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The dataset has been analyzed/visualized using Machine Learning Python Libraries.
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The Spotify data was taken from kaggle and use to analyze in different ways using python libraries.
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The dataset contains different songs and their attributes such as release date, Singer's names, Popularity, Song's Durations, etc.
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I have tried to analyze/visualize the dataset using different python libraries such as Pandas, Numpy, Matplotlib and seaborn as well as from statistical and graphical views.
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The Dataset used is uploaded with the detailed analysis.
NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
matplotlib.pyplot is a collection of functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc.
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Use the package manager pip to install difflib.
pip install numpy
pip install pandas
pip install matplotlib.pyplot
pip install seaborn### import the required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as snsPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.