Step-by-Step Implementation

In this module, we will implement the Stock Price Prediction project using Python in Google Colab. The implementation follows a structured machine learning workflow, beginning with loading the historical stock market dataset and ending with evaluating the K-Nearest Neighbors (KNN) regression model. Along the way, we will inspect the dataset, create meaningful features, split the data into training and testing sets, optimize the model using GridSearchCV, generate predictions, and evaluate its performance using regression metrics.

Each lesson explains both how and why each step is performed, helping learners understand the complete workflow behind stock price prediction.

Before building the prediction model, prepare the Google Colab environment.

If your dataset is stored locally, upload it using the following code.

Code

from google.colab import files

uploaded = files.upload()

Once uploaded, the dataset becomes available inside the Colab notebook.