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a-b-testing

agriculture-water-consumption

agriculture-water-consumption-forecasting

book-recomendation-system

churn-prediction

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

customer-segmentation

datacamp_data-scientist-with-python

DataCamp_Data Scientist with Python

deeplearning-timeseries

Timeseries Forecasting via Recurrent Neural Nets [Keras + Theano]

finding-donors-classifier

Supervised learning classifier that classify people based on their income, 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.

house-pricing

hybrid-model-for-stock-price-predictin

Master Project

p03-telco-churn-model

price-forecaster

Forecasting the future prices of BTC and More using Machine and Deep Learning Models

python-for-data-science-and-machine-learning-bootcamp

pythoncode-tutorials

The Python Code Tutorials

real-estate-market-analysis

rented-biks

singularity-data-science

Data Science Course Materials

stock-price-prediction

Jupyter notebook for predicting the top 5 most negotiated stocks at Brazilian stock exchange

stock-price-prediction-using-gan

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

stock-price-predictor

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

user-classification

The aim is to predict whether students would upgrade their free plan to a paid one.