GitHub topics: seaborn-pairplot

Repositories

AryanBalaji/TimeAnalysis

Conducted in-depth time series analysis on stock market data for major tech companies like Amazon, Google, Apple, and Microsoft. Utilized multivariable analysis to explore the inter-relationship between stock closing prices and daily % return, visualized findings using Seaborn library, and performed value at risk calculations for each company.

Language: Jupyter Notebook - Size: 1.45 MB - Last synced at: 3 months ago - Pushed at: 9 months ago - Stars: 1 - Forks: 0

mohd-faizy/Learn_Seaborn

Seaborn is a visualization library for Python that builds on matplotlib and pandas. It provides beautiful default styles and color palettes for different types of plots, such as histograms, distributions, regression, and matrix plots.

Language: Jupyter Notebook - Size: 6.14 MB - Last synced at: 3 months ago - Pushed at: 12 months ago - Stars: 2 - Forks: 1

MahzaibKhalid/Seaborn_Complete_Hands_on_Notes

This is the complete seaborn playlist. In this almost all plots has been designed and code has been given.

Language: Jupyter Notebook - Size: 1.32 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

RajMaheshP/COASTER_CODING_CLUB_PROJECT

First of all I have uploaded a dataset from Kaggle to the Colab. Then I have imported all the necessary libraries in google colaboratory. Then I started cleaning the data and manipulate the data. After that using matplotlib and seaborn I have created some visualised graphs.

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

Thakursiddhesh/Regression-Model-to-Predict-Cement-Compressive-Strength

Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.

Language: Jupyter Notebook - Size: 604 KB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0