tule2236/NLP-and-Stock-Prediction tule2236/NLP-and-Stock-Prediction

  • Stars
    star
    9
  • Rank 1,939,727 (Top 39 %)
  • Language
    Jupyter Notebook
  • Created over 7 years ago
  • Updated about 6 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
tule2236/NLP-and-Stock-Prediction
github

tule2236

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Use NLP and ML for Sentiment analysis to improve Stock Prediction models. Crawl data, generate training labels, integrate 3 data sources, and experiment with many combinations of labels and algorithms.

More Repositories

1

Airbnb-Dynamic-Pricing-Optimization

[BA project] Dynamic Pricing Optimization for Airbnb listing to optimize yearly profit for host. Use Clustering for competitive analysis, kNN regression for demand forecasting, and find dynamic optimal price with Optimization model.
Jupyter Notebook
181
star
2

Geofence_Vehicle-Tracking-System

(Internship project) Design SQL database and construct website with Django. Act as a Middleware that is currently used in production.
Python
12
star
3

Dynamic-Dispatch-and-Route-Optimization-Application

(Internship Project) Use Unsupervised Learning, Randomized Optimization, Task Assignment algorithms with UI to visualize the dispatch jobs on Singapore maps
JavaScript
4
star
4

Twitter-and-ClickStream-analysis-with-SparkStreaming

Analyze streaming tweets and clickstream data in real-time with Spark Streaming, Spark SQL, and Spark MLLib.
Scala
1
star
5

UnsupervisedML_Customer_Segmentation

Customer Segmentation on Sales data using KMeans, PCA, ICA
Jupyter Notebook
1
star
6

Movie_Recommendation_SageMaker

Build user-based Collaborative Filtering algorithms from scratch. Training and deploying Tensorflow models to AWS SageMaker. Use K-Means to cluster similar users. Deploy recommendation on Serverless application.
Jupyter Notebook
1
star
7

News-Scraping-with-Scrapy

Scraping financial news of 22 big public companies from 2011 to 2017. the result of this project will support my other big project "Sentiment Analysis on Financial News" using Machine Learning and Natural Language Processing
Python
1
star