Kumar Mayank

Machine Learning Techniques: 1. Regression Algorithms: - ARIMA (AutoRegressive Integrated Moving Average) - Decision Trees - Gradient Boosting - LightGBM - XGBoost 2. Classification Algorithms: - Logistic Regression - SVM (Support Vector Machine) - Naive Bayes - AdaBoost - Hybrid models 3. Deep Learning Techniques: - CNN (Convolutional Neural Network) - LSTM (Long Short-Term Memory) - Bi-LSTM (Bidirectional Long Short-Term Memory) - CNN-LSTM (Convolutional Neural Network with Long Short-Term Memory) Applications: 1. Price Optimization: - Predictive modeling for optimal pricing strategies 2. Environmental Monitoring: - Automated classification of floating debris in aquatic environments 3. Healthcare and Medical Diagnosis: - Early detection and diagnosis of breast cancer from medical imaging sources - Prediction and identification of cancer biomarkers 4. Recommendation Systems: - Adventure tourism recommendation system 5. Data Analysis: - Using tools like Matplotlib, Seaborn, Pandas, and Scikit-learn for data analysis and visualization Publications: 1. Recommendation System for Adventure Tourism 2. Cancer Bio-Marker Prediction System 3. An Efficient Detection of Floating Marine Debris using Deep Learning Techniques