CareerBuddy-AI

AI-powered career assistance application that helps job seekers with resume parsing, job recommendations, company search, and career guidance using Machine Learning and Web Scraping.

Project Description:

CareerBuddy AI is designed to streamline the job search process for candidates by leveraging Machine Learning, Natural Language Processing (NLP), and Web Scraping.
The app can take user input (skills and/or job role) or parse a resume to extract skills, predict suitable job titles, recommend companies, and fetch real-time job links from portals like LinkedIn and Naukri.
It also features an interactive chatbot for career-related queries and guidance.

Features:

• Resume parsing to automatically extract skills.
• Job title prediction using Machine Learning models.
• Real-time company and job link recommendations from job portals.
• Interactive chatbot for career advice.
• Skill gap analysis with learning resource suggestions.
• Professional multi-section UI built with Flet.

Running the app:

Flet + Flask:

• Run pip install -r requirements.txt to install all dependencies.
• Ensure the dataset (CSV) is in the data/ directory with required columns (Qualifications, Salary Range, Job Title, Role, Job Description, Skills, Company).
• Run python train.py to train the XGBoost model if not already trained.
• Run python main.py to start the application.

Tech Stack:

• Python
• Flet (Frontend)
• Flask (Backend API)
• XGBoost, Scikit-learn (ML Models)
• NLP (spaCy / Transformers)
• BeautifulSoup, Requests (Web Scraping)
• Pandas, Matplotlib (Data Handling & Visualization)

Dataset:

The dataset contains job-related fields such as:

• Qualifications
• Salary Range
• Job Title
• Role
• Job Description
• Skills
• Company

Note: Dataset should be cleaned and preprocessed before training for better accuracy.

Model Architecture:

• ML Algorithm: XGBoost for job title prediction.
• Input: Skills (from user or resume parsing).
• Output: Recommended job titles & matching companies.
• Web Scraping layer fetches job links for the recommended roles.
• Chatbot integrated using NLP to provide career guidance.

Data Processing and Training:

• Skills are extracted from text input or resume using NLP.
• Job titles and companies are matched using dataset filtering + ML predictions.
• The XGBoost model is trained on preprocessed CSV data.

Current Condition:

The application is fully functional:

• Resume parsing, job title prediction, and job link scraping work as expected.
• Chatbot provides relevant career responses.
• UI is clean, responsive, and divided into three sections for better user experience.

Project Components:

• train.py – Trains the XGBoost model on the dataset.
• main.py – Runs the Flet application (UI + Backend Integration).
• test.py – Tests the model predictions and WHOIS-based phishing detection (if integrated).
• models/ – Stores trained ML models.
• data/ – Contains dataset files.
• templates/ – HTML files if web views are used.
• requirements.txt – List of dependencies.