Final Project Guide

Orange Juice Pricing Analytics Agent

Author

CAIO Executive Program

Published

December 18, 2025

1 Overview

In this project, you will build an AI agent that helps a retail pricing analyst make decisions about orange juice pricing and promotions. The agent will:

Load and explore sales data
Build a regression model to predict sales
Answer business questions using natural language

Time Required: ~2 hours

Prerequisites:

Cursor IDE installed (see Cursor Setup Guide)
Basic familiarity with Cursor from Zoom Session 1
Download the project template to get started quickly

2 Part 1: Project Setup

2.1 Step 1.1: Create Your Project Folder

Open Cursor IDE
Click File → Open Folder
Create a new folder called oj-pricing-agent on your computer
Select that folder to open it in Cursor

2.2 Step 1.2: Create the Main Python File

In the Cursor sidebar, right-click and select New File
Name it oj_agent.py
You’ll see an empty file open in the editor

2.3 Step 1.3: Copy the Dataset

Download the oj_data.csv file and copy it into your oj-pricing-agent folder.

3 Part 2: Data Loading and Exploration

3.1 Step 2.1: Load the Required Libraries

In your oj_agent.py file, start by adding these lines at the top:

# Required libraries
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')

What this does: These libraries help us work with data (pandas), do math (numpy), and build models (sklearn).

3.2 Step 2.2: Load the Data

Add the following code to load the orange juice sales data:

# Load the orange juice dataset
print("Loading data...")
df = pd.read_csv('oj_data.csv')

# Display basic information
print(f"Dataset has {len(df)} rows and {len(df.columns)} columns")
print(f"\nColumns: {list(df.columns)}")
print(f"\nBrands in dataset: {df['brand'].unique()}")
print(f"\nPrice range: ${df['price'].min():.2f} - ${df['price'].max():.2f}")
print(f"\nSample of data:")
print(df.head())

3.3 Step 2.3: Run Your Code (First Test)

Save the file (Ctrl+S or Cmd+S)
Open the terminal in Cursor: View → Terminal
Run the script:

python oj_agent.py

You should see output showing:

The dataset has ~28,000 rows
Three brands: Tropicana, Minute Maid, Dominick’s
Price ranges from about $1 to $4

Troubleshooting: If you get an error about missing packages, run:

pip install pandas numpy scikit-learn

4 Part 3: Building the Regression Model

4.1 Step 3.1: Understanding the Model

We’re building a model that predicts log of sales volume based on:

Price: Higher price → lower sales (negative relationship)
Featured (feat): If the product is in the weekly ad circular (1 = yes, 0 = no)
Brand: Different brands have different base sales levels
Price × Brand interaction: Price sensitivity varies by brand

4.2 Step 3.2: Prepare the Data for Modeling

Add this code to prepare features for the model:

# ============================================
# PART 3: BUILD THE REGRESSION MODEL
# ============================================

print("\n" + "="*50)
print("Building the pricing model...")
print("="*50)

# Create dummy variables for brand (one-hot encoding)
# This converts 'brand' text into numbers the model can use
brand_dummies = pd.get_dummies(df['brand'], prefix='brand', drop_first=False)

# Create the feature matrix
# We include: price, feat, brand dummies, and price*brand interactions
X = pd.DataFrame({
    'price': df['price'],
    'feat': df['feat'],
    'brand_minute.maid': brand_dummies['brand_minute.maid'],
    'brand_tropicana': brand_dummies['brand_tropicana'],
    # Interaction terms: price effect varies by brand
    'price_x_minute.maid': df['price'] * brand_dummies['brand_minute.maid'],
    'price_x_tropicana': df['price'] * brand_dummies['brand_tropicana']
})

# Target variable: log of sales (logmove)
y = df['logmove']

print(f"Features: {list(X.columns)}")
print(f"Target: logmove (log of sales volume)")

4.3 Step 3.3: Fit the Model

Add code to train the regression model:

# Fit the linear regression model
model = LinearRegression()
model.fit(X, y)

# Display the coefficients
print("\nModel Coefficients:")
print("-" * 40)
for feature, coef in zip(X.columns, model.coef_):
    print(f"  {feature}: {coef:.4f}")
print(f"  intercept: {model.intercept_:.4f}")

# Calculate R-squared (how well the model fits)
r_squared = model.score(X, y)
print(f"\nModel R-squared: {r_squared:.3f}")
print("(This means the model explains {:.1f}% of sales variation)".format(r_squared * 100))

4.4 Step 3.4: Run and Verify the Model

Save and run the script again. You should see coefficients like:

price: negative (higher price = lower sales)
feat: positive (being featured increases sales)
brand coefficients: capture baseline differences between brands
interaction terms: show how price sensitivity differs by brand

5 Part 4: Creating Helper Functions for the Agent

5.1 Step 4.1: Add Prediction Functions

Add these functions that the agent will use to answer questions:

# ============================================
# PART 4: HELPER FUNCTIONS FOR THE AGENT
# ============================================

def predict_sales(brand, price, featured=0):
    """
    Predict sales volume for a given brand, price, and feature status.
    
