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hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.
In this comprehensive machine learning project, I executed the entire machine learning life cycle. Designed a streamlined and visually appealing interface using Streamlit. Ensuring a user-friendly experience for individuals to input their relevant information effortlessly. Handed off well-documented and easily modifiable code.
This is a Premiere Project done by Team Gitlab in Hamoye Data Science Program Dec'22. Out of 5 models used on the data, Random Forest Classifier was used to further improve the prediction of characters death. With parameter tuning and few cross validation, we were able to reduce the base error by 5.42% and increase accuracy by 2,42%.
This repository consists the Jupyter Notebook files containing code of Artificial Neural Network with different tuning parameters for a similar scenario.
In this project, we will predict the price for AMES House and learn Machine Learning Algorithms, different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.
This is a supervised machine learning project using telecom customer data to predict customers that would churn based on customer Age Group, Relationship Status, Subscribed Services, Charges, and Financial Responsibilities, etc.
Práctica de clasificación con Machine Learning en el dataset del Titanic, abordando exploración de datos, preprocesamiento, selección de métricas y modelos, con el objetivo de analizar detalladamente los resultados obtenidos.