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SupaSQL
Generate SQL queries with just Natural Language. Powered by LLMs customised with millions of SQL datasets.
Мадэль фармавання цэн.:
Аглядайцеся на падобныя прылады штучнага інтэлекту.
Transformez les interactions avec les clients en informations exploitables grâce au logiciel de recherche utilisateur de Collectif AI pour les logiciels en tant que service (SaaS). Connectez les sources de données, automatisez l'analyse et renforcez la prise de décision grâce à des informations basées sur des preuves.
Learity
Een naadloze data-analyse software gemaakt met het oog op besluitvormers. Learity zet ingewikkelde data om in gemakkelijk te begrijpen inzichten door gebruik te maken van generatieve AI-technologie. Stel vragen in natuurlijke taal en krijg directe antwoorden zonder dat een data-analist nodig is. De eenvoudige installatie, samenwerkingsfuncties en robuuste beveiliging maken data-analyse moeiteloos.
Tumelo ya hao ya ho fumana tlhahlobo e tsoang ho tsoela pele ka litšebeletso tsa khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea khoebo ea 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khoebo ea khoebo ea khoebo ea
The Engine is an end-to-end machine-learning tool that makes it easy for you to go from raw data to prediction in minutes, no coding required.
Upsum.io
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AI analysiert däin Gesiicht an informéiert dech iwwer Hautkonditiounen an Ausbréch.
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Dans ce projet, j'ai effectué une analyse et une visualisation de données. J'ai commencé le programme en important le fichier CSV avec Pandas. J'ai ensuite effectué la visualisation en utilisant les bibliothèques Matplotlib et Seaborn. J'ai choisi parmi de nombreux ensembles de données.