    Args:
        brand: 'tropicana', 'minute.maid', or 'dominicks'
        price: price in dollars (e.g., 2.50)
        featured: 1 if in ad circular, 0 if not
    
    Returns:
        Predicted sales volume (not log-transformed)
    """
    # Create feature vector
    features = {
        'price': price,
        'feat': featured,
        'brand_minute.maid': 1 if brand.lower() == 'minute.maid' else 0,
        'brand_tropicana': 1 if brand.lower() == 'tropicana' else 0,
        'price_x_minute.maid': price if brand.lower() == 'minute.maid' else 0,
        'price_x_tropicana': price if brand.lower() == 'tropicana' else 0
    }
    
    # Convert to dataframe for prediction
    X_pred = pd.DataFrame([features])
    
    # Predict log sales, then convert back
    log_sales = model.predict(X_pred)[0]
    sales = np.exp(log_sales)
    
    return sales


def get_price_elasticity(brand):
    """
    Calculate the price elasticity for a given brand.
    
    Price elasticity tells us: if price increases by 1%, 
    how much does quantity demanded change (in %)?
    
    A more negative number means more price-sensitive.
    """
    # Base price coefficient
    base_coef = model.coef_[0]  # price coefficient
    
    # Add brand-specific interaction if applicable
    if brand.lower() == 'minute.maid':
        interaction_coef = model.coef_[4]  # price_x_minute.maid
    elif brand.lower() == 'tropicana':
        interaction_coef = model.coef_[5]  # price_x_tropicana
    else:  # dominicks (base case)
        interaction_coef = 0
    
    total_elasticity = base_coef + interaction_coef
    return total_elasticity


def get_advertising_lift(brand):
    """
    Calculate the sales lift from being featured in advertising.
    Returns the percentage increase in sales.
    """
    # The 'feat' coefficient tells us the log-sales increase
    feat_coef = model.coef_[1]  # feat coefficient
    
    # Convert from log to percentage change
    percentage_lift = (np.exp(feat_coef) - 1) * 100
    return percentage_lift


def find_optimal_price(brand, min_price=1.0, max_price=4.0, featured=0):
    """
    Find the price that maximizes revenue for a brand.
    Revenue = Price × Quantity
    """
    best_price = min_price
    best_revenue = 0
    
    # Search through price range
    for price in np.arange(min_price, max_price, 0.05):
        sales = predict_sales(brand, price, featured)
        revenue = price * sales
        
        if revenue > best_revenue:
            best_revenue = revenue
            best_price = price
    
    return best_price, best_revenue


def compare_elasticities():
    """
    Compare price elasticity across all three brands.
    """
    brands = ['dominicks', 'minute.maid', 'tropicana']
    results = {}
    
    for brand in brands:
        elasticity = get_price_elasticity(brand)
        results[brand] = elasticity
    
    return results

5.2 Step 4.2: Test the Helper Functions

Add test code to verify the functions work:

# ============================================
# TEST THE HELPER FUNCTIONS
# ============================================

print("\n" + "="*50)
print("Testing helper functions...")
print("="*50)

# Test prediction
test_sales = predict_sales('tropicana', 2.50, featured=0)
print(f"\nPredicted sales for Tropicana at $2.50 (no ad): {test_sales:.0f} units")

# Test elasticity
elasticities = compare_elasticities()
print("\nPrice Elasticities by Brand:")
for brand, elast in elasticities.items():
    print(f"  {brand}: {elast:.3f}")

# Test advertising lift
lift = get_advertising_lift('minute.maid')
print(f"\nAdvertising lift: {lift:.1f}% increase in sales")

# Test optimal price
opt_price, opt_rev = find_optimal_price('dominicks')
print(f"\nOptimal price for Dominick's: ${opt_price:.2f} (revenue: ${opt_rev:.2f})")

Run the script again to verify all functions work correctly.

6 Part 5: Creating the AI Agent

6.1 Step 5.1: Add the Agent Logic

Now we’ll create the agent that interprets natural language questions and calls the appropriate functions. Add this code:

# ============================================
# PART 5: THE AI AGENT
# ============================================

def answer_question(question):
    """
    Simple agent that answers business questions about OJ pricing.
    
