Sheila-Murugi

Causal_Inference

This repository covers a project that deals with logistic optimization where delivery drivers' location is optimized using causal inferencing. It works on company data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.

Contract_Advisor_RAG

This project is designed to build and evaluate a Question-Answer (QA) pipeline using LangChain and OpenAI's GPT models to assist with contract analysis. It aims to simplify the process of extracting key information from legal contracts by leveraging state-of-the-art natural language processing (NLP) techniques.

Data_Warehouse_with__SQL_DBT_AirFlow_Week2

news_correlation_10ac_week0

NLPWarehouse

Scalable Data Warehouse for LLM Finetuning: API Design for High Throughput Data Ingestion and RAG Retrieval. This project collects, cleans, processes, and stores text/audio data for Swahili language. It includes web scraping, database management, API development, and automated workflows to enhance NLP capabilities for African languages.

PromptGenApp

This project tries to harness the power of AI to design a User Interface that can generate well-defined prompts that they can feed to an LLM. The interface will accept a user's question or query, then it will generate prompts that will enable an LLM to generate specific and curated answers.

Redash-chatbot-add-on

Redash chatbot add-on: LLM based chatbot for Advanced Data Analytics, Visualisation, and Automated Insight Extraction

RedashChatbot_add-on

Scalable_Backtesting_Infrastructure_for_Crypto_Trading

Our startup, Mela, aims to simplify cryptocurrency trading for everyone and provide reliable investment sources while mitigating risks. We aim to design and build a reliable, large-scale trading data pipeline that can run various backtests and store useful artifacts in a robust data warehouse.

Storyboard_Synthesis

This project explores how machine learning can enhance advertising by uncovering unexpected insights, improving ad creative, boosting contextual relevance, targeting more defined segments, and optimizing bidding strategies.

Sheila-Murugi/Causal_Inference

This repository covers a project that deals with logistic optimization where delivery drivers' location is optimized using causal inferencing. It works on company data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.

Sheila-Murugi/Contract_Advisor_RAG

This project is designed to build and evaluate a Question-Answer (QA) pipeline using LangChain and OpenAI's GPT models to assist with contract analysis. It aims to simplify the process of extracting key information from legal contracts by leveraging state-of-the-art natural language processing (NLP) techniques.

Sheila-Murugi/Data_Warehouse_with__SQL_DBT_AirFlow_Week2

Sheila-Murugi/news_correlation_10ac_week0

Sheila-Murugi/NLPWarehouse

Scalable Data Warehouse for LLM Finetuning: API Design for High Throughput Data Ingestion and RAG Retrieval. This project collects, cleans, processes, and stores text/audio data for Swahili language. It includes web scraping, database management, API development, and automated workflows to enhance NLP capabilities for African languages.

Sheila-Murugi/PromptGenApp

This project tries to harness the power of AI to design a User Interface that can generate well-defined prompts that they can feed to an LLM. The interface will accept a user's question or query, then it will generate prompts that will enable an LLM to generate specific and curated answers.

Sheila-Murugi/Redash-chatbot-add-on

Redash chatbot add-on: LLM based chatbot for Advanced Data Analytics, Visualisation, and Automated Insight Extraction

Sheila-Murugi/RedashChatbot_add-on

Sheila-Murugi/Scalable_Backtesting_Infrastructure_for_Crypto_Trading

Our startup, Mela, aims to simplify cryptocurrency trading for everyone and provide reliable investment sources while mitigating risks. We aim to design and build a reliable, large-scale trading data pipeline that can run various backtests and store useful artifacts in a robust data warehouse.

Sheila-Murugi/Storyboard_Synthesis

This project explores how machine learning can enhance advertising by uncovering unexpected insights, improving ad creative, boosting contextual relevance, targeting more defined segments, and optimizing bidding strategies.

Sheila-Murugi/Sheila-Murugi

Sheila-Murugi/TellCo_User_Analytics_Week1