Movie Recommender
We all like to watch something when we sit down to eat but how many times has it happened that you are unable to decide what to watch? By the time we are done browsing and finally, decide on something, the food’s already finished or gets cold.
Honestly, we find this annoying and a total waste of time. Websites like IMDB though have great suggestions, but they are too detailed and lengthy. One more problem we found is that, even if we decide on something, we are clueless as to which streaming platform the movie is available on. Searching for the movie on every platform is quite troublesome, time-consuming and there’s always a chance you might not find it anywhere. So, our model is not only simple and easy to use, but it would also guide the users on which platform the selected movie is available on.
A Little Sneak Peek:
Our Model:
The unique features we plan on adding to our movie recommending model is:
1. Our model would be simple and easy to use.
— We are going for a “What you want is what you get” approach. Nothing More, Nothing Ness.
— We are not going to burden our users with unnecessary information.
2. Lots of useful information can be displayed through graphs and charts.
— For example, we could show the users which year had genre specific hit movies instead of writing it as a picture or in this case a graph speaks a thousand words.
3. Our model would also provide the users with information/links, like on which platform their selected movie is available on. This would help them save a lot of time.
Our Competition:
How it works:
So, the user has a choice to either search for the movie title directly or their preferred genre by typing it in the search box. Searching for the movie title in the search box would provide a list of similar movies to choose from, and typing the preferred genre would provide the user with movies based on the typed in genre. Clicking on a movie will open a separate box alongside that would contain required information for making a informed decision like, a brief description, ratings from top movie critics/recommenders like IMDB, and the cast of the movie.
Future Plan:
— We are planning to add links to the streaming platforms on which the movie is available within the description box. Users can click on it, which will direct them to the chosen platform website.
— We also plan on using Big Data and Machine Learning to make our model more efficient.
Our Experience:
Well, none of us had much experience in python. Making the GUI using python was new to us, hence we did the best we could within the time frame provided. Web scraping and cleansing of the dataset also proved to be a hurdle as this was something that we had never done before.
One certain obstacle did set us back a little when one of our teammates had knee surgery in between. He had torn his ligament while playing football and had to get operated on instantly. Though this incident set us back for 2–3 weeks, but we made up for it by spending some sleepless nights and got the work done.
We gave all that we had and did our best. Our end product may not be the best as there is always room for improvement. This journey was one of a kind. This project would be one of those times which we would talk and laugh about later.
Citations and Reference:
1. https://www.youtube.com/watch?v=VMP1oQOxfM0
2. https://www.youtube.com/watch?v=YXPyB4XeYLA
3. https://www.youtube.com/watch?v=XVv6mJpFOb0
4. https://www.youtube.com/watch?v=O6nnVHPjcJU
5. https://www.geeksforgeeks.org/python-gui-tkinter/
6. https://www.javatpoint.com/python-tkinter
7. https://www.edureka.co/blog/web-scraping-with-python/
Acknowledgment
We would like to express our special thanks of gratitude to Bennett University as well as our teacher Mr. Anurag Goswami who gave us the golden opportunity to work on this project. We came to know about so many new things. The skills we learned from this project will surely be of great help to us in the future.
Dev Singhal (8017637028)
Vaibhav Ganeriwala()
Tushar Dadhich()
