Stock-Market-Prediction-with-Linear-Regression-For-Beginners-

Hello everyone, today I'm going to show you how to do stock market prediction using Linear Regression.

Linear Regression is a basic and popular machine learning algorithm that every beginner should understand. It's used for regression problems, where the data has a linear pattern and continuous values.

For this example, we'll use the AAPL.csv file from Kaggle, which contains historical stock price data for Apple Inc.

Here's how to do it:

1. Download the AAPL.csv file from Kaggle.
2. Import the necessary libraries: pandas, numpy, and scikit-learn.
3. Load the AAPL.csv file into a pandas DataFrame.
4. Preprocess the data: handle missing values, convert data types, etc.
5. Split the data into training and testing sets.
6. Create a Linear Regression model and train it on the training data.
7. Evaluate the model's performance on the testing data.
8. Use the model to make predictions on new, unseen data.

This is just a beginner's guide. Good luck!