exploratory-data-visualizations

Components for interactive scientific writing, reactive documents and explorable explanations.

Speed Up Exploratory Data Analysis (EDA)

Jared Kushner and his partners used a program meant for job-starved areas to build a luxury skyscraper

Pre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.

A 60 days+ streak of daily learning of ML/DL/Maths concepts through projects

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

A Flask based Web Application that Predicts the Flight Price using RandomForestRegressor.Its GUI is based on Swagger API. This is hosted on the Heroku platform.

Altair Ally is an companion package to Altair, which provides a few shortcuts to create common plots for exploratory data analysis.

Simple Exploratory Data Analysis web app

Predicting demand of Taxi in a given area and location of New York City.

Real-time, interactive exploration of 3D image stacks

Repository for the reports for Data Analytics virtual internship provided by ANZ.

A case study on MPG dataset.

A repository for the trainee @ codesquad. This repository contains projects that will be executed by trainees to certify that they have master what has been taught to them

Python Package for Quick Exploratory Data Analysis

A python package with standard data visualization functions with reasonable defaults for use in Exploratory Data Analysis and Model Diagnostics.

Open-Sourced Data Science project, that is made on the basis of a data-set from Kaggle.com. This data-set provides data about the Green House Gas Emission in some countries from 1990-2017.

Tools for exploratory data analysis of wearables and mHealth data.

Analysing Disaster related tweets dataset and build a classifier using deep learning and deploy it using Heroku

Exploratory data analysis on datasets available on Kaggle - https://www.kaggle.com/

Class project for Data 601 @ UMBC - Data characterization projects using Python Sci-Kit Learn

Measurement invariance explorer - Shiny app and handy functions to explore invariance

This repository is implementation of Exploratory Data Analysis on Retail data.

Tariff is a list of expenses that incur while transporting the goods from one distance to another distance. Tariff is also dependent on seasonal and non-seasonal factors also. This project is aimed at predicting the tariff ratesfor truck load by using the different machine learning algorithms like lasso regression, elastic net regression, ridge regression and linear regression. Tariffisa combination of lot ofthings and tariff rate is dependent on some ofthe factorslikeYear, Road, SeasonalImpact, Fuel Cost,Distance, Weight, Toll charge, Demand, labour cost, travel expenses etc. Using some ofthese factors and by employing the above-mentioned machine learning regression algorithms we will be trying to predict the tariff rates on the trucks. By doing this we can help the industriesto estimate the tariffratesso that they can take the necessary actions and they can make their business run inprofitable way. This model helps small- and large-scale firms to control and manage the cost on transport.

Demo on performing exploratory data analysis (EDA) on train service disruptions based on scrapped (user generated contents) tweets from the train operator's (SMRT) twitter account

GRIP-Sparks Foundation

Exploratory analysis of weather data for Warsaw, Poland through years 2004-2019.

Linear Regression model for Bike sharing dataset