# Install required packages for data analytics
import piplite
await piplite.install(['seaborn', 'matplotlib', 'pandas', 'numpy', 'scipy', 'plotly'])
print("Packages installed successfully!")
print("You can now import and use: seaborn, matplotlib, pandas, numpy, scipy, plotly")

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Enhanced student dataset with more variables for correlation analysis
student_data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eva', 'Frank', 'Grace', 'Henry'],
    'Age': [16, 17, 16, 18, 17, 16, 17, 18],
    'Grade': ['10th', '11th', '10th', '12th', '11th', '10th', '11th', '12th'],
    'Math_Score': [85, 92, 78, 95, 88, 76, 91, 87],
    'Science_Score': [89, 87, 82, 98, 85, 79, 93, 89],
    'English_Score': [92, 85, 88, 91, 94, 83, 89, 86],
    'Hours_Studied': [5, 8, 4, 10, 6, 3, 9, 7],
    'Extracurriculars': [2, 1, 3, 2, 4, 1, 2, 3],
    'Sleep_Hours': [7, 6, 8, 6, 7, 9, 6, 7],         # New: hours of sleep per night
    'Screen_Time': [4, 6, 3, 2, 5, 7, 3, 4],          # New: recreational screen hours per day
    'Books_Read': [12, 8, 15, 20, 10, 5, 18, 14]      # New: books read per year
}

df = pd.DataFrame(student_data)
df['Average_Score'] = df[['Math_Score', 'Science_Score', 'English_Score']].mean(axis=1)

print("Enhanced Student Dataset for Correlation Analysis:")
print(df[['Name', 'Hours_Studied', 'Average_Score', 'Sleep_Hours', 'Screen_Time', 'Books_Read']])

print("UNDERSTANDING CORRELATION COEFFICIENTS:")
print("┌─────────────────┬────────────────────────────────────┐")
print("│ Correlation     │ What it means                      │")
print("├─────────────────┼────────────────────────────────────┤")
print("│ +1.0            │ Perfect positive relationship      │")
print("│ +0.7 to +0.9    │ Strong positive relationship       │")
print("│ +0.3 to +0.7    │ Moderate positive relationship     │") 
print("│ +0.1 to +0.3    │ Weak positive relationship         │")
print("│ 0.0             │ No relationship                    │")
print("│ -0.1 to -0.3    │ Weak negative relationship         │")
print("│ -0.3 to -0.7    │ Moderate negative relationship     │")
print("│ -0.7 to -0.9    │ Strong negative relationship       │")
print("│ -1.0            │ Perfect negative relationship      │")
print("└─────────────────┴────────────────────────────────────┘")

print("\nPositive correlation: As one goes up, the other goes up")
print("Negative correlation: As one goes up, the other goes down")
print("No correlation: The variables are independent of each other")

# Do students who study more get better grades?
study_grade_corr = df['Hours_Studied'].corr(df['Average_Score'])

print("RELATIONSHIP: Study Hours vs. Average Score")
print(f"Correlation coefficient: {study_grade_corr:.3f}")

# Interpret the correlation
if study_grade_corr > 0.7:
    interpretation = "Strong positive - More study time strongly linked to better grades!"
elif study_grade_corr > 0.3:
    interpretation = "Moderate positive - More study time somewhat linked to better grades"
elif study_grade_corr > 0.1:
    interpretation = "Weak positive - Slight tendency for more study to help grades"
elif study_grade_corr > -0.1:
    interpretation = "No clear relationship between study time and grades"
else:
    interpretation = "Negative relationship - This would be surprising!"

print(f"Interpretation: {interpretation}")

# Create a correlation matrix for all numeric variables
numeric_columns = ['Age', 'Math_Score', 'Science_Score', 'English_Score', 
                  'Hours_Studied', 'Extracurriculars', 'Sleep_Hours', 
                  'Screen_Time', 'Books_Read', 'Average_Score']

correlation_matrix = df[numeric_columns].corr()

print("CORRELATION MATRIX:")
print("(Each number shows correlation between row and column variables)")
print(correlation_matrix.round(3))

# Create a heatmap to visualize the correlation matrix
plt.figure(figsize=(12, 10))
mask = np.triu(np.ones_like(correlation_matrix))  # Hide upper triangle (it's redundant)

