Abdallahelraey/Resturant-tip-analysis-and-prediction

Restaurant Tips Optimization Project Unlock the potential of your restaurant's earnings with our comprehensive Tips Optimization Project. We analyze customer data, unveil insights, and employ advanced machine learning models to predict tip amounts. This project empowers restaurant owners and staff to enhance services, boost earni

Resturant Tips Analysis and Prediction

Restaurant Tips Optimization Project

Welcome to our Restaurant Tips Optimization Project! This project aims to enhance restaurant services and optimize earnings through data analysis and machine learning. Below is a comprehensive guide to the project, highlighting key findings, conclusions, and the modeling process.

Tip Dataset Key Features

Overview

• Source: tip.csv file
• Contains 244 rows/samples of tipping data

Features

• total_bill: the total bill amount
• tip: tip amount paid
• sex: gender of bill payer (Male/Female)
• smoker: whether the bill payer is a smoker (Yes/No)
• day: day of week
• time: meal time
• size: size of dining party

Key Insights

• Provides detailed tipping data across different parties, days, and meal times
• Can analyze differences in tipping behavior by party size, gender, smoking status, and time/day
• Useful for food service business insights and modeling tip percentage prediction

Project Objectives

1. Data Analysis:

   • Explore restaurant tips data.
   • Uncover insights to optimize restaurant processes.

2. Machine Learning Model:

   • Develop a model predicting tips based on various factors.
   • Support restaurant owners and staff in improving services and earnings.

Project Structure

0. Problem framing.

1. Importing necessary libraries and needed dependencies.

2. Data Collection

3. Data Preprocessing

4. Exploratory Data Analysis

5. Feature engineering

6. Model engineering

Key Insights from Data Analysis

1. Customer Demographics:

   • The restaurant attracts more male customers than females.
   • Non-smokers significantly outnumber smokers.

2. Day-wise Analysis:

   • Saturdays, Sundays, and Thursdays observe higher customer turnout.
   • Fridays have lower attendance, while Mondays, Tuesdays, and Wednesdays are missing data.

3. Meal Preference:

   • Dinner is more popular than lunch.
   • Right-skewed total_bill distribution suggests a diverse customer base.

4. Tip Distribution:

   • Right skewness in tip amounts implies potential for tip pooling policies.
   • Majority of tables host around 2 customers.

Key Conclusions from Data Analysis

1. Tipping Behavior:

   • Male customers tend to be higher tippers.
   • High tippers prefer dinner service.

2. Smoking Customers:

   • Smoking customers do not significantly impact tip amounts.

3. Tip Policy Recommendation:

   • Suggested tip range: $1 to $10, with an average of $3.

4. Dining Timing:

   • Male customers prefer early-week dining.

5. Customized Menu Consideration:

   • Customer groups mainly consist of dinner companions, suggesting a need for a tailored menu.

6. Total Bill and Table Size Influence:

   • Both total bill and table size positively impact tip amounts.

Key Considerations about Our Data

1. Assumption of data representativeness.
2. Ignored limitations, e.g., missing days and assumed absence of breakfast menu.

Feature Engineering

• Log-transforming tip data to mitigate extreme tipping behavior's impact.
• Feature scaling to ensure equal influence of variables on the model.

Modeling

1. Baseline Model (The mean):

   • MAE: 0.8184

2. RandomForestRegressor Model:

   • MAE: 0.5788
   • Outperforms the baseline, showcasing its ability to capture complex relationships.

Evaluation Results

• Baseline Model:

  • Predicts tips based on the mean tip value.
  • MAE: 0.8184

• RandomForestRegressor Model:

  • Outperforms the baseline.
  • MAE: 0.5788

The RandomForestRegressor model demonstrates superior predictive accuracy, making it a valuable tool for optimizing restaurant tips.

Conclusion

This project provides actionable insights and a robust machine learning model to optimize restaurant tips, benefiting both owners and staff. Future improvements may involve refining data assumptions and exploring additional features for model enhancement.