Sökning: "consumer prediction"

Visar resultat 1 - 5 av 51 uppsatser innehållade orden consumer prediction.

1. 1. Are expert judgments a reliable tool for predicting farmer and food consumer decisions? : experimental evidence from a forecasting survey

   Master-uppsats, SLU/Dept. of Economics

   Författare :Adam Warren Kirby; [2023]
   Nyckelord :Experimental economics; forecasting; food-chain actors; food choice; agricultural policy;

   Sammanfattning : Often trusted to provide sound recommendations and advice, experts from academia and industry are often relied upon throughout industries around the globe, and the food and agriculture industry is no different. We therefore ask, how accurate are these experts, and are they able to accurately forecast behavior from varying food chain actors such as farmers and consumers? Do these experts have a preconceived bias to one side or the other? These questions become increasingly important when considering policy developments such as the EU Farm to Fork strategy, which seek to integrate the consumer-facing food industry and the producer-forward agriculture industry, two policy realms that have historically remained relatively independent of one another. LÄS MER

3. 2. From Data to Decision: : Using Logistic Regression to Determine Creditworthiness

   Kandidat-uppsats, KTH/Matematisk statistik

   Författare :Joel Norling; Sami Abdu; [2023]
   Nyckelord :Bachelor Thesis; Scorecard modeling; Mathematical Statistics; Logistic Regression; Consumer Credits; Binning; Kandidatuppsats; Scorecard-modellering; Matematisk statistik; Logistisk regression; Konsumentkrediter; Binning;

   Sammanfattning : The development of scorecards for customer credit rating is a well-established field in the financial sector. The aim of this project, conducted in collaboration with a Swedish credit institute, was to develop a statistical model for predicting customer performance. LÄS MER

4. 3. Neural Network-Based Residential Water End-Use Disaggregation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Cajsa Pierrou; [2023]
   Nyckelord :Residential water end-use; Flow disaggregation; Time series classification; Artificial neural network; Smart water meter; Slutanvändning av vatten i hushåll; Flödesdisaggregering; Tidsserieklassificering; Artificiella neurala nätverk; Smart vattenmätare;

   Sammanfattning : Sustainable management of finite resources is vital for ensuring livable conditions for both current and future generations. Measuring the total water consumption of residential households at high temporal resolutions and automatically disaggregating the sole signal into classified end usages (e.g. LÄS MER

5. 4. Predictive Regression Model Evaluation : Evaluating Predictive Machine Learning Models to Reduce Food Waste in the Dairy Industry

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Alexander Carlsson; [2023]
   Nyckelord :Machine learning; Food loss; Food overproduction; Food waste forecast; Food waste prediction; Maskininlärning; Matsvinn; Svinn; Matsvinnsprognos;

   Sammanfattning : Food waste in the food industry is often a result from the complex nature of food production. Uncertainty is always present as yields vary and as there is a chain of consumer demand from stores to producers. Food waste is a waste of both natural and economic resources affecting both the planet and the producer. LÄS MER

7. 5. Prototype of a Range Prediction Interface for an Electric Rescue Boat

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Oskar Pålhagen; Erik Halme; [2023]
   Nyckelord :Electric rescue boat; Battery GUI; Range prediction; Prototyping; Elektrisk räddningsbåt; Batteri GUI; Avståndsestimering; Prototypframställning;

   Sammanfattning : Global warming and its effects on the environment call for electrification of the transportation sector. In this effort, the boating industry has not been a pioneer, while being responsible for almost 3 percent of global anthropogenic carbon emissions. LÄS MER