Pinned Repositories
api-client-python
API v4 client for Python
c_cpp_primer
IACS C/C++ primer
data-labeling
Data Labeling Demo
Email-Scraper
This project was to help a friend in real estate who quickly wanted to pull emails of brokers from certain real-estate companies. Instead of manually copy pasting them from the website I wrote a small script to pull all the emails and append them to a dataframe that was then exported to a csv file.
Estimating-Soil-Moisture-in-Catalonia-with-Deep-Learning
The motivation of this project is to address the need for accurate and timely information about the Earth's surface and understanding flux in the environment's soil moisture during the climate change era. We use different deep learning methods to predict soil moisture levels in the Cataloina region in Spain.
Grouping-similar-Emails-using-the-subject-line-text
This project attempted to group similar enron emails using Kmeans clustering with Word2vec. For this I downloaded the emails from https://www.cs.cmu.edu/~enron/from and used the subject field.
NYC-Covid-19-Dashboard
This was a small project to visualize covid data by zip code in nyc. the data is provided on daily basis by NYC health (https://github.com/nychealth/coronavirus-data). I wrote a python script to pull the latest data from their github repository, followed by some data cleaning and finally appending the data to a historical xlsx file. Lastly I used tableau to create the dashboard.
spacial_data_vis
tadhglooram93
Taliban-Takeover
Data Visualization Project of the Taliban Insurgence and Consequences
tadhglooram93's Repositories
tadhglooram93/Estimating-Soil-Moisture-in-Catalonia-with-Deep-Learning
The motivation of this project is to address the need for accurate and timely information about the Earth's surface and understanding flux in the environment's soil moisture during the climate change era. We use different deep learning methods to predict soil moisture levels in the Cataloina region in Spain.
tadhglooram93/api-client-python
API v4 client for Python
tadhglooram93/c_cpp_primer
IACS C/C++ primer
tadhglooram93/data-labeling
Data Labeling Demo
tadhglooram93/Email-Scraper
This project was to help a friend in real estate who quickly wanted to pull emails of brokers from certain real-estate companies. Instead of manually copy pasting them from the website I wrote a small script to pull all the emails and append them to a dataframe that was then exported to a csv file.
tadhglooram93/Grouping-similar-Emails-using-the-subject-line-text
This project attempted to group similar enron emails using Kmeans clustering with Word2vec. For this I downloaded the emails from https://www.cs.cmu.edu/~enron/from and used the subject field.
tadhglooram93/NYC-Covid-19-Dashboard
This was a small project to visualize covid data by zip code in nyc. the data is provided on daily basis by NYC health (https://github.com/nychealth/coronavirus-data). I wrote a python script to pull the latest data from their github repository, followed by some data cleaning and finally appending the data to a historical xlsx file. Lastly I used tableau to create the dashboard.
tadhglooram93/spacial_data_vis
tadhglooram93/tadhglooram93
tadhglooram93/Taliban-Takeover
Data Visualization Project of the Taliban Insurgence and Consequences
tadhglooram93/Time_Series_Map_Of_Taliban_Takeoverer
This project attempts to map the Taliban’s takeover of Afghanistan’s districts from May 12th to August 16th using articles published by Tolo News, a local publication.
tadhglooram93/Udacity-Machine-Learning-Course---Deep-Learning---Project-2--Image-Classifier
In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset from Oxford of 102 flower categories, you can see a few examples below. The project is broken down into multiple steps: Load the image dataset and create a pipeline. Build and Train an image classifier on this dataset. Use your trained model to perform inference on flower images. We'll lead you through each part which you'll implement in Python. When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.
tadhglooram93/Udacity-Machine-Learning-Course---Supervised-ML---Project-1--Finding-Donors-for-CharityML
Getting Started In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". You can find the article by Ron Kohavi online. The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries
tadhglooram93/Udacity-Machine-Learning-Course---Unsupervised-ML---Project-3---Identifying-Consumer-Segments
In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.