TECH STACK // PROJECT ARCHITECTURE
Technologies Used
A comprehensive overview of the programming languages, machine learning libraries, and web frameworks powering the Skill Evolution Tracking System.
Total Tech Stack
12 Core Technologies
Project Scope
B.Sc AI & Data Science
System Latency
<200ms Data Processing
Development Standard
Industry Best Practices
Filter Tech Stack
CORE ENGINE
40%
AI Model & Data Pipeline
Python-based neural networks for tracking student skill evolution and predicting future demand trends.
Stack Weight40%
Key Objectives
- Implement TensorFlow for deep learning model training and validation
- Utilize Scikit-Learn for predictive analytics and demand forecasting
- Process large-scale student datasets using Pandas and NumPy libraries
Tech Focus:LSTM Networks, Random Forest Regressors, Data Preprocessing
Key Components:Skill Vector Mapping, Demand Trend Analysis, Epoch Training
INTERFACE
30%
Web Framework & UI
Responsive dashboard built with React and Flask to visualize skill gaps and career path predictions.
Stack Weight30%
Key Objectives
- Develop interactive dashboards using React and Tailwind CSS
- Build robust RESTful APIs with Flask for backend communication
- Ensure seamless data visualization with Chart.js and Recharts
Tech Focus:React Components, Flask Routing, API Integration
Key Components:Dashboard Widgets, Skill Evolution Graphs, Prediction Tables
INFRASTRUCTURE
30%
Database & Deployment
Scalable storage and cloud deployment strategies for maintaining student records and model updates.
Stack Weight30%
Key Objectives
- Manage relational data structures using PostgreSQL and SQLAlchemy
- Deploy scalable applications using Docker and cloud platforms
- Optimize database queries for high-speed retrieval and analysis
Tech Focus:PostgreSQL, Docker, Cloud Hosting, CI/CD Pipelines
Key Components:Schema Design, Query Optimization, Containerization