Predict New Restaurant Rating In Yelp
Pro
Details
The restaurant industry is tougher than ever, with reviews on the Internet from the first day a restaurant opens. But as a food lover, you and your friend decide to enter the industry and open your own restaurant. Because a restaurant's success is highly correlated to its reputation, you want to make sure that your restaurant gets the best reviews on the most popular restaurant review site: Yelp!
-
0.68Regression: R2= Coefficient of determination
-
Shape (188593, 25) -
Verified
Data Source
In this API we work with a set of actual data provided by Yelp. We have six files, which are listed below with a brief description:
* `yelp_business.json`: data regarding the location and attributes of all businesses in the data set
* `yelp_review.json`: ratings metadata by company
* `yelp_user.json`: user profile metadata by company
* `yelp_checkin.json`: online billing metadata by company
* `yelp_tip.json`: metadata of tips per company
* `yelp_photo.json`: photo metadata by company
* `yelp_business.json`: data regarding the location and attributes of all businesses in the data set
* `yelp_review.json`: ratings metadata by company
* `yelp_user.json`: user profile metadata by company
* `yelp_checkin.json`: online billing metadata by company
* `yelp_tip.json`: metadata of tips per company
* `yelp_photo.json`: photo metadata by company
Use Cases
If you are launching a new restaurant, or have a set of restaurants you want to predict your rating on Yelp, this endpoint will be very helpful in getting an accurate approximation of that rating.
External API Docs
This API has no external reference documentation
Notebook
This API has no reference notebook
Endpoints (2)
Title:
Endpoint #1 Current Details
Description:
Response
coefficients and interceptions of all data points
Base URL:
Requests:
Token:
every API have unique token. You'll get one once you activate the endpoint
Example responses
Press button to test this endpoint response
Code Examples:
Endpoint #1 Current Details
Description:
Get coefficients and intercepts from the created model
Response
coefficients and interceptions of all data points
Base URL:
https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/get-api
# GET
/yelp-rating-prediction/current-details/<token>
every API have unique token. You'll get one once you activate the endpoint
Press button to test this endpoint response
Code Examples:
const
axios = require('axios');
axios({
"method":
"GET",
"url":
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/get-api/yelp-rating-prediction/current-details/<token>",
"headers": {
"content-type":
"application/json"
}
}).then((response) => {
// handle sucess
console.log(response)
}).catch((error) => {
// handle error
console.log(error)
})
import
requests
url =
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/get-api/yelp-rating-prediction/current-details/<token>"
headers = {
"content-type":
"application/json"
}
response =
requests.request("GET", url, headers=headers)
print
(response.text)
require
'httparty'
url =
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/get-api/yelp-rating-prediction/current-details/<token>",
response =
HTTParty.get(url,
headers: { "Content-Type" => "application/json" })
puts
response.parsed_response
<?php
$curl =
curl_init();
$url =
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/get-api/yelp-rating-prediction/current-details/<token>";
curl_setopt_array($curl, array(
CURLOPT_URL => $url,
CURLOPT_CUSTOMREQUEST => "GET",
CURLOPT_HTTPHEADER => array(
"Content-Type: application/json"
),
));
$response =
curl_exec($curl);
curl_close($curl);
echo
$response;
Title:
Endpoint #2 Predict New Restaurant Rating
Description:
Response
rating prediction in stars
Base URL:
Requests:
Token:
every API have unique token. You'll get one once you activate the endpoint
Body parameters:
25
Code Examples:
Example responses
Enter next parameters to test endpoint response
Endpoint #2 Predict New Restaurant Rating
Description:
Hero you can predict the rating of a new restaurant in Yelp
Response
rating prediction in stars
Base URL:
https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/post-api
# POST
/yelp-rating-prediction/predict/<token>
every API have unique token. You'll get one once you activate the endpoint
Body parameters:
25
| Body parameter name | Body parameter type | Example | Requirement |
|---|---|---|---|
| alcohol | BOOLEAN | 0 | [Required] |
| has_bike_parking | BOOLEAN | 1 | [Required] |
| takes_credit_cards | BOOLEAN | 1 | [Required] |
| good_for_kids | BOOLEAN | 1 | [Required] |
| take_reservations | BOOLEAN | 1 | [Required] |
| has_wifi | BOOLEAN | 1 | [Required] |
| review_count | NUMBER | 10 | [Required] |
| price_range | NUMBER | 2 | [Required] |
| average_caption_length | NUMBER | 3 | [Required] |
