B.H
The project implements foreasting and analysis, to forecast whether an anime be successfull by observing the relationships between the features of all the animes in the anime_filtered CSV dataSet.
#lots of cleaning the data!
I chose to decide that the anime is successful by its rating score (in training ds), if its above 7 = SUCCESSFULL.
I used the anime_filtered dataset from Kaggle: https://www.kaggle.com/azathoth42/myanimelist#anime_filtered.csv
Anime_filtered, which describes anime and their rating. The data is collected from the MyAnimeList site, which helps its users manage their watch list. Thus, the data includes not only information about the anime itself but also data collected from users of the site, and according to their rating.
** Among the features included in the data are: **
- Name anime
- Her genre (comedy for Dodge)
- Is it a movie / series
- Ranking (0-10)
- How many users are watching it
- The length of each episode
- The amount of her episodes
- Age restriction
- Is it still broadcasting
- The time frame in which it was broadcast
Download the code and put it where you can access it. ---You can start creating a graph by the command WGraph_DS g = new Wgraph_DS <> (); Then add nodes using the g.addNode (new NodeInfo (your desired new node key)) command. Continue as you wish. The project is full of descriptions about the functions and their use.
Importing first models:
Cleaning table:
Proccessing data time:
Importing models:
Models Accuracy:
Table for example:
Find me at GitHub: ShaCode256@ -First assignment in computer science and mathematics studies at Ariel University, second year.