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Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
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DeepgraphAnalyze Data with Pandas-based Networks. Documentation:
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ReaperSocial media scraping / data collection tool for the Facebook, Twitter, Reddit, YouTube, Pinterest, and Tumblr APIs
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kenchiA scikit-learn compatible library for anomaly detection
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LasioPython library for reading and writing well data using Log ASCII Standard (LAS) files
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TweetfeelsReal-time sentiment analysis in Python using twitter's streaming api
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Statistical LearningLecture Slides and R Sessions for Trevor Hastie and Rob Tibshinari's "Statistical Learning" Stanford course
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ChirpInterface to manage and centralize Google Alert information
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Suod(MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)
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Prefixspan PyThe shortest yet efficient Python implementation of the sequential pattern mining algorithm PrefixSpan, closed sequential pattern mining algorithm BIDE, and generator sequential pattern mining algorithm FEAT.
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MatminerData mining for materials science
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Zhihu Analysis PythonSocial Network Analysis of Zhihu with Python
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Data Mining ConferencesRanking, acceptance rate, deadline, and publication tips
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Rule Extraction from TreesA toolkit for extracting comprehensible rules from tree-based algorithms
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Awesome Datascience📝 An awesome Data Science repository to learn and apply for real world problems.
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Python Projectssome python projects
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