Pinned Repositories
Book-Smart
DBMS project for getting book recommendation using ratings. Implemented as an android application using google firebase.
Django-modules_stkdeposit
Django-modules_stkdeposit
DS320
Data analysis and visualization of a variety of data sets, including countries, tweets, and movies, as well as market basket analysis practice, with screen scraping, and time series.
E-learning-platform
An E-learning platform with M-pesa Integration for membership payment
FQserverJE
frt_proj
## Project Demo URL https://diabetiesproject.azurewebsites.net •This repository consists of files required to deploy a **Web App** created with **Flask on Microsoft Azure**.# diabetes_predictor The project helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of a Kaggle database. By using the statistical data about how certain aspects like BMI, Glucose level, Insulin level, age etc. impact if an individual is prone to diabetes or not, the project will be able to tell the user if the person has diabetes or not by entering those values. So in a way the project will help in monitoring the likelihood of someone developing diabetes. The project can be extended to include other diseases prediction which I will incorporate later down the road. ### Problem Statement/Oppurtunity: Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. ## Project Discription: Machine learning techniques Provide better result for prediction by con- structing models from datasets collected from patients. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. Which are K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting (GB) and Random Forest (RF). The accuracy is different for every model when compared to other models. The Project work gives the accurate or higher accuracy model shows that the model is capa- ble of predicting diabetes effectively. Our Result shows that Random Forest achieved higher accuracy compared to other machine learning techniques. The is a Microsoft Azure Web App project that helps the user to identify whether someone is suffering from diabetes by simply inputting certain values like BMI, Glucose level, Blood pressure etc. with the help of Kaggle database. ## Primary Ajure Technology: **Azure Web Apps , AI+Machine Learning, Computer Vision, Static Web Apps, Web Apps** ## Conclusion: Hence we succesfully predict that any person having diabities or not. Thank You # f r t p r o j e c t # f r t p r o j e c t
GroupChama
django mpesa
holehe
holehe allows you to check if the mail is used on different sites like twitter, instagram and will retrieve information on sites with the forgotten password function. Cloned from Megadose, thanks to him
Life-Satisfaction-around-world
The dataset I am using here takes 7 parameters to measure the happiness index which is referred to as Life Ladder or Ladder Score with 0 being the lowest and 10 being the highest. The parameters are: Log GDP per capita, Freedom to make life choices, social support, perceptions of corruption, healthy life expectancy at birth, positive and negative affect. The data has been collected from year 2005 to 2021. [1]. Through this project we are going to learn about happiness patterns worldwide, based on the seven factors affecting happiness as mentioned. To get interesting insights from the data, various methods and models are used including data visualization using Graphs and charts, statistic modelling and regression analysis, Hypothesis testing, correlation analysis etc
Machine-Learning-Algorithms
Types of Machine Learning Algorithms There are 3 types of machine learning (ML) algorithms: Supervised Learning Algorithms: Supervised learning uses labeled training data to learn the mapping function that turns input variables (X) into the output variable (Y). In other words, it solves for f in the following equation: Y = f (X) This allows us to accurately generate outputs when given new inputs. We’ll talk about two types of supervised learning: classification and regression. Classification is used to predict the outcome of a given sample when the output variable is in the form of categories. A classification model might look at the input data and try to predict labels like “sick” or “healthy.” Regression is used to predict the outcome of a given sample when the output variable is in the form of real values. For example, a regression model might process input data to predict the amount of rainfall, the height of a person, etc. The first 5 algorithms that we cover in this blog – Linear Regression, Logistic Regression, CART, Naïve-Bayes, and K-Nearest Neighbors (KNN) — are examples of supervised learning. Ensembling is another type of supervised learning. It means combining the predictions of multiple machine learning models that are individually weak to produce a more accurate prediction on a new sample. Algorithms 9 and 10 of this article — Bagging with Random Forests, Boosting with XGBoost — are examples of ensemble techniques. Unsupervised Learning Algorithms: Unsupervised learning models are used when we only have the input variables (X) and no corresponding output variables. They use unlabeled training data to model the underlying structure of the data. We’ll talk about three types of unsupervised learning: Association is used to discover the probability of the co-occurrence of items in a collection. It is extensively used in market-basket analysis. For example, an association model might be used to discover that if a customer purchases bread, s/he is 80% likely to also purchase eggs. Clustering is used to group samples such that objects within the same cluster are more similar to each other than to the objects from another cluster. Dimensionality Reduction is used to reduce the number of variables of a data set while ensuring that important information is still conveyed. Dimensionality Reduction can be done using Feature Extraction methods and Feature Selection methods. Feature Selection selects a subset of the original variables. Feature Extraction performs data transformation from a high-dimensional space to a low-dimensional space. Example: PCA algorithm is a Feature Extraction approach. Algorithms 6-8 that we cover here — Apriori, K-means, PCA — are examples of unsupervised learning. Reinforcement learning: Reinforcement learning is a type of machine learning algorithm that allows an agent to decide the best next action based on its current state by learning behaviors that will maximize a reward. Reinforcement algorithms usually learn optimal actions through trial and error. Imagine, for example, a video game in which the player needs to move to certain places at certain times to earn points. A reinforcement algorithm playing that game would start by moving randomly but, over time through trial and error, it would learn where and when it needed to move the in-game character to maximize its point total.
