Topic: data-projects Goto Github
Some thing interesting about data-projects
Some thing interesting about data-projects
data-projects,These repository includes all my data analytics projects that were basically completed by following their respective tutorials.
User: abhi-deshpande
data-projects,A demonstration of how to create tables in PostgreSQL and import data for analysis.
User: delaney-data
data-projects,Using SQL to build insights and analysis.
User: delaney-data
data-projects,The Data Pulse pipeline processes and transforms web-scraped pageviews using Apache Beam and Google Cloud Dataflow. It reads JSON lines, parses them into PageView objects, filters for "product" post types, enriches with country info, and writes to Google BigQuery. Robust logging and error handling ensure data integrity
User: fatimashehab99
data-projects,A PostgreSQL project using a dataset that pulls job postings from Google's search results for Data Analyst positions in the United States. Dataset created by Luke Barousse.
User: iweld
data-projects,Portfolio of projects taken on by working for Hack for LA. As a member of the Data Science Community of Practice Team at Hack for LA, data science and analysis projects are taken on through a lens of community improvement and service. Code and datasets will be organized based on their respective project.
User: jossus657
data-projects,A portfolio of useful Tableau visualizations and dashboard are located in this repository. The README.md file within this repo contains a summary of each of the different workbooks.
User: kashish-rastogi
data-projects,Dummy project to act as a scaffold example for project 1
User: kirkdotcam
data-projects,Analiza danych z portalu stackexchange.com.
User: lsauchanka
data-projects,This is the final project I developed for the Coursera's IBM Data Science Professional Certificate, from Data Collection to the Presentation of valuable Insights.
User: noissey24
data-projects,Template for open-source and personal project related to data using python. This will work best if you are working with teams!
User: scaredmeow
data-projects,This lecture is part of the "Machine Learning in R" graduate course held at University of Mรผnster, School of Business and Economics (winter term 2021/22). :mortar_board:
User: simonschoe
Home Page: https://simonschoe.github.io/dynamic-programming-with-rmarkdown/
data-projects,Implemented an image search feature on the website to improve the experience of 35% of customers dissatisfied with keyword searches. This enhancement incorporated the CNN model with 95% accuracy for image classification and the Siamese model for the top 5 similar products, leveraging the data set of 24,000+ images across 10 different classes.
User: thao-phan23
data-projects,My Jekyll site
User: tinoswe
Home Page: https://tinoswe.github.io
data-projects,Data: Boston Housing Dataset (HousingData.csv) Programming language(s): R Tool(s): RStudio Business problem: To understand the drivers behind the value of houses in Boston and provide data-driven recommendation to the client on how they can increase the value of housing.The Boston housing dataset consisted of 506 observations and 14 variables. Project challenge(s): MEDV (Median value of homes in Boston) was identified as the dependent variable. While the rest, were the independent variables. The goal was to find out which among the independent variables were statistically significant in driving the house prices (MEDV). The dataset consisted of missing values and outliers. Some of the variables had a skewed distribution. There was multicollinearity among few independent variables. Our Approach: Prior to model building, we tidied up our dataset by eliminating the rows that contained missing values. Replacing the missing values with median and mean of those variables were also done. Considering the three approaches, median imputation(replacing missing values with mean) was found to be the best approach. As the dependent variable "MEDV" (median value of houses) was continuous(numerical) in nature, we implemented the Multiple linear regression to build our model. Additional models were built from Decision trees and Random forest. On further investigation, we discovered that the dependent variable had a skewed distribution. By log transformation of this variable, we were able to get a normal distribution. Post transformation, we found out that the model built from Multiple linear regression with log transformed MEDV was the best in terms of MSE (Mean squared error) value and Adjusted R^2. All the assumptions of linear regression were met.
User: vishalv91
data-projects,The objective of the project was to create innovative and interactive Tableau dashboards that focus on potential commodities, countries, year, trade amount and quantity. The client wanted to launch a new business unit, focusing on global trade and logistics, majorly in the countries such as USA, Canada and Australia The dataset provided by the client contained 59090 observations of 10 variables. The client insisted the data to be cleaned using Excel or R. The Dataset contained missing values and was cleaned using the R programming language. Tableau dashboards were created from the cleaned dataset.
User: vishalv91
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