NumPy
NumPy is an open source library for the Python programming language, adding support for large, multidimensional arrays, and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
- GitHub: https://github.com/topics/numpy
- Wikipedia: https://en.wikipedia.org/wiki/NumPy
- Repo: https://github.com/numpy/numpy
- Created by: Travis Oliphant
- Related Topics: python, scipy,
- Last updated: 2026-06-21 00:17:46 UTC
- JSON Representation
https://github.com/varunbanka/data-insights
Data Insights is a user-friendly tool for analyzing large CSV files. Its advanced analytics helps uncover hidden patterns and trends, making it perfect for data scientists and analysts.
artificial-intelligence automation data-analysis data-science dataanalysis datahive numpy pandas python
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https://github.com/karan-malik/restrictedboltzmannmachine
Python3 implementation of the Unsupervised Deep Learning Algorithm, Restricted Boltzmann Machine.
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https://github.com/kleinyuan/numpy-ml
Implementations of machine learning concepts in modules with basic numpy
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https://github.com/sweety-vigneshg/chatbot-using-python
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https://github.com/udityamerit/python-librearies-for-data-science
Python libraries for data science enable efficient data manipulation, analysis, and modeling. Key libraries include NumPy for numerical computing, pandas for data handling, Matplotlib for visualization, Scikit-learn for machine learning, TensorFlow for deep learning, and BeautifulSoup/requests for web scraping. These libraries simplify complex data
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https://github.com/moindalvs/assignment_hypothesis_test
A F&B manager wants to determine whether there is any significant difference in the diameter of the cutlet between two units. A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validity of the assumptions.
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Used vehicle collision data from NYC Open Data to analyse various factors for collisions, identifying the vehicle type involved in the crashes and using weather dataset to identify how snowfall and precipitation had caused increase in collisons
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Recursively generate large-scale Nengo models using NumPy semantics.
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https://github.com/utsanjan/traffic-prediction-model
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https://github.com/futuresea-dev/pairs-trading-bot
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https://github.com/bhattbhavesh91/self-attention-python
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https://github.com/punitgr/hackerrank
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https://github.com/soumilgit/ai_ml_projects
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https://github.com/n8henrie/simplenet
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https://github.com/pothosware/pothosnumpy
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https://github.com/raravindds/auto_fe
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https://github.com/tmtenbrink/frc
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https://github.com/avinashkranjan/mask-detector
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https://github.com/ndom91/newtelco-powercompare
🔋 Internal Datacenter Power Usage Comparison Tool
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https://github.com/billy-enrizky/image_shuffle
A Python utility for shuffling and augmenting image data stored as .npy files. Designed to enhance machine learning datasets by generating randomized variations of image arrays through grid-based shuffling, split operations, and rotations.
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https://github.com/jwest951227/lstmStockPricePredictor
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https://github.com/stanczakdominik/stochastic-resonance
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https://github.com/karan-malik/uberdataanalysis
Uber Data Analysis and Visualization using Python
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https://github.com/mohitbansal964/image-processing-algorithms
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https://github.com/ruban-edward/ai-virtual-painter
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https://github.com/simeonhristov99/ml_23-24
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https://github.com/gdbecker/100daysofcodepython
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https://github.com/Rahulkumarr2080/Comcast-Telecom-Consumer-Complaints
Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them $2.3 million, after receiving over 1000 consumer complaints. The existing database will serve as a repository of public customer complaints file.
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https://github.com/zehanort/rvg
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Spam detection using Neural Nets and back propagation
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Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite, Accuracy: 98% AUC-ROC
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