Analyzing Online Prices by Using Machine Learning Techniques (master thesis) - Analysis part source code
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Updated
Jan 23, 2020 - R
Analyzing Online Prices by Using Machine Learning Techniques (master thesis) - Analysis part source code
Final project for IEE 520 Stat learning for data mining. Highly imbalanced data set. Sampling methods used.
To know the main reasons for attrition of employees.
Random Oracle Ensembles for Imbalanced Data
Comparing various machine learning techniques to predict strokes in patients based on healthcare attributes.
This notebook shows how the f1 metric differs accuracy on imbalanced data. The heart disease dataset from kaggle is used (https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease).
Project on Fraud Detection using classification algorithm
It is a simple machine learning algorithm to get the latent vector of the Molecules from the datasets. After that we address the imbalance problem in the dataset and handle it by using various resampling techniques. Then we measure the performance of the algorithm by deploying various Classifiers.
Predict customer churn through supervised and unsupervised techniques, perform feature engineering, incorporate network science. The project also covers extensive data preprocessing, making informed churn assumptions and exploratory data analysis.
Customer churn prediction is to measure why customers are leaving a business. In this tutorial we will be looking at customer churn in telecom business. We will build some models to predict the churn and use precision,recall, f1-score to measure performance of our model.
Credit card fraud detection from european cardholders transactions
Working with Imbalance Dataset for classification using SVM model
PySpark를 이용한 불균형 데이터 처리 알고리즘 구현
IBLA - Imbalance Learning Archive
A theoretical analysis of possible approaches to an imbalanced dataset.
The project involves deciding on the mode of transport that the employees prefer while commuting to the office.
Classify people to predict their income class, either above 50K or below 50K based on their age, work class, education level, ... using ScikitLearn
Using a credit score data from Kaggle, determine clients to provide loans and are less likely to default.
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