Class XII Artificial Intelligence Project File | AI-Based Career Advisor
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- Class 12 Artificial Intelligence Practical File
import pandas as pd
print("AI-Based Career Advisor")
print("Type 'q' anytime to quit.")
print("------------------------------------------\n")
# ---------------------------------------------------------------
# MASSIVELY EXPANDED CAREER DATABASE
# ---------------------------------------------------------------
data = {
"subject_keywords": [
["math", "mathematics", "applied math", "statistics"],
["science", "biology", "chemistry", "physics", "botany", "zoology", "biochemistry"],
["computer", "it", "informatics", "programming", "coding", "software", "computer science"],
["english", "literature", "language", "linguistics", "communication"],
["commerce", "account", "economics", "business studies", "finance"],
["arts", "drawing", "painting", "history", "fine arts", "design", "philosophy"],
["psychology", "mental health", "cognitive"],
["geography", "earth science", "climate", "geology"],
["political science", "civics", "public administration"],
["sports", "physical education", "pe", "fitness"],
["music", "dance", "performing arts", "theatre"],
["agriculture", "farming", "soil", "crop science"],
["home science", "nutrition", "family studies"],
["law", "legal studies", "constitution"],
["entrepreneurship", "startup", "business idea"],
["environment", "environmental science", "ecology"],
["medical", "health science", "nursing", "anatomy", "physiology"],
["engineering", "mechanics", "electronics", "electrical", "civil", "mechanical"],
["aviation", "aircraft", "aeronautics"],
["defense", "military science", "combat", "navy", "air force"]
],
"interest_keywords": [
["technology", "computer", "ai", "data", "automation"],
["research", "lab", "experiments"],
["coding", "programming", "software", "developer"],
["teaching", "education", "trainer", "mentor"],
["business", "management", "entrepreneur", "marketing"],
["creativity", "design", "art", "creative"],
["counseling", "helping", "mental", "emotions"],
["travel", "maps", "climate", "environment"],
["politics", "government", "public", "policy"],
["fitness", "sports", "training", "coaching"],
["music", "dance", "perform", "acting"],
["farming", "agriculture", "forestry"],
["nutrition", "food", "health"],
["law", "justice", "legal"],
["startup", "business idea", "innovation"],
["environment", "sustainability", "wildlife"],
["medicine", "healthcare", "doctor", "nurse"],
["engineering", "machines", "robotics"],
["aircraft", "flying", "aviation"],
["army", "navy", "air force", "military"]
],
"careers": [
"Data Scientist, Statistician, Mathematician, AI Specialist",
"Doctor, Scientist, Researcher, Biotechnologist, Pharmacologist",
"Software Developer, AI Engineer, Cybersecurity Specialist, Cloud Architect",
"English Teacher, Journalist, Content Writer, Translator",
"Chartered Accountant, Economist, Financial Analyst, Investment Banker",
"Graphic Designer, Fashion Designer, Historian, Animator, Museum Curator",
"Psychologist, Therapist, Counselor, HR Specialist",
"Geographer, GIS Analyst, Climatologist, Environmental Planner",
"Civil Servant, Policy Analyst, Political Consultant, Diplomat",
"Sports Coach, Fitness Trainer, Sports Physiotherapist, Athlete",
"Singer, Music Producer, Dancer, Actor, Composer",
"Agricultural Scientist, Food Technologist, Agronomist",
"Nutritionist, Dietician, Wellness Coach",
"Lawyer, Legal Advisor, Judge, Cyber Law Expert",
"Entrepreneur, Business Founder, Startup Consultant",
"Environmental Scientist, Ecologist, Wildlife Conservationist",
"Doctor, Surgeon, Nurse, Paramedic, Medical Lab Technician",
"Mechanical Engineer, Civil Engineer, Robotics Engineer, Electrical Engineer",
"Pilot, Aviation Engineer, Air Traffic Controller",
"Army Officer, Navy Officer, Air Force Pilot, Defense Analyst"
]
}
df = pd.DataFrame(data)
# ---------------------------------------------------------------
# MAIN LOOP – Runs until user presses 'q'
# ---------------------------------------------------------------
while True:
print("\n------------------------------------------")
print(" New Career Analysis")
print("------------------------------------------")
name = input("Enter your name (or 'q' to quit): ").lower()
if name == "q":
print("\n Thank you for using the Career Advisor!")
break
subject = input("Enter your favourite subject (or 'q' to quit): ").lower()
if subject == "q":
print("\n Thank you for using the Career Advisor!")
break
interest = input("Enter your main interest (or 'q' to quit): ").lower()
if interest == "q":
print("\n Thank you for using the Career Advisor!")
break
print("\n Analyzing your profile...\n")
# Default suggestion
career_suggestion = (
"You have versatile interests! Explore Civil Services, Digital Marketing, Teaching, or Creative Fields."
)
# Matching logic
for _, row in df.iterrows():
if any(key in subject for key in row["subject_keywords"]):
if any(key in interest for key in row["interest_keywords"]):
career_suggestion = row["careers"]
break
print(f" {name.capitalize()}, based on your inputs, suitable career paths for you include:")
print(f" {career_suggestion}")
print("\n Program Closed Successfully ")