Sentiment Analysis System
NLP-based sentiment analysis tool using LSTM networks to analyze customer reviews and social media posts.
View in Portfolio →AI Final Year Project Center for AI&DS Students
AI and machine learning projects, Artificial Intelligence IEEE projects, Machine Learning projects. Expert AI final year project center for AI&DS engineering students in Erode, Tamil Nadu.
AI & Machine Learning Projects
- • IEEE AI & ML final year projects for AI&DS, CSE, and Data Science students – Cutting-edge technologies aligned with academic standards
- • Real-world problem solving using machine learning and deep learning – Predictive analytics, NLP, computer vision, and automation
- • Projects in NLP, computer vision, predictive analytics, and automation – Sentiment analysis, image classification, fraud detection, recommendation systems
- • Includes data preprocessing, model training, evaluation, and deployment – Complete pipeline with TensorFlow, PyTorch, Scikit-learn
- • Academic + industry-relevant project structure – Real datasets, trained models, web interfaces, and comprehensive documentation
Technologies & Frameworks
Machine Learning
- Scikit-learn
- Pandas & NumPy
- XGBoost
- Linear/Logistic Regression
- Decision Trees & Random Forest
Deep Learning
- TensorFlow
- Keras
- PyTorch
- Neural Networks
- CNN & RNN
Specialized Libraries
- NLTK (NLP)
- OpenCV (Computer Vision)
- Matplotlib & Seaborn
- Jupyter Notebooks
- Flask/FastAPI (APIs)
Project Deliverables
Trained Models
Fully trained and optimized machine learning models with best hyperparameters and evaluation metrics.
Source Code
Complete Python code with data preprocessing, model training, evaluation, and prediction scripts.
Dataset & Analysis
Data collection, cleaning, exploratory data analysis (EDA), and feature engineering documentation.
Model Documentation
Detailed documentation of algorithms used, model architecture, training process, and performance metrics.
Web Interface (Optional)
User-friendly web application or API to interact with your trained model for predictions.
Research Report
Comprehensive project report with literature review, methodology, results, and conclusions.
Common Project Types
Predictive Analytics
Sales forecasting, stock price prediction, weather forecasting, and demand prediction models.
Natural Language Processing
Sentiment analysis, chatbot systems, text classification, language translation, and text summarization.
Computer Vision
Image classification, object detection, face recognition, medical image analysis, and OCR systems.
Recommendation Systems
Movie/product recommendation engines, content-based and collaborative filtering systems.
Fraud Detection
Anomaly detection, credit card fraud detection, and security threat identification systems.
Healthcare AI
Disease prediction, medical diagnosis assistance, drug discovery, and patient monitoring systems.
Timeline & Process
AI/ML projects typically take 3-5 weeks to complete, depending on complexity. The process includes:
1
Problem Definition
Understanding your project requirements, defining objectives, and identifying suitable ML approaches.
2
Data Collection & Preprocessing
Gathering datasets, cleaning data, handling missing values, and feature engineering.
3
Model Development
Selecting algorithms, training models, hyperparameter tuning, and cross-validation.
4
Evaluation & Deployment
Model evaluation, performance metrics, visualization, documentation, and deployment preparation.
Past Work Examples
Image Classification App
Deep learning model using CNN to classify images with web interface for real-time predictions.
View in Portfolio →Sales Prediction Model
Time series forecasting model using ARIMA and LSTM to predict future sales with high accuracy.
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