Get ML Features

Prebuilt intelligence for Machine Learning models.

Access prebuilt Machine Learning features that will take your forecast models and results to the next level, fast. Gain access to over 100 ML-ready features that are guaranteed to improve your forecasting accuracy.

Request

HTTP Request

POST https://api.predicthq.com/v1/features/

Request Headers

Header	Value
Accept	Receive results in JSON or CSV by specifying the appropriate Accept header. Supported values: application/json text/csv

Query Parameters

Parameter	Description
offset number	The number of results to skip. The default is 0. E.g. ?offset=20
limit number	The maximum number of results to return per page. The default limit is 100 and the maximum per page is 100. E.g. ?limit=10

Request Body

Field	Description
active object	The date range to calculate features for. This is named "active" because it includes events that are active within the date range. A multi-day event might start or end outside the specified date range - the days the event is active within the specified range will be included in the calculations. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal Note that all dates here are in local time (not UTC). Features API works on specific locations. Please also note that the maximum supported date range is 90 days. If you require features over a wider date range please make multiple API requests. E.g. { "active": { "gte": "2019-11-28", "lte": "2019-11-29" } }
beam object	An optional beam analysis object which if provided will calculate the location, interval, week_start_day , phq_* features including stats and phq_rank from the Beam analysis and optionally the Beam analysis group. Supports the following fields: analysis_id - Beam analysis id (Required if beam block is present in the request body) group_id - Beam analysis group id (Optional) If beam.analysis_id is provided, the location, interval, week_start_day , phq_* features includingstats and phq_rank will be calculated from the Beam analysis. E.g. { "beam": { "analysis_id": "0SXAmHsoZo0" } } If both beam.analysis_id and beam.group_id are provided, all the above fields will be calculated from the Beam analysis but the phq_* features will be calculated from the Beam analysis Group. Note: This is useful for when you want to request features for a variety of locations while maintaining feature consistency by limiting the returned features to those of the group's feature importance results. E.g. { "beam": { "analysis_id": "0SXaMHsoYo0", "group_id": "QxE9BcLq7ZY" } }
hour_of_day_start object	Time range (per day) to calculate features for. Note: This field is currently only supported on phq_viewership_* features. If your location only operates within certain hours of the day you can use this filter to only include records that are happening within those hours. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal The values are hours between 0 and 23 (i.e. 24h format). E.g. only include events happening between 1pm and 3pm each day. { "hour_of_day_start": { "gte": 13, "lte": 15 } }
location object	Location to calculate features for. You can specify the location as a latitude/longitude (with radius), Place ID(s) or Saved Location ID(s). We recommend using a lat/lon+radius or a saved location id (for a point and radius location) as they could define the location of your interest more accurately. To work out a suitable radius around your location we strongly recommend using our Suggested Radius API. Note: When using Place IDs or Saved Location IDs a maximum of 3 IDs may be used. Note: When using lat/lon+radius, the radius must be in the format <radius><radius_unit>, where <radius> is an integer or a float number up to 2 decimal places and <radius_unit> is one of: m - meters km - kilometers ft - feet mi - miles E.g. using Place IDs: { "location": { "place_id": [ 5224323, 5811704, 4887398 ] } } E.g. using Saved Location IDs: { "location": { "saved_location_id": [ "BN7ZSw8xza9FviPVfyCycd", "X3uyTFbDOUaX2q_Qh5i31b", "X3uyTFbDOUhrfq_Qh5i31A" ] } } E.g. using a latitude/longitude and radius (recommended): { "location": { "geo": { "lat": 41.75038, "lon": -71.49978, "radius": "2.62mi" } } }
interval string optional	Aggregation interval. Possible values: day (default) for daily aggregation week for weekly aggregation
week_start_day string optional	The weekday to be treated as the start of the week. Possible values: monday (default) tuesday wednesday thursday friday saturday sunday Only applicable when interval is set to week.
predicted_events object optional	Include or Exclude predicted events. Supports the following fields: exclude Possible values: true false(default) Include predicted events E.g. { "predicted_events": { "exclude": false } }
<feature_name> object or boolean	The name of the feature you're requesting. You can request multiple features in a single request. Features can be further configured here, or you can set the value as true to perform the default calculations for that feature. Please see the tables below for a list of all currently supported features and how they can be further configured. E.g. requesting certain stats fields and filtering for records with a PHQ Rank over 50. { "phq_attendance_sports": { "stats": ["count", "std_dev", "median"], "phq_rank": { "gt": 50 } } } E.g. requesting the default calculations for a feature. { "phq_attendance_concerts": true }

