Dan Hively's repositories

Air-Passengers-Forecasting

All of the csv files and ipynb files required to run the Air Passengers Forecasting Project can be found in this repository. We implemented Auto Regression Algorithm to forecast the number of passengers for the period of next 5 years.

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autoCV

clean CV LaTex template with GitHub Action that compiles and publishes new changes

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CSE497-Data-Mining-

This repo contains the ipynb files of data mining tasks I performed during the CSE 497 course at Penn State

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danhively.github.io

✨ Build a beautiful and simple website in literally minutes. Demo at https://beautifuljekyll.com

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DataWrangling

Learned how to find or scrape information from networks and manipulate that information to display trends. This was done in Python using Google Colab's .ipynb environment. Libraries used were Pandas, NumPy, Seaborn, Scipy, Matplotlib, SkLearn, and FacebookProphet.

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Dengue-DrivenData

This repository contains all of the csv files, ipynb files, and competition submission csv files required for the Dengue competition hosted on DrivenData.org. DrivenData hosts data science competitions similar to Kaggle that aim to solve real-world problems in the most creative way possible.

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Flipkart-Webscraping-Project

All of the csv files and ipynb files required to run the Web Scraping Sales Project can be found in this repository. We used the libraries such as Beautiful Soup to scrap data from the Flipkart website.

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hack-the-boo-2022

Hack The Box's Halloween CTF

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RbuWSL

Rsync backup using Windows Subsystem for Linux (RbuWSL). Backup your Windows files to external drive quickly using Rsync to copy only new and modified files

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tensorflow-deep-learning

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

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Full-Stack-Learning

This repo will contain multiple files, mainly in .ipynb format for a nice visual and interactive session.

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Grayscale_images_to_color_images.ipynb

Using Autoencoder Grayscale images to color images

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handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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ipynb_to_pdf

A streamlit app to convert ipynb files to PDFs

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jupyter-ipynbs

Collection of ipynbs to learn and revise python

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Machine_Learning_Assignments.ipynb

Machine_Learning_Assingments_GUVI

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ML-For-Beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

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pentest-bookmarks

a collection of handy bookmarks

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PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

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