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jangomango87's Projects

100-pandas-puzzles icon 100-pandas-puzzles

100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

awesome icon awesome

😎 Awesome lists about all kinds of interesting topics

awesome-datascience icon awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-dataviz icon awesome-dataviz

:chart_with_upwards_trend: A curated list of awesome data visualization libraries and resources.

best-data-science-resources icon best-data-science-resources

This repository contains the best Data Science free hand-picked resources to equip you with all the industry-driven skills and interview preparation kit.

data-science icon data-science

:bar_chart: Path to a free self-taught education in Data Science!

data-science-cheatsheet icon data-science-cheatsheet

A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.

data-science-ipython-notebooks icon data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

go icon go

The Open Source Data Science Masters

learn-python icon learn-python

📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.

optapy icon optapy

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

pydata-book icon pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

stockpredictionai icon stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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