Available Features

PHQ Attendance Features

PHQ Attendance features provide daily-level aggregated stats based on the number of people who we predict will attend events on a given day. This takes into account complications like distributing attendance across multi-day events.

We recommend using impact pattern features instead of generic features if you are in one of the supported industries. See #attended-events-impact-patterns.

Attended Events Generic Features

Use the generic features in this table if you are not in one of the industries covered by the impact pattern features listed below.

Feature	Description
phq_attendance_academic_graduation	Academic - Graduation
phq_attendance_academic_social	Academic - Social
phq_attendance_community	Community
phq_attendance_concerts	Concerts
phq_attendance_conferences	Conferences
phq_attendance_expos	Expos
phq_attendance_festivals	Festivals
phq_attendance_performing_arts	Performing Arts
phq_attendance_sports	Sports
phq_attendance_school_holidays	School Holidays

Attended Events Impact Pattern Features

Predicted Impact Patterns model the impact of leading days (days before the event), lagging days (days after an event), and the days the event occurs. In the Features API, Impact Patterns are provided as different features with a feature per industry. We have impact pattern features for the accommodation, hospitality (which covers food & beverage including restaurants), and retail industries.

The features above are generic features and the features in the table below are the impact pattern features per industry. For example, if you were in the accommodation industry and wanted a feature for the conferences category you'd use phq_attendance_conferences_accommodation.

We recommend using impact pattern features instead of generic features if you are in one of the supported industries. See Predicted Impact Patterns for more details.

Feature	Description
phq_attendance_community_accommodation	Community accommodation impact
phq_attendance_concerts_accommodation	Concerts accommodation impact
phq_attendance_conferences_accommodation	Conferences accommodation impact
phq_attendance_expos_accommodation	Expos accommodation impact
phq_attendance_festivals_accommodation	Festivals accommodation impact
phq_attendance_performing_arts_accommodation	Performing Arts accommodation impact
phq_attendance_sports_accommodation	Sports accommodation impact
phq_attendance_community_hospitality	Community hospitality impact
phq_attendance_concerts_hospitality	Concerts hospitality impact
phq_attendance_conferences_hospitality	Conferences hospitality impact
phq_attendance_expos_hospitality	Expos hospitality impact
phq_attendance_festivals_hospitality	Festivals hospitality impact
phq_attendance_performing_arts_hospitality	Performing Arts hospitality impact
phq_attendance_sports_hospitality	Sports hospitality impact
phq_attendance_community_retail	Community Retail impact
phq_attendance_concerts_retail	Concerts Retail impact
phq_attendance_conferences_retail	Conferences Retail impact
phq_attendance_expos_retail	Expos Retail impact
phq_attendance_festivals_retail	Festivals Retail impact
phq_attendance_performing_arts_retail	Performing Arts Retail impact
phq_attendance_sports_retail	Sports Retail impact

Configuration

You can configure PHQ Attendance features using the options below.

Field	Description
stats object optional	You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like. Default fields are count and sum. Supported fields are: count sum min max avg median std_dev E.g. { "stats": [ "count", "std_dev", "median" ] }
phq_rank object optional	Filter for events with a PHQ Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "phq_rank": { "gt": 50, "lt": 80 } }
local_rank object optional	Filter for events with a Local Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "local_rank": { "gt": 50, "lt": 80 } }

PHQ Impact Features

PHQ Impact features provide daily-level aggregated stats based on the predicted impact of an event. This takes into account complications like Impact Patterns (leading and lagging effects of an event).

