Boosted trees in Julia
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Updated
Jun 12, 2024 - Julia
Boosted trees in Julia
Classification in TabularDataset
Large Scale benchmarking of state of the art text vectorizers
Lung Cancer Prediction using Machine Learning Algorithms
This project researched the credit card transaction dataset and tried various machine learning classification models on the dataset to determine the best model that would flag suspicious activity more accurately.
Regression Analysis - Toyota Corolla price prediction
Predicting the Critical Temperature of Superconductors using numerous Machine Learning techniques along with a comparative analysis of their performances.
Random Forest Classification
Implementing Catboost
This is a blog of how machine learning algorithms are used to detect if a person is prone to heart disease or not.
This project aims to detect bone fractures using machine learning and neural networks. It utilizes various machine learning models including AdaBoost, CatBoost, Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, and LightGBM and and neural networks.
This project aims to address the challenge of predicting whether it will rain or snow in Hungary based on various meteorological variables.
What factors influence runners
Course Work on Machine Learning covering Supervised and Unsupervised Algorithms
The telecom operator Interconnect would like to forecast churn of their clients. To ensure loyalty, those who are predicted to leave will be offered promotional codes and special plans.
Predicting popularity of movies using the IMDb movies dataset with multiple regression algorithms such as XGBoost, Gradient Boosting, Regularization Regressors, and Stacking Regressor; Performed extensive data cleaning, feature engineering, and used transformation techniques such as winsorization and log-transformation
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.
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