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11	ML implementation for analyzing and estimating product prices / ML implementation för analys och estimation av produktpriser Kenea, Abel Getachew, Fagerslett, Gabriel January 2024 (has links) Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. This project aims to use machine learning to assist in the pricing of products by estimating the prices to be inserted. Multiple machine learning models have been tested, and an artificial neural network has been implemented for estimating prices effectively. Through additional experimentation, the design of the network was fine-tuned to make it compatible with the project’s needs. The libraries used for implementing and managing the machine learning models are mainly ScikitLearn and TensorFlow. As a result, the trained model has been saved into a file and integrated with an API for accessibility. Machine Learning ML Regression Deep Learning Artificial Neural Network ANN TensorFlow ScikitLearn CUDA cuDNN Estimation Prediction AI Artificial Intelligence Price Tracking Price Logging Price Estimation Supervised Learning Random Forest Decision Trees Batch Learning Hyperparameter Tuning Linear Regression Multiple Linear Regression Maskininlärning Djup lärning Artificiellt Neuralt Nätverk Regression TensorFlow SciktLearn ML ANN Estimation Uppskattning CUDA cuDNN AI Artificiell Intelligens pris loggning pris estimation prisspårning Batchinlärning Hyperparameterjustering Linjär Regression Multipel Linjär Regression Supervised Learning Random Forest Decision Trees Computer Engineering Datorteknik

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ML implementation for analyzing and estimating product prices / ML implementation för analys och estimation av produktpriser