Watch Price Prediction Project

This project aims to develop a watch price prediction system using machine learning regression techniques. The research will employ fundamental techniques such as Linear Regression, Decision Tree, Random Forest, and XGBoost to predict the prices of wristwatches and pocket watches.

Introduction

Wristwatches and pocket watches have evolved from traditional timekeeping devices to encompass modern smartwatches, offering diverse functionalities such as fitness tracking, heart rate monitoring, and seamless connectivity with smartphones. Devices like Apple watches have even been recognized for their potential in detecting irregular heart rhythms, credited with saving lives. However, alongside technological advancements, there has been a rise in counterfeit watch sales, posing significant challenges to the industry.

Counterfeiting and piracy in the watch market are substantial, estimated to range between US$ 200 – 360 billion annually, constituting approximately 5% – 7% of global trade. While counterfeit watch vendors often tout their products as high-quality alternatives available at reduced prices compared to genuine counterparts, these imitations can lead to adverse effects such as skin irritation and susceptibility to water damage. The inadvertent purchase of counterfeit products not only undermines consumer trust but also inflicts substantial economic losses on the industry, potentially resulting in workforce reductions and elevated unemployment rates.

To address the proliferation of counterfeit watches, consumers need to be vigilant about the pricing differentials between authentic and counterfeit offerings, whether making purchases in physical stores or online platforms. This research endeavors to develop a prototype watch price prediction system employing various machine learning algorithms.

Methodology

The study will employ fundamental techniques such as Linear Regression, Decision Tree, Random Forest, and XGBoost.

1. Linear Regression: Linear Regression serves as a cornerstone statistical approach, modeling the relationship between dependent and independent variables by assuming linearity and identifying the best-fitting line for prediction.

2. Decision Tree: Decision Tree methodology employs a tree-like structure to partition datasets based on attributes, iteratively making decisions to yield predictions.

3. Random Forest: Random Forest, an ensemble learning technique, amalgamates multiple decision trees during training to enhance accuracy and mitigate overfitting, thereby bolstering model robustness.

4. XGBoost: XGBoost, an advanced gradient boosting implementation, iteratively constructs decision trees to rectify preceding model errors, culminating in a potent predictive model known for its efficiency and scalability across various machine learning tasks.

Objective

This study endeavors to leverage Linear Regression techniques in conjunction with diverse algorithms to develop a watch price prediction system aimed at achieving heightened accuracy rates. By predicting watch prices accurately, consumers can make informed purchasing decisions, mitigating the risks associated with counterfeit products and promoting transparency in the watch market.