Project Overview:

This project aims to analyze and determine the likelihood of a customer accepting a coupon. By leveraging visualizations and probability distributions, we seek to identify patterns and distinguish between customers who accept driving-related coupons versus those who do not. The analysis will provide insights into customer behavior under various driving scenarios.

Data Description:

The dataset is sourced from the UCI Machine Learning Repository and was collected through a survey conducted on Amazon Mechanical Turk. The survey outlines different driving scenarios, such as:

• Destination: Where the driver is headed.
• Current time: When the scenario occurs.
• Weather: Conditions during the drive.
• Passenger: Who is accompanying the driver.

Respondents were asked whether they would accept a coupon if they were driving. Responses are categorized as follows:

• Y = 1: Indicates acceptance of the coupon (I will drive there right away OR later before the coupon expires).
• Y = 0: Indicates rejection of the coupon (No, I do not want the coupon).

Types of Coupons Analyzed:

The dataset includes five categories of coupons:

1. Less expensive restaurants (under $20).
2. Coffee houses.
3. Carryout and takeaway.
4. Bars.
5. More expensive restaurants ($20–$50).

The project explores patterns within these categories to better understand customer preferences and behavior regarding coupon acceptance.

Project Approach:

Step 1: Data Loading and Cleaning

The dataset is loaded and prepared for analysis by addressing missing or inconsistent data. Cleaning ensures the integrity of the data and reliability of subsequent findings.

Step 2: Data Conversion

Categorical data is converted into numerical formats suitable for analysis, allowing us to apply statistical methods and visualizations effectively.

Step 3: Defining Groups

Key groups are defined based on relevant attributes like age, frequency of bar visits, income level, and social behaviors. These groupings help identify trends and insights within specific demographics.

Step 4: Analyzing Characteristics

The defined groups are analyzed to uncover patterns in coupon acceptance. Characteristics such as social habits, age, and income are considered to explain customer behavior.

Step 5: Visualizing Insights

The insights derived from the analysis are visualized using graphs and charts. This step makes patterns and trends easily interpretable.

Key Findings and Conclusion:

Social and Outgoing Drivers - Frequency of Bar Visits:

Drivers visiting bars more than once a month are likelier to accept bar coupons, highlighting their social and outgoing tendencies.

Younger Demographic - Age Factor:

Drivers under 30 show a higher acceptance rate for bar coupons, indicating their receptiveness to promotional offers associated with social activities.

High Acceptance Among Frequent Bar Visitors:

Regular bar-goers demonstrate a significant inclination to use bar coupons, validating the relationship between frequent visits and promotional acceptance.

Young and Active Lifestyle:

Younger drivers with active social lives are more inclined to accept bar coupons, aligning with trends seen among socially engaged individuals.

Social Engagement:

Drivers with adult passengers and those who are not widowed show a higher acceptance rate, implying a preference for social outings with peers.

Budget-Conscious Behavior:

Lower-income drivers who frequent budget-friendly restaurants are more likely to accept bar coupons, suggesting an attraction to cost-effective social options.

Summary Hypothesis:

Drivers who accept bar coupons are generally younger, socially active, and budget-conscious individuals. Their frequent visits to bars, coupled with social engagement and cost-awareness, make them highly receptive to promotional offers that enhance their social experiences affordably.

For more details on the logic and visualizations used in this project, please refer to the GitHub Pages.