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credit-risk-modeling

Credit Risk Modeling for Retail Loans

credit-risk-modeling-1

credit-risk-modeling

credit-risk-modeling-in-python

Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.

credit-score-ai-models

credit-score-modeling-using-machine-learning

Exploratory Data Analysis and Classification on some financial data to find out which clients default on their loans performed on the Home Equity Dataset

credit_risk_classification

Classification Modeling: Probability of Default

credit_score_analysis

creditriskpaper

Codes for replication and implementation of techniques in our credit risk article

criteo-1tb-benchmark

Benchmark of different ML algorithms on Criteo 1TB dataset

cross-sell-prediction

This project is based on a Hackathon arranged by Analytics Vidhya with the title "Janatahack: Cross-sell Prediction"

croston

csv2sql

A small python utility to convert CSV files into SQL insert statements

cuad

CUAD (NeurIPS 2021)

cuad-demo

cudf

cuDF - GPU DataFrame Library

cueobserve

Timeseries Anomaly detection on data in SQL data warehouses and databases

cuml

cuML - RAPIDS Machine Learning Library

customer-probability-of-default-on-credit-card

This is an Artificial Neural Network that can predict, based on 24 attributes of a customer, if an individual customer will default on their payment next month for their credit card (consumer credit risk).

customer-profiling-for-loan-prediction-in-indian-banking-system

The dataset contains details of 95488 entries who have applied for different categories of loan. It has 92 feature - 26 numerical, 64 categorical and 2 date time feature.The target feature was a binary class. As binary class is a classification problem. The objective is to increase approval rate without altering/changing the Non-Performing Asset/default rate at 1%.

customer-segmentation

Using unsupervised learning methods to help business better understand customers

customer-segmentation-using-k-means-clustering

We cluster mall customers into different categories using Machine Learning's K-Means Clustering Model.

customersegmentation-banking

Develop a customer segmentation to define marketing strategy. Used PCA to reduce dimensions of the dataset and KMeans++ clustering technique is used for clustering and profiling of clusters.

d3-charts-generator

Python script that lets you generate from the command line D3 code base for three types of charts: bar, scatter and timeseries plots.

dagster

A data orchestrator for machine learning, analytics, and ETL.

darts

A python library for user-friendly forecasting and anomaly detection on time series.

dask

Parallel computing with task scheduling

dask-tutorial

Dask tutorial

data-engineering-with-python

Data Engineering with Python, published by Packt

data-science-at-the-command-line

Data Science at the Command Line

data-science-hackathon-and-competition

Grandmaster in MachineHack (3rd Rank Best) | Top 70 in AnalyticsVidya & Zindi | Expert at Kaggle | Hack AI