Creating a fast-efficient pipeline to train Machine learning models.
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Updated
Jul 30, 2023 - Jupyter Notebook
Creating a fast-efficient pipeline to train Machine learning models.
Regression Models - Decision Trees (GBM) and Linear Regression (ElasticNet)
An attempt to predict the tip as a percentage of the total amount the taxi driver recieves
Telecom customer churn example with h2o
Machine learning classification model with streamlit deployment.
2021 Amirkabir Artificial Intelligence Competitions (AAIC): Challenge of forecasting daily internet usage of MCI subscribers
Used R to analyze, explore and process the data, develop models to predict which loans are at risk of default and suggest investment strategies to the client to help them invest in P2P loans with high returns and low risk
All (almost) tree models. reference Repo with ready to use codes and functions
A package to build Gradient boosted trees for ordinal labels
Decompose gbm predictions into feature contributions
Machine Learning approach to predicting comment recommendations based on New York Times articles and comments made on those articles.
Sentiment140 dataset with 1.6 million tweets
Predict prices of diamond data in ggplot2
A R script that runs Boosted Regression Trees(BRT) on epochs of land use datasets with random points to model land use changes and predict and determine the main drivers of change
Twitter sentiment analysis is the process of analyzing tweets posted on the Twitter platform to determine the overall sentiment expressed within them. It involves using natural language processing (NLP) and machine learning techniques to classify tweets.
Crystal Scoring API for PMML
Classification Models
Telecom customer churn example with h2o
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