felixkamau

Analyzing-Airbnb-Data-

Analyzing Airbnb Data for a City using and performing descriptive statistics: mean, median, mode, range, and standard deviation of prices. Visualizing the distribution of prices using histograms and box plots

auth_firebase

awesome-zig

📜Zig Learning Guide & Project Lists

c

C Programming Projects

E-Commerce-Customer-Behavior-Analysis

Exploring and analyzing factors influencing customer behavior in an e-commerce setting. Using statistical methods to uncover patterns, relationships, and insights that can guide business decisions.

eye_gender_machine_learning

Predict some one's gender based on eye images

ml-app-salaryprediction

Salary Prediction Web App With Streamlit

mpesa-express-backend

This repository contains the backend implementation for integrating M-Pesa Express payments using Node.js and Express. The backend handles the initiation of M-Pesa STK push requests, enabling seamless payment processing in your application.

mpesa-express-payment-template

mpesa-express-payment-template: A Node.js starter template for Safaricom's M-Pesa Express API integration. Includes pre-configured environment variables and example code for merchant-initiated payments using the STK Push API. Ideal for quickly adding M-Pesa payment functionality to your app.

Neuralnetwork

Custom Neural Network Implementation in Python Welcome to my repository featuring a custom implementation of a neural network in Python! This project aims to provide a hands-on exploration of neural network fundamentals by building a neural network from scratch, without relying on external libraries.

felixkamau/mpesa-express-backend

This repository contains the backend implementation for integrating M-Pesa Express payments using Node.js and Express. The backend handles the initiation of M-Pesa STK push requests, enabling seamless payment processing in your application.

felixkamau/mpesa-express-payment-template

mpesa-express-payment-template: A Node.js starter template for Safaricom's M-Pesa Express API integration. Includes pre-configured environment variables and example code for merchant-initiated payments using the STK Push API. Ideal for quickly adding M-Pesa payment functionality to your app.

felixkamau/Neuralnetwork

Custom Neural Network Implementation in Python Welcome to my repository featuring a custom implementation of a neural network in Python! This project aims to provide a hands-on exploration of neural network fundamentals by building a neural network from scratch, without relying on external libraries.

felixkamau/Analyzing-Airbnb-Data-

Analyzing Airbnb Data for a City using and performing descriptive statistics: mean, median, mode, range, and standard deviation of prices. Visualizing the distribution of prices using histograms and box plots

felixkamau/auth_firebase

felixkamau/awesome-zig

📜Zig Learning Guide & Project Lists

felixkamau/E-Commerce-Customer-Behavior-Analysis

Exploring and analyzing factors influencing customer behavior in an e-commerce setting. Using statistical methods to uncover patterns, relationships, and insights that can guide business decisions.

felixkamau/ml-app-salaryprediction

Salary Prediction Web App With Streamlit

felixkamau/cloud-flare

felixkamau/Complete-DSA-Preparation

This is A complete DSA preparation Course. A DSA self-paced course for ultimate Interview and Placement Preparation

felixkamau/cost_functions

Implementation of cost functions.

felixkamau/Data_Visualisation

This repository is your go-to resource for exploring a plethora of data visualizations created using Python libraries such as Matplotlib, Seaborn, Plotly, and more. Dive into an extensive collection of interactive and static visualizations covering diverse datasets and topics

felixkamau/deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

felixkamau/felixkamau

felixkamau/flask_intro-1

learning about flask

felixkamau/flutter-frontend

Intro to flutter dev.

felixkamau/intro-to-dotnet-web-dev

Get Started as a Web Developer with .NET, C#, and ASP.NET Core

felixkamau/logistic_regression

Am using logistic regression (Binary classification) to predict what age of people buy insurance based on their age.

felixkamau/machine-learning

felixkamau/ML-From-Scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

felixkamau/mpesa-react-payment-form

A React.js application integrating M-Pesa Daraja API Express for seamless mobile payment processing. This project includes a payment form that securely handles transactions with M-Pesa, providing a user-friendly interface for initiating payments.

felixkamau/NaturalLanguageProcessing-NLP-

Welcome to the NLP Learning Repository! This repository is designed for beginners who are eager to dive into the fascinating world of Natural Language Processing (NLP). Here, you'll find a curated collection of resources, tutorials, and projects to help you understand and apply NLP techniques.

felixkamau/ollama

Get up and running with Llama 3, Mistral, Gemma, and other large language models.

felixkamau/Openai-whisper-large-v3

This project implements the Whisper large v3 model, providing robust speech-to-text capabilities with support for multiple languages and various audio qualities.

felixkamau/phidata

Build AI Assistants using function calling

felixkamau/Porfolio

Personal porfolio

felixkamau/project-based-learning

Curated list of project-based tutorials

felixkamau/rustlings

:crab: Small exercises to get you used to reading and writing Rust code!

felixkamau/Solana-Token

In this project, we are going to be building our own token on Solana. If you’re an absolute beginner in the Solana ecosystem, then marvellous! This is a very simple tutorial to get you going. By the end of the tutorial, you will understand how tokens function in Solana and what token accounts essentially are.

felixkamau/train_test

Explore this repo for concise examples using scikit-learn to split datasets into training and testing sets, a crucial step in evaluating machine learning models. Ideal for beginners, it simplifies the process of assessing model performance on unseen data.