You will get a Fine-tuned LLM Chatbot for Stocks Analysis
You will get a Fine-tuned LLM Chatbot for Stocks Analysis
Project details
This project explores the performance of Apple (AAPL) and Meta (META) stocks since 2022, leveraging the capabilities of large language models (LLMs)
Leveraging the LLM advantage: Specific tools like Streamlit, LangChain, phi 2(LLM), Replicate, and Chroma-db will empower our
investigation. These cutting-edge technologies allow us to:
1.Structure and organize massive amounts of data.
2.Extract key insights from diverse sources.
3.Identify patterns and connections that might not be readily apparent.
4,Present the information in a visually appealing and interactive way.
5,By combining the power of LLMs with human expertise, we aim to deliver an analysis that is both insightful and accessible. This deep dive into Apple and Meta stocks is not just about the numbers; it's about understanding the story behind the data and providing valuable knowledge to investors and anyone interested in the ever-evolving world of the stock market.
Leveraging the LLM advantage: Specific tools like Streamlit, LangChain, phi 2(LLM), Replicate, and Chroma-db will empower our
investigation. These cutting-edge technologies allow us to:
1.Structure and organize massive amounts of data.
2.Extract key insights from diverse sources.
3.Identify patterns and connections that might not be readily apparent.
4,Present the information in a visually appealing and interactive way.
5,By combining the power of LLMs with human expertise, we aim to deliver an analysis that is both insightful and accessible. This deep dive into Apple and Meta stocks is not just about the numbers; it's about understanding the story behind the data and providing valuable knowledge to investors and anyone interested in the ever-evolving world of the stock market.
Chatbot Type
Finance & Banking Chatbot, News ChatbotDevelopment Technology
ChatGPT, Python, QnA MakerWhat's included
| Service Tiers |
Starter
$20
|
Standard
$25
|
Advanced
$30
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 5 days |
Number of Revisions | 2 | 2 | 3 |
Number of Conversation Steps | 4 | 4 | 4 |
Number of Messaging Platforms | 1 | 1 | 1 |
Action Plan | |||
API Integration | - | ||
Bug Fixes | - | - | - |
Chatbot Flow Design | - | ||
Conversation Script | - | - |
About Raghu
Full Stack Data Professional with GEN AI & Google Vertex AI Expertise
Hyderabad, India - 11:49 pm local time
About:
✅Data Scientist at Tata Consultancy Services India (Former) & ASE at Accenture
✅Deep Learning Specialist certified from DeepLearning.AI
✅Azure and IBM Certified Data Engineer Associate
I provide:
🟩Delivery Standards: I will be a Full Stack Data partner that focuses on solving key problems using technology & deliver very detailed documentation & demo videos
🟩Speed: I Provide rapid speed of work with significant business requirements and had a great team & network previously worked with me in multiple companies.
🟩Empathy: I'm dedicated to transparent and honest communication, ensuring a straightforward and sincere approach in all interactions and deliveries.
My areas of expertise include:
🟢𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗶𝗻𝗴 : Python, Postgres, C++,Tensorflow, sklearn,
🟢𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 : Image Generation, Text Generation, Music and Sound Generation ( LLM fine tuning.)
🟢𝐖𝐞𝐛 & 𝐀𝐩𝐩 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: FastAPI & Flask for creating dynamic and interactive web applications.
🟢𝐂𝐥𝐨𝐮𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: Vertex AI, AutoML, BigQuery, Azure Data Factory, Azure Synapse Analytics
🟢𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: Data analysis, Data preparation, Data cleaning, Visualization
🟢𝐄𝐝𝐠𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠: Jetson nano, Raspberry pi, Arduino, Docker, kubernetes
Steps for completing your project
After purchasing the project, send requirements so Raghu can start the project.
Delivery time starts when Raghu receives requirements from you.
Raghu works on your project following the steps below.
Revisions may occur after the delivery date.
Data Extraction and Transformation
Extracting Data and transforming based on Business requirements.
Selecting opensource LLM
Best opensource LLM without spending much money and free resource.