# Create the heatmap
sns.heatmap(correlation_matrix, 
            annot=True,          # Show correlation numbers
            cmap='RdBu_r',       # Red-Blue color scheme
            center=0,            # Center the color map at 0
            mask=mask,           # Apply the mask
            square=True,         # Make cells square
            fmt='.2f',           # Format numbers to 2 decimal places
            cbar_kws={'label': 'Correlation Coefficient'})

plt.title('Student Data Correlation Matrix', fontsize=16, fontweight='bold', pad=20)
plt.tight_layout()
plt.show()

# Create subplots showing our key correlations
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
fig.suptitle('Key Correlation Findings', fontsize=16, fontweight='bold')

# 1. Study Hours vs Performance
axes[0, 0].scatter(df['Hours_Studied'], df['Average_Score'], color='blue', s=100, alpha=0.7)
z1 = np.polyfit(df['Hours_Studied'], df['Average_Score'], 1)
p1 = np.poly1d(z1)
axes[0, 0].plot(df['Hours_Studied'], p1(df['Hours_Studied']), "r--", alpha=0.8)
corr1 = df['Hours_Studied'].corr(df['Average_Score'])
axes[0, 0].set_title(f'Study Hours vs Performance\n(r = {corr1:.3f})')
axes[0, 0].set_xlabel('Hours Studied')
axes[0, 0].set_ylabel('Average Score')

# 2. Sleep vs Performance  
axes[0, 1].scatter(df['Sleep_Hours'], df['Average_Score'], color='green', s=100, alpha=0.7)
z2 = np.polyfit(df['Sleep_Hours'], df['Average_Score'], 1)
p2 = np.poly1d(z2)
axes[0, 1].plot(df['Sleep_Hours'], p2(df['Sleep_Hours']), "r--", alpha=0.8)
corr2 = df['Sleep_Hours'].corr(df['Average_Score'])
axes[0, 1].set_title(f'Sleep Hours vs Performance\n(r = {corr2:.3f})')
axes[0, 1].set_xlabel('Sleep Hours')
axes[0, 1].set_ylabel('Average Score')

# 3. Screen Time vs Performance
axes[1, 0].scatter(df['Screen_Time'], df['Average_Score'], color='red', s=100, alpha=0.7)
z3 = np.polyfit(df['Screen_Time'], df['Average_Score'], 1)
p3 = np.poly1d(z3)
axes[1, 0].plot(df['Screen_Time'], p3(df['Screen_Time']), "r--", alpha=0.8)
corr3 = df['Screen_Time'].corr(df['Average_Score'])
axes[1, 0].set_title(f'Screen Time vs Performance\n(r = {corr3:.3f})')
axes[1, 0].set_xlabel('Screen Hours')
axes[1, 0].set_ylabel('Average Score')

# 4. Books vs English
axes[1, 1].scatter(df['Books_Read'], df['English_Score'], color='purple', s=100, alpha=0.7)
z4 = np.polyfit(df['Books_Read'], df['English_Score'], 1)
p4 = np.poly1d(z4)
axes[1, 1].plot(df['Books_Read'], p4(df['Books_Read']), "r--", alpha=0.8)
corr4 = df['Books_Read'].corr(df['English_Score'])
axes[1, 1].set_title(f'Books Read vs English Score\n(r = {corr4:.3f})')
axes[1, 1].set_xlabel('Books per Year')
axes[1, 1].set_ylabel('English Score')

plt.tight_layout()
plt.show()

print("⚠️  IMPORTANT: CORRELATION ≠ CAUSATION ⚠️")
print("="*50)
print()
print("Just because two things are correlated doesn't mean one CAUSES the other!")
print()
print("Examples from our data:")

# Find a strong correlation
books_english_corr = df['Books_Read'].corr(df['English_Score'])
print(f"Books Read ↔ English Score: {books_english_corr:.3f}")
print()
print("Possible explanations:")
print("1. 📚 Reading more books improves English skills (books → English)")
print("2. 🎓 Students good at English enjoy reading more (English → books)")  
print("3. 👨‍👩‍👧‍👦 Family values education (causes both reading AND good English)")
print("4. 💰 Family income affects both book access AND tutoring")
print()

print("How to think about correlation:")
print("• Correlation suggests a relationship exists")
print("• It helps us ask better questions")  
print("• We need experiments or more data to prove causation")
print("• Always consider alternative explanations")