House Price Prediction using Machine Learning Article Swipe
Rutwika Sudhakar Patil
,
Samiksha Devidas Patil
,
Raksha Dilip Attarde
,
Dinesh Patil
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48175/ijarsct-1693
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48175/ijarsct-1693
Machine Learning is seeing like its growth is much more rapid in this decade. Lots of applications of Machine Learning and algorithms of Machine Learning is evolving day to day. One of the application found in journals which is nothing but house price prediction. Prices of the houses are increasing each and every year which has postulate the modeling of house price prediction. All these models constructed using Machine Learning, help the customers for purchasing a house which is suitable as per their needs. The work which is proposed makes the use of all the attributes or all features of houses such as travelling facility from the location, In the proposed work, the house availability based on desired features of the house and house price prediction are modelled.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.48175/ijarsct-1693
- OA Status
- diamond
- Cited By
- 7
- References
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3179975697
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3179975697Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48175/ijarsct-1693Digital Object Identifier
- Title
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House Price Prediction using Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
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2021-07-13Full publication date if available
- Authors
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Rutwika Sudhakar Patil, Samiksha Devidas Patil, Raksha Dilip Attarde, Dinesh PatilList of authors in order
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https://doi.org/10.48175/ijarsct-1693Publisher landing page
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.48175/ijarsct-1693Direct OA link when available
- Concepts
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Purchasing, Machine learning, Computer science, Artificial intelligence, Work (physics), House price, Predictive modelling, Econometrics, Engineering, Economics, Operations management, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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4Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.their | 82 |
| abstract_inverted_index.these | 64 |
| abstract_inverted_index.using | 67 |
| abstract_inverted_index.which | 37, 54, 77, 86 |
| abstract_inverted_index.work, | 111 |
| abstract_inverted_index.Prices | 44 |
| abstract_inverted_index.growth | 6 |
| abstract_inverted_index.houses | 47, 100 |
| abstract_inverted_index.models | 65 |
| abstract_inverted_index.needs. | 83 |
| abstract_inverted_index.seeing | 3 |
| abstract_inverted_index.Machine | 0, 18, 23, 68 |
| abstract_inverted_index.decade. | 13 |
| abstract_inverted_index.desired | 117 |
| abstract_inverted_index.nothing | 39 |
| abstract_inverted_index.Learning | 1, 19, 24 |
| abstract_inverted_index.evolving | 26 |
| abstract_inverted_index.facility | 104 |
| abstract_inverted_index.features | 98, 118 |
| abstract_inverted_index.journals | 36 |
| abstract_inverted_index.modeling | 58 |
| abstract_inverted_index.proposed | 88, 110 |
| abstract_inverted_index.suitable | 79 |
| abstract_inverted_index.Learning, | 69 |
| abstract_inverted_index.customers | 72 |
| abstract_inverted_index.location, | 107 |
| abstract_inverted_index.modelled. | 127 |
| abstract_inverted_index.postulate | 56 |
| abstract_inverted_index.algorithms | 21 |
| abstract_inverted_index.attributes | 95 |
| abstract_inverted_index.increasing | 49 |
| abstract_inverted_index.prediction | 125 |
| abstract_inverted_index.purchasing | 74 |
| abstract_inverted_index.travelling | 103 |
| abstract_inverted_index.application | 33 |
| abstract_inverted_index.constructed | 66 |
| abstract_inverted_index.prediction. | 43, 62 |
| abstract_inverted_index.applications | 16 |
| abstract_inverted_index.availability | 114 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.70878676 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |