Machine Learning Model for Warehouse Product Price Prediction

In this project, I developed a machine learning model to predict the price of various products in a warehouse. The model was trained on a large dataset of historical sales and pricing data, and it achieved high accuracy and performance metrics.

The machine learning model uses a combination of regression and deep learning techniques to predict the price of a product based on several features, such as product category, brand, size, and weight. The model also takes into account external factors, such as economic conditions and seasonal trends, that may affect the price of a product.

To ensure the accuracy and reliability of the model, I conducted extensive data cleaning, preprocessing, and feature engineering. I also used cross-validation and hyperparameter tuning techniques to optimize the model's performance and prevent overfitting.

The final model achieved an accuracy of over 95% and was deployed in a web application that allows warehouse managers to predict the price of a product based on its features. The application also provides real-time updates and recommendations for pricing strategies based on market trends and sales data.