Suppose there is a table like below (1 info row only):
| Blows Minute (BPM) |
Speed (RPM) | Power, PSI | Flow, PSI | Tool Sys |
|---|---|---|---|---|
| 0-2500 | 0-250 | 1.8 HP | 2.6-13.2 GPM | SDS Max |
How to scrape it using cheerio.js as a parser?
Month: April 2021
The Bike Sharing Demand problem requires using historical data on weather conditions and bicycle rental to predict the number of occupied bicycles (rentals) for a certain hour of a certain day.
In the original problem statement, there are 11 features available. The feature set contains both real, categorical, and binary data. For the demonstration, a training sample bike_sharing_demand.csv is used from the original data.
See the Bike Sharing Demand, part 1 of the task where we performed some initial problem analysis.
The problem is taken from kaggle.com. Based on historical data on bicycle rental and weather conditions, it is necessary to evaluate the demand for bicycle rental.
In the original problem statement, there are 11 features available. The feature set contains both real, categorical, and binary data. For the demonstration, a training sample bike_sharing_demand.csv is used from the original data.
See the Bike Sharing Demand, part 2 of the task where we performed advanced problem analysis.