Recipe for Rating: Predict Food Ratings using ML

This Kaggle project, "Recipe for Rating," focuses on predicting food ratings using machine learning. By analyzing various factors that influence how people rate recipes, we aim to develop a model that can accurately predict the rating a recipe will receive.

Table of Contents

• Project Overview
• Data Preparation
• Exploratory Data Analysis (EDA)
• Model Selection and Training
• Evaluation
• Results and Conclusion

Project Overview

The dataset captures a variety of culinary details, including recipe names, user reviews, and other key features. task is to explore this data and develop predictive models to forecast the ratings for each recipe.

Data Preparation

The dataset undergoes thorough preprocessing, including handling missing values, encoding categorical variables, and normalizing numerical features. Feature engineering is employed to create new features that may enhance the predictive power of the model.

Exploratory Data Analysis (EDA)

EDA is conducted to understand the distribution of ratings, the relationship between different features, and to uncover any hidden trends in the data. Visualizations such as histograms, box plots, and scatter plots help in gaining insights into the dataset.

Model Selection and Training

Several machine learning algorithms are explored, including linear regression, decision trees, random forests, gradient boosting, and neural networks. Hyperparameter tuning is performed using techniques like GridSearchCV to optimize the models. The models are trained on a training set and validated on a separate validation set to ensure their generalizability.

Evaluation

The performance of the models is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared. Cross-validation is used to ensure that the model does not overfit and performs well on unseen data.

Results and Conclusion

The best-performing model is selected based on its predictive accuracy and generalization capability. Insights from the model are discussed, highlighting which features have the most significant impact on recipe ratings. This project demonstrates the potential of machine learning in predicting food ratings, providing valuable insights for food enthusiasts and businesses in the culinary industry.

Acknowledgments

• This project was inspired by the need to understand how various factors affect food ratings.
• Thanks to the Kaggle community for providing the dataset and resources.

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For the full implementation and code, visit the GitHub repository.