pairplot

Car Price Prediction

This project performs **exploratory data analysis (EDA)** and **multidimensional visualizations** on the UCI Wine Quality dataset (Red & White wines).

The dataset used for this project is taken from the official UCI Machine Learning Repository.

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 y…

Multiple-Linear-Regression-1. Consider only the below columns and prepare a prediction model for predicting Price of Toyota Corolla.

The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset

Iris-Dataset-Prediction-using-Unsupervised-ML This project involves the analysis of the Iris dataset using Python. It includes code to determine the optimum number of clusters using the K-means clustering algorithm, visualizations such as scatter plot, pair plots and hist plots, and other insights into the dataset.

A simple analysis of Zlatan Ibrahimović's club career.

Visualization using Matplotlib and Seaborn

Seaborn Visualization on Titanic Dataset Visual exploration of different features on No. of people survived or otherwise Visualization using FacetGrid function, Lambda function and criterion function Visualization of subplots

Strip Plot, Grouping with Strip Plot, Swarm Plot, Box and Violin Plot, placing plots together, Combining the plots, Joint Plot, Density Plot, Pair Plot

HHA507 / Data Science / Assignment 3b / Data Visualization with Seaborn and Plotly

Python matplotlib, seaborn and plotly plots cheat sheet

Seaborn “Beginner-to-Pro” EDA guide: clean, modular plot patterns + reusable mini EDA pipeline.

1st Project for the Post Graduate Programme in Data Science and Business Analytics at the University of Texas at Austin - Exploratory Data Analysis

Here we will be taking two dataset from the seaborn library itself i.e. the tip and iris dataset to perform continuous and categorical datapoint visualisations.

This repository contains the file for the task that was done as part of my internship in The Sparks Foundation with specialization - Data Science & Business Analytics.

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