    This is a rule-based agent that matches keywords in the question
    to determine which analysis to perform.
    """
    question_lower = question.lower()
    
    # Question 1: Predict sales for specific scenario
    if 'predict' in question_lower or 'sales volume' in question_lower:
        # Extract brand and price from question if possible
        if 'tropicana' in question_lower:
            brand = 'tropicana'
        elif 'minute maid' in question_lower:
            brand = 'minute.maid'
        else:
            brand = 'dominicks'
        
        # Look for price (default to $2.50 if not found)
        import re
        price_match = re.search(r'\$?(\d+\.?\d*)', question_lower)
        price = float(price_match.group(1)) if price_match else 2.50
        
        # Check for advertising
        featured = 1 if 'advertis' in question_lower or 'feature' in question_lower else 0
        if 'no advertis' in question_lower or 'without advertis' in question_lower:
            featured = 0
        
        sales = predict_sales(brand, price, featured)
        
        response = f"""
**Predicted Sales Analysis**

Brand: {brand.title().replace('.', ' ')}
Price: ${price:.2f}
Featured in Ad: {'Yes' if featured else 'No'}

**Predicted Sales Volume: {sales:,.0f} units**

This prediction is based on our regression model that accounts for:
- Base demand for this brand
- Price sensitivity (elasticity)  
- Advertising effects
"""
        return response
    
    # Question 2: Which brand is most price-sensitive?
    elif 'price-sensitive' in question_lower or 'price sensitive' in question_lower or 'most sensitive' in question_lower:
        elasticities = compare_elasticities()
        
        # Find most price-sensitive (most negative elasticity)
        most_sensitive = min(elasticities, key=elasticities.get)
        
        response = f"""
**Price Sensitivity Analysis**

Price Elasticity by Brand:
"""
        for brand, elast in sorted(elasticities.items(), key=lambda x: x[1]):
            sensitivity = "HIGH" if elast < -3 else "MEDIUM" if elast < -2 else "LOW"
            response += f"- {brand.title().replace('.', ' ')}: {elast:.3f} ({sensitivity} sensitivity)\n"
        
        response += f"""
**Most Price-Sensitive: {most_sensitive.title().replace('.', ' ')}**

Interpretation: A 1% price increase leads to a {abs(elasticities[most_sensitive]):.1f}% decrease in sales for {most_sensitive.title().replace('.', ' ')}.

Business Implication: Be careful with price increases on {most_sensitive.title().replace('.', ' ')} - customers are very responsive to price changes.
"""
        return response
    
    # Question 3: Should we feature a brand in advertising?
    elif 'feature' in question_lower or 'ad circular' in question_lower or 'advertising' in question_lower:
        if 'minute maid' in question_lower:
            brand = 'minute.maid'
        elif 'tropicana' in question_lower:
            brand = 'tropicana'
        else:
            brand = 'dominicks'
        
        lift = get_advertising_lift(brand)
        
        # Calculate example impact
        base_sales = predict_sales(brand, 2.50, featured=0)
        featured_sales = predict_sales(brand, 2.50, featured=1)
        
        response = f"""
**Advertising Impact Analysis for {brand.title().replace('.', ' ')}**

Expected Sales Lift from Featuring: **{lift:.1f}%**

Example at $2.50:
- Without advertising: {base_sales:,.0f} units
- With advertising: {featured_sales:,.0f} units  
- Additional sales: {featured_sales - base_sales:,.0f} units

**Recommendation:** {'Yes, feature this product!' if lift > 20 else 'Consider the advertising cost vs. the sales lift.'}

The advertising effect is consistent across price points. Factor in your advertising costs to determine if the sales lift justifies the expense.
"""
        return response
    
    # Question 4: Optimal price for a brand
    elif 'optimal price' in question_lower or 'maximize revenue' in question_lower or 'best price' in question_lower:
        if 'minute maid' in question_lower:
            brand = 'minute.maid'
        elif 'tropicana' in question_lower:
            brand = 'tropicana'
        else:
            brand = 'dominicks'
        
        opt_price, opt_revenue = find_optimal_price(brand)
        opt_sales = predict_sales(brand, opt_price, featured=0)
        
        # Compare with current average price
        avg_price = df[df['brand'] == brand]['price'].mean()
        avg_revenue = avg_price * predict_sales(brand, avg_price, featured=0)
        
        response = f"""
**Revenue Optimization for {brand.title().replace('.', ' ')}**

**Optimal Price: ${opt_price:.2f}**

At optimal price:
- Predicted sales: {opt_sales:,.0f} units
- Revenue per store-week: ${opt_revenue:,.2f}

Comparison with current average (${avg_price:.2f}):
- Current revenue: ${avg_revenue:,.2f}
- Potential improvement: ${opt_revenue - avg_revenue:,.2f} ({((opt_revenue/avg_revenue)-1)*100:.1f}%)

Note: This optimization assumes no competitor response and stable market conditions.
"""
        return response
    
    # Question 5: Compare elasticities across brands
    elif 'compare' in question_lower or 'elasticity' in question_lower or 'across' in question_lower:
        elasticities = compare_elasticities()
        
        response = """
**Price Elasticity Comparison Across Brands**

| Brand | Elasticity | Interpretation |
|-------|------------|----------------|
"""
        for brand, elast in sorted(elasticities.items(), key=lambda x: x[1]):
            interp = f"1% price ↑ → {abs(elast):.1f}% sales ↓"
            response += f"| {brand.title().replace('.', ' ')} | {elast:.3f} | {interp} |\n"
        
        response += """
**Key Insights:**

1. **Dominick's** (store brand) is least price-sensitive - customers buying store brands may prioritize value and be less responsive to small price changes.

