Sökning: "Random Forest model"

Visar resultat 1 - 5 av 439 uppsatser innehållade orden Random Forest model.

1. 1. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Habib Jabeli; [2024]
   Nyckelord :Machine Learning; Flight Comparison; Flygresor.se; Neural Networks; Flight Ranking; Random Forest; XGBoost;

   Sammanfattning : Flygresor.se, a leading flight comparison platform, uses machine learning to rankflights based on their likelihood of being clicked. The main goal of this project was toimprove this flight sorting to obtain a better user experience. The platform's existingmodel is based on a neural network approach and a limited set of features. LÄS MER

3. 2. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Nikolaos Staikos; [2024]
   Nyckelord :Physical Geography; Ecosystem Analysis; Bike-sharing demand; Machine learning; Deep learning; Spatial regression; Graph Convolutional Neural Network; Multiple Linear Regression; Multilayer Perceptron Regressor; Support Vector Machine; Random Forest Regressor; Urban environment; Micro-mobility; Station planning; Geomatics; Earth and Environmental Sciences;

   Sammanfattning : Public Bike-Sharing systems have been employed in many cities around the globe. Shared bikes are an efficient and convenient means of transportation in advanced societies. Nonetheless, station planning and local bike-sharing network effectiveness can be challenging. LÄS MER

4. 3. ML implementation for analyzing and estimating product prices

   Kandidat-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Abel Getachew Kenea; Gabriel Fagerslett; [2024]
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

5. 4. Detecting Fraudulent User Behaviour : A Study of User Behaviour and Machine Learning in Fraud Detection

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Analys och partiella differentialekvationer

   Författare :Patrik Gerdelius; Sjönneby Hugo; [2024]
   Nyckelord :Fraud Detection; User Behaviour; Random Forest; PCA; SMOTE;

   Sammanfattning : This study aims to create a Machine Learning model and investigate its performance of detecting fraudulent user behaviour on an e-commerce platform. The user data was analysed to identify and extract critical features distinguishing regular users from fraudulent users. LÄS MER

7. 5. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

   Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Alexander Florean; [2024]
   Nyckelord :Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

   Sammanfattning : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. LÄS MER