Tourist Recommendation System

Recommendation Systems/tourist_recommendation_system/README.md

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+## Tourist Recommendation System
+
+### 🎯 **Goal**
+
+The primary goal of the Tourist Recommendation System is to provide personalized travel recommendations to users based on their preferences, past travel experiences, and interests. The purpose of this project is to enhance the travel planning experience by suggesting popular tourist attractions, activities, and accommodations tailored to individual users, ultimately making their trips more enjoyable and efficient.
+
+### 🧵 **Dataset**
+
+Link: https://www.kaggle.com/datasets/saketk511/travel-dataset-guide-to-indias-must-see-places
+
+### 🧮 **What I had done!**
+
+- Data Collection: Gathered the dataset from Kaggle, which includes details about tourist attractions and user ratings.
+- Data Preprocessing: Cleaned the dataset by handling missing values, standardizing formats, and removing duplicates.
+- Exploratory Data Analysis (EDA): Analyzed the dataset to understand trends, distributions, and insights about popular attractions.
+- Feature Engineering: Created relevant features such as average ratings, location tags, and user preferences to enhance the recommendation process.
+- Model Selection: Chose collaborative filtering and content-based filtering methods for generating recommendations based on user preferences.
+- Model Training: Implemented the selected algorithms and trained the models using the prepared dataset.
+- Evaluation: Assessed the performance of the recommendation system using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
+
+### 🚀 **Models Implemented**
+
+- Content-Based Filtering: This model is selected to recommend attractions based on their features and attributes (e.g., type of attraction, location). It ensures that users receive recommendations aligned with their specific interests.
+
+### 📚 **Libraries Needed**
+
+- Pandas: For data manipulation and analysis.
+- NumPy: For numerical operations and handling arrays.
+- Scikit-learn: For implementing machine learning algorithms and evaluation metrics.
+- Matplotlib: For data visualization.