AI & Machine Learning

# Simple house price predictor using machine learning
import pandas as pd
from sklearn.linear_model import LinearRegression

# Sample house data (size in sq ft, price in thousands)
house_data = {
    'size': [1000, 1500, 2000, 2500, 3000],
    'bedrooms': [2, 3, 3, 4, 4],
    'price': [200, 300, 400, 500, 600]
}

# Create DataFrame
df = pd.DataFrame(house_data)
print("🏠 House Data:")
print(df)

# Prepare the data
X = df[['size', 'bedrooms']]  # Features (input)
y = df['price']               # Target (output)

# Create and train the AI model
ai_model = LinearRegression()
ai_model.fit(X, y)

# Make a prediction for a new house
new_house = [[1800, 3]]  # 1800 sq ft, 3 bedrooms
predicted_price = ai_model.predict(new_house)

print(f"\n🤖 AI Prediction:")
print(f"A 1800 sq ft house with 3 bedrooms costs: ${predicted_price[0]:.0f}k")

# Test the model accuracy
predictions = ai_model.predict(X)
print(f"\n📊 Model Accuracy Score:")
print(f"R² Score: {ai_model.score(X, y):.3f}")

# Fun fact about the model
print(f"\n✨ The AI learned that:")
print(f"- Each sq ft adds ${ai_model.coef_[0]:.2f}k to price")
print(f"- Each bedroom adds ${ai_model.coef_[1]:.2f}k to price")

# Simple image classifier for cats vs dogs
import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np

# Create a simple neural network for image classification
def create_pet_classifier():
    model = models.Sequential([
        # Input layer - expects 64x64 RGB images
        layers.Reshape((64, 64, 3), input_shape=(64, 64, 3)),
        
        # Feature extraction layers
        layers.Conv2D(32, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        
        # Classification layers
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dropout(0.5),  # Prevents overfitting
        layers.Dense(1, activation='sigmoid')  # Output: cat(0) or dog(1)
    ])
    
    # Configure the learning process
    model.compile(
        optimizer='adam',
        loss='binary_crossentropy',
        metrics=['accuracy']
    )
    
    return model

# Create the AI model
pet_ai = create_pet_classifier()

# Display model structure
print("🧠 Pet Classifier AI Model:")
pet_ai.summary()

# Function to make predictions
def predict_pet(image_array):
    """
    Predicts if an image contains a cat or dog
    Returns: probability and prediction
    """
    # Reshape for prediction
    image_input = np.expand_dims(image_array, axis=0)
    
    # Get prediction
    prediction = pet_ai.predict(image_input)
    confidence = prediction[0][0]
    
    if confidence > 0.5:
        return f"🐶 Dog (confidence: {confidence:.1%})"
    else:
        return f"🐱 Cat (confidence: {1-confidence:.1%})"

print("\n🎯 Model ready for training!")
print("📸 Feed it thousands of cat and dog images to teach it!")
print("🚀 After training, it can identify pets in new photos!")

# Simple AI chatbot using natural language processing
import random
import re

class SmartChatbot:
    def __init__(self):
        self.name = "AI Assistant"
        self.responses = {
            'greeting': [
                "Hello! How can I help you today? 😊",
                "Hi there! What would you like to know? 🤖",
                "Greetings! I'm your AI assistant!"
            ],
            'goodbye': [
                "Goodbye! Have a great day! 👋",
                "See you later! Keep learning! 🚀",
                "Bye! Remember, AI is everywhere! 🌟"
            ],
            'ai_question': [
                "AI is like giving computers super-smart brains! 🧠",
                "AI helps computers learn patterns, just like humans do! 📚",
                "Think of AI as digital wizards that solve problems! 🧙‍♂️"
            ],
            'learning': [
                "Machine learning is like teaching computers through examples! 📖",
                "The more data we show AI, the smarter it becomes! 📊",
                "AI learns by finding patterns in lots and lots of data! 🔍"
            ],
            'default': [
                "That's interesting! Tell me more! 🤔",
                "I'm still learning about that topic! 🌱",
                "Can you ask me about AI or machine learning? 🤖"
            ]
        }
    
    def analyze_message(self, message):
        """Analyze user message and determine response type"""
        message = message.lower()
        
        # Check for greeting patterns
        if re.search(r'\b(hi|hello|hey|greetings)\b', message):
            return 'greeting'
        
        # Check for goodbye patterns
        if re.search(r'\b(bye|goodbye|see you|farewell)\b', message):
            return 'goodbye'
        
        # Check for AI-related questions
        if re.search(r'\b(ai|artificial intelligence|what.*ai)\b', message):
            return 'ai_question'
        
        # Check for learning questions
        if re.search(r'\b(learn|learning|machine learning|ml)\b', message):
            return 'learning'
        
        return 'default'
    
    def respond(self, user_message):
        """Generate appropriate response based on user input"""
        response_type = self.analyze_message(user_message)
        responses = self.responses[response_type]
        return random.choice(responses)

# Create and test the chatbot
chatbot = SmartChatbot()

print("🤖 AI Chatbot Started!")
print("💬 Type 'quit' to exit\n")

# Example conversation
sample_messages = [
    "Hello there!",
    "What is artificial intelligence?",
    "How does machine learning work?",
    "That's amazing!",
    "Goodbye!"
]

print("📝 Sample Conversation:")
for message in sample_messages:
    response = chatbot.respond(message)
    print(f"👤 User: {message}")
    print(f"🤖 AI: {response}\n")

print("✨ This chatbot uses NLP patterns to understand and respond!")
print("🚀 Real chatbots use much more advanced AI techniques!")