Regression Diagnostics for Seattle Hotels Recommender

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Recommender systems are a popular application of machine learning that suggest products or services to users based on their preferences and past behaviors. In the hospitality industry, recommender systems can help travelers find the best hotels that meet their needs and preferences. Regression diagnostics can help ensure the accuracy and reliability of the recommender system by identifying outliers, influential observations, and violations of model assumptions.

In this project, we aim to develop a Seattle hotels recommender system using regression diagnostics. The proposed workflow for the Seattle Hotels Recommender project includes the following steps:

  1. Data Collection and Preprocessing: We will collect a dataset of hotel features, such as location, amenities, and customer ratings, and preprocess it by cleaning and normalizing the data, removing outliers, and performing feature selection and engineering.
  2. Feature Selection and Engineering: We will select a subset of relevant features from the dataset, such as hotel category, neighborhood, and distance from popular attractions. We will also engineer new features, such as customer satisfaction index or price-to-value ratio, to improve the model’s performance.
  3. Model Training and Selection: We will train a regression model, such as linear regression or ridge regression, on the preprocessed dataset. The model will predict customer ratings based on the input features. We will evaluate the performance of the model using metrics such as mean squared error (MSE) or root mean squared error (RMSE).
  4. Regression Diagnostics and Analysis: We will perform regression diagnostics on the trained model to identify outliers, influential observations, and violations of model assumptions. We will also analyze the factors that contribute to model accuracy, such as the impact of influential observations or the relationship between input features and customer ratings.
  5. Recommender System Deployment and Integration: We will deploy the regression model to a cloud-based platform or mobile app, which can provide hotel recommendations based on the input features and customer preferences. We will also integrate the model into existing systems, such as travel planning or booking platforms.

The expected outcomes of this project include an accurate and reliable Seattle hotels recommender system using regression diagnostics, a comprehensive dataset of hotel features, and a set of best practices and guidelines for applying machine learning algorithms to recommender systems. The project has numerous applications, including travel planning, marketing research, and customer service. The insights gained from this project can also inform decision-making in other domains, such as tourism management or hospitality operations.

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