Stock Price Prediction for Beginners using Data Science

Stock Price Prediction for Beginners using Data Science

Learn how to build a Stock Price Prediction system using Python and the K-Nearest Neighbors (KNN) algorithm. This intermediate-level handbook covers historical stock market analysis, feature engineering, GridSearchCV optimization, regression evaluation metrics, and visualization techniques to forecast stock closing prices.

Stock Price Prediction Using KNN Regression – Intermediate Data Science Project

This handbook guides learners through building a complete Stock Price Prediction system using K-Nearest Neighbors (KNN) Regression. It explains how to preprocess historical stock market data, engineer meaningful features, optimize the model using GridSearchCV, generate predictions, and evaluate performance using MAE, MSE, RMSE, and R² Score. By working with real financial data, learners gain practical experience in regression modeling and machine learning for stock market forecasting.

Stock Price Prediction Using KNN Regression – Intermediate Data Science Project

This handbook guides learners through building a complete Stock Price Prediction system using K-Nearest Neighbors (KNN) Regression. It explains how to preprocess historical stock market data, engineer meaningful features, optimize the model using GridSearchCV, generate predictions, and evaluate performance using MAE, MSE, RMSE, and R² Score. By working with real financial data, learners gain practical experience in regression modeling and machine learning for stock market forecasting.

Prerequisites

This course is suitable for:

• Basic knowledge of Python programming
• Understanding of Data Science fundamentals
• Familiarity with Machine Learning concepts
• Basic understanding of regression algorithms
• A Google account to access Google Colab
• A Kaggle account to download the stock market dataset
• Internet connection to access datasets and required Python libraries