Linear Regression
Task 1: Initial E-commerce Performance Analysis
Objective: Analyze basic patterns and relationships in e-commerce performance metrics.
Instructions:
Load the ecommerce_data.csv and display the first 5 rows.
Calculate daily average metrics for marketing_spend, website_visitors, conversion_rate, and
daily_sales.
Compare weekend vs weekday performance using these metrics.
List 3 key observations about daily performance patterns.
Expected Outcome: Basic statistical summary and initial insights about daily operations.
Task 2: Marketing Spend Effectiveness Visualization
Objective: Visualize how marketing spend impacts sales and conversion rates.
Instructions:
Create a scatter plot of marketing_spend vs daily_sales, colored by month.
Plot average conversion rates for different marketing spend brackets.
Generate a line plot showing marketing spend efficiency (sales per marketing dollar) over time.
Write a brief interpretation of each visualization highlighting key patterns.
Expected Outcome: Three visualizations with written analysis of marketing effectiveness.
Task 3: Custom Performance Metrics Creation
Objective: Develop metrics that combine multiple factors to measure daily success.
Instructions:
Calculate marketing_efficiency_ratio as (daily_sales / marketing_spend).
Create visitor_value_score as (daily_sales / website_visitors).
Identify the top 10 most efficient days based on these metrics.
Expected Outcome: New performance metrics and analysis of best-performing days.
Task 4: Sales Prediction Baseline Model
Objective: Create a simple model to predict daily_sales.
Instructions:
Use marketing_spend and website_visitors to predict daily_sales using Linear Regression.
Split data into training (80%) and test (20%) sets.
Calculate and evaluate R-squared and Mean Absolute Error.
Expected Outcome: Basic predictive model with performance metrics.
Task 5: Comparative Model Analysis
Objective: Compare different regression models for sales prediction.
Instructions:
Train Linear Regression, Ridge, and Lasso models using all numerical features.
Perform 5-fold cross-validation for each model.
Compare R-squared and MAE across models.
Expected Outcome: Model comparison results and recommendation.
Task 6: Seasonal Performance Analysis
Objective: Analyze how business metrics vary across different months.
Instructions:
Calculate monthly averages for all key metrics.
Create separate regression models for each month.
Compare model coefficients to identify seasonal patterns.
Expected Outcome: Analysis of seasonal impact on business performance.
Task 7: Customer Satisfaction Impact Study
Objective: Analyze factors affecting customer satisfaction scores.
Instructions:
Build a regression model to predict customer_satisfaction.
Identify thresholds where operational metrics affect satisfaction.
Compare satisfaction during promotional vs non-promotional periods.
Expected Outcome: Analysis of factors influencing customer satisfaction.
Task 8: Promotion Effectiveness Analysis
Objective: Measure the impact of promotions on key metrics.
Instructions:
Compare conversion rates and sales between promotional and non-promotional periods.
Calculate the average lift in sales during promotions.
Analyze marketing efficiency during promotional periods.
Expected Outcome: Quantified impact of promotional activities.
Task 9: Traffic Quality Analysis
Objective: Analyze the relationship between traffic volume and conversion quality.
Instructions:
Create traffic volume brackets and analyze conversion rates within each.
Build a model to predict conversion_rate based on traffic and other metrics.
Identify optimal traffic levels for maximum conversion.
Expected Outcome: Analysis of traffic-conversion relationship.
Task 10: Holiday Season Performance Prediction
Objective: Build a model specifically for holiday season performance.
Instructions:
Filter data for November and December.
Create a regression model to predict daily_sales during this period.
Compare accuracy with non-holiday season predictions.
Expected Outcome: Holiday-specific prediction model and performance analysis.