Train a model using a multi-column text-filled excel sheetAsk Question Asked5 years, 5 months agoModifiedtodayViewed1k times Asked5 years, 5 months ago 0$\begingroup$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):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.machine-learningpythonsupervised-learningscikit-learnShareImprove this questionFollowaskedJun 3, 2020 at 12:13Prototype133 bronze badges$\endgroup$1$\begingroup$Create an embedding of 'description' field, using word embeddings or perhaps contextualized embeddings from a language model. The 'director' field you could either one-hot encode, or create embeddings, since there might be similarities between directors in a vector space, those you could train from scratch using word2vec. The rating field you could use as it is by converting it to an integer. Then you could concatenate all the vectors and train a model, or train 3 different models and ensemble them :) The 'Movie Name' field does prob not contain any signal. Good luck :)$\endgroup$Isbister–Isbister2020-10-07 21:00:33 +00:00CommentedOct 7, 2020 at 21:00Add a comment|1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first)0$\begingroup$You will need to convert that to something that a neural network can understand.Movie NameIs useless. At least you want to judge a movie by its name.DescriptionYou will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to seethese videosfrom TensorFlow. There you can find the Google Colab Links for the job.DirectorThere are two options. A One Hot Array or an integer for every director. One Hot Array may be better if you don't have an order of similarity of the directors. But will increase the size of the inputs.If you have an order of similarity of the directors an integer for each director will work fine.RatingNothing to do here. Is ready to go.You can perform this work directly on excel, but Tensorflow has great tools for it. It's hard to run the model after you have trained it on excel. If you are to comfortable on excel or you can install new software on your computer, I'vemade a backpropagation algorithmthat runs on excel and give you a formula to paste in a module.ShareImprove this answerFolloweditedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badgesansweredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges$\endgroup$Add a comment|You mustlog into answer this question.Start asking to get answersFind the answer to your question by asking.Ask questionExplore related questionsmachine-learningpythonsupervised-learningscikit-learnSee similar questions with these tags. 0$\begingroup$I have an excel sheet filled with my own personal appreciations of movies I've watched, and I want to use it to train an AI model so that it can predict if I'll like a specific movie or not, based on the ones I've already seen.My data is formatted as following (just a sample, the spreadsheet is filled with hundreds of movies):And I would like to use all the columns to train my model. Because I am going to say if I liked the movie or not, I know it will be Supervised Learning. I already cleaned the data so there's no blank or missing data, but I do not know how to train my model using every column.If required, I can be more specific on something, just ask and I'll edit the post.machine-learningpythonsupervised-learningscikit-learnShareImprove this questionFollowaskedJun 3, 2020 at 12:13Prototype133 bronze badges$\endgroup$1$\begingroup$Create an embedding of 'description' field, using word embeddings or perhaps contextualized embeddings from a language model. The 'director' field you could either one-hot encode, or create embeddings, since there might be similarities between directors in a vector space, those you could train from scratch using word2vec. The rating field you could use as it is by converting it to an integer. Then you could concatenate all the vectors and train a model, or train 3 different models and ensemble them :) The 'Movie Name' field does prob not contain any signal. Good luck :)$\endgroup$Isbister–Isbister2020-10-07 21:00:33 +00:00CommentedOct 7, 2020 at 21:00Add a comment| 0$\begingroup$I have an excel sheet filled with my own personal appreciations of movies I've watched, and I want to use it to train an AI model so that it can predict if I'll like a specific movie or not, based on the ones I've already seen.My data is formatted as following (just a sample, the spreadsheet is filled with hundreds of movies):And I would like to use all the columns to train my model. Because I am going to say if I liked the movie or not, I know it will be Supervised Learning. I already cleaned the data so there's no blank or missing data, but I do not know how to train my model using every column.If required, I can be more specific on something, just ask and I'll edit the post.machine-learningpythonsupervised-learningscikit-learnShareImprove this questionFollowaskedJun 3, 2020 at 12:13Prototype133 bronze badges$\endgroup$1$\begingroup$Create an embedding of 'description' field, using word embeddings or perhaps contextualized embeddings from a language model. The 'director' field you could either one-hot encode, or create embeddings, since there might be similarities between directors in a vector space, those you could train from scratch using word2vec. The rating field you could use as it is by converting it to an integer. Then you could concatenate all the vectors and train a model, or train 3 different models and ensemble them :) The 'Movie Name' field does prob not contain any signal. Good luck :)$\endgroup$Isbister–Isbister2020-10-07 21:00:33 +00:00CommentedOct 7, 2020 at 21:00Add a comment| $\begingroup$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):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.machine-learningpythonsupervised-learningscikit-learnShareImprove this questionFollowaskedJun 3, 2020 at 12:13Prototype133 bronze badges$\endgroup$ 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):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. 