Answers.Body: <p>This cannot be done as described, unless you find a way to create a feasible and measurable link or association between the input data and output data, that exists already in your training data, and that could be analysed statistically.</p>
  <p>Basic supervised machine learning works by being given example inputs and outputs - so called &quot;ground truth&quot; - that already answer your question for some subset of a population. Your suggested input dataset of text, images, videos about US presidents does not appear to include any poll results that could be learned from.</p>
  <p>A variation of your dataset might be used to predict poll preferences. If you had some simple summary data about people who have voted in similar polls in the past (e.g. demographics such as age, political party membership, location) and also summarised data about each president (approval ratings, number of terms etc), then you may be able to train a machine learning system to learn a little how personal preferences work in the context of your poll. You could then use it to predict poll results for unseen people, and unseen presidents, provided you have the same summary data for the new people and new presidents.</p>
  <p>This is quite similar to recommendation systems, so if instead of structured data about people and presidents, you had results of multiple similar polls from multiple people, then you might be able to train a system that predicted outcome of a new poll if you knew what a person chose in one or more previous similar polls.</p>
  <p>Processing that included unstructured data such as text articles, images and videos is harder, but not impossible. The trick would be to reduce this data down into simpler numerical format - e.g. you could use a language model to extract sentiment (positive or negative articles) from texts. You would still need to have some previous poll results in your training data in order to help predict what these summaries might mean for a new poll.</p>
  <p>What you should <em>not</em> do is pour lots of unstructured data into some summary machine, and expect it to output anything usefully predictive about a specific function that you want to know about. The chances of the results being correct is no better than you simply guessing at the start.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-08T05:37:55.833Z
Answers.Text: Thanks. So input dataset would include additional "you had some simple summary data about people who have voted in similar polls in the past (e.g. demographics such as age, political party membership, location) and also summarised data about each president (approval ratings, number of terms etc), then you may be able to train a machine learning system to learn a little how personal preferences work in the context of your poll. You could then use it to predict poll results for unseen people, and unseen presidents, provided you have the same summary data for the new people and new presidents"?
Answers.UserDisplayName: Prashant Akerkar
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-08T15:30:05.887Z
Answers.Text: @PrashantAkerkar: Yes if you had that kind of data, you could build a supervised learning model that predicted how much a person might prefer certain choices in a poll, based on things you know about the person and the presidents they should choose between. The quality of such a model would vary a lot, depending on what data you had, and how much of it. The usual, *least work* method of collecting this data in case like your example would be to actually run the poll, and then maybe adjust for demographics of the responses.
Answers.UserDisplayName: Neil Slater
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-07T10:57:28.223Z
Answers.LastActivityDate: 2023-06-07T11:04:11.210Z
Answers.LastEditDate: 2023-06-07T11:04:11.210Z
Answers.UserDisplayName: Neil Slater
Body: <p>Can artificial intelligence and Machine Learning Prediction Algorithms assist in deciding the Outome Results of a Hypothetical Online Poll?</p>
  <p>Poll: Selecting favorite American President till date.</p>
  <p>Example: Input dataset for All Presidents of USA till date.
  This includes Text, Images, Videos etc. I.e. Multimedia.</p>
  
Tags: <machine-learning><deep-learning><datasets><prediction><training-datasets>
Title: AI and Machine Learning Prediction Algorithms for predicting outcome results of Hypothetical poll
UserDisplayName: Prashant Akerkar
ThreadText: # AI and Machine Learning Prediction Algorithms for predicting outcome results of Hypothetical poll
  
  (Asked by: username_0 on 2023-06-07)
  
  Can artificial intelligence and Machine Learning Prediction Algorithms assist in deciding the Outome Results of a Hypothetical Online Poll?
  
  Poll: Selecting favorite American President till date.
  
  Example: Input dataset for All Presidents of USA till date.
  This includes Text, Images, Videos etc. I.e. Multimedia.
  
  **Tags:** machine-learning, deep-learning, datasets, prediction, training-datasets
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-06-07. Score: 0 | Accepted answer
  
  This cannot be done as described, unless you find a way to create a feasible and measurable link or association between the input data and output data, that exists already in your training data, and that could be analysed statistically.
  
  Basic supervised machine learning works by being given example inputs and outputs - so called "ground truth" - that already answer your question for some subset of a population. Your suggested input dataset of text, images, videos about US presidents does not appear to include any poll results that could be learned from.
  
  A variation of your dataset might be used to predict poll preferences. If you had some simple summary data about people who have voted in similar polls in the past (e.g. demographics such as age, political party membership, location) and also summarised data about each president (approval ratings, number of terms etc), then you may be able to train a machine learning system to learn a little how personal preferences work in the context of your poll. You could then use it to predict poll results for unseen people, and unseen presidents, provided you have the same summary data for the new people and new presidents.
  
  This is quite similar to recommendation systems, so if instead of structured data about people and presidents, you had results of multiple similar polls from multiple people, then you might be able to train a system that predicted outcome of a new poll if you knew what a person chose in one or more previous similar polls.
  
  Processing that included unstructured data such as text articles, images and videos is harder, but not impossible. The trick would be to reduce this data down into simpler numerical format - e.g. you could use a language model to extract sentiment (positive or negative articles) from texts. You would still need to have some previous poll results in your training data in order to help predict what these summaries might mean for a new poll.
  
  What you should *not* do is pour lots of unstructured data into some summary machine, and expect it to output anything usefully predictive about a specific function that you want to know about. The chances of the results being correct is no better than you simply guessing at the start.
  
  > **Comments:**
  > **username_0**: Thanks. So input dataset would include additional "you had some simple summary data about people who have voted in similar polls in the past (e.g. demographics such as age, political party membership, location) and also summarised data about each president (approval ratings, number of terms etc), then you may be able to train a machine learning system to learn a little how personal preferences work in the context of your poll. You could then use it to predict poll results for unseen people, and unseen presidents, provided you have the same summary data for the new people and new presidents"?  
  > **username_1**: @PrashantAkerkar: Yes if you had that kind of data, you could build a supervised learning model that predicted how much a person might prefer certain choices in a poll, based on things you know about the person and the presidents they should choose between. The quality of such a model would vary a lot, depending on what data you had, and how much of it. The usual, *least work* method of collecting this data in case like your example would be to actually run the poll, and then maybe adjust for demographics of the responses.  
  
  ---
  
  