import numpy as np
from sklearn.linear_model import LinearRegression

# Imagine this is your project data:
# 'Size' of house (sqft) vs 'Price' (in thousands)
X = np.array([500, 700, 900, 1100, 1300, 1500]).reshape(-1, 1)
y = np.array([150, 180, 200, 230, 250, 280])

# 🚀 STEP 1: Create the model
model = LinearRegression()

# ⚙️ STEP 2: Train the model (the AI learns patterns here!)
model.fit(X, y)

# 🔮 STEP 3: Make a prediction!
new_house_size = np.array([[1000]]) # A 1000 sqft house
predicted_price = model.predict(new_house_size)

print(f"Predicted price for a {new_house_size[0][0]} sqft house: ${predicted_price[0]:.2f}k")
# Output: Predicted price for a 1000 sqft house: $215.00k (approx)

import numpy as np
from sklearn.linear_model import LinearRegression

# Let's predict exam scores based on hours studied!
# X = Hours Studied (Our feature)
# y = Exam Score (What we want to predict)
X = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).reshape(-1, 1)
y = np.array([55, 60, 65, 70, 75, 80, 85, 90, 95])

# 1. Initialize the Linear Regression model
model = LinearRegression()

# 2. Train the model (it learns the relationship between X and y)
model.fit(X, y)

# 3. Make a prediction!
# What score would someone get if they studied 7.5 hours?
predicted_score = model.predict(np.array([[7.5]]))

print(f"If you study 7.5 hours, your predicted score is: {predicted_score[0]:.2f}")
# Output: If you study 7.5 hours, your predicted score is: 82.50

# ✨ Your AI Project Power-Up! ✨
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

# Sample data (your project's text and categories)
texts = [
    "This movie was fantastic, highly recommend!",
    "Terrible service, wasted my money.",
    "The product works perfectly.",
    "Customer support was unhelpful and rude.",
    "Absolutely loved the experience!"
]
labels = ["positive", "negative", "positive", "negative", "positive"]

# Create a simple text classification pipeline
# TfidfVectorizer converts text to numbers
# LogisticRegression is our classification model
model = make_pipeline(TfidfVectorizer(), LogisticRegression())

# Train your model with your data
model.fit(texts, labels)

# Make a prediction on new text!
new_review = ["This is the worst thing I've ever seen."]
prediction = model.predict(new_review)

print(f"The predicted sentiment is: {prediction[0]}")
# Output for new_review: The predicted sentiment is: negative

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from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 1. Load the dataset
data = load_iris()
X, y = data.data, data.target

# 2. Split into Training data (80%) and Test data (20%)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 3. Initialize the ML model (Random Forest)
model = RandomForestClassifier()

# 4. Train the model
model.fit(X_train, y_train)

# 5. Predict and check accuracy
predictions = model.predict(X_test)
print(f"Model Accuracy: {accuracy_score(y_test, predictions) * 100:.2f}%")