Answers.Body: <p>You will need to convert that to something that a neural network can understand.</p>
  <ul>
  <li><p>Movie Name</p>
  <p>Is useless. At least you want to judge a movie by its name.</p>
  </li>
  <li><p>Description</p>
  <p>You will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to see <a href="https://www.youtube.com/watch?v=fNxaJsNG3-s&amp;t=18s" rel="nofollow noreferrer">these videos</a> from TensorFlow. There you can find the Google Colab Links for the job.</p>
  </li>
  <li><p>Director</p>
  <p>There are two options. A One Hot Array or an integer for every director. One Hot Array may be better if you don't have an order of similarity of the directors. But will increase the size of the inputs.<br />
  If you have an order of similarity of the directors an integer for each director will work fine.</p>
  </li>
  <li><p>Rating</p>
  <p>Nothing to do here. Is ready to go.</p>
  </li>
  </ul>
  <p>You can perform this work directly on excel, but Tensorflow has great tools for it. It's hard to run the model after you have trained it on excel. If you are to comfortable on excel or you can install new software on your computer, I've <a href="https://github.com/TorrensJoaquin/Multivariant-Nonlinear-Regression-in-VBA-Small-Neural-Network" rel="nofollow noreferrer">made a backpropagation algorithm</a> that runs on excel and give you a formula to paste in a module.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-10-07T20:48:29.393Z
Answers.LastActivityDate: 2024-10-22T07:05:53.500Z
Answers.LastEditDate: 2024-10-22T07:05:53.500Z
Answers.UserDisplayName: Joaquin Torrens
Body: <p>I have an excel sheet filled with my own personal appreciations of movies I've watched, and I want to use it to train an AI model so that it can predict if I'll like a specific movie or not, based on the ones I've already seen.</p>
  
  <p>My data is formatted as following (just a sample, the spreadsheet is filled with hundreds of movies):</p>
  
  <p><a href="https://i.sstatic.net/84df6.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/84df6.png" alt="enter image description here"></a></p>
  
  <p>And I would like to use all the columns to train my model. Because I am going to say if I liked the movie or not, I know it will be Supervised Learning. I already cleaned the data so there's no blank or missing data, but I do not know how to train my model using every column.</p>
  
  <p>If required, I can be more specific on something, just ask and I'll edit the post.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-07T21:00:33.210Z
Comments.Text: Create an embedding of 'description' field, using word embeddings or perhaps contextualized embeddings from a language model. The 'director' field you could either one-hot encode, or create embeddings, since there might be similarities between directors in a vector space, those you could train from scratch using word2vec. The rating field you could use as it is by converting it to an integer. Then you could concatenate all the vectors and train a model, or train 3 different models and ensemble them :) The 'Movie Name' field does prob not contain any signal. Good luck :)
Comments.UserDisplayName: Isbister
Tags: <machine-learning><python><supervised-learning><scikit-learn>
Title: Train a model using a multi-column text-filled excel sheet
UserDisplayName: Prototype
ThreadText: # Train a model using a multi-column text-filled excel sheet
  
  (Asked by: username_0 on 2020-06-03)
  
  I have an excel sheet filled with my own personal appreciations of movies I've watched, and I want to use it to train an AI model so that it can predict if I'll like a specific movie or not, based on the ones I've already seen.
  
  My data is formatted as following (just a sample, the spreadsheet is filled with hundreds of movies):
  
  [![enter image description here](https://i.sstatic.net/84df6.png)](https://i.sstatic.net/84df6.png)
  
  And I would like to use all the columns to train my model. Because I am going to say if I liked the movie or not, I know it will be Supervised Learning. I already cleaned the data so there's no blank or missing data, but I do not know how to train my model using every column.
  
  If required, I can be more specific on something, just ask and I'll edit the post.
  
  **Tags:** machine-learning, python, supervised-learning, scikit-learn
  > **Comments:**
  > **username_1**: Create an embedding of 'description' field, using word embeddings or perhaps contextualized embeddings from a language model. The 'director' field you could either one-hot encode, or create embeddings, since there might be similarities between directors in a vector space, those you could train from scratch using word2vec. The rating field you could use as it is by converting it to an integer. Then you could concatenate all the vectors and train a model, or train 3 different models and ensemble them :) The 'Movie Name' field does prob not contain any signal. Good luck :)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-10-07. Score: 0
  
  You will need to convert that to something that a neural network can understand.
  
  * Movie Name
  
    Is useless. At least you want to judge a movie by its name.
  * Description
  
    You will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to see [these videos](https://www.youtube.com/watch?v=fNxaJsNG3-s&t=18s) from TensorFlow. There you can find the Google Colab Links for the job.
  * Director
  
    There are two options. A One Hot Array or an integer for every director. One Hot Array may be better if you don't have an order of similarity of the directors. But will increase the size of the inputs.  
    If you have an order of similarity of the directors an integer for each director will work fine.
  * Rating
  
    Nothing to do here. Is ready to go.
  
  You can perform this work directly on excel, but Tensorflow has great tools for it. It's hard to run the model after you have trained it on excel. If you are to comfortable on excel or you can install new software on your computer, I've [made a backpropagation algorithm](https://github.com/TorrensJoaquin/Multivariant-Nonlinear-Regression-in-VBA-Small-Neural-Network) that runs on excel and give you a formula to paste in a module.
  
  
  ---
  
  