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Published 2 ML models, more ML / LLM models will be created Check more here  Andrometocs Profile | Kaggle   Top three major domains for xBusinessTech !! 1. SpaceTech datasets, ML models, and notebooks 2. InsurTech datasets, ML models and Notebooks 3. FinTech datasets, ML Models and notebooks

  Embedded Kaggle Notebook The notebook contains various code examples and functionalities, particularly focusing on using a language model called "Gemma LM" for text generation, problem-solving, and fine-tuning tasks. 1. Introduction This notebook demonstrates the use of a language model (Gemma LM) to perform various tasks, including text generation, mathematical problem-solving, and insurance domain research. 2. Purpose of This Notebook The primary purpose is to showcase how the Gemma LM can be utilized in diverse scenarios, ranging from generating innovative ideas in specific domains (like insurance) to solving mathematical problems and planning tasks. 3. Business Problem It Will Address The notebook highlights how large language models (LLMs) can support businesses by: Automating content creation. Enhancing decision-making through data-driven insights. Streamlining domain-specific research. 4. Business Research Details and Scenario One example in the notebook generates th...

 Learning Materials on Kaggle The 5 Day data Challenge

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Here are SQL-focused interview questions with only the relevant SQL code: 1. Find the second highest salary from an Employee table. SELECT MAX(Salary) AS SecondHighestSalary FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees); Using ROW_NUMBER(): WITH RankedSalaries AS (   SELECT Salary, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS Rank   FROM Employees ) SELECT Salary AS SecondHighestSalary FROM RankedSalaries WHERE Rank = 2; --- 2. Write a query to calculate a running total of sales. SELECT   OrderID,   OrderDate,   Amount,   SUM(Amount) OVER (ORDER BY OrderDate) AS RunningTotal FROM Orders; --- 3. Retrieve customers who placed no orders using a LEFT JOIN. SELECT c.CustomerID, c.CustomerName FROM Customers c LEFT JOIN Orders o ON c.CustomerID = o.CustomerID WHERE o.OrderID IS NULL; --- 4. Write a query to find the top 3 highest salaries. SELECT DISTINCT Salary FROM Employees ORDER BY Salary DESC LIMIT 3; Using DENSE_RANK(): WIT...

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The space technology insurance sector is evolving rapidly, driven by increased commercial space activities, technological advancements, and emerging risks. Key trends shaping this industry include: 1. Expansion of Coverage Areas Satellite Insurance: This remains crucial for both governmental and commercial entities, covering various stages from pre-launch to in-orbit operations. Policies typically address launch failures, in-orbit malfunctions, and third-party liabilities.  Launch and In-Orbit Coverage: Insurers offer protection against potential losses during the critical phases of satellite deployment and operation, ensuring financial safeguards against unforeseen events. Personal Accident Insurance for Astronauts: With the rise of human spaceflight and space tourism, there's a growing demand for policies that cover accidents, injuries, or fatalities during missions. 2. Rising Demand Due to Commercial Space Activities Proliferation of Small Satellites: The surge in sm...

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  Source Code here in kaggle:  SpaceTech_Insurance_Agile_Strories_Dataset V1.0   When we have private agile user stories, we can able to refine the COT / TOT framework related prompt responses more and lot refined and easier eye catching solutions. Concept Overview: SpaceTech Insurance Dataset Creation and Agile Workflow Planning Business Context SpaceTech insurance is an emerging field addressing risks associated with space exploration, satellite launches, and orbital operations. Insurance companies require robust datasets and workflows to assess risks, underwrite policies, manage claims, and ensure regulatory compliance. By creating an agile dataset and workflow planning approach, insurers can streamline operations, improve decision-making, and adapt to the dynamic nature of the SpaceTech industry. Business Research Market Demand: Increased reliance on satellites for communication, GPS, and Earth observation. Growing investments in private space exploration companies li...

 Kaggle Notebook Gemma 2B Fine Tuned Lightweight model Step 1: Configure GPU for Memory Growth python gpus = tf.config.list_physical_devices( 'GPU' ) if gpus: try : for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True ) print ( "GPU memory growth enabled." ) except RuntimeError as e: print (e) else : print ( "No GPU found. Using CPU." ) Purpose : Ensures the GPU is set to dynamically allocate memory instead of pre-allocating all available GPU memory. This approach prevents memory wastage and allows multiple processes to use the GPU without running into memory allocation errors. Technical Details : tf.config.list_physical_devices('GPU') : Lists available GPUs. tf.config.experimental.set_memory_growth(gpu, True) : Allows TensorFlow to allocate GPU memory on demand. Fallback : If no GPU is found, the code defaults to CPU computation. Step 2: Enable Mixed Precision for Memory Optimizatio...

  Source Code  Kaggle Notebook:  Space Research COT TOT Prompt - Gemma 2B Responses Google Colab:  space-research-cot-tot-prompt-gemma-2b-responses (1).ipynb - Colab   Leveraging Chain of Thoughts (CoT) and Tree of Thoughts (ToT) Models for Space Research and Innovation Subject Details: Introduction to CoT and ToT Methodologies : Overview of Chain of Thoughts (CoT) as a step-by-step reasoning framework. Introduction to Tree of Thoughts (ToT) for multi-branch explorations, allowing parallel evaluation of diverse hypotheses. Applications in Research Paper Development : Enhancing clarity and logical structuring in academic papers. Automating the drafting of hypotheses, problem-solving approaches, and iterative revisions. Innovation in Space Research : Hypothesis generation for challenges like propulsion systems or asteroid mining. Multi-path exploration to cover cost, feasibility, and ethical dimensions of research. Data-driven insights to predict space weather or ...

Scenario: Underwriting Natural Disaster Risk in Hurricane-Prone Regions Key Players: 1. Sophia (Underwriting Manager): Oversees policy design and premium determination. 2. Ethan (Actuarial Analyst): Specializes in catastrophe modeling and predictive analytics. 3. Amara (Risk Assessment Lead): Focuses on environmental risk and regional vulnerability analysis. 4. Liam (Claims Analyst): Provides historical claims data and disaster impact insights. 5. Maya (GenAI Specialist): Leverages AI-driven tools for data processing and scenario simulations. --- Step 1: Initial Meeting Sophia: "Team, we’re planning to underwrite policies in a hurricane-prone region. I need a detailed analysis of potential risks and pricing recommendations." Ethan: "I’ll gather data on past hurricanes and run catastrophe models to estimate potential losses." Amara: "I’ll analyze the geographical and environmental factors contributing to the region's risk." Maya: "I’ll ...