Introduction to Machine Learning with Python

Machine Learning (ML) is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Python is a popular language for ML due to its simplicity, vast ecosystem of libraries, and strong community support.

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Types of Machine Learning

1. Supervised Learning – The model is trained on labeled data (e.g., predicting stock prices based on past trends).
2. Unsupervised Learning – The model identifies patterns in unlabeled data (e.g., customer segmentation).
3. Reinforcement Learning – The model learns by interacting with an environment and receiving rewards (e.g., training a self-driving car).

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Example: Predicting House Prices using Linear Regression

Let’s implement a simple machine learning model using Python and Scikit-Learn.

Step 1: Install Required Libraries

pip install numpy pandas scikit-learn matplotlib

Step 2: Import Required Libraries

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

Step 3: Load Sample Data

We’ll use a simple dataset where square_feet is the feature (input) and price is the target (output).

# Sample dataset
data = {
    "square_feet": [650, 700, 720, 800, 850, 900, 950, 1000, 1100, 1200],
    "price": [70000, 75000, 76000, 85000, 88000, 92000, 98000, 105000, 115000, 125000]
}

df = pd.DataFrame(data)

# Split into features (X) and target (y)
X = df[["square_feet"]]
y = df["price"]

# Split into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 4: Train a Linear Regression Model

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

Step 5: Evaluate the Model

# Calculate errors
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)

print(f"Mean Absolute Error: {mae}")
print(f"Mean Squared Error: {mse}")

Step 6: Visualizing the Results

# Plot training data
plt.scatter(X_train, y_train, color="blue", label="Training Data")
# Plot test data
plt.scatter(X_test, y_test, color="red", label="Test Data")
# Plot regression line
plt.plot(X, model.predict(X), color="green", linestyle="dashed", label="Prediction Line")
plt.xlabel("Square Feet")
plt.ylabel("Price")
plt.legend()
plt.show()

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Conclusion

This was a basic introduction to Machine Learning with Python using a Linear Regression model. You can extend this by:

• Using larger datasets
• Experimenting with different ML algorithms
• Applying feature engineering techniques