Build a model that predicts the rating of a movie based on features like genre, director, and actors. Use regression techniques, detecting data types, null values, duplicates, creation of new data frames, etc. to tackle this problem.
The goal is to analyze historical movie data and develop a model that accurately estimates the rating given to a movie by users or critics.
Approach:
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Data Preparation: Begin by cleaning the dataset, addressing missing values, and transforming categorical features into numerical representations.
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Model Implementation: Develop predictive models using Random Forest, Logistic Regression, and K-Nearest Neighbors algorithms to estimate movie ratings.
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Evaluation: Train these models and evaluate their performance using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Accuracy to gauge their predictive accuracy.
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Feature Importance Analysis: Investigate feature importance scores to gain insights into which factors have the most influence on movie ratings.
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Model Comparison: Compare the performance of the different algorithms to identify the most effective one for this task.
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User Interface Development: Create a user-friendly interface that allows users to input movie features and receive predicted ratings, making the model accessible and practical for users.