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.github/workflows/main.yml

@@ -42,4 +42,4 @@ jobs:
           upload_url: ${{ steps.create_release.outputs.upload_url }}
           asset_path: output/SeniorThesis.pdf
           asset_name: SeniorThesis.pdf
-          asset_content_type: pdf
+          asset_content_type: pdf

abstract.md

@@ -1,4 +1,4 @@
 # Abstract
 
 
-By looking at the emerging trends in the entertainment industry, it can be inferred that individuals are becoming increasingly interested in the deep personalization of their movie choices. Most streaming platforms now utilize different comprehensive algorithms that keep track of user’s choices, which should further perpetuate the need for unique streaming. However, the research done in thus far has only touched on the different factors affecting movie success and not the rising demand for individualization in streaming. This knowledge gap requires one to explore different ways for streaming services and other sectors of the movie industry to tailor their services for their consumers. This is where the research done in this paper comes in, where the analysis of streaming platforms (Netflix, Hulu, etc.), movie production (actors, budget, directors, etc.), and overall revenue is used to determine what exactly makes a movie successful. This paper will be a comprehensive analysis of different determinants that affect movie success, such as actors, genres, production budget, movie sales, and directors. These determinants will be analyzed with a <insert algorithm name once found> algorithm, which will then be put into a machine-learning model. Once the model is appropriately trained, then two outputs will be generated by the model. For movide industry personnel, there will be a streamlit-based application that will allow them to fill out the factors that are being determined by the model with their personal data and then output the liklihood of their movie being successful, based on movie trends determined by the model. For regular users who would like personalized movie recommendations, there will be an interface in the streamlit-run application that will allow users to input their preferred data for the movie factors, with the output being a downloadable pdf of all of the different movie recommendations that the model chooses, based on user preferences and movie trends.
+By looking at the emerging trends in the entertainment industry, it can be inferred that individuals are becoming increasingly interested in the deep personalization of their movie choices. Most streaming platforms now utilize different comprehensive algorithms that keep track of user’s choices, which should further perpetuate the need for unique streaming. However, the research done in thus far has only touched on the different factors affecting movie success and not the rising demand for individualization in streaming. This knowledge gap requires one to explore different ways for streaming services and other sectors of the movie industry to tailor their services for their consumers. This is where the research done in this paper comes in, where the analysis of streaming platforms (Netflix, Hulu, etc.), movie production (actors, budget, directors, etc.), and overall revenue is used to determine what exactly makes a movie successful. This paper will be a comprehensive analysis of different determinants that affect movie success, such as actors, genres, production budget, movie sales, and directors. These determinants will be analyzed with a <insert algorithm name once found> algorithm, which will then be put into a machine-learning model. Once the model is appropriately trained, then two outputs will be generated by the model. For movide industry personnel, there will be a streamlit-based application that will allow them to fill out the factors that are being determined by the model with their personal data and then output the liklihood of their movie being successful, based on movie trends determined by the model. For regular users who would like personalized movie recommendations, there will be an interface in the streamlit-run application that will allow users to input their preferred data for the movie factors, with the output being a downloadable pdf of all of the different movie recommendations that the model chooses, based on user preferences and movie trends.

references.bib

@@ -32,4 +32,4 @@ @article{movie_success_1
   journal={http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208506},
   year={2017},
   publisher={Digitala Vetenskapliga Arkivet}
-}
+}

thesis.md

@@ -101,4 +101,4 @@ add future ethics concerns here.
 # References
 
 ::: {#refs}
-:::
+:::