Stock Price Prediction PPT
- 1. STOCK PRICE FORECASTING USING LSTM ARTIFICIAL INTELLIGENCE
- 2. INTRODUCTION A market where shares are publicly issued and traded is known as a share market. Implementing the concept of algorithmic trading, which uses automated, pre-programmed trading strategies to predict stock prices. Time series forecasting (predicting future values based on historical values) applies well to stock forecasting. Developed a User Interface
- 3. FUTURE WORK • Machine learning and Data science is a game changer in this domain so there is a lot of data to find patterns in for predicting with high degree of accuracy. • In future we’ll try to predict the values based on multiple factors such as politics, global economic conditions, unexpected events like covid, companies financial performance, and so on. • We are going to implement multiple types of algorithms because different types of data requires different types of techniques. • Decided to implement a simple User Interface to operate this whole process for users so to make people engage in Stock market.
- 4. STEPS PERFORMED 1. Importing and Cleaning data 2. Split the Data into training / test sets 3. Creating and Training the Model 4. Making Predictions 5. Evaluating and Improving Predictions
- 5. Need of Project The stock market is known for being volatile, dynamic, & nonlinear Accurate stock price prediction is extremely challenging because of multiple factors. But, all of this also means that there’s a lot of data to find patterns in. So, we keep exploring analytics techniques to detect stock market trends. So, they can be analyzed as a sequence of discrete-time data Despite the volatility, stock prices aren’t just randomly generated numbers.
- 6. Python Language Python is a rich language for Data Science and AI Algorithm Long Short Term Memory ( LSTM ) Libraries Pandas, Numpy, Sklearn, Tensorflow, etc Streamlit UI Provided User Interface using Streamlit P A L S METHODOLOGY