2. **Tropicana** shows moderate price sensitivity - as a premium brand, some customers are loyal but others will switch if prices rise.

3. **Minute Maid** is most price-sensitive - positioned between store and premium brands, these customers actively compare prices.

**Strategic Implications:**
- Use competitive pricing on Minute Maid to capture price-sensitive shoppers
- Tropicana can sustain moderate price premiums
- Dominick's margins can be optimized with less risk of volume loss
"""
        return response
    
    else:
        return """
I can help you with these types of questions:

1. **Sales Prediction:** "What is the predicted sales volume if we price Tropicana at $2.50?"
2. **Price Sensitivity:** "Which brand is most price-sensitive?"
3. **Advertising Impact:** "Should we feature Minute Maid in the ad circular?"
4. **Price Optimization:** "What price should we set for Dominick's to maximize revenue?"
5. **Elasticity Comparison:** "Compare the price elasticity across brands"

Please try one of these questions!
"""

6.2 Step 5.2: Add the Interactive Interface

Finally, add code to let users interact with the agent:

# ============================================
# PART 6: INTERACTIVE AGENT INTERFACE
# ============================================

def run_agent():
    """
    Run the interactive agent interface.
    """
    print("\n" + "="*60)
    print("🍊 ORANGE JUICE PRICING ANALYTICS AGENT 🍊")
    print("="*60)
    print("\nHello! I'm your pricing analytics assistant.")
    print("I can help you analyze orange juice pricing and promotions.")
    print("\nTry asking me questions like:")
    print("  - What is the predicted sales if we price Tropicana at $2.50?")
    print("  - Which brand is most price-sensitive?")
    print("  - Should we feature Minute Maid in the ad circular?")
    print("  - What price maximizes revenue for Dominick's?")
    print("  - Compare price elasticity across brands")
    print("\nType 'quit' to exit.\n")
    
    while True:
        question = input("Your question: ").strip()
        
        if question.lower() in ['quit', 'exit', 'q']:
            print("\nThank you for using the OJ Pricing Agent. Goodbye!")
            break
        
        if not question:
            continue
        
        print("\n" + "-"*50)
        response = answer_question(question)
        print(response)
        print("-"*50 + "\n")


# ============================================
# MAIN: RUN THE AGENT
# ============================================

if __name__ == "__main__":
    # Run the interactive agent
    run_agent()

7 Part 6: Testing Your Agent

7.1 Step 6.1: Run the Complete Script

Save the file and run:

python oj_agent.py

7.2 Step 6.2: Test All Five Required Questions

Test your agent with these exact questions:

“What is the predicted sales volume if we price Tropicana at $2.50 with no advertising?”
“Which brand is most price-sensitive?”
“Should we feature Minute Maid in this week’s ad circular? What’s the expected sales lift?”
“What price should we set for Dominick’s brand to maximize revenue?”
“Compare the price elasticity across the three brands.”

Record the answers for your summary document.

8 Part 7: Writing Your Summary

Create a 1-page document (Word or PDF) that includes:

8.1 Section 1: Key Findings (half page)

Which brand is most/least price-sensitive and why this matters
The impact of advertising on sales
The optimal pricing recommendations

8.2 Section 2: Surprises and Insights (quarter page)

What surprised you about the results?
How do these findings compare to your intuition?

8.3 Section 3: Business Implications (quarter page)

How would you recommend a retailer use these insights?
What additional data would make this analysis more useful?

9 Part 8: Preparing Your Demo

For Zoom Session 3, prepare a 2-3 minute demonstration:

Setup (30 sec): Briefly explain what the agent does
Demo (1.5 min): Show 2-3 questions and responses
Insight (1 min): Share your most interesting finding

Tips:

Have your script running before you share screen
Pre-type a question so you’re not typing live
Focus on business insights, not technical details

10 Troubleshooting

10.1 “ModuleNotFoundError: No module named ‘pandas’”

Run: pip install pandas numpy scikit-learn

10.2 “FileNotFoundError: oj_data.csv”

Make sure the data file is in the same folder as your Python script.

10.3 “Model coefficients look wrong”

Check that your data loaded correctly - you should have ~28,000 rows.

10.4 “Agent doesn’t understand my question”

Try rephrasing using keywords like “predict”, “price-sensitive”, “feature”, “optimal”, or “compare”.

11 Complete Code Reference

The complete oj_agent.py file should be approximately 350-400 lines. If you get stuck, ask Cursor’s AI assistant for help by selecting your code and pressing Cmd+K (Mac) or Ctrl+K (Windows), then describing your issue. I also prepared a complete code reference for you to refer.

