{"id":21810003,"url":"https://github.com/saylidholam/data_analytics","last_synced_at":"2025-04-13T21:52:18.936Z","repository":{"id":221943268,"uuid":"755842542","full_name":"SayliDholam/Data_Analytics","owner":"SayliDholam","description":"Data Analytics with Python using Jupyter notebook","archived":false,"fork":false,"pushed_at":"2024-07-09T08:14:46.000Z","size":165,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T12:12:31.777Z","etag":null,"topics":["csv-files","jupyter-notebook"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data_Analytics\n\n\n#### Popular Libraries in Python for Data Analytics\u003c/br\u003e\u003c/br\u003e\n\n##### NumPy:\u003c/br\u003e\nProvides support for large multi-dimensional arrays and matrices.\nContains a collection of mathematical functions to operate on these arrays.\u003c/br\u003e\n```import numpy as np```\n\u003c/br\u003e\u003c/br\u003e\n\n##### Pandas:\u003c/br\u003e\nOffers data structures like Series and DataFrame for data manipulation and analysis.\nProvides functionalities for reading and writing data, handling missing values, and merging datasets.\u003c/br\u003e\n```import pandas as pd```\n\u003c/br\u003e\u003c/br\u003e\n\n##### Matplotlib:\u003c/br\u003e\nA plotting library for creating static, interactive, and animated visualizations in Python.\u003c/br\u003e\n```import matplotlib.pyplot as plt```\n\u003c/br\u003e\u003c/br\u003e\n\n##### Seaborn:\u003c/br\u003e\nBuilt on top of Matplotlib, it provides a high-level interface for drawing attractive and informative statistical graphics.\u003c/br\u003e\n``` import seaborn as sns ```\n\u003c/br\u003e\u003c/br\u003e\n\n##### Scikit-learn:\u003c/br\u003e\nA machine learning library that provides simple and efficient tools for data mining and data analysis.\nIncludes algorithms for classification, regression, clustering, and more.\u003c/br\u003e\n```from sklearn.model_selection import train_test_split```\u003c/br\u003e\n```from sklearn.linear_model import LinearRegression```\u003c/br\u003e\n```from sklearn.metrics import mean_squared_error```\u003c/br\u003e\n\u003c/br\u003e\n\n##### SciPy:\u003c/br\u003e\nBuilds on NumPy and provides additional tools for optimization, integration, and other scientific computations.\u003c/br\u003e\n\u003c/br\u003e\u003c/br\u003e\n\n##### Statsmodels:\u003c/br\u003e\nProvides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests.\u003c/br\u003e\n\u003c/br\u003e\u003c/br\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaylidholam%2Fdata_analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaylidholam%2Fdata_analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaylidholam%2Fdata_analytics/lists"}