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stock_prediction_project

stock_price_predictor

Stock Price Predictor with Deep Learning

tensorflow_basics

Basic TensorFlow mechanics, operations, class definitions, and neural networks building. Examples from deeplearning.ai Tensorflow course using Google Colab platform.

test-stock-prediction-algorithms

Use deep learning, genetic programming and other methods to predict stock and market movements

text-as-data-lab-spring-2019

Materials for the lab component of DS-GA 1015 Text-as-Data (Spring 2019).

text-clustering-api

Implementation of a text clustering algorithm using Kmeans clustering in order to derive quick insights from unstructured text

text-scraping-document-clustering-topic-modeling

The objective of this project is to scrape a corpus of news articles from a set of web pages, pre-process the corpus, and then to apply unsupervised clustering algorithms to explore and summarise the contents of the corpus. Part 1. Text Data Scraping This part of the project should be implemented as a Python script 1. Identify the URLs for all news articles listed on the website: http://mlg.ucd.ie/modules/COMP41680/news/index.html 2. Retrieve all web pages corresponding to these article URLs. 3. From the web pages, extract the main body text containing the content of each news article. Save the body of each article as plain text. Part 2. Corpus Exploration Tasks to be completed in your IPython notebook: 1. Load the text corpus generated in Part 1. Apply any appropriate pre-processing steps and construct a document-term matrix representation of the corpus. 2. Summarise the overall corpus by identifying the most characteristic terms and phrases in the corpus. 3. Apply two alternative clustering algorithms of your choice to the document-term matrix to produce clusters of related documents. This might require applying each algorithm several times with different parameter values. 4. For each clustering generated in Step 3, summarise the contents of the clusters. Based on your summary, suggest a topic/theme for each cluster.

text_clustering

k-means text clustering using cosine similarity.

time-series-forecasting-of-amazon-stock-prices-using-neural-networks-lstm-and-gan-

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

time-series-transformer

A data preprocessing package for time series data. Design for machine learning and deep learning.

timeseries2018

Course page for DS-GA 3001.001 Modeling Time Series Data

trading-bot

Stock Trading Bot using Deep Q-Learning

visualization

Data Visualization using Matplotlib, Pandas Visualization, Seaborn, ggplot, and Plotly.

xgboost-autotune

Wrap-up to automatically tune xgboost in Python.

youtubedataanalysis

This is a data analysis of youtube data.