A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.
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Updated
Aug 4, 2024 - TypeScript
A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.
Mini projects worked on during my study at the Institute of Data
This project focuses on predicting the weather for the next day using a classification model. Both RandomForest and GradientBoosting classifiers were tested with grid search for hyperparameter tuning. The dataset used for this project is available at Kaggle.
PUBG player data (4.5million+) processed using Pandas, NumPy in Python for preprocessing, and CatBoost for match predictions. Achieved RMSE 0.08, R² close to 1, optimizing gameplay metrics.
An interesting app for predicting the price of houses in Tehran
Logistic Regression and Decision Tree models to predict Customers purchasing a Loan from the bank.
A summative coursework for CSC8635 Machine Learning with Project
Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.
Combine grid search with early stopping via cross validation
Breast Cancer Wisconsin Dataset Classifier with Scikit-learn and Streamlit
Python project for Banknotes Analysis.
A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning
Backpropagation and automatic differentiation, and grid search from scratch.
🔮 Mastermind puzzle solver using Genetic Algorithm and Grid Search for optimization
Cat vs. Dog classification model using traditional ML methods, including data collection, splitting, HOG feature extraction, model training (e.g., SVM, Decision Tree), and fine-tuning via Grid Search.
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