Investigate Ford GoBike Project
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Updated
Sep 1, 2023 - HTML
Investigate Ford GoBike Project
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.
This is the final community contribution for EDAV Fall 2023, Columbia University. Author: Xinyi Zhao, Jean Law
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.
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.
Online Gaming Case Study
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.
Exploratory & Explanatory Analysis of Prosper Loan Data.
Analysing the data of uber using R
This project explores the relationship between features and diagnosis in cancer data. Using methods like boxplots, scatterplots, PCA, k-means clustering, and logistic regression, we analyze and visualize data to understand health indicators.
Predicting House Prices using Linear Regression Model
performed end-to-end reproducible data analyzes in the IE48A - Essentials of Data Analysis course taught at Boğaziçi University
Exploratory and explanatory analysis of a small, generated dataset. pandas + missingno + seaborn.
Bitcoin price fluctuation prediction model using headline sentiment scores from top newpaper articles. This is the repository that includes all the data and python scripts used while creating the project.
An analysis of Prosper Loan dataset
Exploratory and Descriptive Data Analysis on Indonesian data using R. This project involves reading data, feature analysis, correlation analysis, logistic regression, PCA, MDS, and clustering. Visualizations include boxplots, scatter plots, corrgrams, and dendrograms. Comprehensive report available in report.docx.
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
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