AI/ML · 2025 · DESCRIPTION
Predicting Microsoft Stock Values
A comprehensive stock prediction system using deep learning techniques including LSTM networks for time series forecasting. The project handles data preprocessing, time filtering, and timezone considerations.
This project uses time series analysis and Keras to predict Microsoft stock values. The model was trained and evaluated, showing a Root Mean Squared Error (RMSE) of 16.94 (5.75% error) on data from 2020-2025.
- Machine Learning
- Python
- Keras
- Data Science
Model performance
- RMSE Root mean squared error
- 16.94USD avg. miss per close
- ERR Error percentage
- 5.75% relative to price
- FIT Tracking accuracy
- 94.25% 100 − error %
- WIN Evaluation window
- 2020–2025 daily closes, UTC
Actual vs predicted close
- Actual close
- LSTM prediction
- Error band
Generated to echo the shape of the notebook's 2020–2025 plot (see window 1 below for the real output). The model runs a little high, but it follows every turn.
Model pipeline
- 01INGESTDaily MSFT price historyloaded into a pandas DataFrame
- 02CLEANDates parsed, UTC-awarefiltered to the 2020–2025 window
- 03SCALENormalised + windowedsliding look-back sequences
- 04LSTMStacked recurrent layersKeras / TensorFlow
- 05DENSERegression headone value: the next close
- 06EVALScored on held-out dataRMSE 16.94 · 5.75% error
- 07PLOTActual vs predictedmatplotlib overlay
MODEL.SUMMARY() schematic ─────────────────────────────── Input look-back × 1 (close) LSTM return_sequences=True LSTM → hidden state Dense → 1 next close (USD) ─────────────────────────────── loss mse · metric rmse
Weekly bars · tail
- LAST
- 429.63
- CHG
- -5.83
- PRED
- 445.25
Case wire
- PREDICTING MICROSOFT STOCK VALUESThis project uses time series analysis and Keras to predict Microsoft stock values. The model was trained and evaluated, showing a Root Mean Squared Error (RMSE) of 16.94 (5.75% error) on data from 2020-2025.
- APPROACHA comprehensive stock prediction system using deep learning techniques including LSTM networks for time series forecasting. The project handles data preprocessing, time filtering, and timezone considerations.
- STATUS: COMPLETEDData Science · May 2025
Chart windows
How it was built
LOADING CASE FILE
Session summary
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DISCLAIMER Educational project. Charts marked illustrative are generated for this page. Nothing here is financial advice.