Stock Price Prediction Project using TensorFlow

This project is a stock price prediction project using TensorFlow. Stock market price analysis is a time series approach and can be performed using a recurrent neural network (RNN). To implement this, we will use TensorFlow, an open-source Python framework for machine learning and deep learning.

Importing Libraries and Dataset Manipulation

The following Python libraries are needed for this project:

• Pandas: For loading and manipulating the dataset
• Numpy: For numerical operations
• Matplotlib/Seaborn: For data visualization
• TensorFlow: For building and training the machine learning model

Data Preparation

The stock price dataset can be obtained from a variety of sources, such as Yahoo Finance or Google Finance.

• The file I'm using is Yahoo for the Nasdaq. Oct, 15 2022 - Oct 15, 2023

Model Building and Training

The stock price dataset can be obtained from a variety of sources, such as Yahoo Finance or Google Finance.

Once the dataset is loaded and preprocessed, the RNN model can be built and trained. The following steps are involved in model building and training:

• Define the model architecture: This involves specifying the number of layers in the RNN, the number of units in each layer, and the type of activation function.
• Compile the model: This involves specifying the optimizer and loss function to be used for training.
• Train the model: This involves feeding the model the training data and allowing it to learn the relationship between the input features and the target output.

Model Evaluation

Once the model is trained, it needs to be evaluated on a held-out test set. This will give an idea of how well the model will generalize to new data.

Conclusion

This project provides a basic template for building a stock price prediction model using TensorFlow. The model can be further improved by using more advanced techniques, such as feature engineering and hyperparameter tuning.

Additional Notes

• This project can be extended to predict other stock market variables, such as trading volume or volatility.
• The RNN model can be replaced with other types of neural networks, such as Long Short-Term Memory (LSTM) networks.
• The model can be used to generate real-time predictions by feeding it the latest stock price data.

SETUP Local environment

• setup a python venv enviroment and install the requirment.txt file

• python3.8 -m venv env

• source env/Scripts/activate

• pip install -r requirements.txt

• deactivate