Stack Index Prediction Using Time-Series Analysis
The Prevalence of Community support and engagement for different domains in the tech industry has changed and evolved throughout the years. In this study, we aim to understand, analyze and predict the trends of technology in a scientific manner, having collected data on numerous topics and their gro...
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| creator | Raman, Raja CSP Mahadevan, Rohith Perumal, Divya Sankar, Vedha Rahman, Talha Abdur |
| description | The Prevalence of Community support and engagement for different domains in
the tech industry has changed and evolved throughout the years. In this study,
we aim to understand, analyze and predict the trends of technology in a
scientific manner, having collected data on numerous topics and their growth
throughout the years in the past decade. We apply machine learning models on
collected data, to understand, analyze and forecast the trends in the
advancement of different fields. We show that certain technical concepts such
as python, machine learning, and Keras have an undisputed uptrend, finally
concluding that the Stackindex model forecasts with high accuracy and can be a
viable tool for forecasting different tech domains. |
| doi_str_mv | 10.48550/arxiv.2108.08120 |
| format | Article |
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the tech industry has changed and evolved throughout the years. In this study,
we aim to understand, analyze and predict the trends of technology in a
scientific manner, having collected data on numerous topics and their growth
throughout the years in the past decade. We apply machine learning models on
collected data, to understand, analyze and forecast the trends in the
advancement of different fields. We show that certain technical concepts such
as python, machine learning, and Keras have an undisputed uptrend, finally
concluding that the Stackindex model forecasts with high accuracy and can be a
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the tech industry has changed and evolved throughout the years. In this study,
we aim to understand, analyze and predict the trends of technology in a
scientific manner, having collected data on numerous topics and their growth
throughout the years in the past decade. We apply machine learning models on
collected data, to understand, analyze and forecast the trends in the
advancement of different fields. We show that certain technical concepts such
as python, machine learning, and Keras have an undisputed uptrend, finally
concluding that the Stackindex model forecasts with high accuracy and can be a
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the tech industry has changed and evolved throughout the years. In this study,
we aim to understand, analyze and predict the trends of technology in a
scientific manner, having collected data on numerous topics and their growth
throughout the years in the past decade. We apply machine learning models on
collected data, to understand, analyze and forecast the trends in the
advancement of different fields. We show that certain technical concepts such
as python, machine learning, and Keras have an undisputed uptrend, finally
concluding that the Stackindex model forecasts with high accuracy and can be a
viable tool for forecasting different tech domains.</abstract><doi>10.48550/arxiv.2108.08120</doi><oa>free_for_read</oa></addata></record> |
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| source | arXiv.org |
| subjects | Computer Science - Learning |
| title | Stack Index Prediction Using Time-Series Analysis |
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