Let’s talk about Microsoft stock price prediction in this Python tutorial. Microsoft is now one of the world’s top technological corporations, employing over 163,000 people globally. It is well-known for producing the Windows operating system, which is one of the most widely used computer operating systems.
This post will teach you how to forecast Microsoft stock values in the future. In this essay, I will guide you through the process of predicting Microsoft stock prices with machine learning using Python.
Importing necessary modules/libraries and the dataset
Let’s get started on the challenge of forecasting Microsoft stock prices by loading the relevant Python modules and dataset:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
plt.style.use('seaborn')
data = pd.read_csv("MSFT.csv")
data.head()
Data Visualization
The Close column in this dataset comprises the values whose future values we wish to anticipate. So, let’s take a deeper look at Microsoft’s stock price’s historical close prices:
plt.figure(figsize=(10, 4))
plt.title("Microsoft Stock Prices")
plt.xlabel("Date")
plt.ylabel("Close")
plt.plot(data["Close"])
plt.show()
Finding Co-relation between data
Let’s have a look at the correlation between the dataset’s characteristics now:
print(data.corr())
sns.heatmap(data.corr(),cmap="Greens")
plt.show()
Splitting Data into train and test data
I’ll now prepare the data for the machine learning model. In this phase, I’ll add the most significant characteristics to x and the target column to y, then divide the dataset into training and test sets:
x = data[["Open", "High", "Low"]]
y = data["Close"]
x = x.to_numpy()
y = y.to_numpy()
y = y.reshape(-1, 1)
from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)
Applying the machine learning model
Let’s now use the Decision Tree Regression Algorithm to train the Microsoft Stock Price prediction model and look at the projected stock prices for the next 5 days:
from sklearn.tree import DecisionTreeRegressor
model = DecisionTreeRegressor()
model.fit(xtrain, ytrain)
ypred = model.predict(xtest)
data = pd.DataFrame(data={"Predicted Rate": ypred})
print(data.head())
So, using the Python programming language, you can anticipate Microsoft stock values using Machine Learning. Microsoft has captured the interest of the entire globe once again since the introduction of Windows 11 is just around the horizon.
As a result, it is a fantastic moment to forecast Microsoft’s stock price because it is receiving a lot of attention.
Conclusion
Congratulations! You just learned how to predict Microsoft Stock Price. Hope you enjoyed it! 😇
Liked the tutorial? In any case, I would recommend you to have a look at the tutorials mentioned below:
- Stock Price Prediction using Python
- Crypto Price Prediction with Python
- Stock Price Prediction using Python
- Box Office Revenue Prediction in Python – An Easy Implementation
Thank you for taking your time out! Hope you learned something new!! 😄