Table of Contents
- Introduction
- Prerequisites
- Setup
- Data Acquisition
- Data Preprocessing
- Feature Engineering
- Model Building
- Model Evaluation
- Conclusion
Introduction
In this tutorial, we will walk through the process of creating a stock price prediction model using Python. We will use historical stock price data, preprocess and engineer relevant features, and build a machine learning model to predict future stock prices. By the end of this tutorial, you will have a better understanding of how to use Python libraries and modules for data science and create practical applications using stock price prediction.
Prerequisites
To effectively follow along with this tutorial, it is recommended to have a basic understanding of Python programming and machine learning concepts. Familiarity with libraries such as Pandas, NumPy, and scikit-learn would be beneficial.
Setup
To start, make sure you have Python and the necessary libraries installed. You can use the following commands to check the versions:
shell
python --version
pip show pandas numpy scikit-learn
If any of the libraries are missing, you can install them using pip:
shell
pip install pandas numpy scikit-learn
Now that we have our environment set up, let’s proceed to the next steps.
Data Acquisition
To build a stock price prediction model, we need historical stock price data. There are several options to obtain this data, including using APIs or downloading datasets from websites. For this tutorial, we will utilize the yfinance
library, which provides an easy way to access historical stock price data directly in Python.
To install yfinance
, use the following command:
shell
pip install yfinance
Once installed, we can import the library and retrieve the historical stock price data:
```python
import yfinance as yf
# Define the stock symbol and period of interest
symbol = "AAPL"
start_date = "2010-01-01"
end_date = "2021-01-01"
# Retrieve the historical stock price data
data = yf.download(symbol, start=start_date, end=end_date)
``` ## Data Preprocessing
Now that we have our historical stock price data, let’s preprocess it to prepare it for model training. The preprocessing steps may include handling missing values, scaling the data, and splitting it into training and testing sets.
Handling Missing Values
First, we need to check if there are any missing values in the dataset and decide how to handle them. One common approach is to fill missing values with the mean or median of the respective column. For simplicity, we will use the fillna
method from the Pandas library to fill any missing values with the mean:
```python
# Check for missing values
data.isnull().sum()
# Fill missing values with the mean
data = data.fillna(data.mean())
``` ### Scaling the Data
Since stock prices can have different scales, it is important to scale the data before training the model to ensure all features contribute equally. We will use the MinMaxScaler
from the scikit-learn library to scale the data:
```python
from sklearn.preprocessing import MinMaxScaler
# Scale the data
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data)
``` ### Train-Test Split
To evaluate the performance of our model, we need to split the data into training and testing sets. We will use the first 80% of the data for training and the remaining 20% for testing: ```python # Split the data into training and testing sets train_data = scaled_data[:int(0.8len(data))] test_data = scaled_data[int(0.8len(data)):]
# Separate the input (X) and target (y) variables
X_train, y_train = train_data[:, :-1], train_data[:, -1]
X_test, y_test = test_data[:, :-1], test_data[:, -1]
``` ## Feature Engineering
To improve the performance of our stock price prediction model, we can engineer additional features based on the existing data. Feature engineering involves creating new features that capture relevant patterns or relationships in the data.
Rolling Window
One common feature in stock price prediction is the rolling window, which calculates statistics within a fixed-size window. We can create rolling window features to capture short-term trends in the stock price. Here’s an example of creating a rolling mean feature: ```python # Compute the rolling mean feature window_size = 5 rolling_mean = data[‘Close’].rolling(window=window_size).mean()
# Add the rolling mean feature to the dataset
data['Rolling Mean'] = rolling_mean
``` ## Model Building
With our preprocessed data and engineered features, we can now move on to building the stock price prediction model. For this tutorial, we will use a simple linear regression model as an example, but feel free to experiment with different models.
Linear Regression
To build a linear regression model, we can use the LinearRegression
class from the scikit-learn library:
```python
from sklearn.linear_model import LinearRegression
# Create the linear regression model
model = LinearRegression()
# Train the model
model.fit(X_train, y_train)
``` ## Model Evaluation
After training the model, it’s important to evaluate its performance to determine its accuracy and effectiveness. In this tutorial, we will use the mean squared error (MSE) as the evaluation metric: ```python from sklearn.metrics import mean_squared_error
# Make predictions
train_predictions = model.predict(X_train)
test_predictions = model.predict(X_test)
# Calculate mean squared error
train_mse = mean_squared_error(y_train, train_predictions)
test_mse = mean_squared_error(y_test, test_predictions)
print(f"Train MSE: {train_mse:.4f}")
print(f"Test MSE: {test_mse:.4f}")
``` ## Conclusion
In this tutorial, we covered the process of creating a stock price prediction model using Python. We started by acquiring historical stock price data using the yfinance
library. Then, we preprocessed the data by handling missing values and scaling the features. Next, we performed feature engineering by creating a rolling mean feature. Finally, we built a linear regression model and evaluated its performance using mean squared error.
By applying the concepts and techniques discussed in this tutorial, you can further explore stock price prediction and potentially apply more advanced machine learning algorithms for better accuracy.