1552 Open source projects that are alternatives of or similar to Rsparkling

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Koalas: pandas API on Apache Spark

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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Materials for GWU DNSC 6279 and DNSC 6290.

简单的可视化湖州通话数据 假设数据量很大，没法用浏览器直接绘制热力图，把绘制热力图这一步骤放到线下计算分析。使用Apache Spark并行计算数据之后，再使用Apache Spark绘制热力图，然后用leafletjs加载OpenStreetMap图层和热力图图层，以达到良好的交互效果。现在使用Apache Spark实现绘制，可能是Apache Spark不擅长这方面的计算或者是我没有设计好算法，并行计算的速度比不上单机计算。Apache Spark绘制热力图和计算代码在这 https://github.com/yuanzhaokang/ParallelizeHeatmap.git .

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Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.

VerticaPy is a Python library that exposes sci-kit like functionality to conduct data science projects on data stored in Vertica, thus taking advantage Vertica’s speed and built-in analytics and machine learning capabilities.

⛈️ Rumble 1.11.0 "Banyan Tree"🌳 for Apache Spark | Run queries on your large-scale, messy JSON-like data (JSON, text, CSV, Parquet, ROOT, AVRO, SVM...) | No install required (just a jar to download) | Declarative Machine Learning and more

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❄️ 🐳 Awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++ etc

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