Mercari is a popular online marketplace that allows users to buy and sell a wide variety of products. Accurately pricing products is essential for both buyers and sellers on the platform. Machine learning algorithms, such as Lightgbm, can provide a solution by modeling the complex relationships between product attributes and pricing.
In this project, we aim to use machine learning algorithms to predict product prices on Mercari using the Lightgbm algorithm. The proposed workflow for the Mercari Price Suggestion project includes the following steps:
- Data Collection and Preprocessing: We will collect a dataset of product attributes, such as brand, category, condition, and description, and preprocess it by cleaning and normalizing the data, removing outliers, and performing feature selection and engineering.
- Feature Selection and Engineering: We will select a subset of relevant features from the dataset, such as brand popularity, category hierarchy, and descriptive language analysis. We will also engineer new features, such as sentiment analysis of the product description or the average price of similar products, to improve the model’s performance.
- Model Training and Selection: We will train a Lightgbm model on the preprocessed dataset. Lightgbm is a type of gradient boosting algorithm that can handle large-scale datasets with high-dimensional features. We will evaluate the performance of the model using metrics such as root mean squared error (RMSE) and mean absolute error (MAE), and select the best-performing model.
- Price Prediction and Analysis: We will use the selected Lightgbm model to predict product prices based on the input features. We will also analyze the factors that contribute to price, such as brand reputation or product condition, and identify opportunities for pricing optimization.
- Model Evaluation and Deployment: We will evaluate the performance of the Lightgbm model using cross-validation and backtesting techniques. We will then deploy the model to a cloud-based platform or mobile app, which can provide price suggestions for products on Mercari in real-time based on the input features.
The expected outcomes of this project include a scalable and efficient machine learning algorithm for price suggestion on Mercari using Lightgbm, a comprehensive dataset of product attributes, and a set of best practices and guidelines for applying machine learning algorithms to online marketplace pricing analysis. The project has numerous applications, including pricing optimization, competitor analysis, and customer behavior analysis. The insights gained from this project can also inform decision-making in other domains, such as demand forecasting and inventory management.
