explanatory-data-analysis

There are 2 repositories under explanatory-data-analysis topic.

• NouranHany / Instacart-Market-Basket-Analysis

  A Recommender system that predicts your next order based on your previous purchases. Also, it discuss the association between product purchases.

  kaggle-competitionmarket-basket-analysisrecommender-systemclassificationexplanatory-data-analysispowerbiassociation-rules
  Language:Jupyter Notebook 17

• RohitMidha23 / Explained

  Basics of ML libraries Explained through Jupyter Notebooks

  jupyter-notebookpandas-tutorialnumpy-tutorialdata-visualizationpython3python-libraryexplanatory-data-analysistutorialtutorial-codeeducationcommunity
  Language:Jupyter Notebook 16

• mhezarei / divar-data-analyst-summercamp-entrance-task

  Divar's 2021 Data Analyst summer camp entrance task.

  data-analysisexplanatory-data-analysis
  Language:Jupyter Notebook 7

• alinasahoo / python-data-science-essentials-2

  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.

  data-sciencemachine-learninglinear-regressionlogistic-regressionk-means-clusteringexplanatory-data-analysisneural-networksbayesian-methodsensemble-model
  Language:Jupyter Notebook 4

• antran28 / House-Price-Prediction

  Predict house price using linear regression model

  analysisdata-cleaningdata-sciencedata-visualizationexplanatory-data-analysislinear-regressionpythonpredictive-modeling
  Language:Jupyter Notebook 3

• fezzibasma / Speed-Dating-Experiment

  What attributes influence the selection of a romantic partner?

  cleaning-dataedaexplanatory-data-analysispreprocessing-datapythonspeed-datingvisualization
  Language:Jupyter Notebook 3

• hellofromtheothersky / Laptop-price-analysis-and-prediction

  Crawl data, process data, visualize, and create ML model for laptop price prediction

  cleaning-dataexplanatory-data-analysisfeature-engineeringmachine-learning
  Language:Jupyter Notebook 3

• roma-glushko / kaggle-house-prices

  🏘 Ames house dataset modelled and explained

  kaggle-competitionkaggle-house-pricesregressionmachine-learningexplanatory-data-analysishouse-price-prediction
  Language:Jupyter Notebook 3

• Shoh96 / ALX-Data-Analyst

  This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course

  cleaning-data-in-pythondatadataanalyticsexplanatory-data-analysisexploratory-data-analysisgathering-datajupyter-notebookmatplotlibnumpypresentation-slidespythonreportingseaborntwitter-apivisualizationwrangling-data
  Language:HTML 3

• alinasahoo / titanic-kaggle-eda

  This repository was just for my practice. Here, I have performed explanatory data analysis on the famous titanic dataset from kaggle.

  explanatory-data-analysistitanic-datasetlogistic-regression
  Language:Jupyter Notebook 2

• EnkiDoctor / The_TMDB_data_analysis

  The analysis and prediction of TMDB dataset

  data-analysisexplanatory-data-analysismachine-learningxgboost-algorithm
  Language:Jupyter Notebook 2

• Fuenj / Prosper-Loan-Data-Analysis

  The objective of this work is to investigate factors affecting borrower rate and loan amount.

  udacitydataanalysisprosperloandatainterestratevisualizationplotmatplotlibviolinplotboxplotlineplotpointplotborrower-rateloan-statusprosper-ratingscollateralexplanatory-data-analysisexploratory-data-analysis
  Language:HTML 2

• kk289 / Stock_Price_Prediction

  Stock Price Prediction of APPLE Using Python

  pythonmachine-learning-algorithmslinear-regressionknn-algorithmsvm-modelrmse-scoreaccuracy-scorestock-price-predictionexplanatory-data-analysis
  Language:Python 2

• nadineamin / pisa_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.

  pandasnumpydata-sciencedata-analysisjupyter-notebookmatplotlibseabornexploratory-data-analysisexplanatory-data-analysisdata-visualization
  Language:HTML 2

• roma-glushko / kaggle-wine-quality

  🍷 Quality analysis of red and white variants of the Portuguese "Vinho Verde" wine

  classificationwine-qualityexplanatory-data-analysiskaggleregressionmachine-learning
  Language:Jupyter Notebook 2

• swilliamc / Tableau

  University of California Davis Specialization Certificate in Data Visualization with Tableau

  data-visualizationtableaustorytellingstoryboarddashboardsdata-analysisdata-analyticsexploratory-data-analysisexplanatory-data-analysiscredit-cardpredictive-analyticsdata-vizdata-visualization-project
  2

• ahujaya / Wrangle-and-Analyze-Twitter-Data-Python

  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.

  data-wranglingweb-crawlingjson-dataquery-apidata-visualizationexplanatory-data-analysisexploratory-data-analysis
  Language:Jupyter Notebook 1

• morikaglobal / EDA_starwars_survey

  EDA with Python (Pandas and Matplotlib)

  pythonpandasdata-sciencedataanalysisedaexplanatory-data-analysismatplotlibdata-visualization
  Language:Jupyter Notebook 1

