Sökning: "Purchase Probability Prediction"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Purchase Probability Prediction.

1. 1. Predicting Customer Churn in E-commerce Using Statistical Modeling and Feature Importance Analysis : A Comparison of Random Forest and Logistic Regression Approaches

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Amanda Rudälv; [2023]
   Nyckelord :Customer behavior; E-commerce; Churn prediction; Statistical model; Machine learning; Random forest; Logistic regression; Feature importance; Kundbeteende; E-handel; Kundbortfall; Statistisk modell; Maskininlärning; Random forest; Logistisk regression; Variabelsignifikans;

   Sammanfattning : While operating in online markets offers opportunities for expanded assortment and convenience, it also poses challenges such as increased competition and the need to build personal relationships with customers. Customer retention be- comes crucial in maintaining a successful business, emphasizing the importance of understanding customer behavior. LÄS MER

3. 2. Prediction of Short-term Default Probability of Credit Card Invoices Using Behavioural Data

   Master-uppsats, KTH/Matematisk statistik

   Författare :Billy Lu; [2022]
   Nyckelord :Probability of Default; Credit Risk; Short-term Default Prediction; Machine Learning; Gradient Boosting; Thresholding; Sannolikheten för Fallissemang; Kreditrisk; Kortsiktig Fallissemang Prediktion; Maskininlärning; Gradientförstärkning; Tröskling;

   Sammanfattning : Probability of Default (PD) is a standard metric to model and monitor credit risk, a major risk facing financial institutions. Traditional PD models are used to forecast risk levels in the long-term, while short-term PD predictions are rarer, but they can support management decisions on an operational level. LÄS MER

4. 3. Purchase Probability Prediction : Predicting likelihood of a new customer returning for a second purchase using machine learning methods

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Olivia Alstermark; Evangelina Stolt; [2021]
   Nyckelord :Purchase Probability Prediction; Machine Learning Models; Well-Calibrated Probabilities; Imbalanced Data; Data Protection;

   Sammanfattning : When a company evaluates a customer for being a potential prospect, one of the key questions to answer is whether the customer will generate profit in the long run. A possible step to answer this question is to predict the likelihood of the customer returning to the company again after the initial purchase. LÄS MER

5. 4. Estimating Prediction Intervals with Machine Learning and Monte Carlo Methods in Online Advertising

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

   Författare :Emil Häglund; [2020]
   Nyckelord :;

   Sammanfattning : Online advertising presents a complex environment. The vast amount of available websites, platforms and formats as well as the trend of programmatic adpurchasing makes assessing a proposed advertisement in terms of cost and expected return challenging. LÄS MER

7. 5. Algorithm that creates productcombinations based on customerdata analysis : An approach with Generalized Linear Modelsand Conditional Probabilities

   Kandidat-uppsats, KTH/Matematisk statistik

   Författare :Enkhzul Uyanga; Lida Wang; [2017]
   Nyckelord :E-commerce; Customer data; Generalized linear model; Conditional probability; ; E-handel; Kunddata; Generaliserad linjär modell; Betingad sannolikhet; Produktkombination; SWOT; Algoritm;

   Sammanfattning : This bachelor’s thesis is a combined study of applied mathematical statistics and industrial engineering and management implemented to develop an algorithm which creates product combinations based on customer data analysis for eleven AB. Mathematically, generalized linear modelling, combinatorics and conditional probabilities were applied to create sales prediction models, generate potential combinations and calculate the conditional probabilities of the combinations getting purchased. LÄS MER