datetime.strptime("09:00", "%H:%M")
check_out = datetime.strptime("17:30", "%H:%M")

hours = (check_out - check_in).seconds / 3600

status = "Present" if hours >= 8 else "Absent"
print("Working Hours:", hours, "Status:", status)
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That alone is enough to explain the project.

Keywords to memorize

  • Process automation
  • Attendance management
  • Time calculation
  • Report generation
  • Data accuracy

Interview Q&A (ONLY THESE)

Q: Why use CSV?

CSV is lightweight, easy to maintain, and suitable for small-scale systems.

Q: What happens if time input is wrong?

Basic exception handling can be added to handle invalid inputs.

Q: Where is this used in real life?

HR systems, payroll processing, and workforce management.

ONE LINE THAT SAVES HER IN INTERVIEWS

Tell her to say this confidently at the end of every answer:

“The project was designed with simplicity and scalability in mind, suitable for enterprise environments.”

Recruiters love that sentence. It means nothing and everything at the same time.

Absolute Final Prep Checklist

She should be able to:

  • Explain what problem the project solves
  • Explain basic flow
  • Name 2 improvements
  • Say why Python was used

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