Stock market analysis and prediction using long short term memory network,predicted the differnt stock market prices using the lstm model which is sequential network in deep learning

2. Team Leader :
-Sai Jai Bhargav . J (41611081)
Team Members :
-Madhan Gajawada (41611104)
-Dhamodhar . K (41611097)
- Jaya Baaskar . B (41611075)
-Joshan Harish . J (41611079)

3. Presentation Outline
-Introduction
-Objectives
-Project Implementation
-Methodology
-LSTM
-Results and Discussions
-Conclusion

4. Introduction
The project "Stock market Analysis and Prediction" focuses on utilizing Long
Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN),
for predicting stock market trends specifically for Amazon. By solely relying on
historical stock data, the LSTM model is trained to forecast future price
movements of Amazon's stock. The analysis involves evaluating the accuracy of
LSTM predictions against actual market data and exploring the effectiveness of
LSTM in capturing the complex patterns inherent in stock market dynamics. The
findings provide valuable insights into the feasibility and limitations of using LSTM
for stock market prediction tasks, particularly in the context of a prominent
company like Amazon.

5. Objectives of the Work
-The primary objective of the work is to develop a robust
framework for predicting the daily stock price movement
of Amazon.
-The predictive framework is based on Long Short Term
Memory(LSTM) Network.
-And perform some technical analysis like moving
averages,volume analysis,Fibonacci retracement,support
and recistance analysis.

6. • Stock Price Prediction using machine learning helps in discovering the
future values of a company's stocks and other assets. Predicting stock
prices helps in gaining significant profits.
• Accuracy Improvement: Aim to improve the accuracy of stock price
predictions compared to traditional methods or other forecasting
techniques. This involves minimizing forecasting errors and optimizing
model performance metrics such as Mean Absolute Error (MAE), Mean
Squared Error (MSE), or Root Mean Squared Error (RMSE).
• Model Interpretability: Prophet provides intuitive interpretation of its
components such as trend, seasonality, and holidays. If interpretability is
crucial for your application, ensure that your objective includes building a
model that stakeholders can easily understand and trust.
• Real-time Prediction: Depending on your application, real-time or near-
real-time prediction capability might be essential. If so, design your
workflow to ensure timely updates of the Prophet model using the latest
available data.

7. Project Implementation
PROBLEM STATEMENT : Stock Market Analysis and Prediction
HARDWARE REQUIREMENTS :
-PC or Laptop
-Mouse
-Keyboard
SOFTWARE REQUIREMENTS :
-Anaconda Navigator
-Python3
-Jupyter Notebook
-visual studio
DATASET :
Downloading a set of data from Yahoo finance.Yahoo! Finance is the media
asset which is a member of Yahoo! network. Provides financial information,
data and comments including stock quote, media release, financial details, and

8. Methodology
Methodology for Stock Market Analysis and Prediction using LSTM:
1.Data Collection:
1.Gather historical stock market data including price, volume, and other relevant
indicators.
2.Utilize financial APIs or databases to access real-time and historical market data.
2.Data Preprocessing:
1.Cleanse the data by handling missing values, outliers, and inconsistencies.
2.Normalize the data to ensure uniformity and remove scale effects that might
affect model training.
3.Feature Engineering:
1.Select relevant features such as price movements, trading volumes, technical
indicators, and sentiment scores.
2.Extract additional features that might impact stock prices, such as

9. 4. LSTM Model Development:
- Design an LSTM (Long Short-Term Memory) architecture suitable for time-series
forecasting.
- Define the input sequence length and output prediction horizon.
- Configure the LSTM layers with appropriate units, activation functions, and
regularization techniques.
5. Model Training:
- Split the dataset into training, validation, and testing sets.
- Train the LSTM model using the training data while monitoring performance on the
validation set.
- Optimize hyperparameters such as learning rate, batch size, and dropout rate to
improve model performance.
6. Model Evaluation:
- Evaluate the trained LSTM model using the testing dataset.
- Measure the performance metrics such as Mean Squared Error (MSE), Root Mean
Squared Error (RMSE), and Mean Absolute Error (MAE).
- Conduct sensitivity analysis to assess the model's robustness to variations in input

10. 7. Prediction and Analysis:
- Utilize the trained LSTM model to make predictions on unseen data.
- Visualize the predicted stock prices alongside actual prices to analyze the model's
accuracy.
- Assess the model's ability to capture trends, identify turning points, and react to
market dynamics.
8. Strategy Implementation:
- Develop trading strategies based on LSTM predictions and market insights.
- Implement risk management techniques to mitigate potential losses.
- Backtest the trading strategies on historical data to evaluate their effectiveness
and refine the approach if necessary.
9. Continuous Improvement:
- Monitor model performance over time and retrain the LSTM model periodically to
adapt to changing market conditions.
- Incorporate new data sources or features to enhance prediction accuracy and
robustness.

12. LSTM (Long Short-Term Memory)
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN)
architecture specifically designed to address the vanishing gradient problem
encountered in traditional RNNs.
Unlike standard RNNs, LSTM networks incorporate memory cells and gating
mechanisms that allow them to selectively remember or forget information over long
sequences.
The key components of an LSTM unit include:
1.Cell State: This represents the long-term memory of the network and is regulated by
gates.
2.Forget Gate: Determines which information to discard from the cell state.
3.Input Gate: Regulates the updating of the cell state with new information.
4.Output Gate: Controls the output of the LSTM unit based on the current cell state.
These gates, consisting of sigmoid and tanh activation functions, enable LSTM
networks to learn long-range dependencies in sequential data, making them
particularly effective for time-series forecasting tasks like stock market prediction.

14. THANK YOU!!