> For the complete documentation index, see [llms.txt](https://docs.predicthq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.predicthq.com/getting-started/guides/features-api-guides/increase-accuracy-with-the-features-api.md). # Increase Accuracy with the Features API {% embed url="" %} Features API Product Demo {% endembed %} Once you’ve familiarized yourself with our data, you’ll likely find that focusing on individual spikes often leads to a data set too small to accurately correlate. By doing it at an aggregate level, a data science team will be looking at the volume of spike days to prove a correlation between demand and events based on category features. Features API aggregates PHQ Attendance figures, PHQ Viewership figures and PHQ Rank counts (in buckets by rank range) for a given category feature in a particular location on a given day, and returns desired statistics. These evaluated statistics can be used to quickly gauge and understand the demand impact on a location for a given day for a particular category. For example, at a future date in Sydney, there is a major sports game, a street fair, an international film festival, the Symphony orchestra playing, and more. The combined impact of all these events might result in a total aggregate attendance (when the various category aggregated attendance values are summed up) score of 150,000 and this could be across a hundred events or more. This represents a prediction of 150,000 people attending events on that day in the location. The Features API returns requested statistical values (`sum`, `count`, `average`, `min`, `max`, `median`, `std_dev`) per day for a specified date range, across a specified attendance category feature - *see* [PHQ Attendance Response](/api/features/get-features.md). Similarly, Features API returns requested statistical values, across a specified viewership category feature. For non-attendance-based events the rank of those events impacting that location on those days are bucketed into a relevant rank range in the response for evaluation. See [the API documentation](/api/features/get-features.md) for more details on the API. See also [this tutorial](/getting-started/guides/tutorials/improving-demand-forecasting-models-with-event-features.md) for how to incorporate features from the Features API into demand forecasting models. ### Features API Endpoint {% embed url="" %} How to use Features API {% endembed %} {% hint style="success" %} **Recommended: use `beam.analysis_id`**\ The most reliable way to call the Features API is by supplying a `beam.analysis_id`. This automatically applies the correct location boundary (from your Saved Location), selects only the event categories that materially drive demand at that location, and applies calibrated rank thresholds — no manual configuration needed. [Run Beam first](/getting-started/guides/beam-guides.md), then pass the returned `analysis_id` to the Features API. {% endhint %} #### With a Beam Analysis ID (Recommended) Supplying `beam.analysis_id` automatically configures the location, feature selection, and rank filters from your Beam results. You do not need to specify individual feature names or a location separately. ```python import requests data = { "active": { "gte": "2019-11-16", "lte": "2019-11-27" }, "beam": { "analysis_id": "$ANALYSIS_ID" } } response = requests.post( url="https://api.predicthq.com/v1/features/", headers={ "Authorization": "Bearer $API_TOKEN", "Accept": "application/json" }, json=data ) print(response.json()) ``` #### Without Beam (Manual Configuration) If you haven’t run Beam yet, you can configure the Features API manually by specifying a location and the feature names you want. Use a `saved_location_id` for location (preferred), or provide a `place_id` or lat/lon with radius. Choose feature names based on your industry’s [recommended categories](/getting-started/guides/industry-specific-event-filters.md) until you can run Beam. {% tabs %} {% tab title="python" %} ```python import requests data = { "active": { "gte": "2019-11-16", "lte": "2019-11-27" }, "location": { "saved_location_id": "$SAVED_LOCATION_ID" }, "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 $API_TOKEN", "Accept": "application/json" }, json=data ) print(response.json()) ``` {% endtab %} {% tab title="python sdk" %} ```python from predicthq import Client phq = Client(access_token="$API_TOKEN") for feature in phq.features.obtain_features( active__gte="2019-11-16", active__lte="2019-11-27", location__saved_location_id="$SAVED_LOCATION_ID", 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) ``` {% endtab %} {% endtabs %} A snippet of the full results are shown below: ```json { "results": [ { "date": "2020-02-01", "phq_attendance_community": { "stats": { "count": 24, "sum": 3135, "avg": 130.625 } }, "phq_attendance_concerts": { "stats": { "count": 38, "sum": 25478, "avg": 670.4736842105264 } }, "phq_attendance_conferences": { "stats": { "count": 2, "sum": 5100, "avg": 2550.0 } }, "phq_attendance_sports": { "stats": { "count": 6, "sum": 34259, "avg": 5709.833333333333 } }, "phq_viewership_sports_american_football_nfl": { "stats": { "count": 2, "sum": 16544, "avg": 8272 } } }, ... ] } ``` --- # Agent Instructions This documentation is published with GitBook. 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