Holidays and Observances Impact Pattern Features

These features include the Predicted Impact Patterns for public holidays and observances. For example, these features will show when people typically arrive and book accommodation before a holiday and if they tend to leave after the holiday. See Predicted Impact Patterns

We recommend that if you operate in the industries listed below you use the demand impact features for holidays and observances instead of the generic features as these will result in greater forecast accuracy as they include the impact before an event starts and after it finishes.

Feature	Category	Industry
phq_impact_public_holidays	Public Holidays	N/A
phq_impact_public_holidays_accommodation	Public Holidays	Accomodation
phq_impact_public_holidays_hospitality	Public Holidays	Hospitality/Food & Beverage*
phq_impact_public_holidays_retail	Public Holidays	Retail
phq_impact_observances	Observances	N/A
phq_impact_observances_accommodation	Observances	Accomodation
phq_impact_observances_retail	Observances	Retail
phq_impact_observances_hospitality	Observances	Hospitality/Food & Beverage
phq_impact_school_holidays	School Holidays	N/A
phq_impact_academic_exam	Academic	N/A
phq_impact_academic_holiday	Academic	N/A
phq_impact_academic_session	Academic	N/A

Severe Weather Impact Features

Currently supported industries are: Retail.

Feature	Description	Industry
phq_impact_severe_weather_air_quality_retail	Severe Weather - Air Quality	Retail
phq_impact_severe_weather_blizzard_retail	Severe Weather - Blizzard	Retail
phq_impact_severe_weather_cold_wave_retail	Severe Weather - Cold Wave - (All)	Retail
phq_impact_severe_weather_cold_wave_snow_retail	Severe Weather - Cold Wave - Snow	Retail
phq_impact_severe_weather_cold_wave_storm_retail	Severe Weather - Cold Wave - Storm	Retail
phq_impact_severe_weather_dust_retail	Severe Weather - Dust - (All)	Retail
phq_impact_severe_weather_dust_storm_retail	Severe Weather - Dust - Storm	Retail
phq_impact_severe_weather_flood_retail	Severe Weather - Flood	Retail
phq_impact_severe_weather_heat_wave_retail	Severe Weather - Heat Wave	Retail
phq_impact_severe_weather_hurricane_retail	Severe Weather - Hurricane	Retail
phq_impact_severe_weather_thunderstorm_retail	Severe Weather - Thunderstorm	Retail
phq_impact_severe_weather_tornado_retail	Severe Weather - Tornado	Retail
phq_impact_severe_weather_tropical_storm_retail	Severe Weather - Tropical Storm	Retail

Attended Events Impact Features

See #attended-events-impact-pattern-features

Configuration

You can configure PHQ Impact features using the options below.

Field	Description
stats object optional	You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like. Default fields are count and sum. Supported fields are: count sum min max avg median std_dev E.g. { "stats": [ "count", "std_dev", "median" ] }
phq_rank object optional	Filter for events with a PHQ Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "phq_rank": { "gt": 50, "lt": 80 } }
local_rank object optional	Filter for events with a Local Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "local_rank": { "gt": 50, "lt": 80 }

PHQ Spend Features

PHQ Spend features provide daily-level aggregated stats based on total USD we predict will be spent during events on a given day. This takes into account complications like distributing attendance across multi-day events.

You can request industry-specific features which are tuned to one of three potential industries:

• Accommodation: Event spend relating to hotels and hosts for the purposes of staying at during events. Spend can extend before and after an event actually starts/ends.

• Hospitality: Event spend on restaurants, food and drinks. Hotel restaurants are included in this industry.

• Transportation: Ground-based transportation for the purposes of getting to and from an event. Includes public and private transport, such as taxis, rails, busses and rideshares.

