Feature Engineering for ML

Hello and welcome! In today's lesson, we will explore Feature Engineering for Machine Learning using Tesla ($TSLA) stock data. Feature engineering is a crucial step in preparing financial data to build effective machine-learning models. We'll go through how to create new features from existing stock data using Python and Pandas to enhance our predictive capabilities.

Feature engineering involves creating new input variables (features) from raw data to improve the performance of machine learning models. This process is especially vital in financial markets, where capturing the correct patterns and relationships can significantly impact trading decisions.

For example, features like price differences, volatility, and moving averages can reveal underlying patterns in stock movements, aiding in more accurate predictions.

By the end of this lesson, you will understand how to generate meaningful features from stock data, setting a strong foundation for more advanced machine-learning techniques in trading.

First, let's load our Tesla stock dataset using Pandas. Using Pandas is essential for handling financial data efficiently, enabling us to load, manipulate, and analyze large datasets with ease.

We'll be using the datasets library to import our dataset. Here's how you can load the dataset and convert it into a Pandas DataFrame:

import pandas as pd
import datasets

# Load the dataset
data = datasets.load_dataset('codesignal/tsla-historic-prices')
tesla_df = pd.DataFrame(data['train'])
print(tesla_df.head())

Executing the above code will load the Tesla stock data into a DataFrame called tesla_df. The DataFrame looks like this:

         Date      Open      High       Low     Close  Adj Close     Volume
0  2010-06-29  1.266667  1.666667  1.169333  1.592667   1.592667  281494500
1  2010-06-30  1.719333  2.028000  1.553333  1.588667   1.588667  257806500
2  2010-07-01  1.666667  1.728000  1.351333  1.464000   1.464000  123282000
3  2010-07-02  1.533333  1.540000  1.247333  1.280000   1.280000   77097000
4  2010-07-06  1.333333  1.333333  1.055333  1.074000   1.074000  103003500

Our dataset includes columns like 'Open', 'High', 'Low', and 'Close', representing the stock's opening, highest, lowest, and closing prices for each day. Here’s a brief description:

• Open: The price at which the stock opened.
• High: The highest price reached during the trading day.
• Low: The lowest price reached during the trading day.
• Close: The price at which the stock closed.