https://github.com/archanakokate/movielens-case-study-eda-prediction-

Exploratory Data Analysis on Movielens data files and Model building using Decision Tree Classifier , Random Forest Classifier and XG Boost.
https://github.com/archanakokate/movielens-case-study-eda-prediction-

data-visualization dataengineering exploratory-data-analysis machine-learning-algorithms

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Exploratory Data Analysis on Movielens data files and Model building using Decision Tree Classifier , Random Forest Classifier and XG Boost.

• Host: GitHub
• URL: https://github.com/archanakokate/movielens-case-study-eda-prediction-
• Owner: Archanakokate
• Created: 2023-02-14T11:04:06.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-14T11:16:46.000Z (almost 2 years ago)
• Last Synced: 2024-11-23T04:30:28.662Z (2 months ago)
• Topics: data-visualization, dataengineering, exploratory-data-analysis, machine-learning-algorithms
• Language: Jupyter Notebook
• Homepage:
• Size: 7.25 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Movielens-Case-Study-EDA and Prediction-

Exploratory Data Analysis on Movielens data files and Model building using Decision Tree Classifier , Random Forest Classifier and XG Boost.

### Problem Objective :

Here, we ask you to perform the analysis using the Exploratory Data Analysis technique. You need to find features affecting the ratings of any particular movie and build a model to predict the movie ratings.

### Dataset Description :

These files contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.

        Ratings.dat - UserID::MovieID::Rating::Timestamp

    

        Users.dat - UserID::Gender::Age::Occupation::Zip-code

        

        Movies.dat - Format - MovieID::Title::Genres

### Analysis Tasks to be performed:

•	Import the three datasets

•	Create a new dataset [Master_Data] with the following columns MovieID Title UserID Age Gender Occupation Rating.

•	Explore the datasets using visual representations (graphs or tables), also include your comments on the following:

         1.User Age Distribution

    

         2.User rating of the movie “Toy Story”

    

         3.Top 25 movies by viewership rating

    

         4.Find the ratings for all the movies reviewed by for a particular user of user id = 2696

    

•	Feature Engineering:

    Use column genres:

    

        1.	Find out all the unique genres (Hint: split the data in column genre making a list and then process the data to find out only the unique categories of genres)

        

        2.	Create a separate column for each genre category with a one-hot encoding ( 1 and 0) whether or not the movie belongs to that genre. 

        

        3.	Determine the features affecting the ratings of any particular movie.

•	Develop an appropriate model to predict the movie ratings