📈 StockLSTM – Deep Learning for Tesla Price Forecasting

This project demonstrates how to build a Long Short-Term Memory (LSTM) model using Keras to predict Tesla's stock closing prices based on historical data.

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Tags: deep-learning lstm stock-prediction time-series-analysis tensorflow keras tesla tsla yahoo-finance machine-learning python numpy pandas matplotlib minmaxscaler price-forecasting sequential-mod

🧠 Project Objective

The main objective is to:

• Load historical Tesla stock price data
• Preprocess and normalize the data
• Create time-series sequences for the LSTM model
• Train the LSTM neural network
• Predict and visualize future stock prices

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📂 Dataset

• Dataset used: TSLA.csv
• Source: Yahoo Finance
• Important feature: Close price of the stock
• The Date column is set as the index for time series analysis

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🧪 Model Architecture

• Model: Sequential
• Layers:
  • LSTM layer with 50 units
  • Dense layer with 1 output
• Loss function: Mean Squared Error (MSE)
• Optimizer: Adam
• Epochs: 100
• Batch size: 32

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🔁 Data Preprocessing Steps

1. Load and parse the Date column
2. Extract only the Close prices
3. Apply MinMaxScaler to scale the data between 0 and 1
4. Create sequences of 60 days to predict the 61st day price
5. Split the dataset into 80% training and 20% testing

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📊 Results

Training Accuracy: Achieved low loss values indicating good learning

Predictions: Accurately predicted trends and closing prices

Visualization: Plotted predicted vs actual prices for clear comparison