1. Project Title: Spam Mail Detection Using Machine Learning
Objective: Built a model to classify emails as spam or non-spam.
Techniques Used: Utilized algorithms like Naïve Bayes or Logistic Regression.
Tools/Skills: Python, Scikit-Learn, data preprocessing, and evaluation metrics (accuracy, precision).
2. Fake News Prediction Using Machine Learning
Objective: Developed a model to classify news articles as real or fake to combat misinformation.
Techniques Used: Employed Natural Language Processing (NLP) techniques, including TF-IDF, with algorithms like Logistic Regression for text classification.
Tools/Skills: Python, Scikit-Learn, Pandas, and NLTK for text processing.
3. Breast Cancer Classification with Neural Networks
Objective: Built a neural network model to classify breast cancer tumors as benign or malignant based on medical data.
Techniques Used: Designed a neural network with Python using libraries like Keras or TensorFlow, focusing on accuracy and performance metrics.
Tools/Skills: Python, Keras/TensorFlow, data preprocessing, and model evaluation.
4. Image Data Processing for Deep Learning Applications
Objective: Processed and prepared image datasets for deep learning model training to improve accuracy and performance.
Techniques Used: Applied techniques such as resizing, normalization, augmentation, and image transformation to enhance model robustness.
Tools/Skills: Python, OpenCV, TensorFlow/Keras, image preprocessing.
5. Content-Based Movie Recommender System
Objective: Developed a recommendation system that suggests movies based on user preferences and content similarity.
Techniques Used: Utilized Natural Language Processing (NLP) and cosine similarity to analyze movie metadata for personalized recommendations.
Tools/Skills: Python, Pandas, Scikit-Learn, NLP techniques.
6. Credit Card Fraud Detection
Objective: Built a machine learning model to identify fraudulent credit card transactions to improve financial security.
Techniques Used: Applied techniques like data balancing, anomaly detection, and classification algorithms to achieve high accuracy.
Tools/Skills: Python, Scikit-Learn, Pandas, data preprocessing, and model evaluation.
7. IMDB Reviews Sentiment Analysis with LSTM
Objective: Developed an LSTM-based model to classify IMDB movie reviews as positive or negative, leveraging deep learning for accurate sentiment analysis.
Techniques Used: Utilized Long Short-Term Memory (LSTM) networks for sequence modeling and text classification.
Tools/Skills: Python, Keras/TensorFlow, NLP, text preprocessing.
8. Parkinson’s Disease Detection
Objective: Created a machine learning model to detect Parkinson’s disease based on voice measurements, improving early diagnosis potential.
Techniques Used: Employed feature engineering and classification algorithms (e.g., SVM, Random Forest) to identify patterns in biomedical data.
Tools/Skills: Python, Scikit-Learn, data preprocessing, model evaluation.