7eaddd9a54
ML - устранение неточностей и багов ML - оптимизация производительности DecisionTreeRegressor.Fit, RandomForestRegressor.Fit ML - тесты
49 lines
1.7 KiB
Plaintext
49 lines
1.7 KiB
Plaintext
name = MoscowHousing
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task = regression
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features = rooms,area,kitchen_area,floor,floors_total,metro_minutes,renovation
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target = price
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description.en = Apartment prices in Moscow with basic structural and location features.
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description.ru = Цены на квартиры в Москве с основными характеристиками жилья и расположения.
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source = Real estate listings (aggregated dataset)
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url = https://example.com/moscow-housing
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feature.price = float
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feature.rooms = int
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feature.area = float
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feature.kitchen_area = float
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feature.floor = int
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feature.floors_total = int
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feature.metro_minutes = float
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feature.renovation = categorical
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feature.price.ru = цена квартиры
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feature.rooms.ru = число комнат
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feature.area.ru = площадь квартиры
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feature.kitchen_area.ru = площадь кухни
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feature.floor.ru = этаж
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feature.floors_total.ru = этажей в доме
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feature.metro_minutes.ru = минуты до метро
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feature.renovation.ru = тип ремонта
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feature.price.en = apartment price
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feature.rooms.en = number of rooms
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feature.area.en = apartment area
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feature.kitchen_area.en = kitchen area
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feature.floor.en = floor
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feature.floors_total.en = total floors in building
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feature.metro_minutes.en = minutes to metro
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feature.renovation.en = renovation type
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value.renovation.without.ru = без ремонта
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value.renovation.cosmetic.ru = косметический
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value.renovation.euro.ru = евроремонт
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value.renovation.designer.ru = дизайнерский
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value.renovation.without.en = without renovation
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value.renovation.cosmetic.en = cosmetic
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value.renovation.euro.en = european renovation
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value.renovation.designer.en = designer renovation |