analysis of BOAT LISTING SURVEY
Prepared By : VIVEK PATEL
Presented to : The Product Manager
Agenda for a Meeting
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Work Motivation
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Exploratory Data Analysis Findings
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Predictive Model’s Findings
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Results Summary and Next Steps Suggestions.
Motivation
Business Problem :
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Boost Traffic to Website
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To list and stop boats that do not receive many views.
Work Plan:
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Analyze the relationship between the number of views with respect to all the other characteristics.
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Build a predictive model in order to prevent listing boats that won’t get high number of views.
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Assess the predictive power of the boat’s characteristics.
All Types of Boats vs Views
→What's Going On in This Graph?
Exploratory Data Analysis Findings:
(i) Pie chart of Boat Types
(ii) Boats we can remove from Website
Exploratory Data Analysis Findings:
Let's Understand Where to Focus
Exploratory Data Analysis Findings:
Predictive Model Findings:
Correlation of Features with Number of views last 7 days
Model Comparison(Lower the better)
Summary and Future Work
Work impact
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Explore the areas where we see high number of views for a boat in each of its characteristics.
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identified boats from listing which are not getting views
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Built a predictive model that can help prevent listing boats that won’t get high number of views.
Next Steps
- I would recommend optimizing this predictive model for deployment and hosting.
- I would also recommend to try increase Number of listings using promotion countries where boats are less listed.
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I can assess in the future if this would lead to an increase in the number of people who sign up.
Thank You ❤️
DATACAMP
By vivekcodes