🌲 Random Forest ML - Travel Dataset

A simple implementation of Random Forest for predictive modeling on a travel dataset.

📌 "Random Forest model trained to analyze and predict travel-related patterns."

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📖 What is Random Forest?

Random Forest is an ensemble machine learning algorithm that builds multiple decision trees during training and merges their outputs to improve prediction accuracy and reduce overfitting.

• Each tree votes, and the majority decision (for classification) or average (for regression) is taken.
• It works well on both classification and regression problems.
• Advantages: Handles missing values, reduces variance, and provides feature importance.

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📂 Project Contents

• Travel.csv → Dataset used for training and testing.
• work.pkl → Pickled Random Forest model.
• new.ipynb → Jupyter Notebook with preprocessing, model training, and evaluation steps.

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⚙️ How it Works Here

1. Load and preprocess the Travel dataset.
2. Train a Random Forest model on relevant features.
3. Save the trained model as work.pkl for reuse.
4. Evaluate predictions and analyze feature importance.

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▶️ Running the Project

1. Clone the repository:

   git clone https://github.com/YOUR-USERNAME/random-forest-ml.git
   cd random-forest-ml