Stock Price Prediction 📈📊

---

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.