Stock Price Predictor

📌 Project: Stock Price Predictor

👨‍💻 Author: Shailesh Gupta

🔗 GitHub: View Source Code

📈 Objective:
Develop a predictive model that forecasts future stock prices using historical data — combining time series insights with deep learning techniques.

📊 Use Case:
Predicting next-day or next-week stock trends can give traders, analysts, and retail investors a valuable edge. This model is focused on learning **price patterns** from past data and providing **next-value estimations**.

🔍 Dataset Overview:

  • Used a historical CSV of daily stock prices
  • Preprocessed with MinMax scaling
  • Split into training/testing sequences using time steps

🧠 Model Highlights:

  • LSTM-based model trained on sequences of past prices
  • Captures time dependencies and patterns in sequential data
  • Plots predicted vs actual values to visualize model performance

🛠️ Tools Used:

  • Python, Pandas, NumPy
  • Keras & TensorFlow (LSTM)
  • Matplotlib for graph visualization
  • Streamlit for frontend interaction

📷 App Preview:



📚 Key Learning Outcomes:

  • Learned how LSTMs can model time series dependencies
  • Improved preprocessing and scaling techniques for sequential input
  • Enhanced deployment experience through Streamlit UX improvements

📁 GitHub Repo: Visit here

🙌 Stay Connected:
If you found this useful, feel free to star the GitHub repo ⭐ or drop me a message on LinkedIn.

🔖 Tags:
Stock Prediction, LSTM, Time Series Forecasting, Streamlit, Deep Learning, Financial Data, Python, TensorFlow, Keras, Data Science

Location: India

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