# Simple Linear Regression in Python! 🚀
# Predict exam scores based on study hours!

import numpy as np
from sklearn.linear_model import LinearRegression

# Your project data:
# X (Input): Study Hours
study_hours = np.array([2, 4, 3, 5, 6, 1]).reshape(-1, 1)

# y (Output): Exam Scores
exam_scores = np.array([50, 70, 60, 80, 90, 40])

# Create and train the model
model = LinearRegression()
model.fit(study_hours, exam_scores)

# Make a prediction! 🚀
# Let's predict the score for someone who studied 4.5 hours
predicted_score = model.predict(np.array([[4.5]]))

print(f"Predicted score for 4.5 hours: {predicted_score[0]:.2f}")
# Output will be around: Predicted score for 4.5 hours: 75.00

from textblob import TextBlob

# Our text data examples
text1 = "I absolutely love learning to code, it's so much fun!"
text2 = "This bug is making me pull my hair out, so frustrating."
text3 = "The sky is blue today."

# Create TextBlob objects
blob1 = TextBlob(text1)
blob2 = TextBlob(text2)
blob3 = TextBlob(text3)

# Get sentiment (polarity ranges from -1 for negative to 1 for positive)
print(f"'{text1}' -> Polarity: {blob1.sentiment.polarity:.2f}")
print(f"'{text2}' -> Polarity: {blob2.sentiment.polarity:.2f}")
print(f"'{text3}' -> Polarity: {blob3.sentiment.polarity:.2f}")

# 🚀 Interview Tip: Explain what polarity and subjectivity mean!
# Polarity: How positive or negative the text is (-1 to 1).
# Subjectivity: How much of an opinion the text contains (0 for factual, 1 for opinionated).

# 🐍 Your First "AI" Predictor (Mini-ML Style!)

from sklearn.linear_model import LinearRegression
import numpy as np

# Imagine this is your project data: (study_hours, exam_score)
# X = input (features), y = output (target)
X = np.array([ [2], [3], [4], [5], [6] ]) # Study Hours
y = np.array([ [50], [60], [70], [80], [90] ]) # Exam Scores

# Create and 'train' your simple AI model
model = LinearRegression()
model.fit(X, y) # This is where the magic happens! ✨

# Now, predict a new student's score!
new_study_hours = np.array([[7]])
predicted_score = model.predict(new_study_hours)

print(f"🧑‍💻 If a student studies for {new_study_hours[0][0]} hours, their predicted score is: {predicted_score[0][0]:.2f}")

#💡 Real-world use: Predicting sales, analyzing trends, recommendation systems!

import numpy as np
from sklearn.linear_model import LinearRegression

# Let's predict 'study hours' vs 'exam score'! 📈
# X = hours studied (our feature)
# y = exam score (our target)
hours_studied = np.array([2, 3, 4, 5, 6]).reshape(-1, 1) 
exam_score = np.array([50, 60, 70, 80, 90])              

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

# 2. Train the model with your data
model.fit(hours_studied, exam_score)

# 3. Predict a score for 7 hours of study!
future_study = np.array([[7]])
predicted_score = model.predict(future_study)

print(f"If you study 7 hours, your predicted score is: {predicted_score[0]:.2f}!") 
# Output: If you study 7 hours, your predicted score is: 100.00!

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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