Feature	Description
phq_spend_conferences	Conferences
phq_spend_expos	Expos
phq_spend_sports	Sports
phq_spend_community	Community
phq_spend_concerts	Concerts
phq_spend_festivals	Festivals
phq_spend_performing_arts	Performing Arts
phq_spend_conferences_accommodation	Conferences - Accommodation
phq_spend_expos_accommodation	Expos - Accommodation
phq_spend_sports_accommodation	Sports - Accommodation
phq_spend_community_accommodation	Community - Accommodation
phq_spend_concerts_accommodation	Concerts - Accommodation
phq_spend_festivals_accommodation	Festivals - Accommodation
phq_spend_performing_arts_accommodation	Performing Arts - Accommodation
phq_spend_conferences_hospitality	Conferences - Hospitality
phq_spend_expos_hospitality	Expos - Hospitality
phq_spend_sports_hospitality	Sports - Hospitality
phq_spend_community_hospitality	Community - Hospitality
phq_spend_concerts_hospitality	Concerts - Hospitality
phq_spend_festivals_hospitality	Festivals - Hospitality
phq_spend_performing_arts_hospitality	Performing Arts - Hospitality
phq_spend_conferences_transportation	Conferences - Transportation
phq_spend_expos_transportation	Expos - Transportation
phq_spend_sports_transportation	Sports - Transportation
phq_spend_community_transportation	Community - Transportation
phq_spend_concerts_transportation	Concerts - Transportation
phq_spend_festivals_transportation	Festivals - Transportation
phq_spend_performing_arts_transportation	Performing Arts - Transportation

Configuration

You can configure PHQ Spend features using the options below.

Field	Description
stats object optional	You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like. Default fields are count and sum. Supported fields are: count sum min max avg median std_dev E.g. { "stats": [ "count", "std_dev", "median" ] }
phq_rank object optional	Filter for events with a PHQ Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "phq_rank": { "gt": 50, "lt": 80 } }
local_rank object optional	Filter for events with a Local Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "local_rank": { "gt": 50, "lt": 80 } }

PHQ Viewership Features

PHQ Viewership features provide daily-level aggregated stats based on the number of people who we predict will view broadcasts on a given day.

Feature	Description
phq_viewership_sports	Sports - (All)
phq_viewership_sports_american_football	American Football - (All)
phq_viewership_sports_american_football_ncaa_men	American Footbal - NCAA Men's
phq_viewership_sports_american_football_nfl	American Football - NFL
phq_viewership_sports_auto_racing	Automotive Racing - All
phq_viewership_sports_auto_racing_indy_car	Automotive Racing - Indy Car
phq_viewership_sports_auto_racing_nascar	Automotive Racing - NASCAR
phq_viewership_sports_baseball	Baseball - (All)
phq_viewership_sports_baseball_mlb	Baseball - MLB
phq_viewership_sports_baseball_ncaa_men	Baseball - NCAA Men's
phq_viewership_sports_basketball	Basketball - (All)
phq_viewership_sports_basketball_nba	Basketball - NBA
phq_viewership_sports_basketball_ncaa_men	Basketball - NCAA Men's
phq_viewership_sports_basketball_ncaa_women	Basketball - NCAA Women's
phq_viewership_sports_boxing	Boxing - (All)
phq_viewership_sports_golf	Golf - (All)
phq_viewership_sports_golf_masters	Golf - Masters
phq_viewership_sports_golf_pga_championship	Golf - PGA Championships
phq_viewership_sports_golf_pga_tour	Golf - PGA Tours
phq_viewership_sports_golf_us_open	Golf - US Open
phq_viewership_sports_horse_racing	Horse Racing - (All)
phq_viewership_sports_horse_racing_belmont_stakes	Horse Racing - Belmont Stakes
phq_viewership_sports_horse_racing_kentucky_derby	Horse Racing - Kentucky Derby
phq_viewership_sports_horse_racing_preakness_stakes	Horse Racing - Preakness Stakes
phq_viewership_sports_ice_hockey	Ice Hockey - (All)
phq_viewership_sports_ice_hockey_nhl	Ice Hockey - NHL
phq_viewership_sports_mma	Mixed Martial Arts - (All)
phq_viewership_sports_mma_ufc	Mixed Martial Arts - UFC
phq_viewership_sports_soccer	Soccer - (All)
phq_viewership_sports_soccer_concacaf_champions_league	Soccer - CONCACAF Champions League
phq_viewership_sports_soccer_concacaf_gold_cup	Soccer - CONCACAF Gold Cup
phq_viewership_sports_soccer_copa_america_men	Soccer - COPA America Men's
phq_viewership_sports_soccer_fifa_world_cup_women	Soccer - FIFA World Cup Women's
phq_viewership_sports_soccer_fifa_world_cup_men	Soccer - FIFA World Cup Men's
phq_viewership_sports_soccer_mls	Soccer - MLS
phq_viewership_sports_soccer_uefa_champions_league_men	Soccer - UEFA Champions League Men's
phq_viewership_sports_softball	Softball - (All)
phq_viewership_sports_softball_ncaa_women	Softball - NCAA Women's
phq_viewership_sports_tennis	Tennis - (All)
phq_viewership_sports_tennis_us_open	Tennis - US Open
phq_viewership_sports_tennis_wimbledon	Tennis - Wimbledon

