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: 2026-07-02 00:21:34 UTC
- JSON Representation
https://github.com/shreenidhi7700/rice-leaf-disease-prediction-using-convolutional-neural-network
Classifying images of rice leaf disease plants into various disease categories using Convolutional Neural Network
convolutional-neural-networks efficientnet inceptionv3 jupyter-notebook keras machine-learning matplotlib numpy os python resnet50 seaborn shutil-python tensorflow transfer-learning xception
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https://github.com/luizabash/wmap-tt-eda
EDA of the WMAP 5-Year TT Power Spectrum data, the analysis includes summary statistics, data visualization, polynomial regression for model fitting, and hypothesis testing
astrophysics matplotlib numpy pandas scipy
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https://github.com/hasanyahya101/pareto-tutorial-python
This tutorial demonstrates how to create Pareto charts using Python libraries like pandas, NumPy, and Matplotlib. All code for this is given in the Jupyter Notebook.
chart charts code-blocks ipynb jupyter-notebook license markdown matplotlib mit numpy pandas pareto py python
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https://github.com/legendarym4x/data_science
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jupyter-notebook keras matplotlib nltk numpy pandas scikit-learn spacy tensorflow
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bootcamp data-analysis data-science education jupyter machine-learning matplotlib numpy pandas python scikit-learn
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https://github.com/ankit21111/carpredict
This project predicts car prices using machine learning models, including Simple and Multiple Linear Regression. It covers data acquisition, feature selection, and optimization techniques like Ridge Regression. The best model, Multiple Linear Regression, achieved an R² score of 0.84. Check out the full analysis in the repository!
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