This project analyzes operational data from Shishu Mela, a theme park, to understand visitor behavior, spending patterns, and satisfaction levels. Using a simulated dataset of 200 visitors, I developed an interactive Power BI Dashboard, performed statistical hypothesis testing using SPSS, and built a predictive model using BigML.
- Data Visualization: Microsoft Power BI (Dashboard & DAX)
- Statistical Analysis: IBM SPSS Statistics
- Machine Learning: BigML (Decision Tree)
- Data Cleaning: Microsoft Excel
The dataset contains 200 records with the following key attributes:
Visitor_Age: Age of the customer.Gender: Male/Female.Ticket_Price: Amount spent on tickets.Ride_Name: The specific ride chosen by the visitor.Satisfaction_Score: Rated on a scale of 1 to 5.
I designed a dynamic Power BI dashboard to visualize the park's daily operations and KPIs.
- Revenue Tracking: Visualized total ticket sales and revenue trends.
- Ride Popularity: Analyzed which rides are most preferred by different age groups using bar charts.
- Customer Demographics: Breakdown of visitors by Gender and Age.
- KPIs: Calculated Average Satisfaction Score and Total Footfall using DAX measures.
I conducted three specific statistical tests to validate business hypotheses:
- Objective: To determine if the average customer satisfaction is significantly different from Neutral (Score: 3).
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Hypothesis:
$H_0: \mu = 3$ vs$H_1: \mu \neq 3$ - Result: With a mean score of 3.42 and a significance value of <.001, we rejected the Null Hypothesis.
- Insight: Visitors are significantly satisfied with the park's services.
- Objective: To analyze if there is a significant difference in spending between Boys and Girls.
- Result: The significance value (p-value) was .302 (> 0.05).
- Insight: There is no significant difference in spending behavior based on gender. Both groups spend roughly the same amount.
- Objective: To predict Ticket Price based on Visitor Age.
- Result: The analysis showed a weak linear relationship (R-Square near 0.01, Sig .164).
- Insight: Age is not a strong predictor of spending in this specific dataset; visitors of all ages choose a mix of expensive and affordable rides.
To predict visitor preferences, I constructed a Decision Tree model using BigML.
- Target Variable:
Ride_Name - Outcome: The model generated rules to predict which ride a visitor is likely to choose based on their age group (e.g., Children <10 prefer 'Toy Train', Teenagers prefer '3D Cinema').
- High Satisfaction: The management is doing well in maintaining customer happiness (Score > 3).
- Universal Pricing: Since gender doesn't affect spending, marketing campaigns can be gender-neutral.
- Targeted Operations: The Decision Tree reveals distinct preferences by age, allowing for better queue management.
[Jahid Hasan]
- LinkedIn: https://www.linkedin.com/in/jahidstm/
- Email: jahidhasanstm@gmail.com
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