12 Next Steps (Optional Enhancements)

If you finish early and want to explore further:

Add more questions: What other business questions could the agent answer?
Improve the NLP: Use fuzzy matching to better understand varied phrasings
Add visualizations: Create charts showing price vs. sales by brand
Connect to an LLM: Use the OpenAI API to make the agent truly conversational

Good luck with your project! 🍊

--- title: "Final Project Guide" subtitle: "Orange Juice Pricing Analytics Agent" author: "CAIO Executive Program" date: today format: html: toc: true toc-depth: 3 number-sections: true theme: cosmo code-fold: false code-tools: true execute: eval: false echo: true --- ## Overview In this project, you will build an AI agent that helps a retail pricing analyst make decisions about orange juice pricing and promotions. The agent will: 1. Load and explore sales data 2. Build a regression model to predict sales 3. Answer business questions using natural language **Time Required:** ~2 hours **Prerequisites:** - Cursor IDE installed (see [Cursor Setup Guide](cursor-setup-guide.qmd)) - Basic familiarity with Cursor from Zoom Session 1 - Download the [project template](project_template.py) to get started quickly --- ## Part 1: Project Setup ### Step 1.1: Create Your Project Folder 1. Open Cursor IDE 2. Click **File → Open Folder** 3. Create a new folder called `oj-pricing-agent` on your computer 4. Select that folder to open it in Cursor ### Step 1.2: Create the Main Python File 1. In the Cursor sidebar, right-click and select **New File** 2. Name it `oj_agent.py` 3. You'll see an empty file open in the editor ### Step 1.3: Copy the Dataset Download the [oj_data.csv](oj_data.csv) file and copy it into your `oj-pricing-agent` folder. --- ## Part 2: Data Loading and Exploration ### Step 2.1: Load the Required Libraries In your `oj_agent.py` file, start by adding these lines at the top: ```python # Required libraries import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.preprocessing import OneHotEncoder import warnings warnings.filterwarnings('ignore') ``` **What this does:** These libraries help us work with data (pandas), do math (numpy), and build models (sklearn). ### Step 2.2: Load the Data Add the following code to load the orange juice sales data: ```python # Load the orange juice dataset print("Loading data...") df = pd.read_csv('oj_data.csv') # Display basic information print(f"Dataset has {len(df)} rows and {len(df.columns)} columns") print(f"\nColumns: {list(df.columns)}") print(f"\nBrands in dataset: {df['brand'].unique()}") print(f"\nPrice range: ${df['price'].min():.2f} - ${df['price'].max():.2f}") print(f"\nSample of data:") print(df.head()) ``` ### Step 2.3: Run Your Code (First Test) 1. Save the file (Ctrl+S or Cmd+S) 2. Open the terminal in Cursor: **View → Terminal** 3. Run the script: ```bash python oj_agent.py ``` You should see output showing: - The dataset has ~28,000 rows - Three brands: Tropicana, Minute Maid, Dominick's - Price ranges from about $1 to $4 **Troubleshooting:** If you get an error about missing packages, run: ```bash pip install pandas numpy scikit-learn ``` --- ## Part 3: Building the Regression Model ### Step 3.1: Understanding the Model We're building a model that predicts **log of sales volume** based on: - **Price:** Higher price → lower sales (negative relationship) - **Featured (feat):** If the product is in the weekly ad circular (1 = yes, 0 = no) - **Brand:** Different brands have different base sales levels - **Price × Brand interaction:** Price sensitivity varies by brand ### Step 3.2: Prepare the Data for Modeling Add this code to prepare features for the model: ```python # ============================================ # PART 3: BUILD THE REGRESSION MODEL # ============================================ print("\n" + "="*50) print("Building the pricing model...") print("="*50) # Create dummy variables for brand (one-hot encoding) # This converts 'brand' text into numbers the model can use brand_dummies = pd.get_dummies(df['brand'], prefix='brand', drop_first=False) # Create the feature matrix # We include: price, feat, brand dummies, and price*brand interactions X = pd.DataFrame({ 'price': df['price'], 'feat': df['feat'], 'brand_minute.maid': brand_dummies['brand_minute.maid'], 'brand_tropicana': brand_dummies['brand_tropicana'], # Interaction terms: price effect varies by brand 'price_x_minute.maid': df['price'] * brand_dummies['brand_minute.maid'], 'price_x_tropicana': df['price'] * brand_dummies['brand_tropicana'] }) # Target variable: log of sales (logmove) y = df['logmove'] print(f"Features: {list(X.columns)}") print(f"Target: logmove (log of sales volume)") ``` ### Step 3.3: Fit the Model Add code to train the regression model: ```python # Fit the linear regression model model = LinearRegression() model.fit(X, y) # Display the coefficients print("\nModel Coefficients:") print("-" * 40) for feature, coef in zip(X.columns, model.coef_): print(f" {feature}: {coef:.4f}") print(f" intercept: {model.intercept_:.4f}") # Calculate R-squared (how well the model fits) r_squared = model.score(X, y) print(f"\nModel R-squared: {r_squared:.3f}") print("(This means the model explains {:.1f}% of sales variation)".format(r_squared * 100)) ``` ### Step 3.4: Run and Verify the Model Save and run the script again. You should see coefficients like: - **price:** negative (higher price = lower sales) - **feat:** positive (being featured increases sales) - **brand coefficients:** capture baseline differences between brands - **interaction terms:** show how price sensitivity differs by brand --- ## Part 4: Creating Helper Functions for the Agent ### Step 4.1: Add Prediction Functions Add these functions that the agent will use to answer questions: ```python # ============================================ # PART 4: HELPER FUNCTIONS FOR THE AGENT # ============================================ def predict_sales(brand, price, featured=0): """ Predict sales volume for a given brand, price, and feature status. Args: brand: 'tropicana', 'minute.maid', or 'dominicks' price: price in dollars (e.g., 2.50) featured: 1 if in ad circular, 0 if not Returns: Predicted sales volume (not log-transformed) """ # Create feature vector features = { 'price': price, 'feat': featured, 'brand_minute.maid': 1 if brand.lower() == 'minute.maid' else 0, 'brand_tropicana': 1 if brand.lower() == 'tropicana' else 0, 'price_x_minute.maid': price if brand.lower() == 'minute.maid' else 0, 'price_x_tropicana': price if brand.lower() == 'tropicana' else 0 } # Convert to dataframe for prediction X_pred = pd.DataFrame([features]) # Predict log sales, then convert back log_sales = model.predict(X_pred)[0] sales = np.exp(log_sales) return sales def get_price_elasticity(brand): """ Calculate the price elasticity for a given brand. Price elasticity tells us: if price increases by 1%, how much does quantity demanded change (in %)? A more negative number means more price-sensitive. """ # Base price coefficient base_coef = model.coef_[0] # price coefficient # Add brand-specific interaction if applicable if brand.lower() == 'minute.maid': interaction_coef = model.coef_[4] # price_x_minute.maid elif brand.lower() == 'tropicana': interaction_coef = model.coef_[5] # price_x_tropicana else: # dominicks (base case) interaction_coef = 0 total_elasticity = base_coef + interaction_coef return total_elasticity def get_advertising_lift(brand): """ Calculate the sales lift from being featured in advertising. Returns the percentage increase in sales. """ # The 'feat' coefficient tells us the log-sales increase feat_coef = model.coef_[1] # feat coefficient # Convert from log to percentage change percentage_lift = (np.exp(feat_coef) - 1) * 100 return percentage_lift def find_optimal_price(brand, min_price=1.0, max_price=4.0, featured=0): """ Find the price that maximizes revenue for a brand. Revenue = Price × Quantity """ best_price = min_price best_revenue = 0 # Search through price range for price in np.arange(min_price, max_price, 0.05): sales = predict_sales(brand, price, featured) revenue = price * sales if revenue > best_revenue: best_revenue = revenue best_price = price return best_price, best_revenue def compare_elasticities(): """ Compare price elasticity across all three brands. """ brands = ['dominicks', 'minute.maid', 'tropicana'] results = {} for brand in brands: elasticity = get_price_elasticity(brand) results[brand] = elasticity return results ``` ### Step 4.2: Test the Helper Functions Add test code to verify the functions work: ```python # ============================================ # TEST THE HELPER FUNCTIONS # ============================================ print("\n" + "="*50) print("Testing helper functions...") print("="*50) # Test prediction test_sales = predict_sales('tropicana', 2.50, featured=0) print(f"\nPredicted sales for Tropicana at $2.50 (no ad): {test_sales:.0f} units") # Test elasticity elasticities = compare_elasticities() print("\nPrice Elasticities by Brand:") for brand, elast in elasticities.items(): print(f" {brand}: {elast:.3f}") # Test advertising lift lift = get_advertising_lift('minute.maid') print(f"\nAdvertising lift: {lift:.1f}% increase in sales") # Test optimal price opt_price, opt_rev = find_optimal_price('dominicks') print(f"\nOptimal price for Dominick's: ${opt_price:.2f} (revenue: ${opt_rev:.2f})") ``` Run the script again to verify all functions work correctly. --- ## Part 5: Creating the AI Agent ### Step 5.1: Add the Agent Logic Now we'll create the agent that interprets natural language questions and calls the appropriate functions. Add this code: ```python # ============================================ # PART 5: THE AI AGENT # ============================================ def answer_question(question): """ Simple agent that answers business questions about OJ pricing. This is a rule-based agent that matches keywords in the question to determine which analysis to perform. """ question_lower = question.lower() # Question 1: Predict sales for specific scenario if 'predict' in question_lower or 'sales volume' in question_lower: # Extract brand and price from question if possible if 'tropicana' in question_lower: brand = 'tropicana' elif 'minute maid' in question_lower: brand = 'minute.maid' else: brand = 'dominicks' # Look for price (default to $2.50 if not found) import re price_match = re.search(r'\$?