• morikaglobal / waterpipe_breakage_data_analysis

  Data Analysis of potential factors affecting water pipe breakage

  data-analysisdata-sciencedata-visualizationexplanatory-data-analysisdata-cleaning-and-preprocessingdata-insightspython
  Language:Jupyter Notebook 1

• nabousaab / FordGoBike_Explanatory_Dataset

  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

  fordgobikeexplanatory-data-analysisvisualization
  Language:HTML 1

• OrNixz / case-study-dicoding-collection

  This is a part of the exercise project provided by Dicoding in "Learn Data Analytics with Python" course.

  data-visualizationdata-wranglingexplanatory-data-analysisexploratory-data-analysisstreamlit-dashboard
  Language:Jupyter Notebook 1

• Paul-Asamoah-Boadu / Prosper-Loan-Data

  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.

  csv-filesdata-analysisdata-visualizationexplanatory-data-analysisexploratory-data-analysisjupyter-notebookmatplotlib-pyplotnumpypandaspython3seabornslide-deck
  Language:HTML 1

• Usama-Tariq / Udacity_Communicat-Data-Findings_Project-5_DAND

  Performed an exploratory data analysis using python and presented explanatory plots that convey insights of data.

  pythoncommand-linenumpyjupyter-notebookpandasdata-visualizationseaborndata-analysismultivariate-analysisudacity-data-analyst-nanodegreecsv-filesbivariate-analysismatplotlib-pyplotunivariate-analysisexploratory-data-analysisexplanatory-data-analysisexploratory-data-visualizationsexplanatory-data-visualization
  Language:HTML 1

• yabiola / udacity-data-analyst-projects

  This repository contains 3 projects that were carried out and submitted for my ALX Udacity Data Analyst Course

  cleaning-data-in-pythondatadataanalyticsexplanatory-data-analysisexploratory-data-analysisgathering-datamatplotlibnumpypresentation-slidespythonreportingseaborntwitter-apivisualizationwrangling-data
  Language:HTML 1

• ZSoumia / EDA-for-Monica-dataset

  This is the 6th project in my data analysis nanodegree and it focuses on prforming exploratory data analysis ( or EDA for short ) in R

  explanatory-data-analysisrggplot2data-analysisdata-visualization
  Language:R 1

• ZSoumia / US-flights-data-story-

  This project was the last project of my data analyst nanodegree : Creating a data story with Tableau

  explanatory-data-analysisdata-storiesdata-analysis
  1

• Andrew2077 / 102Flowers-EDA-Classification

  FellowshipAi project

  explanatory-data-analysisgradcam-visualizationpythonpytorchtensorboardxaiflowers102
  Language:Python 0

• collins-kimotho / Wrangle-and-analyze-project

  Data Wrangling and Analysis Project: Analyzing WeRateDogs Twitter Account Data

  matplotlibnumpypandaspythontwittertwitter-apiexplanatory-data-analysisexploratory-data-analysis
  Language:HTML 0

• Hide-A-Pumpkin / Obesity-Data-Analysis

  This is the final community contribution for EDAV Fall 2023, Columbia University. Author: Xinyi Zhao, Jean Law

  data-visualizationexplanatory-data-analysis
  Language:Jupyter Notebook 0

• kae4ka / anime-recommendations

  Explanatory data analysis of anime ratings dataset 🕵🏻‍♀️

  explanatory-data-analysisanime-titlespandas
  Language:Jupyter Notebook 0

• MAZENN72 / CreditScore_Classification

  Machine Learning Model on Credit_Score Prediction Including Data Cleaning, EDA, DataPreprocessing, Modeling

  classificationdata-sciencedatacleaningdatapreprocessingexplanatory-data-analysismachine-learning
  Language:Jupyter Notebook 0

• Sadiq-marcelo / investigate-FordGoBike-tripdata

  Investigate Ford GoBike Project

  data-visualizationexplanatory-data-analysisexploratory-data-analysismatplotlib-pyplotnumpypandaspythonwrangling-cleaning
  Language:HTML 0

• Ch3ekiBr3eki / popular_videogames

  EDA analysis and a couple of models from classical machine learning on actual data as of 12.07.2023 about video games. Dataset from kaggle link in readme.

  explanatory-data-analysiskaggleknn-regressionliner-regestionrandom-forestvideo-game
  Language:Jupyter Notebook

• fadiyahsutopo / AnalisisDataPython

  Proyek Akhir kelas Belajar Analisis Data dengan Python dari Dicoding Indonesia

  dashboardexplanatory-data-analysispythonstreamlit
  Language:Python

• sandhya-0310 / Worldwide-Mortality-Analysis-2021

  Worldwide-Mortality-Analysis-2021 examines COVID-19's impact on global mortality rates and national responses, revealing significant age-related effects and highlighting disparities linked to institutional trust rather than income inequality.

  covid-19explanatory-data-analysisjupyter-notebookpythonvizualization
  Language:Jupyter Notebook

• VaderSame / Iris-Dataset

  This GitHub repository contains a comprehensive analysis of the popular Iris dataset using various machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), and Random Forest. Additionally, it explores the impact of different data split ratios (80-10-10 vs. 60-20-20) on model performance.

  machine-learningexplanatory-data-analysismetrics-visualizationregression-analysis
  Language:Jupyter Notebook