Configuration

You can configure PHQ Attendance features using the options below.

Field	Description
stats object optional	You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like. Default fields are count and sum. Supported fields are: count sum min max avg median std_dev E.g. { "stats": [ "count", "std_dev", "median" ] }
phq_rank object optional	Filter for events with a PHQ Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "phq_rank": { "gt": 50, "lt": 80 } }
local_rank object optiona	Filter for events with a Local Rank within a certain range. Supports the following fields: gt - greater than gte - greater than or equal lt - less than lte - less than or equal E.g. { "local_rank": { "gt": 50, "lt": 80 } }

PHQ Rank Features

PHQ Rank features provide the daily-level aggregated sum of events bucketed by PHQ Rank level (1-5).

PHQ Rank Impact Pattern Features

See the "Holidays and Observances Impact Pattern Features" under PHQ Impact in the tab above. These features cover the Accommodation, Retail, and Hospitality (Food & Beverage) industries.

We recommend that if you operate in the supported industries you use the demand impact features for holidays and observances instead of the generic features as these will result in greater forecast accuracy as they include the impact before an event starts and after it finishes.

PHQ Rank Generic Features

These are generic features that do not include Predicted Impact Patterns and should be used if you are not in one of the industries that we have impact patterns for.

Feature	Description
phq_rank_observances	Observances
phq_rank_public_holidays	Public Holidays
phq_rank_school_holidays	School Holidays
phq_rank_academic_session	Academic - Session
phq_rank_academic_exam	Academic - Exam
phq_rank_academic_holiday	Academic - Holiday
phq_rank_daylight_savings	Daylight savings
phq_rank_health_warnings	Health Warnings
phq_rank_politics	Politics

Configuration

PHQ Rank features cannot currently be configured further. When requesting phq_rank_* features set the value as true indicating you require the default calculations.

Response

Response Fields

Field	Description
results array	List of results where each item is a Feature. Please refer to the Feature Response Fields section below for the structure of each record. Note that pagination is not required in this API.

Feature Response Fields

Other than the date, the structure of each result here will depend on how you configured the feature in your request and the type of feature.

PHQ Attendance Features

Field	Description
date string	Date in local time. E.g. 2023-10-01
<phq_attendance_*> object	Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request. PHQ Attendance features are stats-based. Default fields are count and sum. E.g. { "stats": { "count": 5, "sum": 17307, "min": 1000, "max": 9215, "avg": 3461.4, "median": 2620.0, "std_dev": 2978.810473997968 } }

PHQ Impact Features

Field	Description
date string	Date in local time. E.g. 2023-10-01
<phq_impact_*> object	Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request. PHQ Impact features are stats-based. Default fields are count and sum. E.g. { "stats": { "count": 5, "sum": 17307, "min": 1000, "max": 9215, "avg": 3461.4, "median": 2620.0, "std_dev": 2978.810473997968 } }

PHQ Rank Features

Field	Description
date string	Date in local time. E.g. 2023-10-01
<phq_rank_*> object	Daily-level feature result. The structure of the result here is always the same as PHQ Rank features cannot currently be configured. Will contain a rank_levels field which indicates the sum of matching events active on the date at each PHQ Rank level. PHQ Rank is on a scale of 0 to 100 and the levels are bucketed as: 1 - Minor (rank between 0 and 20). 2 - Moderate (rank between 21 and 40). 3 - Important (rank between 41 and 60). 4 - Significant (rank between 61 and 80). 5 - Major (rank between 81 and 100). E.g. { "rank_levels": { "1": 0, "2": 0, "3": 0, "4": 2, "5": 0 } }

