Stock Predictor using Machine Learning

This is a Machine Learning–based web application that predicts the next day's stock closing price using historical data.
It uses yfinance to fetch stock data, a trained ML model to make predictions, and a Streamlit web interface for visualization and interaction.

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Features

• Predict stock prices using a trained TensorFlow/Keras model

• Fetches real-time stock data using yfinance

• Visualizations using matplotlib and seaborn

• Interactive and simple web UI with Streamlit

• Option to save predictions into a CSV file

• Clean and beginner-friendly project structure

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Machine Learning Workflow

• Collect and preprocess historical stock data

• Normalize the data using MinMaxScaler

• Build and train a Keras neural network model

• Make predictions on unseen data

• Save predicted values to CSV

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Libraries Used

• Python 3.10

• Streamlit

• yfinance

• pandas

• numpy

• matplotlib

• seaborn

• scikit-learn

• tensorflow

• keras

To install them all:

pip install -r requirements.txt