🧠 Build a Stock Price Predictor Using Python in 10 Easy Steps

Have you ever wondered if you could predict the stock market using Python? While we can’t promise you’ll become the next Wall Street guru, building a stock price predictor is a great way to sharpen your data science and Python skills.

In this post, you’ll learn how to use real stock data, clean it, train a simple machine learning model, and predict future prices — all using Python.

🚀 What You'll Build

We’ll create a simple app that:

Pulls real-time stock data (e.g., Apple)
Trains a Linear Regression model
Predicts the stock’s price for the next 30 days
Plots the results

🧰 Tools You’ll Need

Install these libraries if you haven’t yet:

bashCopyEditpip install yfinance pandas numpy scikit-learn matplotlib seaborn

🔟 Step-by-Step Guide

1. 📦 Import Libraries

Start by importing the necessary packages:

pythonCopyEditimport yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

2. 📈 Download Stock Data

We’ll use Apple (AAPL) stock for this example:

pythonCopyEditstock_symbol = 'AAPL'
data = yf.download(stock_symbol, start="2018-01-01", end="2024-12-31")
data = data[['Close']]  # Use only the 'Close' price

3. 🔮 Add a Prediction Column

Let’s predict the closing price 30 days into the future:

pythonCopyEditfuture_days = 30
data['Prediction'] = data[['Close']].shift(-future_days)

4. 🎯 Prepare the Data

Separate the data into features and labels:

pythonCopyEditX = np.array(data.drop(['Prediction'], axis=1))[:-future_days]
y = np.array(data['Prediction'])[:-future_days]

5. 🧪 Train/Test Split

Split the data for training and testing:

pythonCopyEditX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

6. 🧠 Train the Model

Now let’s fit our model:

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

7. 📊 Make Predictions

Test it on unseen data:

pythonCopyEditpredictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse}")

8. 📅 Predict Future Prices

Use the last 30 days to predict the next 30:

pythonCopyEditx_future = data.drop(['Prediction'], axis=1)[-future_days:]
future_pred = model.predict(x_future)

9. 🖼️ Visualize the Prediction

Let’s see the results:

pythonCopyEditplt.figure(figsize=(14,6))
plt.plot(data['Close'], label='Original Price')
plt.plot(data.index[-future_days:], future_pred, label='Predicted Price (Next 30 Days)', linestyle='--')
plt.title(f"{stock_symbol} Stock Price Prediction")
plt.xlabel("Date")
plt.ylabel("Price (USD)")
plt.legend()
plt.grid()
plt.show()

📈 Output

You'll see a graph that shows:

The historical closing prices
The predicted prices for the next 30 days

🧠 What You Learned

How to fetch real stock data with yfinance
How to prepare and clean data for ML
Basics of Linear Regression
How to plot predictions using matplotlib

💡 Bonus: Take It Further

Use more features like Open, High, Volume
Try advanced models like LSTM or Random Forest
Add a GUI using Streamlit

🔚 Conclusion

Predicting stock prices isn't easy — but understanding the process using Python gives you a strong foundation in both data science and finance. Try tweaking this model, test different stocks, and explore deeper insights.

Stock Price Predictor using python