pair-plot

There are 11 repositories under pair-plot topic.

  • kanchanchy/Data-Visualization-in-Python

    Data Visualization in Python using Matplotlib, Seaborn and Plotly Express

    Language:Jupyter Notebook9108

  • ZubayerOjhor/EDA-of-Wine-Quality-Data-in-Python

    Conducting Exploratory Data Analysis of White Wine Quality Dataset

    Language:Python1100

  • Zulnoorain-Ashfaq/pair_plot

    modified pair plot to show relationships in data

    Language:Python1100

  • Abhi-Pat/Time-Series-Project-S-P-500-Stock-Market-Case-Study-

    This repository analyzes S&P 500 stock data for tech companies like Apple, Amazon, Google, and Microsoft. It covers data prep, price analysis, moving averages, daily returns, resampling, and correlation analysis. Visualizations include line plots, pair plots, and heatmaps, using pandas, seaborn, and matplotlib.

    Language:Jupyter Notebook0100

  • AnooshaHegde/Data-Visualization

    Language:Jupyter Notebook0100

  • gkrusta/dslr

    A machine learning project applying logistic regression for multi-class classification, ETL, model training, and visualization.

    Language:Python0100

  • shwetapardhi/Assignment-05-Multiple-Linear-Regression-2

    Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t

    Language:Jupyter Notebook0100

  • ThePush/dslr

    Exploratory Data Analysis and Multinomial Logistic Regression from scratch

    Language:Python0110

  • Ankit152/HaberMan-Dataset

    Exploratory Data Analysis on Haberman Dataset

    Language:Jupyter Notebook20

  • Fufulooky/Time-Series-Project-S-P-500-Stock-Market-Case-Study-

    This repository analyzes S&P 500 stock data for tech companies like Apple, Amazon, Google, and Microsoft. It covers data prep, price analysis, moving averages, daily returns, resampling, and correlation analysis. Visualizations include line plots, pair plots, and heatmaps, using pandas, seaborn, and matplotlib.

    10

  • summerolmstead/Spotify-Top-Songs-2023-Analysis

    Using a dataset of the Top Spotify Songs of 2023, I create a stacked bar chart, clustered bar chart, and pair plots to utilizing a Spotify Top Songs of 2023 dataset, I explore the attributes of energy, danceability, valence, liveliness, acousticness, and speechiness and its relation for the balance of a hit song.

    Language:Jupyter Notebook10