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| Publish by | Mohd Arman |
| Project Name | Customer Retention Prediction Using Machine Learning |
| Upload Date | April 8, 2023 |
| Platform | Python |
| Programming Language | python |
| Database | Mysql |
| Front end | |
| Back end | |
| Project Type | desktop Application |
| View | 836 |
The customer retention prediction project is a machine learning project that helps businesses predict which customers are likely to churn and take actions to retain them. By analyzing customer data from various sources, the project can identify patterns and behaviors that are associated with customer churn, such as low engagement levels or negative feedback. The predictive model generated by the project can provide businesses with real-time insights on which customers are at risk of leaving, enabling them to take proactive steps to retain them. The project involves several steps, including data collection, data preprocessing, feature engineering, model selection, model training, model evaluation, deployment, testing, and maintenance. Ultimately, the project can help businesses improve customer satisfaction, reduce customer churn, and increase customer retention rates.
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An autonomous AI agent that searches the live web in real time to answer any question. Uses the ReAct framework where the agent thinks about what to search, performs the search, observes the results and repeats until confident answer is found. Features include live web search via Tavily API, multi-step ReAct reasoning loop, transparent step by s ... [ Download Source Code ]
Upload any PDF and ask natural language questions about it using Retrieval-Augmented Generation. The program splits the PDF into chunks, embeds them into a FAISS vector store, and retrieves the most relevant sections to answer each question accurately. Key features include PDF ingestion and smart chunking, semantic search via FAISS vector store, ... [ Download Source Code ]
A conversational chatbot built with LangChain and GPT-4o that remembers the full session history. Users type any question and receive intelligent context-aware replies. The bot uses ConversationBufferMemory to retain all previous messages. Features include persistent conversation memory, GPT-4o integration, custom system prompt, clean CLI interf ... [ Download Source Code ]