PHQ Spend Features

Field	Description
date string	Date in local time. E.g. 2023-10-01
<phq_spend_*> object	Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request. PHQ Spend features are stats-based. Default fields are count and sum. E.g. { "stats": { "count": 5, "sum": 17307, "min": 1000, "max": 9215, "avg": 3461.4, "median": 2620.0, "std_dev": 2978.810473997968 } }

PHQ Viewership Features

Field	Description
date string	Date in local time. E.g. 2023-10-01
<phq_viewership_*> object	Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request. PHQ Viewership features are stats-based. Default fields are count and sum. E.g. { "stats": { "count": 5, "sum": 17307, "min": 1000, "max": 9215, "avg": 3461.4, "median": 2620.0, "std_dev": 2978.810473997968 } }

Response Format

You can receive responses formatted as JSON (default) or CSV. Use the Accept header when performing your request to define which format you would like to receive.

JSON

With the Accept header set to application/json you will receive the results as JSON like the example below:

{
  "results": [
    {
      "date": "2019-11-16",
      "phq_attendance_concerts": {
        "stats": {
          "count": 20,
          "sum": 6751
        }
      },
      "phq_attendance_conferences": {
        "stats": {
          "min": 1500,
          "max": 1500
        }
      },
      "phq_attendance_sports": {
        "stats": {
          "count": 5,
          "sum": 17307,
          "min": 1000,
          "max": 9215,
          "avg": 3461.4,
          "median": 2620.0,
          "std_dev": 2978.810473997968
        }
      },
      "phq_rank_public_holidays": {
        "rank_levels": {
          "1": 0,
          "2": 0,
          "3": 0,
          "4": 0,
          "5": 0
        }
      }
    },
    {
      "date": "2019-11-17",
      "phq_attendance_concerts": {
        "stats": {
          "count": 2,
          "sum": 241
        }
      },
      "phq_attendance_conferences": {
        "stats": {
          "min": 67,
          "max": 67
        }
      },
      "phq_attendance_sports": {
        "stats": {
          "count": 1,
          "sum": 852,
          "min": 852,
          "max": 852,
          "avg": 852.0,
          "median": 852.0,
          "std_dev": 0.0
        }
      },
      "phq_rank_public_holidays": {
        "rank_levels": {
          "1": 0,
          "2": 0,
          "3": 0,
          "4": 0,
          "5": 0
        }
      }
    }
  ]
}

CSV

With the Accept header set to text/csv you will receive the results as CSV like the example below:

date,phq_attendance_concerts_stats_count,phq_attendance_concerts_stats_sum,phq_attendance_conferences_stats_min,phq_attendance_conferences_stats_max,phq_attendance_sports_stats_count,phq_attendance_sports_stats_sum,phq_attendance_sports_stats_min,phq_attendance_sports_stats_max,phq_attendance_sports_stats_avg,phq_attendance_sports_stats_median,phq_attendance_sports_stats_std_dev,phq_rank_public_holidays_rank_levels_1,phq_rank_public_holidays_rank_levels_2,phq_rank_public_holidays_rank_levels_3,phq_rank_public_holidays_rank_levels_4,phq_rank_public_holidays_rank_levels_5
2019-11-16,43,24546,11,1000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-17,25,13440,11,146,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-18,6,2021,11,700,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-19,10,6047,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-20,14,59704,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-21,21,60851,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-22,35,25760,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-23,29,25425,11,394,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-24,18,23410,11,500,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-25,6,5122,11,1000,1,18064,18064,18064,18064.0,18064.0,0.0,0,0,0,0,0
2019-11-26,8,6861,11,1000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-27,12,6225,11,500,1,18064,18064,18064,18064.0,18064.0,0.0,0,0,0,0,0

