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22 lines
2.3 KiB
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22 lines
2.3 KiB
Markdown
# Examples of useful models
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Data scientists armed with access to high-quality datasets are now primed to build useful models for a variety of use cases. Model building is often the most enjoyable part of the 
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* **Government Open Data**
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* Real Estate Valuation Model: Use real estate data to build a machine learning model that predicts property valuations. This can be useful for both buyers and sellers to understand market trends.
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* Traffic Management System: Utilize transportation data to develop an AI-based traffic management system that predicts traffic congestion and suggests optimal routes.
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* Disease Prediction and Outbreak Management: Leverage healthcare data to build a machine learning model for disease prediction and management of potential outbreaks.
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* **Public APIs**
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* Weather Forecast Model: Using weather APIs, build a machine learning model to accurately predict weather conditions. This can be beneficial for farmers, event organizers, and other stakeholders.
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* Stock Market Trend Analysis: Leverage financial APIs to create a predictive model for stock market trends, providing insights for investors.
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* **On-Chain Data**
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* Web3 Customer Cohort Analysis: Build a model to analyze the user activity of DeFi protocols to understand their major customer cohorts and their various key metrics like retention rate and customer lifetime value. Sell the model as a B2B product for protocols to improve their business or as premium research for traders
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* DeFi Credit Scoring System: Build a decentralized finance credit scoring system using DeFi data, allowing for more accurate risk assessment in lending and unlocking under collateralized lending in DeFi.
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* Social Data Analysis: Using decentralized social data, create a model that analyses trends, sentiments, and behaviors in decentralized social platforms.
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* **Fine-Tuned Foundation Models**
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* Personalized Recommendation System: Fine-tune an existing foundation model with labeled datasets to create a recommendation system that can be personalized based on individual user preferences.
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* Medical Diagnosis Assistance: Train a model with labeled medical images to assist in diagnosing a wide range of diseases.
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* Customized Chatbot: Fine-tune a conversational AI model with labeled conversational data to create a chatbot customized to specific industries or companies.
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