(\d+\.?\d*)', question_lower) price = float(price_match.group(1)) if price_match else 2.50 # Check for advertising featured = 1 if 'advertis' in question_lower or 'feature' in question_lower else 0 if 'no advertis' in question_lower or 'without advertis' in question_lower: featured = 0 sales = predict_sales(brand, price, featured) response = f""" **Predicted Sales Analysis** Brand: {brand.title().replace('.', ' ')} Price: ${price:.2f} Featured in Ad: {'Yes' if featured else 'No'} **Predicted Sales Volume: {sales:,.0f} units** This prediction is based on our regression model that accounts for: - Base demand for this brand - Price sensitivity (elasticity) - Advertising effects """ return response # Question 2: Which brand is most price-sensitive? elif 'price-sensitive' in question_lower or 'price sensitive' in question_lower or 'most sensitive' in question_lower: elasticities = compare_elasticities() # Find most price-sensitive (most negative elasticity) most_sensitive = min(elasticities, key=elasticities.get) response = f""" **Price Sensitivity Analysis** Price Elasticity by Brand: """ for brand, elast in sorted(elasticities.items(), key=lambda x: x[1]): sensitivity = "HIGH" if elast < -3 else "MEDIUM" if elast < -2 else "LOW" response += f"- {brand.title().replace('.', ' ')}: {elast:.3f} ({sensitivity} sensitivity)\n" response += f""" **Most Price-Sensitive: {most_sensitive.title().replace('.', ' ')}** Interpretation: A 1% price increase leads to a {abs(elasticities[most_sensitive]):.1f}% decrease in sales for {most_sensitive.title().replace('.', ' ')}. Business Implication: Be careful with price increases on {most_sensitive.title().replace('.', ' ')} - customers are very responsive to price changes. """ return response # Question 3: Should we feature a brand in advertising? elif 'feature' in question_lower or 'ad circular' in question_lower or 'advertising' in question_lower: if 'minute maid' in question_lower: brand = 'minute.maid' elif 'tropicana' in question_lower: brand = 'tropicana' else: brand = 'dominicks' lift = get_advertising_lift(brand) # Calculate example impact base_sales = predict_sales(brand, 2.50, featured=0) featured_sales = predict_sales(brand, 2.50, featured=1) response = f""" **Advertising Impact Analysis for {brand.title().replace('.', ' ')}** Expected Sales Lift from Featuring: **{lift:.1f}%** Example at $2.50: - Without advertising: {base_sales:,.0f} units - With advertising: {featured_sales:,.0f} units - Additional sales: {featured_sales - base_sales:,.0f} units **Recommendation:** {'Yes, feature this product!' if lift > 20 else 'Consider the advertising cost vs. the sales lift.'} The advertising effect is consistent across price points. Factor in your advertising costs to determine if the sales lift justifies the expense. """ return response # Question 4: Optimal price for a brand elif 'optimal price' in question_lower or 'maximize revenue' in question_lower or 'best price' in question_lower: if 'minute maid' in question_lower: brand = 'minute.maid' elif 'tropicana' in question_lower: brand = 'tropicana' else: brand = 'dominicks' opt_price, opt_revenue = find_optimal_price(brand) opt_sales = predict_sales(brand, opt_price, featured=0) # Compare with current average price avg_price = df[df['brand'] == brand]['price'].mean() avg_revenue = avg_price * predict_sales(brand, avg_price, featured=0) response = f""" **Revenue Optimization for {brand.title().replace('.', ' ')}** **Optimal Price: ${opt_price:.2f}** At optimal price: - Predicted sales: {opt_sales:,.0f} units - Revenue per store-week: ${opt_revenue:,.2f} Comparison with current average (${avg_price:.2f}): - Current revenue: ${avg_revenue:,.2f} - Potential improvement: ${opt_revenue - avg_revenue:,.2f} ({((opt_revenue/avg_revenue)-1)*100:.1f}%) Note: This optimization assumes no competitor response and stable market conditions. """ return response # Question 5: Compare elasticities across brands elif 'compare' in question_lower or 'elasticity' in question_lower or 'across' in question_lower: elasticities = compare_elasticities() response = """ **Price Elasticity Comparison Across Brands** | Brand | Elasticity | Interpretation | |-------|------------|----------------| """ for brand, elast in sorted(elasticities.items(), key=lambda x: x[1]): interp = f"1% price ↑ → {abs(elast):.1f}% sales ↓" response += f"| {brand.title().replace('.', ' ')} | {elast:.3f} | {interp} |\n" response += """ **Key Insights:** 1. **Dominick's** (store brand) is least price-sensitive - customers buying store brands may prioritize value and be less responsive to small price changes. 