LSTM Stock Price Prediction

This project predicts Microsoft stock prices using a LSTM (Long Short-Term Memory) model.
It is built with Python, TensorFlow/Keras, and data visualization with Matplotlib and Seaborn.

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Project Structure

LSTM-STOCK-PREDICTION/
├── main.py
├── MicrosoftStock.csv
├── plots/
│   ├── open_close_prices.png
│   ├── volume_over_time.png
│   ├── feature_correlation.png
│   ├── price_over_time.png
│   └── predictions_vs_actual.png
└── requirements.txt

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How it works

1. Data Loading & Exploration:
   Load Microsoft stock data (MicrosoftStock.csv) and perform basic EDA (head, info, description).

2. Visualization:

   • Open vs Close Prices
   • Trading Volume
   • Feature Correlation Heatmap
   • Price over Time

3. Data Preprocessing:

   • Scale data using StandardScaler
   • Create sliding window dataset for LSTM (60 days)

4. Model Architecture:

   • Two LSTM layers
   • Dense layer with 128 neurons
   • Dropout layer (0.5)
   • Output Dense layer (1 neuron for stock price)

5. Training:

   • 20 epochs, batch size 32
   • Metrics: MAE & RMSE

6. Prediction & Plotting:

   • Test set predictions
   • Compare predictions with actual stock prices
   • Save all plots in the plots/ folder

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Example Plots

Open vs Close Prices

Trading Volume

Feature Correlation Heatmap

Price over Time

Predictions vs Actual

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How to Run

1. Clone this repo:

bash git clone https://github.com/Sparkydev007/LSTM-STOCK-PREDICTION.git cd LSTM-STOCK-PREDICTION

2. Install dependencies:

pip install -r requirements.txt

3. Run the model:

python main.py

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Tech Stack

Python 3.x
TensorFlow / Keras
Pandas, Numpy
Matplotlib, Seaborn
Scikit-learn

Author,

Prathmesh Bunde.

Stock Prediction Projects | ML Enthusiast | Python Developer