A cinematic, single-page analytics dashboard that delivers deep intelligence on any movie or TV show in real time. Users search for a title and instantly see box office financials, revenue trend modelling, financial breakdown charts, genre radar profiles, audience score rings, cast & crew grids, season browsers, network info, and popularity metrics — all wrapped in a gold-on-dark responsive UI built entirely in vanilla HTML, CSS, and JavaScript.

Entertainment data exists across dozens of scattered sources — box office trackers, review aggregators, streaming databases — making it hard for a user to get a holistic picture of any title in one place. The challenge was to unify all of this into a single zero-backend dashboard that feels as polished as a native app, loads instantly, and works on any device.

• Dual Mode: Seamless switch between Movies and TV Shows with context-aware UI — profit bars for movies, season grids and network info for TV
• Live Autocomplete Search: Real-time suggestions via TMDB search API with full keyboard navigation (↑ ↓ Enter Escape) and ⌘K shortcut
• Revenue Trend Modelling: Cumulative and period-based revenue charts built using a theatrical decay curve — since TMDB does not provide weekly breakdowns, the distribution is modelled mathematically
• Financial Breakdown: Animated bar chart comparing budget, worldwide box office, and net profit/loss with colour-coded profit/loss states
• Genre Radar Chart: Six-axis radar mapping Action, Adventure, Drama, Thriller, Comedy, and Romance scores derived from a hand-crafted genre scoring matrix
• Audience Score Ring: Animated SVG stroke-dashoffset ring that fills dynamically, colour-coded green/gold/red by rating tier
• Cast & Crew Grid: Director/Creator highlighted in gold, full cast with profile photos, character names, and roles
• Season Browser: TV-only grid showing every season with poster, episode count, and air year

• Zero Backend: Pure static frontend — no server, no build step, no framework. HTML + CSS + vanilla JS only
• TMDB API v3: Two parallel fetch calls on search selection — /movie/{id} + /movie/{id}/credits (or TV equivalents) — resolved with Promise.all for speed
• Chart.js 4.4: Six chart instances (cumulative line, period bar, financial bar, popularity bar, genre radar, episodes bar) with shared tooltip and scale defaults
• chartjs-adapter-date-fns: Enables time-axis charts with type: "time" for the revenue trend panels
• CSS Custom Properties: Full design token system (10 colour variables, layout variables) enabling consistent theming across all components

• No weekly box office data: TMDB only provides a lifetime revenue total. Solved by modelling distribution using an exponential decay function with a 120-day theatrical window and a 12% home-video tail
• Chart instance memory leaks: Each new search destroys all six previous chart instances via a destroyChart() wrapper before re-creating them
• Mobile image cropping: Fixed by replacing fixed-height poster containers with aspect-ratio: 2/3 + max-height caps per breakpoint

HTML5, CSS3 (Custom Properties, Grid, Flexbox, Animations), Vanilla JavaScript (ES2020+), TMDB API v3, Chart.js 4.4, chartjs-adapter-date-fns, Bebas Neue + DM Sans (Google Fonts)

A full-stack, AI-powered web vulnerability scanner built with Streamlit and powered by a dual-intelligence engine: Groq Cloud AI (LLaMA 3.3 70B) for contextual threat analysis and a trained Random Forest ML model for pattern-based detection. Users authenticate via a login system, enter any URL or domain, and the scanner automatically discovers subdomains, crawls pages, and surfaces vulnerabilities across SQL Injection, XSS, CSRF, insecure headers, and security misconfigurations — all exported as JSON, Markdown, or CSV reports.

Manual penetration testing is slow, expensive, and hard to scale. Rule-based scanners miss context-dependent vulnerabilities and generate noisy false positives. This project combines a trained ML model with a real Groq LLM to create a scanner that understands page context, validates findings intelligently, and produces executive-grade reports.

