rajannaap100

langflow

Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.

langroid

Harness LLMs with Multi-Agent Programming

Good-to-hear-from-you-ChatGPT

This small project integrates 3 AI models in order to have an oral conversation with ChatGPT: one model to convert speech into text (Whisper), a conversational agent (ChatGPT), and a final model to generate speech from text (Google Could Text-to-Speech)

ai-alchemist

Machine Learning Notes for AI enthusiasts

PyBot-A-ChatBot-For-Answering-Python-Queries-Using-NLP

Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

mmm_stan

Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

lightweight_mmm

LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

mmm_pydata_global_2022

PowerBi-Contact-Center--Debit-Collection-Agents-Analysis

awesome-power-bi

Collection of resources for Power BI

twitter_reccommendationAlgorithm

Source code for Twitter's Recommendation Algorithm

Customer_Churn_Analysis

This project investigates customer churn in debt collection, emphasizing efficient partners, optimized debt allocation, and improved recovery. Insights from visualizations and analyses guide recommendations to retain loyal customers, conduct competitive analysis, strategic marketing, and address feedback for reduced churn and higher satisfaction.

Identify-intention-BERT

Debt-collection AI models, includings IdentifyClassifier( 身份环节），IntentionClassifer(意图环节）， Loop3Classifer(追问环节）and ModelService

avance_bpo

Predicting the recovery rates on non-performing loans (NLP) using a private database from a debt collection agency. Freelance project.

python-debtcollector

A collection of Python deprecation patterns and strategies that help you collect your technical debt in a non-destructive manner

HervalDeepMailing

Simple machine learning project in order to identify payment score for debt-collection aggreements

Collecting_Debts_by_Analyzing_Statute-Barred

This project is about developing a machine-learning model to identify statute-barred accounts, crucial in debt collection. It utilizes a dataset with various attributes and focuses on classifying accounts based on the "IsStatBarred" field, revolutionizing debt recovery and compliance.

DeepMailing

Project created to investigate the most relevant features for debt collection callcenters

NegotiationChatBot-

Chat Bot for automated collection of revenue from customers having a debt

indebted

DA takehome project to dashboard the success of debt collection messages

social-data-driven-debt-collection

本项目设计并开发了一种多源异构大数据驱动的智能催收系统，该系统框架主要包括坏账收回概率等级预测模块和文明催收智能辅助模块(智能生成最佳催收对象、时间、用语和互动方式等)。

DEBT-COLLECTION-PROBABILITY-QUESTION

QUESTION: Based on demographics, what action or combination of action taken against a debtor will have said debtor pay their outstanding debt. The available actions to take are a "Phone Call', "Text Message' Or "Legal Summons"

debt_collection_doc_scraping

Policy Lab '24

mail_debt_collection

debt-in-collections

Intelligent-Debt-Collection

docassemble-DebtCollectionDefense

DebtCollectionApp

Debt collection dashboard with Shiny App

DebtCollectionPrediction

Debt Collection prediction module using XGBoost.