Ai Python Tutorial

Getting Started with AI in Python: A Simple Machine Learning Example

Artificial Intelligence (AI) is a rapidly evolving field, and Python has emerged as the go-to programming language for AI development. Its simplicity, extensive library support, and strong community make it an excellent choice for beginners and experts alike. In this article, we'll walk through a simple AI example in Python: building a machine learning model to predict house prices.

Why Python for AI?

• Rich Libraries: Libraries like TensorFlow, PyTorch, and scikit-learn simplify AI tasks.
• Ease of Learning: Python’s readable syntax makes it accessible to beginners.
• Community Support: A large community ensures abundant resources and tutorials.

Setting Up Your Environment

Before we start coding, ensure you have Python installed. Then, install the necessary libraries by running:

pip install numpy pandas scikit-learn matplotlib

AI Python Code Example: Predicting House Prices

We will create a machine learning model that predicts house prices based on features like size, number of bedrooms, and age.

Step 1: Import Required Libraries

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

Step 2: Prepare the Dataset

data = {
    "Size (sq ft)": [1500, 2000, 2500, 3000, 3500],
    "Bedrooms": [3, 4, 4, 5, 5],
    "Age (years)": [10, 15, 20, 25, 30],
    "Price (USD)": [300000, 400000, 500000, 600000, 700000]
}
df = pd.DataFrame(data)
print("Dataset:")
print(df)

Step 3: Split the Data

X = df[["Size (sq ft)", "Bedrooms", "Age (years)"]]
y = df["Price (USD)"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print("Training Features:", X_train)
print("Testing Features:", X_test)

Step 4: Train the AI Model

model = LinearRegression()
model.fit(X_train, y_train)
print("Model Trained Successfully!")

Step 5: Make Predictions

predictions = model.predict(X_test)
print("Predicted Prices:", predictions)
print("Actual Prices:", y_test.values)

Step 6: Evaluate the Model

mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse:.2f}")

Step 7: Visualize the Results

plt.scatter(y_test, predictions)
plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linewidth=2)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs Predicted Prices")
plt.show()

Understanding the Output

• Dataset: A table displaying house features and their corresponding prices.
• Predictions: The model predicts house prices based on test data.
• Mean Squared Error: Indicates the error in the model’s predictions.
• Visualization: A scatter plot comparing actual and predicted prices.

Key Takeaways

This example demonstrated how to:

• Prepare a dataset using pandas.
• Split data into training and testing sets.
• Train a linear regression model using scikit-learn.
• Evaluate and visualize the model’s predictions.

Applications of AI in Python

Python-powered AI models are widely used in various domains, including:

• Predictive Modeling: Used in real estate, finance, and marketing.
• Image Recognition: Leveraging TensorFlow for image classification.
• Natural Language Processing (NLP): Using libraries like NLTK and spaCy for text analysis.
• Automation: AI-driven automation for customer support and fraud detection.

Conclusion

This tutorial introduced the fundamentals of AI using Python by building a simple machine learning model to predict house prices. With Python’s rich ecosystem of libraries, AI development is accessible even for beginners.

Now that you’ve learned the basics, you can explore more advanced AI topics, such as deep learning with TensorFlow or PyTorch. Start experimenting today and unlock the power of AI!