Stock Market Prediction using Machine Learning in 2025

Stock price prediction is one of the most popular use cases of machine learning today. With AI in the finance market expected to reach $50.87 billion by 2029, more people are exploring how technology can help make better investment decisions. In this article, you’ll learn stock price prediction using machine learning and deep learning. We’ll show you how to use an LSTM (Long Short-Term Memory) model to work with Google stock data and make accurate predictions.

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Importance of the Stock Market

A stock market is a public market where you can buy and sell shares of publicly listed companies. The stocks, also known as equities, represent ownership in the company. The stock exchange is the mediator that allows the buying and selling of shares. Here’s how the stock market plays a crucial role in the global economy:

• It offers investors the opportunity to buy and sell shares of publicly traded companies, contributing to wealth generation and economic growth.
• The stock market also serves as a barometer for economic health, reflecting investor sentiment and trends that can influence business strategies, government policies, and market regulations.
• It facilitates liquidity, enabling investors to easily buy and sell assets, thus fostering confidence in the financial system and supporting long-term economic stability.

Stock Price Prediction Using Machine Learning Algorithms

Stock Price Prediction using machine learning algorithm helps you discover the future value of company stock and other financial assets traded on an exchange. The entire idea of predicting stock prices is to gain significant profits. Predicting how the stock market will perform is a hard task. Other factors are involved in the prediction, such as physical and psychological factors, rational and irrational behavior, etc. All these factors contibute to make share prices dynamic and volatile. This makes it very difficult to predict stock prices with high accuracy.

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Machine Learning using PythonExplore Program

Google Stock Price Prediction Using LSTM

Let’s now walk through how to build a stock prediction using machine learning by leveraging an LSTM network to forecast stock price movements:

Step 1: Collect Historical Stock Data

To begin your prediction, you’ll want to get historical stock pricing data from trusted finance data sources such as Yahoo Finance, Alpha Vantage, or Quandl. This data set should have price information pertaining to Open price, Close price, High price, Low price and Volume. It’s reasonable to start with daily stock data for the last 5 - 10 years because that timeframe offers a sufficient time period to identify price behavior and seasonal trends.

Step 2: Select the Relevant Feature

Decide which price metric you'll use to train your model; typically, the Open or Close price is preferred. If you're interested in predicting the price at which a stock begins trading each day, stick with the Open column. Choosing the right feature ensures the model is aligned with your prediction objective.

Step 3: Prepare the Data

Once you collect your data from a reliable source, make it easy to analyze the data by creating a clean, structured, and organized data set. In order to clean the data, first, organize the data by date, remove values that are missing or corrupt, and make sure each column has the proper data type. If a row has a missing price value or a symbol, that information needs to be corrected or removed so that it does not cause problems for use during training.

Step 4: Normalize the Data

As LSTM models perform best on data that is scaled, please execute normalisation, so that all the values are in a similar range, usually between 0 and 1. If we use a technique like Min-Max Scaling, the model can learn the trend rather than the effect of numerical prices, which are many times the difference in raw prices.

Step 5: Create Training Sequences

LSTM models are based on sequences of data. You must create sliding windows of historical prices. For example, you might take the previous 60 days' prices to predict the next day, the 61st day. This method will allow your LSTM to learn how prices change over time and will create a sequential and structured training set of input-output pairs.

Step 6: Reshape the Data for LSTM

Reshuffle the data in such a way that it conforms LSTM’s input, i.e., [samples, time steps, features]. So let’s say you have created 1000 training sequences, with the time step = 60 and we have 1 feature, then our input shape will be (1000, 60,1). This structure allows the model to learn time dependencies in your data.

Step 7: Build and Train the LSTM Model

Use a deep learning library like TensorFlow or Keras to construct your model. A typical architecture includes stacked LSTM layers followed by dropout layers and a dense output layer. You'll then compile the model with an appropriate optimizer and loss function, and train it on the prepared sequences using multiple epochs to allow for deep learning.

Step 8: Testing the Model on New Data

After the model has been trained, it's time to see how the model performs on new data. You will be utilizing the last 60-day price window for future predictions. Once you feed this new sequence into the model, you will receive predictions that you can then compare against actual prices, giving you practical experience of the model's performance.

Step 9: Compare Predicted vs. Actual Prices

Plot the predicted vs actual values to visualize the model performance. One simple way to visually inspect this is to plot a line graph and see if the model accurately tracks price movement. If both of these lines move in parallel, the model is doing a good job of capturing market behavior. Any unfilled gaps should lead to model re-tuning or additional training data.

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Stock Analysis: Fundamental vs. Technical

There are two main approaches to analyzing stocks: fundamental and technical analysis. Each method offers a different way of understanding a stock's potential.

• Fundamental Analysis

This method looks at a company’s overall health to figure out its real value. It includes factors like financial reports, revenue, assets, liabilities, how good the management is, and even customer behavior.

Fundamental analysis is often used for long-term investing. It uses both past and present data to understand how strong the company is. One key point, its results usually don’t swing much based on short-term news.

• Technical Analysis

The technical analysis zooms in on the numbers, including stock prices, trading volume, and price movements. It allows traders to identify patterns and signals for making short-term decisions.

Technical analysis is more reactive to recent news and events than fundamental analysis. It uses tools like such as moving averages, support and resistance levels, and trend lines to make forecasts on the stock's potential future trajectory.