https://github.com/pronzzz/stock_price_prediction

This repository is dedicated to exploring different machine learning models for stock price prediction. Our goal is to determine which model is the most accurate and efficient in forecasting stock market prices. We'll be using historical stock data and various machine learning techniques to build and evaluate models.
https://github.com/pronzzz/stock_price_prediction

knn-classifier knn-regression linear-regression lstm-model random-forest svm-model

Last synced: 14 days ago
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This repository is dedicated to exploring different machine learning models for stock price prediction. Our goal is to determine which model is the most accurate and efficient in forecasting stock market prices. We'll be using historical stock data and various machine learning techniques to build and evaluate models.

• Host: GitHub
• URL: https://github.com/pronzzz/stock_price_prediction
• Owner: pronzzz
• Created: 2024-01-21T06:57:45.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-01-28T07:31:07.000Z (10 months ago)
• Last Synced: 2024-01-29T05:25:43.593Z (10 months ago)
• Topics: knn-classifier, knn-regression, linear-regression, lstm-model, random-forest, svm-model
• Language: Jupyter Notebook
• Homepage:
• Size: 3.24 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
 # Stock Price Prediction 📈📊

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## Project Description:

We'll be focusing on Tata Global Stock Price Prediction using various machine learning models. We'll start with a simple K-Nearest Neighbors (KNN) model using Quandl data. As we progress, we'll explore more advanced models like Linear Regression, Support Vector Machines, Random Forest, and LSTM Neural Networks. Our aim is to find the model that provides the most accurate predictions and insights into Tata Global stock price movements.

## List of Machine Learning Models:

Here's a list of machine learning models that we'll be investigating:

- K-Nearest Neighbors (KNN) 

- Linear Regression

- Support Vector Machines (SVM)

- Random Forest

- Long Short-Term Memory (LSTM) Neural Networks

## Repository Sections:

The repository is structured into several sections:

1. **Data**: This section contains the historical stock data we'll be using for our analysis.

2. **Models**: Here, you'll find the code for implementing and evaluating different machine learning models.

3. **Results**: This section presents the results of our analysis, including accuracy metrics and visualizations.

4. **Documentation**: We'll provide detailed documentation explaining the project, methodology, and findings.

5. **Resources**: A collection of helpful resources, such as tutorials, articles, and datasets.

## Contributing:

Contributions to this repository are welcome! Feel free to submit issues, suggest improvements, or share your own findings related to stock price prediction. Let's collaborate to build a comprehensive resource for stock market analysis.