An AI and System Dev expert. Contact me for projects
Repositories
SilasAmisi/Moti
This project focuses on developing an ecommerce site for buying and selling second hand cars in Kenya
SilasAmisi/meridian-chatbot
Meridian Electronics — Customer Support Chatbot
SilasAmisi/elimu360
AI-Powered Curriculum-Aligned Learning Platform for Kenyan Primary and Secondary School Students
SilasAmisi/production
Repo for my course on Generative AI and Agentic AI in Production
SilasAmisi/agents
Repo for the Complete Agentic AI Engineering Course
SilasAmisi/llm_engineering
Repo to accompany my mastering LLM engineering course
SilasAmisi/intro_to_npm
This an introduction to npm and sas
SilasAmisi/Email_Notification
This repository contains a robust Email Notification System built with Phoenix Elixir and PostgreSQL. It supports user registration, role-based access control, and tiered features, including priority bulk messaging and email retry functionality.
SilasAmisi/Phoenix-Postgress-Forum
SilasAmisi/Vue
Vue JS Basics and Projects
SilasAmisi/Crime-Analysis-and-Prediction
Description: The aim of this project is to analysis the crime reportings from 2018 to April 2024 in the city of Calgary and predict the crime count using neural network. Technologies Used: The notebooks uses LSTM Neural Network to predict the crime count by using adam optimizer.
SilasAmisi/Predicting-Heart-Stroke
Description: The project predicts the risk of heart stroke on studying the person's demographics and medical info Technologies Used: The notebooks uses logistic regression, support vector machine, decision tree and knn Results: The logistic regression, SVM and KNN performs the best with 93.8 % accuracy
SilasAmisi/Predicting-Crop-Yield
Description: The aim of this data science project is to predict crop yield using the dataset provided from Crop Yield Prediction.. Technologies Used: The notebooks uses Decision Tree Regressor and Random Forest Regressor. Results: The random forest regressor gave 80.2% accuracy
SilasAmisi/Breast-Cancer-Detection
Description: The project predicts the diagnosis (M = malignant, B = benign) of the Breast Cancer Technologies Used: The notebooks uses Decision Tree Classification and Logistic Regression Results: The logistic regression gave 97% accuracy and decision tree gave 93.5% accuracy
SilasAmisi/sylogic-lab
Config files for my GitHub profile.