Research Domain/Project:
This dataset was created for a machine learning experiment aimed at developing a classification model to predict outcomes based on a set of features. The primary research domain is disease prediction in patients. The dataset was used in the context of training, validating, and testing.

Purpose of the Dataset:
The purpose of this dataset is to provide training, validation, and testing data for the development of machine learning models. It includes labeled examples that help train classifiers to recognize patterns in the data and make predictions.

Dataset Creation:
Data preprocessing steps involved cleaning, normalization, and splitting the data into training, validation, and test sets. The data was carefully curated to ensure its quality and relevance to the problem at hand. For any missing values or outliers, appropriate handling techniques were applied (e.g., imputation, removal, etc.).

Structure of the Dataset:
The dataset consists of several files organized into folders by data type:

• Training Data: Contains the training dataset used to train the machine learning model.

• Validation Data: Used for hyperparameter tuning and model selection.

• Test Data: Reserved for final model evaluation.

Each folder contains files with consistent naming conventions for easy navigation, such as train_data.csv, validation_data.csv, and test_data.csv. Each file follows a tabular format with columns representing features and rows representing individual data points.

Software Requirements:
To open and work with this dataset, you need VS Code or Jupyter, which could include tools like:

• Python (with libraries such as pandas, numpy, scikit-learn, matplotlib, etc.)

Reusability:
Users of this dataset should be aware that it is designed for machine learning experiments involving classification tasks. The dataset is already split into training, validation, and test subsets. Any model trained with this dataset should be evaluated using the test set to ensure proper validation.

Limitations:
The dataset may not cover all edge cases, and it might have biases depending on the selection of data sources. It's important to consider these limitations when generalizing model results to real-world applications.

Research Domain/Project:
This dataset is part of the Tour Recommendation System project, which focuses on predicting user preferences and ratings for various tourist places and events. It belongs to the field of Machine Learning, specifically applied to Recommender Systems and Predictive Analytics.

Purpose:
The dataset serves as the training and evaluation data for a Decision Tree Regressor model, which predicts ratings (from 1-5) for different tourist destinations based on user preferences. The model can be used to recommend places or events to users based on their predicted ratings.

Creation Methodology:
The dataset was originally collected from a tourism platform where users rated various tourist places and events. The data was preprocessed to remove missing or invalid entries (such as #NAME? in rating columns). It was then split into subsets for training, validation, and testing the model.

Structure of the Dataset:
The dataset is stored as a CSV file (user_ratings_dataset.csv) and contains the following columns:

• place_or_event_id: Unique identifier for each tourist place or event.

• rating: Rating given by the user, ranging from 1 to 5.

The data is split into three subsets:

• Training Set: 80% of the dataset used to train the model.

• Validation Set: A small portion used for hyperparameter tuning.

• Test Set: 20% used to evaluate model performance.

Folder and File Naming Conventions:
The dataset files are stored in the following structure:

• user_ratings_dataset.csv: The original dataset file containing user ratings.

• tour_recommendation_model.pkl: The saved model after training.

• actual_vs_predicted_chart.png: A chart comparing actual and predicted ratings.

Software Requirements:
To open and work with this dataset, the following software and libraries are required:

• Python 3.x

• Pandas for data manipulation

• Scikit-learn for training and evaluating machine learning models

• Matplotlib for chart generation

• Joblib for saving and loading the trained model

The dataset can be opened and processed using any Python environment that supports these libraries.

Additional Resources:

• The model training code, README file, and performance chart are available in the project repository.

• For detailed explanation and code, please refer to the GitHub repository (or any other relevant link for the code).

Dataset Reusability:
The dataset is structured for easy use in training machine learning models for recommendation systems. Researchers and practitioners can utilize it to:

• Train other types of models (e.g., regression, classification).

• Experiment with different features or add more metadata to enrich the dataset.

Data Integrity:
The dataset has been cleaned and preprocessed to remove invalid values (such as #NAME? or missing ratings). However, users should ensure they understand the structure and the preprocessing steps taken before reusing it.

Licensing:
The dataset is provided under the CC BY 4.0 license, which allows free usage, distribution, and modification, provided that proper attribution is given.