Ravi Rohilla

TinyLlama Text Generation A simple Python project demonstrating how to use the "TinyLlama-1.1B-Chat" pre-trained language model with Hugging Face Transformers to generate responses from user prompts. By using Python, PyTorch, Hugging Face Transformers, TinyLlama

Built a CNN model using TensorFlow/Keras to classify cat and dog images. Applied image preprocessing, data augmentation, convolutional layers, pooling, dropout, and binary classification. Trained the model and evaluated performance using accuracy and loss curves.

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No project description available.

This project implements a single-layer Perceptron to predict student placement using CGPA and IQ. The dataset is cleaned, visualized, split into training and testing sets, and evaluated using accuracy. Although a single Perceptron only handles linearly separable data, it provides a strong foundation for understanding neural network.

This program applies machine learning to predict employee attrition by cleaning and preprocessing HR data, training multiple models, evaluating their performance, and identifying key factors influencing attrition using XGBoost feature importance.