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!
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0.68Regression: R2= Coefficient of determination
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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.
0 Comments
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
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