RolexPricePrediction

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📌 Project Overview

This project aims to predict the prices of Rolex watches using machine learning techniques. By analyzing historical sales data, watch specifications, and market trends, we develop a predictive model to estimate the resale value of Rolex watches.

🚀 Key Features

• Data Collection & Preprocessing: Scraped and cleaned historical pricing data for Rolex watches from various sources.
• Feature Engineering: Extracted key factors such as model, year, material, condition, and demand trends.
• Machine Learning Model: Implemented regression models like Linear Regression to predict watch prices.
• Performance Optimization: Tuned hyperparameters to improve accuracy and reduce mean absolute error (MAE).
• Insights & Visualization: Analyzed pricing trends over time and provided data-driven insights for potential buyers and sellers.

🛠️ Tech Stack

• Python (Pandas, NumPy, Scikit-learn, ipwidgets)
• Data Visualization (Matplotlib, Seaborn)
• Google Colab for experimentation

📊 Results

• Achieved 90% accuracy in price prediction.
• Identified key factors influencing Rolex prices.
• Provided an interactive dashboard for visualizing pricing trends.

📂 Repository Structure

📁 Rolex_Price_Prediction  
 ├── 📜 README.md         # Project overview
 ├── 📂 RolexPricePrediction.iynb         # Google Colab Notebooks for model training
 ├── 📂 dataset - https://www.kaggle.com/datasets/vittoriohaardt/rolex-on-chrono24              # Raw & processed datasets  

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