Pre-requisites & Tech Stack Used

Before developing a stock price prediction model, it is important to prepare the working environment and understand the technologies used throughout the project. Stock market datasets contain historical financial information that must be cleaned, analyzed, and transformed before a machine learning model can learn meaningful patterns.

In this project, we will use Google Colab as the development environment and implement the K-Nearest Neighbors (KNN) algorithm to predict stock closing prices. Along with Python libraries for data analysis and visualization, we will also use GridSearchCV to optimize the model by selecting the most suitable value of K.

By the end of this module, you will understand the project requirements, dataset structure, technologies involved, and the complete workflow followed to build the stock price prediction model.

Before starting this project, ensure that you have the following:

Google Colab

Google Colab provides a cloud-based environment where Python notebooks can be created and executed without installing software locally.

Google Account

A Google account is required to access Google Colab and save notebooks to Google Drive.

Stock Market Dataset

A historical stock market dataset containing daily trading information is required for model training and prediction.

Basic Python Knowledge

Learners should be familiar with:

• Variables
• Loops
• Functions
• Conditional statements
• Python syntax

Basic Machine Learning Knowledge

A basic understanding of the following concepts is recommended:

• Regression
• Features and target variables
• Training and testing datasets
• Model evaluation

Since this project uses Google Colab, no local installation of Python or additional software is required.