2. **Tropicana** shows moderate price sensitivity - as a premium brand, some customers are loyal but others will switch if prices rise. 3. **Minute Maid** is most price-sensitive - positioned between store and premium brands, these customers actively compare prices. **Strategic Implications:** - Use competitive pricing on Minute Maid to capture price-sensitive shoppers - Tropicana can sustain moderate price premiums - Dominick's margins can be optimized with less risk of volume loss """ return response else: return """ I can help you with these types of questions: 1. **Sales Prediction:** "What is the predicted sales volume if we price Tropicana at $2.50?" 2. **Price Sensitivity:** "Which brand is most price-sensitive?" 3. **Advertising Impact:** "Should we feature Minute Maid in the ad circular?" 4. **Price Optimization:** "What price should we set for Dominick's to maximize revenue?" 5. **Elasticity Comparison:** "Compare the price elasticity across brands" Please try one of these questions! """ ``` ### Step 5.2: Add the Interactive Interface Finally, add code to let users interact with the agent: ```python # ============================================ # PART 6: INTERACTIVE AGENT INTERFACE # ============================================ def run_agent(): """ Run the interactive agent interface. """ print("\n" + "="*60) print("🍊 ORANGE JUICE PRICING ANALYTICS AGENT 🍊") print("="*60) print("\nHello! I'm your pricing analytics assistant.") print("I can help you analyze orange juice pricing and promotions.") print("\nTry asking me questions like:") print(" - What is the predicted sales if we price Tropicana at $2.50?") print(" - Which brand is most price-sensitive?") print(" - Should we feature Minute Maid in the ad circular?") print(" - What price maximizes revenue for Dominick's?") print(" - Compare price elasticity across brands") print("\nType 'quit' to exit.\n") while True: question = input("Your question: ").strip() if question.lower() in ['quit', 'exit', 'q']: print("\nThank you for using the OJ Pricing Agent. Goodbye!") break if not question: continue print("\n" + "-"*50) response = answer_question(question) print(response) print("-"*50 + "\n") # ============================================ # MAIN: RUN THE AGENT # ============================================ if __name__ == "__main__": # Run the interactive agent run_agent() ``` --- ## Part 6: Testing Your Agent ### Step 6.1: Run the Complete Script Save the file and run: ```bash python oj_agent.py ``` ### Step 6.2: Test All Five Required Questions Test your agent with these exact questions: 1. **"What is the predicted sales volume if we price Tropicana at $2.50 with no advertising?"** 2. **"Which brand is most price-sensitive?"** 3. **"Should we feature Minute Maid in this week's ad circular? What's the expected sales lift?"** 4. **"What price should we set for Dominick's brand to maximize revenue?"** 5. **"Compare the price elasticity across the three brands."** Record the answers for your summary document. --- ## Part 7: Writing Your Summary Create a 1-page document (Word or PDF) that includes: ### Section 1: Key Findings (half page) - Which brand is most/least price-sensitive and why this matters - The impact of advertising on sales - The optimal pricing recommendations ### Section 2: Surprises and Insights (quarter page) - What surprised you about the results? - How do these findings compare to your intuition? ### Section 3: Business Implications (quarter page) - How would you recommend a retailer use these insights? - What additional data would make this analysis more useful? --- ## Part 8: Preparing Your Demo For Zoom Session 3, prepare a 2-3 minute demonstration: 1. **Setup (30 sec):** Briefly explain what the agent does 2. **Demo (1.5 min):** Show 2-3 questions and responses 3. **Insight (1 min):** Share your most interesting finding **Tips:** - Have your script running before you share screen - Pre-type a question so you're not typing live - Focus on business insights, not technical details --- ## Troubleshooting ### "ModuleNotFoundError: No module named 'pandas'" Run: `pip install pandas numpy scikit-learn` ### "FileNotFoundError: oj_data.csv" Make sure the data file is in the same folder as your Python script. ### "Model coefficients look wrong" Check that your data loaded correctly - you should have ~28,000 rows. ### "Agent doesn't understand my question" Try rephrasing using keywords like "predict", "price-sensitive", "feature", "optimal", or "compare". --- ## Complete Code Reference The complete `oj_agent.py` file should be approximately 350-400 lines. If you get stuck, ask Cursor's AI assistant for help by selecting your code and pressing Cmd+K (Mac) or Ctrl+K (Windows), then describing your issue. I also prepared a [complete code reference](project_template.py) for you to refer. --- ## Next Steps (Optional Enhancements) If you finish early and want to explore further: 1. **Add more questions:** What other business questions could the agent answer? 2. **Improve the NLP:** Use fuzzy matching to better understand varied phrasings 3. **Add visualizations:** Create charts showing price vs. sales by brand 4. **Connect to an LLM:** Use the OpenAI API to make the agent truly conversational Good luck with your project! 🍊