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Python Development Tools and Platforms for Machine Learning and Data Science Projects

Software Requirements

  • Operating System : Ubuntu 14.04 LTS / Windows
  • IDE: Spyder /PyCharm
  • Databases: PostgreSQL / MySQL / SQLite
S.No. Python Libraries Type Description
1 NumPy Numerical Operations Supporting the scientific computing that is high-level mathematical functions over large, multi-dimensional arrays and matrices
2 Matplotlib, Seaborn, Bokeh, Plotly, NetworkX, Basemap,d3py, ggplot, prettyplotlib Visualization Visualizing the data from Python quickly Plotting 2D graphs in various formats such as bar charts, plots, histograms, error charts, power spectra, and scatter plots across platforms using a few lines of code
3 Scikit- Learn, Shogun, Pattern, PyLearn2, PyMC Machine Learning (ML) Algorithms Providing machine learning algorithms such as classification, clustering, and regression Interoperating with the numerical and scientific libraries such as NumPy and SciPy
4 Pandas Data Analysis Offering high-performance operations and data structures for time series and numerical tables manipulation
5 NLTK Natural Language Processing (NLP) Analyzing and understanding the English written human language data Providing easy interfaces over 50 lexical resources and corpora Supporting functionalities include tokenization, stemming, tagging, parsing, and semantic reasoning
6 Statsmodel Statistical Analysis Conducting statistical data exploration and statistical tests Performing statistical computations such as descriptive statistics and providing classes and functions to estimate different statistical models
7 PyBrain Neural Network Providing algorithms for reinforcement learning, neural networks, unsupervised learning, and evolution to analyze large-scale data
8 Gensim Topic Modeling Supporting natural language processing and unsupervised topic modeling through statistical machine learning Supporting automatic extraction of semantic topics from documents
9 Keras, TensorFlow, Theano Deep Learning Providing fast computing of numerical data with deep neural networks Effectively handling mathematical expressions, especially, matrix values
10 Scrapy Web Crawling Extracting the required data from the websites in a simple and fast way
11 SciPy, Dask, Numba, HPAT, Cython Data Science tools Performing scientific computing involves special functions, integration, linear algebra, optimization, interpolation, Ordinary Differential Equation (ODE) solvers, Fast Fourier Transform (FFT), and image processing Optimizing the machine code at runtime
12 HDF5 Data manipulation Enabling the storage of huge amounts of numerical data and manipulating the data easily from NumPy
13 SymPy Statistical Applications Supporting symbolic mathematics and modeling the full-featured Computer Algebra System (CAS)
14 csvkit, PyTables, SQLite3 Storage and Data Formatting Converting to CSV formats from different file formats such as JSON and Excel and working with CSV Managing hierarchical datasets and accessing the large-scale databases
15 Cryptography, pyOpenSSL, passlib, requests- oauthlib, ecdsa, PyCryptodome, service- identity Security Providing low-level primitives Supporting extensive error handling and providing cryptographic authority Reporting bugs and hashing the data
16 NumPy, SciPy, matplotlib, OpenCV, scikit-learn, scikit-image, ilastik Image Processing Providing a set of algorithms for image processing Supporting geometric transformations, segmentation, filtering, color space manipulation, morphology, analysis, and feature detection