stock market prediction project powerpoint

2. • Introduction
• Objectives of the Project
• Problem Statement
• Solution Methodology
• Results
• References
Contents

3. Introduction
 A market where shares are publicly issued and
traded is known as a share market.
 Implementing the concept of predicting stock
price by using historical data, which uses
automated, pre-programmed trading models to
predict stock prices.
 Random forest model is used, and prediction of
directionality is made whether the stock price will
go up or down the next day.

4. Objectives
 The main objective of our project is to develop an Random
Regression Method as well as LSTM-based model for accurately
predicting stock prices.
 To study and improve the supervised learning algorithms to
predict the stock price.
 The technical objectives will be implemented in Python.
 The system must be able to access a list of historical prices.
 It must calculate the estimated price of stock based on the
historical data.

5. Problem Statement
 Stock markets are known for their inherent volatility and
uncertainty, making accurate price prediction a challenging task.
Also, the stock prices are influenced by various external factors,
including economic indicators, news, and global events, making it
difficult to predict their movements solely based on historical data.
 Stock markets are subject to dynamic changes and trends. The
model needs to adapt to evolving market conditions. Dealing with
large volumes of noisy and unstructured historical stock price data
can pose data preprocessing challenges that affect the quality of
predictions

7. Data Collection
 Data Source: We collected historical stock price data from Yahoo
Finance Package of Python.
 Data Frequency: We gathered all the S&P data from 1990 to present
day from yfinance(yahoo finance).
 Data Retrieval Method: We retrieved the data using Python libraries
such as pandas and yfinance to fetch daily stock price information.
 We performed data validation checks and removed outliers to ensure the
accuracy and integrity of the dataset.

8. Module
Random Forest
• Random Forest is an ensemble learning technique that combines
the predictions of multiple decision trees that can help capture
complex patterns and reduce overfitting. This ensemble approach
often results in more accurate predictions than individual models.
• Stock price data can be noisy, and outliers may occur due to
sudden market events or errors in data collection.
• Random Forest can be parallelized, allowing it to take advantage of
multi-core processors and efficiently handle large datasets. This can
be important when working with extensive historical stock price data.

9. Diagram

10. Result :

11. References
 Wikipedia
 Google Search
 Chat GPT
 Yahoo finance