Topic: explanatory-data-analysis Goto Github
Some thing interesting about explanatory-data-analysis
Some thing interesting about explanatory-data-analysis
explanatory-data-analysis,The dataset that I will be wrangling, analyzing and visualizing is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because "they're good dogs Brent." WeRateDogs has over 4 million followers and has received international media coverage.
User: ahujaya
explanatory-data-analysis,This repository contains my learning path of python for data-science essential training(part-2). Here, I have included chapter-wise topics and my practice problems. Also, feel free to checkout for better understanding.
User: alinasahoo
explanatory-data-analysis,This repository was just for my practice. Here, I have performed explanatory data analysis on the famous titanic dataset from kaggle.
User: alinasahoo
explanatory-data-analysis,FellowshipAi project
User: andrew2077
explanatory-data-analysis,Predict house price using linear regression model
User: antran28
explanatory-data-analysis,ExcelR_Assignment---Clustering---Assignment---7
User: arif-rehman
explanatory-data-analysis,Exploratory and explanatory analysis of a small, generated dataset. pandas + missingno + seaborn.
User: arkadiuszslowik
explanatory-data-analysis,This repository demonstrates the use of Pandas Profiling library for Exploratory Data Analysis (EDA) within a Jupyter Notebook. By automating much of the EDA process, the library generates comprehensive and interactive reports, complete with insightful visualizations to facilitate data understanding.
User: briankim254
explanatory-data-analysis,Online Gaming Case Study
User: denistanjingyu
explanatory-data-analysis,This project is conducted as a part of Udacity Data Analyst Nanodegree. The purpose of this project is to perform exploratory data analysis, then create a presentation with explanatory charts that conveys findings and insights from the data set provided.
User: dorothy-nguyen
explanatory-data-analysis,The analysis and prediction of TMDB dataset
User: enkidoctor
explanatory-data-analysis,This is an implementation for the famous Kaggle competition "House Prices" using EDA tools, feature engineering, handling outliers and missing data and finally machine learning linear models and regularization.
User: eslamwael
explanatory-data-analysis,Proyek Akhir kelas Belajar Analisis Data dengan Python dari Dicoding Indonesia
User: fadiyahsutopo
explanatory-data-analysis,What attributes influence the selection of a romantic partner?
User: fezzibasma
explanatory-data-analysis,The objective of this work is to investigate factors affecting borrower rate and loan amount.
User: fuenj
explanatory-data-analysis,Crawl data, process data, visualize, and create ML model for laptop price prediction
User: hellofromtheothersky
explanatory-data-analysis,SAS and R Assignments completed at Undergraduate Applied Statistics Program
User: hey-heidi
explanatory-data-analysis,This is the final community contribution for EDAV Fall 2023, Columbia University. Author: Xinyi Zhao, Jean Law
User: hide-a-pumpkin
Home Page: https://hide-a-pumpkin.github.io/Obesity-Data-Analysis/
explanatory-data-analysis,Stock Price Prediction of APPLE Using Python
User: kk289
explanatory-data-analysis,
User: lirong-zhang
explanatory-data-analysis,Auto_MPG (Linear Regression)
User: madandahal
explanatory-data-analysis,
User: mattiabraida
explanatory-data-analysis,Divar's 2021 Data Analyst summer camp entrance task.