| number_pics | NUMBER | 10 | [Required] |
| average_review_age | NUMBER | 10 | [Required] |
| average_review_length | NUMBER | 1200 | [Required] |
| average_review_sentiment | NUMBER | 0.9 | [Required] |
| number_funny_votes | NUMBER | 3 | [Required] |
| number_cool_votes | NUMBER | 6 | [Required] |
| number_useful_votes | NUMBER | 5 | [Required] |
| average_tip_length | NUMBER | 50 | [Required] |
| number_tips | NUMBER | 3 | [Required] |
| average_number_friends | NUMBER | 50 | [Required] |
| average_days_on_yelp | NUMBER | 1800 | [Required] |
| average_number_fans | NUMBER | 12 | [Required] |
| average_review_count | NUMBER | 123 | [Required] |
| average_number_years_elite | NUMBER | 0.5 | [Required] |
| weekday_checkins | NUMBER | 0 | [Required] |
| weekend_checkins | NUMBER | 0 | [Required] |
Code Examples:
const
axios = require('axios');
axios({
"method":
"POST",
"url":
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/post-api/yelp-rating-prediction/predict/<token>",
"data":
{
"alcohol":
0
,
"has_bike_parking":
1
,
"takes_credit_cards":
1
,
"good_for_kids":
1
,
"take_reservations":
1
,
"has_wifi":
1
,
"review_count":
10
,
"price_range":
2
,
"average_caption_length":
3
,
"number_pics":
10
,
"average_review_age":
10
,
"average_review_length":
1200
,
"average_review_sentiment":
0.9
,
"number_funny_votes":
3
,
"number_cool_votes":
6
,
"number_useful_votes":
5
,
"average_tip_length":
50
"number_tips":
3
,
"average_number_friends":
50
,
"average_days_on_yelp":
1800
,
"average_number_fans":
12
,
"average_review_count":
123
,
"average_number_years_elite":
0.5
,
"weekday_checkins":
0
,
"weekend_checkins":
0
,
}
"headers": {
"content-type":
"application/json"
}
}).then((response) => {
// handle sucess
console.log(response)
}).catch((error) => {
// handle error
console.log(error)
})
import
requests
url =
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/post-api/yelp-rating-prediction/predict/<token>"
data =
{
"alcohol":
0
,
"has_bike_parking":
1
,
"takes_credit_cards":
1
,
"good_for_kids":
1
,
"take_reservations":
1
,
"has_wifi":
1
,
"review_count":
10
,
"price_range":
2
,
"average_caption_length":
3
,
"number_pics":
10
,
"average_review_age":
10
,
"average_review_length":
1200
,
"average_review_sentiment":
0.9
,
"number_funny_votes":
3
,
"number_cool_votes":
6
,
"number_useful_votes":
5
,
"average_tip_length":
50
"number_tips":
3
,
"average_number_friends":
50
,
"average_days_on_yelp":
1800
,
"average_number_fans":
12
,
"average_review_count":
123
,
"average_number_years_elite":
0.5
,
"weekday_checkins":
0
,
"weekend_checkins":
0
,
}
headers = {
"content-type":
"application/json"
}
response =
requests.request("POST", url, data=data, headers=headers)
print
(response.text)
require
'httparty'
url =
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/post-api/yelp-rating-prediction/predict/<token>",
data =
{
"alcohol" =>
0
,
"has_bike_parking" =>
1
,
"takes_credit_cards" =>
1
,
"good_for_kids" =>
1
,
"take_reservations" =>
1
,
"has_wifi" =>
1
,
"review_count" =>
10
,
"price_range" =>
2
,
"average_caption_length" =>
3
,
"number_pics" =>
10
,
"average_review_age" =>
10
,
"average_review_length" =>
1200
,
"average_review_sentiment" =>
0.9
,
"number_funny_votes" =>
3
,
"number_cool_votes" =>
6
,
"number_useful_votes" =>
5
,
"average_tip_length" =>
50
"number_tips" =>
3
,
"average_number_friends" =>
50
,
"average_days_on_yelp" =>
1800
,
"average_number_fans" =>
12
,
"average_review_count" =>
123
,
"average_number_years_elite" =>
0.5
,
"weekday_checkins" =>
0
,
"weekend_checkins" =>
0
,
}
response =
HTTParty.post(url,
body: data.to_json,
headers: { "Content-Type" => "application/json" })
puts
response.parsed_response
<?php
$curl =
curl_init();
$url =
"https://dataendpoint.co/machine-learning-apis/predict-new-restaurant-rating-in-yelp/post-api/yelp-rating-prediction/predict/<token>";
curl_setopt_array($curl, array(
CURLOPT_URL => $url,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => "
{
"alcohol":
0
,
"has_bike_parking":
1
,
"takes_credit_cards":
1
,
"good_for_kids":
1
,
"take_reservations":
1
,
"has_wifi":
1
,
"review_count":
10
,
"price_range":
2
,
"average_caption_length":
3
,
"number_pics":
10
,
"average_review_age":
10
,
"average_review_length":
1200
,
"average_review_sentiment":
0.9
,
"number_funny_votes":
3
,
"number_cool_votes":
6
,
"number_useful_votes":
5
,
"average_tip_length":
50
"number_tips":
3
,
"average_number_friends":
50
,
"average_days_on_yelp":
1800
,
"average_number_fans":
12
,
"average_review_count":
123
,
"average_number_years_elite":
0.5
,
"weekday_checkins":
0
,
"weekend_checkins":
0
,
}
",
CURLOPT_HTTPHEADER => array(
"Content-Type: application/json"
),
));
$response =
curl_exec($curl);
curl_close($curl);
echo
$response;
Enter next parameters to test endpoint response
Enter parameters
