📊 Stock Price Prediction Using LSTM This project is part of the ML Internship by FutureIntern and focuses on building a machine learning model using LSTM (Long Short-Term Memory networks) to predict future stock prices.
🎯 Objective To develop a machine learning model based on historical stock market data to estimate future stock prices. The project aims to apply deep learning techniques to capture temporal patterns in time series data for accurate stock forecasting.
🛠 Tools & Technologies Python — Core programming language Pandas — Data manipulation and analysis NumPy — Numerical computing Scikit-learn — Preprocessing, model evaluation, and scaling TensorFlow / Keras — Deep learning model (LSTM) yfinance — Fetching real-time historical stock data Matplotlib — Data visualization
📚 Project Workflow
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Data Collection Use the yfinance API to download historical stock price data (e.g., closing prices).
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Data Preprocessing Handle missing values (if any). Normalize the data using MinMaxScaler. Create input sequences (e.g., use 60 previous days to predict the next day). Split the dataset into training and testing sets.
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Model Building Build an LSTM model using the Keras Sequential API.
Architecture: One or more LSTM layers Dropout layers for regularization Dense output layer for prediction
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Model Compiling Compile the model with a suitable optimizer, loss function, and evaluation metrics.
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Model Training Train the model and monitor both training and validation loss.
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Model Evaluation Evaluate using metrics like Mean Squared Error (MSE) and R² Score. Visualize loss curves (training vs. validation).
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Prediction & Visualization Forecast stock prices on the test set. Plot actual vs. predicted prices for visual evaluation.
📈 Predict and Visualize Unseen Data (Next n Days) Predict stock prices for the next n days. Visualize the predicted prices using line plots.