wilsonmwiti

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/Django-modules_stkdeposit

Django-modules_stkdeposit

wilsonmwiti/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

wilsonmwiti/SequentialSearchNFA_Y3

Software- Netbeans. Language- Java. Sequential Search for NFA.

wilsonmwiti/ada-2021-dol-eta

wilsonmwiti/bluise

wilsonmwiti/bodaBodampez

wilsonmwiti/chama001

cchama mpesa

wilsonmwiti/Codility-C-

The best possible (100%) Codility answers in C++

wilsonmwiti/CsvDataAnalyzer

This is a visualization ans analysis GUI tool for csv file.

wilsonmwiti/darajaloan

daraja loan

wilsonmwiti/dariapp

wilsonmwiti/erpnext

Free and Open Source Enterprise Resource Planning (ERP)

wilsonmwiti/FinalExamReviewPartD

wilsonmwiti/FirebaseLogin-1

This app demonstrates my ability to integrate Firebase authentication.

wilsonmwiti/GlamourHaven

An application to help manage bookings of salon appointments

wilsonmwiti/GradleVscodetests

Java vsCode Gradle testing

wilsonmwiti/Hyplex

wilsonmwiti/IceTube

wilsonmwiti/Lindenmayer-System-Renderer-cp5

L-System generator written in C++ using Qt

wilsonmwiti/login_form

Registration / Login Form using NodeJS, MongoDB, and Angular

wilsonmwiti/mifs

This flask system allows users to conduct financial transactions that happen in a bank

wilsonmwiti/Mwitijava

wilsonmwiti/nodejsmaintemplate

wilsonmwiti/pigi-backend

wilsonmwiti/ProceduralVegetation-LSystem-P5js-js14

Procedural vegetation generator algorithm with L-System developed in Javascript using P5.js

wilsonmwiti/SLRTools

wilsonmwiti/spacehuru_v1

Spacehuru MVP -> to be launched!

wilsonmwiti/sparkRDDs

A project to perform analytics on twitter data.

wilsonmwiti/STUDY

프로그래밍 문제 및 알고리즘 정리

wilsonmwiti/UNet-data-Analysis