TL;DR:

In this project, we systematically measure watermelons using 38 different parameters in an effort to predict watermelon sweetness based on its externally measurable characteristics. Generally speaking, the metrics may be grouped as follows:

  • Color: Coloration on the exterior of the watermelon, including bands, scarring, and yellow spot.
  • Acoustics: Frequency analysis of “knocking” on the watermelon in different locations.
  • Structure: The dimensions of the watermelon, including its ability to stand upright.
  • Surface: Identification and surface area of scarring and the yellow spot.

Introduction

The Problem

In

The Company

As

Data Collection

In-the-moment metric extraction may include concurrent viewer retention, livestream sales metrics, and chatroom activity; in the long-term, metrics like average impact of event giveaways and long-term viewer retention can be examined. Relationships between livestream events and viewer retention provide statistically significant feedback to Cyclops about how to improve the structure of their next livestream — the long viewers are retained, the longer they have to pitch their products. Each livestream builds an ever-growing dataset of business-to-consumer (B2C) interactions unique to Cyclops’ development.

The Event

Registration

Prior

Attendance

Each

Fig. 1. Concurrent viewers VS time into stream

Within

Giveaways

During

Data Collection

Scraping

For

Fig. 2. The livestream data capture pipeline

When

Transcript parsing

After

Data Analysis

Concurrent viewers

Concurrent

Fig. 3. Plots show concurrent viewer count versus absolute time for (left) all streams and (right) select streams.

Additionally,

The impact of events

During

Fig. 4. Concurrent viewer count during (left) a single stream and (right) averaged over 11 streams.

Fig. 4 s

Event timing

The

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Analyzing the data left to right reveals some interesting points:

Results

Once

Inference results for each trained model

Of

Conclusion

The