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Social Media Tourism Prediction

Overview This project aims to predict tourism trends using social media data. We employ three machine learning algorithms: logistic regression, random forest, and decision tree, to analyze social media posts and predict tourism patterns.

Data The dataset used for this project consists of social media posts related to travel and tourism. Each post contains various features such as text content, user engagement metrics, location tags, and timestamps.

Algorithms Used

1. Logistic Regression
2. Random Forest
3. Decision Tree

Methodology

1. Data Preprocessing: Cleaning and preprocessing of social media posts including text normalization, feature extraction, and handling missing values.
2. Feature Engineering: Creation of new features from existing data to improve model performance.
3. Model Training: Training logistic regression, random forest, and decision tree models using the preprocessed data.
4. Model Evaluation: Evaluating the performance of each model using appropriate evaluation metrics such as accuracy, precision, recall, and F1 score.
5. Model Comparison: Comparing the performance of logistic regression, random forest, and decision tree models to identify the most suitable algorithm for tourism prediction.

Usage

1. Ensure that you have Python installed on your system.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run the preprocessing script to clean and preprocess the data: python preprocessing.py.
4. Run the logistic regression model script: python logistic_regression.py.
5. Run the random forest model script: python random_forest.py.
6. Run the decision tree model script: python decision_tree.py.

Results The results of each model are stored in separate output files for further analysis and comparison.

Conclusion Based on the evaluation metrics and model performance, we conclude the most effective algorithm for predicting tourism trends using social media data. Additionally, insights gained from this analysis can be used to inform tourism marketing strategies and decision-making processes.

Contributors

• Saritha Verma
• Ajay Kumar S

License This project is licensed under the MIT License - see the LICENSE file for details.