Essays about: "Random Forest"

Showing result 1 - 5 of 711 essays containing the words Random Forest.

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

   University essay from KTH/Hälsoinformatik och logistik

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

   Abstract : 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. READ MORE

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

   University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Author : Nikolaos Staikos; [2024]
   Keywords : 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;

   Abstract : 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. READ MORE

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

   University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Author : Abel Getachew Kenea; Gabriel Fagerslett; [2024]
   Keywords : 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;

   Abstract : 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. READ MORE

5. 4. Land cover classification using machine-learning techniques applied to fused multi-modal satellite imagery and time series data

   University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Author : Anastasia Sarelli; [2024]
   Keywords : Geography; GIS; Land Cover Classification; Landsat; Machine Learning; Earth and Environmental Sciences;

   Abstract : Land cover classification is one of the most studied topics in the field of remote sensing, involving the use of data from satellite sensors to analyze and categorize different land surface types. There are numerous satellite products available, each offering different spatial, spectral, and temporal resolutions. READ MORE

6. 5. Android Malware Detection Using Machine Learning

   University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Author : Rahul Sai Kesani; [2024]
   Keywords : Malware; Machine Learning; Random Forest; Sequential Neural Network.;

   Abstract : Background. The Android smartphone, with its wide range of uses and excellent performance, has attracted numerous users. Still, this domination of the Android platform also has motivated the attackers to develop malware. The traditional methodology which detects the malware based on the signature is unfit to discover unknown applications. READ MORE