Personal Learning Roadmap Generator

Overview

The Personal Learning Roadmap Generator is a complete machine learning web application that answers a question every student and career-switcher actually asks: "I know these skills, I want this job, so what do I learn next and in what order?" Instead of giving generic advice, the app studies around 9,000 real job postings scraped from Naukri.com, works out what the Indian job market genuinely demands for a chosen career, and hands you a clean week-by-week study plan that fits the hours you have free.

Most career-guidance tools rely on hand-written rules or someone's opinion. This project does it the data-driven way. It pulls the actual skills out of thousands of job descriptions, builds a skill profile for each career using TF-IDF, and uses cosine similarity to find where you stand and how far you have to go. For a final year submission it sits in a strong spot, because it touches natural language processing, recommendation systems, data cleaning and full-stack web development in one project. If you like this kind of work, it pairs well with the rest of our AI and machine learning final year projects.

Key Features

• Skill Gap Analysis: Compares the skills you already have against the top skills a career demands, and lists exactly what is missing.
• Weekly Study Planner: Packs the missing skills into weekly buckets based on how many hours you can study, scheduling foundations like Python, SQL and Git before frameworks so nothing feels out of order.
• Related Career Suggestions: Cosine similarity recommends careers that sit closest to your current skill set, so you can see other paths you are already half-qualified for.
• Demand Badges: Every skill is labelled High Demand, Moderate or Niche based on how often it shows up across job postings.
• Difficulty Badges: Skills are tagged Beginner, Intermediate or Advanced using the ratio of fresher to senior postings that ask for them.
• Salary Insights: A real minimum-to-maximum salary range in LPA, parsed from actual Naukri payrate data, shown for each career.
• Skill Autocomplete: A tag-pill input with suggestions drawn from the full skills vocabulary, so entry stays fast and clean.
• Progress Tracker: A per-skill checklist with an animated progress bar, saved in SQLite so your progress survives a page refresh.
• Roadmap History: Every roadmap you generate is saved, with mini progress bars, so you can revisit and compare plans.

How the ML Engine Works

The real intelligence sits in the data pipeline, and this is the part that makes the project stand out in a viva. The skills column in the raw dataset only contains broad industry tags like "ITES", which are useless for real recommendations. So the engine instead reads the job description text and extracts the genuine skill list that recruiters wrote between the Keyskills and Desired Candidate Profile markers, using a regular expression. Spam rows advertising fake data-entry earnings are filtered out before anything else happens.

Noisy job titles such as "Sr. Lead Java Developer - 5+ Yrs" are mapped down to about 17 clean career categories using keyword rules. All the extracted skills for a career are then combined into a single document, and word frequency gives the top fifteen skills per career. A TF-IDF vectoriser, fitted on these career documents, turns everything into numbers. When you submit your own skills, they are transformed into the same vector space and cosine similarity ranks every career by how close it is to you. The gap is simply the career's top skills minus the skills you already listed, and those gap skills are then sorted foundations-first and bin-packed into your weekly schedule.

Applications and Use Cases

• Students planning a career switch who want a clear, ordered study path instead of a random list of topics.
• Training institutes and bootcamps that want to show prospective learners a personalised roadmap before enrolment.
• College placement cells guiding final year batches towards the skills their target roles actually require.
• Self-learners who need a realistic weekly plan tied to the hours they can genuinely commit.
• A reference build for anyone studying recommendation systems, TF-IDF, or text-based feature extraction.

What You Get

The package includes the complete source code, the Jupyter notebook that builds the model pipeline end to end, the dataset, 38 pytest unit tests, and a college-format project report. You also get our standard add-on support: a live setup session over Google Meet or AnyDesk, a walkthrough of the logic and database design, and the option of a custom report, research paper or PPT. If you want to compare it against other Python builds before deciding, you can browse our full collection of final year projects with source code, including data-driven machine learning projects like our Car Price Prediction system.

Why This Makes a Strong Final Year Project

Examiners look for projects that solve a believable problem with a method you can defend. This one does both. The dataset is real, the pipeline handles messy data the way industry actually does, and the recommendation logic is something you can explain on a whiteboard without hand-waving. It demonstrates data cleaning, feature engineering, an unsupervised similarity model, a working database, and a full Flask front-end, which covers most of what a CSE or IT panel wants to see in a single submission.