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): 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. machine-learningpythonsupervised-learningscikit-learn machine-learningpythonsupervised-learningscikit-learn machine-learningpythonsupervised-learningscikit-learn ShareImprove this questionFollowaskedJun 3, 2020 at 12:13Prototype133 bronze badges ShareImprove this questionFollowaskedJun 3, 2020 at 12:13Prototype133 bronze badges ShareImprove this questionFollow ShareImprove this questionFollow ShareImprove this questionFollow Improve this question askedJun 3, 2020 at 12:13Prototype133 bronze badges askedJun 3, 2020 at 12:13Prototype133 bronze badges askedJun 3, 2020 at 12:13 askedJun 3, 2020 at 12:13 Prototype133 bronze badges $\begingroup$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 :)$\endgroup$Isbister–Isbister2020-10-07 21:00:33 +00:00CommentedOct 7, 2020 at 21:00Add a comment| $\begingroup$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 :)$\endgroup$Isbister–Isbister2020-10-07 21:00:33 +00:00CommentedOct 7, 2020 at 21:00 $\begingroup$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 :)$\endgroup$Isbister–Isbister2020-10-07 21:00:33 +00:00CommentedOct 7, 2020 at 21:00 $\begingroup$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 :)$\endgroup$Isbister–Isbister2020-10-07 21:00:33 +00:00CommentedOct 7, 2020 at 21:00 2020-10-07 21:00:33 +00:00 1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first)0$\begingroup$You will need to convert that to something that a neural network can understand.Movie NameIs useless. At least you want to judge a movie by its name.DescriptionYou will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to seethese videosfrom TensorFlow. There you can find the Google Colab Links for the job.DirectorThere 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.RatingNothing 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'vemade a backpropagation algorithmthat runs on excel and give you a formula to paste in a module.ShareImprove this answerFolloweditedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badgesansweredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges$\endgroup$Add a comment|You mustlog into answer this question.Start asking to get answersFind the answer to your question by asking.Ask questionExplore related questionsmachine-learningpythonsupervised-learningscikit-learnSee similar questions with these tags. 1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) 1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) Sorted by:Reset to default Highest score (default)Date modified (newest first)Date created (oldest first) 0$\begingroup$You will need to convert that to something that a neural network can understand.Movie NameIs useless. At least you want to judge a movie by its name.DescriptionYou will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to seethese videosfrom TensorFlow. There you can find the Google Colab Links for the job.DirectorThere 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.RatingNothing 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'vemade a backpropagation algorithmthat runs on excel and give you a formula to paste in a module.ShareImprove this answerFolloweditedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badgesansweredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges$\endgroup$Add a comment| 0$\begingroup$You will need to convert that to something that a neural network can understand.Movie NameIs useless. At least you want to judge a movie by its name.DescriptionYou will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to seethese videosfrom TensorFlow. There you can find the Google Colab Links for the job.DirectorThere 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.RatingNothing 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'vemade a backpropagation algorithmthat runs on excel and give you a formula to paste in a module.ShareImprove this answerFolloweditedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badgesansweredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges$\endgroup$Add a comment| $\begingroup$You will need to convert that to something that a neural network can understand.Movie NameIs useless. At least you want to judge a movie by its name.DescriptionYou will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to seethese videosfrom TensorFlow. There you can find the Google Colab Links for the job.DirectorThere 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.RatingNothing 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'vemade a backpropagation algorithmthat runs on excel and give you a formula to paste in a module.ShareImprove this answerFolloweditedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badgesansweredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges$\endgroup$ You will need to convert that to something that a neural network can understand.Movie NameIs useless. At least you want to judge a movie by its name.DescriptionYou will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to seethese videosfrom TensorFlow. There you can find the Google Colab Links for the job.DirectorThere 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.RatingNothing 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'vemade a backpropagation algorithmthat runs on excel and give you a formula to paste in a module. You will need to convert that to something that a neural network can understand. Is useless. At least you want to judge a movie by its name. You will need to perform a Tokenization. Grab the x's more common words and convert them on an array. I recommend you to seethese videosfrom TensorFlow. There you can find the Google Colab Links for the job. 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. 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'vemade a backpropagation algorithmthat runs on excel and give you a formula to paste in a module. ShareImprove this answerFolloweditedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badgesansweredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges ShareImprove this answerFolloweditedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badgesansweredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges ShareImprove this answerFollow ShareImprove this answerFollow ShareImprove this answerFollow editedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badges editedOct 22, 2024 at 7:05cottontail13111 gold badge33 silver badges99 bronze badges editedOct 22, 2024 at 7:05 editedOct 22, 2024 at 7:05 cottontail13111 gold badge33 silver badges99 bronze badges 13111 gold badge33 silver badges99 bronze badges answeredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges answeredOct 7, 2020 at 20:48Joaquin Torrens4266 bronze badges answeredOct 7, 2020 at 20:48 answeredOct 7, 2020 at 20:48 Joaquin Torrens4266 bronze badges Start asking to get answersFind the answer to your question by asking.Ask questionExplore related questionsmachine-learningpythonsupervised-learningscikit-learnSee similar questions with these tags. Start asking to get answersFind the answer to your question by asking.Ask question Start asking to get answersFind the answer to your question by asking.Ask question Start asking to get answers Find the answer to your question by asking. Explore related questionsmachine-learningpythonsupervised-learningscikit-learnSee similar questions with these tags. Explore related questionsmachine-learningpythonsupervised-learningscikit-learnSee similar questions with these tags. Explore related questions machine-learningpythonsupervised-learningscikit-learn See similar questions with these tags. 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