This repository includes a machine learning modeling study about estimating customers hotel cancellation and what are the reasons for these cancellations.
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May 23, 2024
#
xgboost
Here are 3,098 public repositories matching this topic...
Projeto de Regressão Linear utilizando o XGBRegressor, com base no dataset Flight Price Prediction de Shubham Bathwal e disponibilizado no Kaggle
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May 23, 2024 - Jupyter Notebook
An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
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May 23, 2024 - Python
Standardized Serverless ML Inference Platform on Kubernetes
kubernetes
machine-learning
tensorflow
sklearn
pytorch
artificial-intelligence
xgboost
k8s
service-mesh
hacktoberfest
istio
model-serving
kubeflow
mlops
knative
model-interpretability
kubeflow-pipelines
mlops-community
kserve
hacktoberfest2023
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May 23, 2024 - Python
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
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May 23, 2024 - C++
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
machine-learning
data-mining
statistics
kafka
graph-algorithms
clustering
word2vec
regression
xgboost
classification
recommender
recommender-system
apriori
feature-engineering
flink
fm
flink-ml
graph-embedding
flink-machine-learning
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May 23, 2024 - Java
This is the analysis from well known beginner Titanic Dataset from Kaggle Competition. It reached 0.77 score on Kaggle.
python
data-science
numpy
sklearn
machine-learning-algorithms
jupyter-notebook
eda
pandas
data-visualization
seaborn
xgboost
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May 23, 2024 - Jupyter Notebook
This is a repository of AI projects that i did during my master's degree in computer science.
machine-learning
deep-reinforcement-learning
recurrent-neural-networks
artificial-intelligence
xgboost
long-short-term-memory
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May 23, 2024 - Jupyter Notebook
Data Science Feature Engineering and Selection Tutorials
python
data-science
machine-learning
tutorial
jupyter
notebook
scikit-learn
exploratory-data-analysis
tutorials
pandas
feature-selection
xgboost
feature-engineering
features
data-cleaning
pandas-profiling
sweetviz
pyrasgo
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May 22, 2024 - Jupyter Notebook
Distributed ML Training and Fine-Tuning on Kubernetes
python
kubernetes
machine-learning
ai
tensorflow
gpu
pytorch
distributed
xgboost
fine-tuning
kubeflow
jax
mlops
huggingface
llm
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May 23, 2024 - Go
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
data-science
machine-learning
neural-network
random-forest
scikit-learn
xgboost
hyperparameter-optimization
lightgbm
ensemble
feature-engineering
decision-tree
hyper-parameters
automl
automated-machine-learning
catboost
automatic-machine-learning
mljar
shap
tuning-algorithm
models-tuning
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May 22, 2024 - Python
Scalable Machine Learning and Deep Learning, Final Project, 2023/2024
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May 22, 2024 - Python
Ranks hotel to a user search on the likeliness that it will be booked
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May 22, 2024 - Jupyter Notebook
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn
python
data-science
machine-learning
linear-regression
exploratory-data-analysis
cross-validation
data-visualization
supervised-learning
xgboost
logistic-regression
regularization
decision-trees
predictive-modeling
ridge-regression
support-vector-machines
elastic-net
oversampling
lasso-regression
random-forests
k-nearest-neighbors
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May 21, 2024 - Jupyter Notebook
Fraudulent Activities - anomaly detection
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May 21, 2024 - Jupyter Notebook
This is a collection of all the machine learning techniques required in any machine learning project. It contains detailed descriptions, videos, book recommendations, and additional material to properly grasp all the concepts. It also contains implementations in various frameworks.
monitoring
deployment
random-forest
machine-learning-algorithms
cross-validation
pipelines
xgboost
feature-engineering
basics
evaluation-metrics
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May 21, 2024 - Jupyter Notebook
The goal of this project is to predict the age of abalone using various physical measurements. The dataset used for this task is the Abalone dataset from Kaggle. The age of abalone is determined by the number of rings, which is the dependent variable in this study.
machine-learning
random-forest
regression
kaggle
kaggle-competition
xgboost
random-forest-regressor
xgboost-regressor
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May 21, 2024 - Jupyter Notebook
All the Data Science projects I've done, during the course, or after.
data-science
random-forest
rstudio
regression
neural-networks
xgboost
classification
decision-trees
timeseries-analysis
datapreprocessing
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May 21, 2024 - R
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