• Layer 1 — Random Forest ML Model: A trained scikit-learn Random Forest classifier predicts SQL Injection, XSS, or Misconfiguration with per-class probability scores. Only predictions above 60% confidence are surfaced as findings
• Layer 2 — Traditional Pattern Detection: Regex and DOM heuristics scan page HTML for known dangerous patterns — eval(), innerHTML=, document.write(), GET-method forms, POST forms without CSRF tokens
• Layer 3 — Groq AI Analysis (LLaMA 3.3 70B): The GroqOrchestrator sends HTML snippets, form structures, and script counts to Groq's API for deep contextual analysis, returning structured JSON vulnerability objects with CWE IDs, proof of concept, and remediation guidance

• SQL Injection (CWE-89), XSS (CWE-79), Missing CSRF Protection (CWE-352)
• Sensitive Data Exposure (CWE-200) via HTML comment scanning
• Insecure Protocol (CWE-319) and five missing security headers (CWE-16)
• ML-detected Misconfiguration based on CSP absence and script/form count thresholds

Python, Streamlit, Groq Cloud API (LLaMA 3.3 70B), Scikit-learn (Random Forest), Joblib, Pandas, NumPy, BeautifulSoup4, Requests, python-dotenv, lxml, concurrent.futures, socket

A machine learning-powered system designed to detect malicious network activity in real time, providing proactive cybersecurity through traffic pattern analysis and threat classification.

Modern networks face threats including malware, DoS attacks, and unauthorized access. Traditional rule-based tools struggle with unknown threats. This project applies ML to recognize attack patterns in network traffic.

Random Forest, SVM, and ensemble learners classify traffic as benign or malicious. Models are evaluated using accuracy, precision, recall, and F1-score metrics.

• Network traffic preprocessing and feature preparation
• Real-time inference through Flask API
• Confidence scores for predictions
• Attack category visualisation

Enhances traditional security tools by detecting complex attack patterns, supporting faster incident response and reducing false positives. Achieved 94% detection accuracy during the RedKross internship deployment.

A real-time music recommendation system built using Python and the Spotify Web API. Its goal is to help users discover new music tailored to their listening preferences by analyzing their historical listening behavior and track audio features.

With millions of songs available on streaming platforms, users often struggle to find tracks that match their tastes. This project creates a system that recommends songs dynamically based on listening patterns and audio characteristics.

• User Authentication: Secure login via Spotify OAuth 2.0
• Data Retrieval: Collects recently played tracks and audio features
• Feature Processing: Normalizes song vectors for similarity comparison
• Similarity Calculation: Uses cosine similarity for matching
• Recommendations: Real-time suggestions through web interface

• Real-time analysis enables highly personalized recommendations
• Audio feature-based similarity outperforms playlist matching
• API optimization significantly improves responsiveness

Python, Flask, Pandas, NumPy, Spotify Web API

ML-powered car price prediction system estimating vehicle values based on mileage, manufacture year, brand, and other features through regression models.

Predicting fair market value for used cars is challenging. Buyers and sellers rely on rough estimates. This model learns from historical data to provide accurate predictions.

Data cleaning handled missing values and categorical encoding. Feature engineering created variables like vehicle age, enhancing the model's pattern recognition.

Regression models trained on historical car data. Evaluation using MAE and R² score ensures generalisation to unseen data.

• Data preprocessing and transformation pipeline
• Real-time prediction via Flask API
• Cloud deployment ready
• Serialised model for quick inference

A Python-based recommendation system providing personalized movie suggestions using content similarity algorithms and collaborative filtering.

Users struggle to find relevant movies among thousands of titles. Static charts don't cater to individual tastes. This system analyses metadata and similarity patterns for personalisation.

Precomputed similarity matrices enable fast recommendations. Cosine similarity between movie feature vectors (genres, ratings) drives the logic.

• Interactive web interface
• Fast real-time recommendations
• Personalised suggestions
• Lightweight Flask deployment

Enhances content discovery and user engagement by applying ML techniques for personalized recommendations in large catalogs.

In-depth exploratory analysis of IPL cricket (2008–2019), uncovering patterns in team performance, player contributions, and match outcomes using historical data.

IPL generates massive datasets, but raw statistics don't explain performance trends. This project structures data to answer questions about consistency, toss influence, and venue effects.

• Team Performance: Win percentages and dominance patterns
• Toss Impact: Correlation with match outcomes
• Player Analysis: Strike rates and consistency metrics
• Venue Insights: Stadium influence on scoring

• Small group of teams showed long-term dominance
• Toss had limited impact on outcomes
• Individual performance influenced success more than conditions
• Venue characteristics affected scoring trends