Automated Tool for Optimized Modelling
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Updated
Jul 15, 2024 - HTML
Automated Tool for Optimized Modelling
🍸 Digital Collections Framework
Multidimensional data explorer and visualization tool.
Ford GoBike System Data Exploration and Findings Communication: This document explores a dataset with information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area in 2019
Project 4 on Udacity's Data Analyst Nanodegree
We will analyze a dataset provided by an e-commerce marketplace called [Olist](https://www.olist.com) to answer the CEO's question: Should Olist remove underperforming sellers from its marketplace? How to increase customer satisfaction (so as to increase profit margin) while maintaining a healthy order volume?
A CLI wrapper for Sweetviz Python exploratory data analysis (EDA) tool.
Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.
Biologically Plausible Programming
Explore valuable insights into the performance of a coffee chain across various locations, including key attributes such as Area Code, COGS, Profit, Sales, and more. Dive into sales trends, financial performance, and market dynamics.
User interface for data exploration
Asteroid Feature Prediction Machine Learning Models
The dataset I wrangled (and analysed and visualized) 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.
Predicting Next Booking Destinations for Airbnb Users. Feel free to access the Streamlit App in the link below.
Analyze data from airbnb bookings since July 2019 in R
I learnt from Google employees whose foundations in data analytics served as launchpads for their own careers. At under 10 hours per week, you can complete the certificate in less than 6 months.
A MLR algorithm that analyzes diabetes data in African Americans to predict a diabetes diagnosis
Data exploration of loans using charts
Finding which apps characteristics contribute the most to apps ratings, using visualizations - IPython Notebook.
This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. It was requested to investigate this dataset to get at least some insights.
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