Necessary Concepts

Before implementing the Stock Price Prediction model, it is important to understand the machine learning concepts that make price forecasting possible. Unlike classification problems that predict categories, stock price prediction is generally a regression problem, where the objective is to predict a continuous numerical value.

In this project, we use the K-Nearest Neighbors (KNN) algorithm to estimate future stock closing prices based on historical trading patterns. We also explore how the same stock dataset can be converted into a simple classification problem by predicting whether a stock should be bought or sold.

Understanding these concepts will help you build the model with confidence and interpret its predictions correctly.

Machine Learning problems are generally categorized into two main types:

• Classification
• Regression

Classification predicts predefined categories such as:

• Spam or Not Spam
• Fraud or Genuine
• Buy or Sell

Regression predicts continuous numerical values such as:

• House prices
• Temperature
• Stock prices

Since this project predicts the closing price of a stock, it is considered a regression problem.

The objective is to estimate a numerical value as accurately as possible using historical stock market data.