https://github.com/sabyasachi-seal/stockmarketprediction

Stock Market Prediction using Numerical and Textual Analysis
https://github.com/sabyasachi-seal/stockmarketprediction

aiml analysis data-science data-visualization machine-learning notebook prediction python

Last synced: about 15 hours ago
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Stock Market Prediction using Numerical and Textual Analysis

• Host: GitHub
• URL: https://github.com/sabyasachi-seal/stockmarketprediction
• Owner: Sabyasachi-Seal
• License: mit
• Created: 2022-03-01T08:54:40.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2022-03-06T17:49:52.000Z (almost 3 years ago)
• Last Synced: 2023-03-04T18:20:39.434Z (almost 2 years ago)
• Topics: aiml, analysis, data-science, data-visualization, machine-learning, notebook, prediction, python
• Language: Jupyter Notebook
• Homepage: https://github.com/Sabyasachi-Seal/StockMarketPrediction
• Size: 30.1 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Task for The Sparks Foundation

## Data Science and Business Analytics

### Stock Market Prediction using Numerical and Textual Analysis (Task 7) 

### - By Sabyasachi Seal



Packages Used: 


- Math

- Numpy

- Pandas

- Matplotlib

- Keras

- SciKit Learn



Models Made: 


- Logistic Regression Model

- Random Forest Model

- Decision Tree Model

- Linear Discriminant Model

- AdaBoost Model

- Gradient Boosting Model



In the end, we can see that Gradient Boosting Model has the highest accuracy as compared to all other models. Gradient Boosting Model performs better on Analysis and prediction of Stock price/performance than the other 5 Neural Network Models.




Youtube Explanation