The same data represented as a table:

date	phq_attendance_concerts_stats_count	phq_attendance_concerts_stats_sum	phq_attendance_conferences_stats_min	phq_attendance_conferences_stats_max	phq_attendance_sports_stats_count	phq_attendance_sports_stats_sum	phq_attendance_sports_stats_min	phq_attendance_sports_stats_max	phq_attendance_sports_stats_avg	phq_attendance_sports_stats_median	phq_attendance_sports_stats_std_dev	phq_rank_public_holidays_rank_levels_1	phq_rank_public_holidays_rank_levels_2	phq_rank_public_holidays_rank_levels_3	phq_rank_public_holidays_rank_levels_4	phq_rank_public_holidays_rank_levels_5
2019-11-16	43	24546	11	1000	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-17	25	13440	11	146	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-18	6	2021	11	700	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-19	10	6047	11	171000	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-20	14	59704	11	171000	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-21	21	60851	11	171000	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-22	35	25760	11	171000	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-23	29	25425	11	394	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-24	18	23410	11	500	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-25	6	5122	11	1000	1	18064	18064	18064	18064.0	18064.0	0.0	0	0	0	0	0
2019-11-26	8	6861	11	1000	0	0	0	0	0.0	0.0		0	0	0	0	0
2019-11-27	12	6225	11	500	1	18064	18064	18064	18064.0	18064.0	0.0	0	0	0	0	0

Examples

curl

curl -X POST "https://api.predicthq.com/v1/features/?offset=0&limit=100" \
     -H "Accept: application/json" \
     -H "Authorization: Bearer $ACCESS_TOKEN" \
     --data @<(cat <<EOF
    {
        "active": {
            "gte": "2019-11-16",
            "lte": "2019-11-17"
        },
        "location": {
            "geo": {
                "lat": "37.78428",
                "lon": "-122.40075",
                "radius": "2.6mi"
            }
        },
        "phq_attendance_conferences": {
            "stats": [
                "min",
                "max"
            ]
        },
        "phq_attendance_sports": {
            "stats": ["count", "std_dev", "median"],
            "phq_rank": { 
                "gt": 50
            }    
        },
        "phq_attendance_concerts": true,
        "phq_rank_public_holidays": true
    }
    EOF
    )

python

import requests

data = {
    "active": {
        "gte": "2019-11-16",
        "lte": "2019-11-27"
    },
    "location": {
        "geo": {
            "lat": "37.78428",
            "lon": "-122.40075",
            "radius": "2.6mi"
        }
    },
    "phq_attendance_conferences": {
        "stats": [
            "min",
            "max"
        ]
    },
    "phq_attendance_sports": {
        "stats": ["count", "std_dev", "median"],
        "phq_rank": { 
            "gt": 50
        }    
    },
    "phq_attendance_concerts": True,
    "phq_rank_public_holidays": True
}

response = requests.post(
    url="https://api.predicthq.com/v1/features/",
    headers={
      "Authorization": "Bearer $ACCESS_TOKEN",
      "Accept": "application/json"
    },
    params={
      "offset": 0,
      "limit": 100
    },
    json=data
)

print(response.json())

python sdk

from predicthq import Client

phq = Client(access_token="$ACCESS_TOKEN")

for feature in phq.features.obtain_features(
        active__gte="2019-11-16",
        active__lte="2019-11-27",
        location__geo={
            "lat": "37.78428",
            "lon": "-122.40075",
            "radius": "2.6mi"
        },
        phq_attendance_conferences__stats=["min", "max"],
        phq_attendance_sports__stats=["count", "std_dev", "median"],
        phq_attendance_sports__phq_rank={
            "gt": 50
        },
        phq_attendance_concerts=True,
        phq_rank_public_holidays=True
):
    print(feature.date, feature.phq_attendance_conferences.stats.min, 
        feature.phq_attendance_conferences.stats.max,
        feature.phq_attendance_sports.stats.count,
        feature.phq_attendance_sports.stats.std_dev,
        feature.phq_attendance_sports.stats.median,
        feature.phq_attendance_concerts.stats.count,
        feature.phq_attendance_concerts.stats.sum,
        feature.phq_rank_public_holidays.rank_levels)

Guides

Below are some guides relevant to this API:

• Increase Accuracy with the Features API

• Get ML Features

• Demand Forecasting with Event Features

• Aggregating Live TV Events