Building a Stock Price Prediction Model with Python and the Pandas Library

Stock price prediction is a frequent use case in machine learning and data analysis. We can construct models that forecast future stock prices with fair accuracy by analysing past trends and patterns in the stock market. In this lesson, we'll look at how to use Python and the pandas package to create a stock price prediction model.

The pandas library is a popular Python data analysis package. It includes a comprehensive collection of tools for working with structured data, such as data frames and series. We'll use pandas to analyse and manipulate stock data before developing a machine learning model to forecast future stock values.

Getting Started

Before we dive into using the Pandas library, we first need to install the library using pip. However, since it does not come built−in, we must first install the Pandas library. This can be done using the pip package manager.

To install the Pandas library, open your terminal and type the following command:

pip install pandas

This will download and install the pandas library and its dependencies. Once installed, we can import pandas in our Python code using the following statement:

import pandas as pd

Collecting and Preprocessing Data

To create a stock price prediction model, we must first collect data for the stock under consideration. We can acquire data from a variety of sources, including Yahoo Finance, Alpha Vantage, and Google Finance. In this tutorial, we will collect data using Yahoo Finance.

We may use the pandas_datareader package to gather data from Yahoo Finance, which provides a straightforward interface for collecting data from multiple sources, including Yahoo Finance. Using pip, we can install pandas_datareader:

pip install pandas_datareader

Once installed, we can use the following code to collect data for a specific stock:

import pandas_datareader.data as web
start_date = '2010-01-01'
end_date = '2021-04-30'
stock_symbol = 'AAPL'

stock_data = web.DataReader(stock_symbol, 'yahoo', start_date, end_date)

We're gathering stock data for Apple Inc. (AAPL) in this code from January 1st, 2010 to April 30th, 2021. The data is obtained from Yahoo Finance using the pandas_datareader DataReader function. We can now analyse and manipulate the data using the stock_data variable.

We must first preprocess the data before we can utilise it to develop our prediction model. This includes cleaning the data, dealing with missing values, and translating the data into a format that our model can use. In this lesson, we will use the stock's closing price as our goal variable and the opening, high, low, and volume as our features.

To begin preprocessing the data, we'll construct a new DataFrame with only the required columns:

df = pd.DataFrame(data=stock_data, columns=['Open', 'High', 'Low', 'Close', 'Volume'])

We'll then handle any missing values in the data by replacing them with the previous day's value:

df.fillna(method='ffill', inplace=True)

Finally, we'll add a new column to the DataFrame that contains the percentage change in the closing price from the previous day:

df['Price_Change'] = df['Close'].pct_change()

Building the Prediction Model

After gathering and cleaning our data, we can begin creating our stock price prediction model with Python and the pandas module. Based on historical data, we will use a machine learning method called Linear Regression to forecast future stock prices.

Linear Regression is a supervised learning technique that predicts the outcome of a dependent variable using one or more independent variables. The dependent variable in our situation is the stock price, and the independent variables are the numerous attributes collected from our previous stock data.

# Split the data into training and testing sets
train_size = int(len(df) * 0.8)
train_data, test_data = df[0:train_size], df[train_size:len(df)]

Next, we need to define our dependent and independent variables. Our dependent variable is the closing stock price, while our independent variables are the various features that we have extracted from our historical data.

# Define dependent and independent variables
X_train, y_train = train_data.drop(['Close'], axis=1), train_data['Close']
X_test, y_test = test_data.drop(['Close'], axis=1), test_data['Close']

Now that we have our training and testing data, we can start building our Linear Regression model using the scikit−learn library.

# Build Linear Regression model
from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

We have now trained our Linear Regression model on the training data. Next, we can use it to predict the stock prices on the testing data and evaluate its performance using various metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).

# Make predictions on the testing data
y_pred = model.predict(X_test)

# Evaluate the performance of the model
from sklearn.metrics import mean_squared_error, r2_score

mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print('Mean Squared Error:', mse)
print('Root Mean Squared Error:', rmse)
print('R2 Score:', r2)

The preceding code will return the various performance metrics for our model. The Mean Squared Error (MSE) is a measure of the average squared difference between predicted and actual values, whereas the RMSE is the square root of the MSE. The R2 number is a metric that indicates how well the model fits the data, with a higher number suggesting a better fit.

Plotting a Graph

We can also use a line graph to compare our forecasted stock prices to the actual stock prices.

# Visualize the predicted vs actual stock prices
import matplotlib.pyplot as plt

plt.plot(y_test.index, y_test.values, label='Actual')
plt.plot(y_test.index, y_pred, label='Predicted')
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.title('Actual vs Predicted Stock Prices')
plt.legend()
plt.show()

The output of the above code will give us a line graph that shows the actual stock prices and the predicted stock prices based on our model.

Conclusion

In this tutorial, we looked at how to use Python and the pandas package to create a stock price prediction model. The pandas library is a strong tool for data manipulation and analysis, and it can be used to develop complicated machine learning models when combined with other libraries such as scikit−learn.

The stock price prediction model developed in this tutorial is just one of many data science applications in finance. The possibilities are limitless with the availability of data and the tools to analyze it.