data-projects
There are 1 repository under data-projects topic.
vishalv91 / Global-Trade-Analytics
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.
Language:R 35iweld / data-analyst-job-postings
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.
scaredmeow / template-py-data-project
Template for open-source and personal project related to data using python. This will work best if you are working with teams!
Language:Python 4- Language:HTML 2
kirkdotcam / dummydataproject1
Dummy project to act as a scaffold example for project 1
Language:Jupyter Notebook 1Noissey24 / Applied-Data_Science-Capstone-Project
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.
Language:Jupyter Notebook 1Data: 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.
Language:R 1delaney-data / SQL-CreateTablesImport
A demonstration of how to create tables in PostgreSQL and import data for analysis.
0Using SQL to build insights and analysis.
0jossus657 / hack4la-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.
Language:Jupyter Notebook 0- Language:R 0
simonschoe / dynamic-programming-with-rmarkdown
This lecture is part of the "Machine Learning in R" graduate course held at University of Münster, School of Business and Economics (winter term 2021/22). :mortar_board:
Language:HTML 0thao-phan23 / Proposal_for_image_search_feature_for_NITA_fashion_ecommerce_company
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.
Language:Jupyter Notebook 0Kashish-Rastogi / Tableau
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.
- Language:Python
