-Implemented a gamified education project that facilitated 24/7 accessibility, prioritizing personalized learning through data-driven customization. -Integrated machine learning enhancements such as a chatbot using Python and the NLTK library, real-time weather using ML agents in unity and an interactive navigation robot and Non-Performing Characters using NavMesh.-Added real-time progress tracking data analytics, enabling students to monitor metrics such as study hours, concepts covered, and performance scores.

-Successfully applied transfer learning models, including ResNet101, InceptionV3, InceptionResNetV2, ResNet50, MobileNetV2, DenseNet169, VGG19, and VGG16, for Alzheimer's disease detection using Keras and TensorFlow.-Leveraged Scikit-Learn for data preprocessing and model evaluation, while managing data with NumPy. Visualized model performance and data characteristics with Matplotlib.-Analyzed confusion matrices and ROC-AUC curves, to gain insights into model behavior, including overfitting or underfitting, and fine-tuned model hyperparameters accordingly. Obtained an accuracy of 98.0% using VGG16.