User: mhezarei
explanatory-data-analysis,EDA with Python (Pandas and Matplotlib)
User: morikaglobal
explanatory-data-analysis,Data Analysis of potential factors affecting water pipe breakage
User: morikaglobal
explanatory-data-analysis,Cleaned FordGoBike data for 2019 was analyzed using different pots (univariate and multivariate) to draw conclusion over the distribution relation between different categorical and numerical variables
User: nabousaab
explanatory-data-analysis,# PISA 2012 Data ## by Nadine Amin ## Dataset > PISA is a survey of students' skills and knowledge as they approach the end of compulsory education. It focuses on examining how well prepared the students are for life beyond school. > Around 510,000 students in 65 economies took part in the PISA 2012 assessment of reading, mathematics and science representing about 28 million 15-year-olds globally. Of those economies, 44 took part in an assessment of creative problem solving and 18 in an assessment of financial literacy. ## Summary of Findings > Before starting this study, I thought the features that would affect the total scores the most were the teachers' influences, the students' immigration status, the class size, and the parents' highest schooling. However, almost none of my assumptions were correct once I started to see the relationships of the variables with the total scores and with other variables. > The number of cellphones, TVs, computers & books, the parents' schooling & jobs, and the homework study time were the variables that affected the total scores. > The higher the number of cellphones, TVs, computers and books, the higher the chances of getting a better total score. This could be because the family's social status was better, and therefore provided better support for the students. > As long as the parents' schooling was level 3A or higher, there is a good chance that the students would get higher grades. Furthermore, parents who had full-time jobs resulted in their children getting higher scores. This could be because having role models to look up to will make you work harder and believe in yourself more. > Finally, students who studied for longer hours had a higher chance of scoring better. ## Key Insights for Presentation > In the presentation, I will show the plots that had an effect on the total score the most. Those include the bivariate plots of the variables mentioned above against the total score. I will also include the multivariate plot of the father and mother's jobs vs. the number of cellphones vs. the total score.
User: nadineamin
explanatory-data-analysis,A Recommender system that predicts your next order based on your previous purchases. Also, it discuss the association between product purchases.
User: nouranhany
explanatory-data-analysis,This is a part of the exercise project provided by Dicoding in "Learn Data Analytics with Python" course.
User: ornixz
explanatory-data-analysis,
User: paras-singh7
explanatory-data-analysis,Analysing the data of uber using R
User: paras-singh7
explanatory-data-analysis,This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others. The analysis explore the factors and patterns in the creditworthiness of borrowers and the borrowing trend of Prosper Loan Business.
User: paul-asamoah-boadu
explanatory-data-analysis,In this data analysis project, I have explored the Prosper dataset and used Tableau to create my visualizations. Prosper is a peer-to-peer platform that lends money and its goal is to connect people who need money with those people who have the money to invest.
User: praxitelisk
explanatory-data-analysis,An analysis of Prosper Loan dataset
User: princess227
explanatory-data-analysis,Basics of ML libraries Explained through Jupyter Notebooks
User: rohitmidha23
explanatory-data-analysis,🏘 Ames house dataset modelled and explained
User: roma-glushko
explanatory-data-analysis,🍷 Quality analysis of red and white variants of the Portuguese "Vinho Verde" wine
User: roma-glushko
explanatory-data-analysis,A project is to make a simple Exploratory Data Analysis to find if there is a direct relationship between income and the level of education in Canada.
User: saboye
explanatory-data-analysis,Investigate Ford GoBike Project
User: sadiq-marcelo
explanatory-data-analysis,Using linear regression to explain housing prices in Brooklyn, NY from 2016-2020 and estimate how prices changed from quarter 3 and quarter 4 of 2020
User: sakshi-shende
explanatory-data-analysis,Predicting House Prices using Linear Regression Model
User: shikhargupta-in
Home Page: https://shikhargupta-in.github.io/portfolio/Predicting-House-Prices.html
explanatory-data-analysis,This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course
User: shoh96
explanatory-data-analysis,University of California Davis Specialization Certificate in Data Visualization with Tableau
User: swilliamc
explanatory-data-analysis,Explanatory data analysis of Hong Kong Airbnb data; Data-driven insights regarding 5 business questions were provided.
User: timchansdp
explanatory-data-analysis,Performed an exploratory data analysis using python and presented explanatory plots that convey insights of data.
User: usama-tariq
Home Page: https://www.udacity.com/course/data-analyst-nanodegree--nd002
explanatory-data-analysis,Exploratory & Explanatory Analysis of Prosper Loan Data.
User: xinqilin1994
explanatory-data-analysis,This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course
User: yabiola
explanatory-data-analysis,Report emphasizing on exploratory and explanatory data visualization techniques used for analyzing FordGoBike bike-sharing system data
User: yashmotwani
explanatory-data-analysis,This is the 6th project in my data analysis nanodegree and it focuses on prforming exploratory data analysis ( or EDA for short ) in R
User: zsoumia
explanatory-data-analysis,This project was the last project of my data analyst nanodegree : Creating a data story with Tableau
User: zsoumia
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