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To predict likelihood of a liability customer buying personal loans, using some Machine Learning algorithms.
internship studio project for The classification goal is to predict the likelihood of a liability customer buying personal loans.
Creating and comparing accuracies of different machine learning classification models to classify whether a transaction is fraud or not on imbalanced dataset. Imbalanced datasets are those where there is a severe skew in the class distribution, such as 1:100 or 1:1000 examples in the minority class to the majority class
A predictive model that uses several machine learning algorithms to predict the eligibility of loan applicants based on several factors
What is CLV or LTV? CLV or LTV is a metric that helps you measure the customer's lifetime value to a business. In this kernel, I am sharing the customer lifetime value prediction using BG-NBD, Pareto, NBD & Gamma Model on top of RFM in Python.
Our goals here are finding CLV each customer, segement customer using RFM and CLV, and making recommendation
Customer Segementaion using RFM modeling and KMeans Clustering
Customer segmentation by using the RFM method and K-Means clustering
This repo contains my loan approval prediction project in Python.
Loan Application Data Analysis
The solution to the Loan Prediction Practice Problem on Analytics Vidhya (https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/)
A Classification Problem which predicts if a loan will get approved or not.
https://habr.com/ru/post/276725/
Unsupervised Clustering on Online Retail Dataset
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