wilsonmwiti's Repositories
wilsonmwiti/AI-Trading-Bot-By-GPT-4
wilsonmwiti/BabyRobotGym
An OpenAI Gym Environment for Baby Robot
wilsonmwiti/Binary-Bot-XML
This is a collection of https://bot.binary.com and https://app.deriv.com xml bots
wilsonmwiti/Binary-Robots
This repository is the largest collection of scripts for use on Binary or Deriv bots. It is powered by Deriv and contains a wide variety of scripts that can be used to enhance and automate trading strategies on these platforms.
wilsonmwiti/blue-phish
Phishing analysis and prevention
wilsonmwiti/brettamdur.github.io
r using vscode
wilsonmwiti/cortx-PULSAR
CORTX Community Object Storage is 100% open source object storage uniquely optimized for mass capacity storage devices.
wilsonmwiti/DataStructure-cp3
L-System made in C++
wilsonmwiti/demo
A working demo of Laravel with all Backpack packages installed.
wilsonmwiti/Dental-Clinic-Management-System
Dental Clinic Management System (DCMS) for a dental centre with clinics in major cities across Canada using PHP and PostgreSQL.
wilsonmwiti/final-project-2008-financialcrash
Team Neo's Final Project Repository
wilsonmwiti/FrozenLake-v1
The FrozenLake Environment Project using OpenAI gym
wilsonmwiti/Hospital-Management-System
This Project has pdf file in Business Analysis of Hospital Management System.
wilsonmwiti/Inventory-Management-System-Laravel-SPA
An Inventory and Sales Management System using Laravel 7, Vue js and RESTful API (SPA)
wilsonmwiti/JetBrainsActiveCode
Jetbrains Active Code
wilsonmwiti/kenya-counties-subcounties
A comprehensive repository hosting structured .JSON data files for all 47 Kenyan counties, including their codes, capitals, subcounties, and geoJSON information (polygon coordinates). It also provides OpenStreetMap data and multipolygon coordinates for each county.
wilsonmwiti/laravel
Laravel is a web application framework with expressive, elegant syntax. We’ve already laid the foundation for your next big idea — freeing you to create without sweating the small things.
wilsonmwiti/LindenmayerPlantSimulation
wilsonmwiti/LSystemsInUnity
L-Systems in Unity
wilsonmwiti/nexfil-shizuku
OSINT tool for finding profiles by username
wilsonmwiti/notebooks
Training and presentation materials (mostly) in Jupyter Notebook format
wilsonmwiti/openprojects-im
wilsonmwiti/robotic-warehouse
Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment
wilsonmwiti/Simple-MEXC-TradingView-Bot
A simplified example MEXC TradingBot using TradingView Webhooks and the PENDAX SDK.
wilsonmwiti/springboot-crud-restful-webservices
Spring Boot, MySQL, JPA, Hibernate Restful CRUD API Tutorial
wilsonmwiti/SPX_vs_VIX_Analysis
An analysis of the inverse relationship of the S&P 500 Index ETF ($SPY) against VIX Futures
wilsonmwiti/terraform-zero-to-hero
Master Terraform in 7 days using this Zero to Hero course.
wilsonmwiti/Top-Laravel-Interview-Questions-Wiki
Top Laravel Interview Questions & Answers
wilsonmwiti/treemapper
TreeMapper extends the Plant-for-the-Planet Platform and allows on site data registration for restoration initiatives.
wilsonmwiti/trufflehog
Find and verify credentials