Pius Mutuma's repositories
Predicting-Time-of-Arrival-for-Deliveries-in-Nairobi
This is a project that is aimed at predicting the estimated time of arrival (ETA) for motorbike deliveries in Nairobi
citi-bike-data
This project pulls data from Citi Bike Trip website, stores it in a PostgreSQL database for use in Data Visualization.
Jumia-data-scraping
This project aims to scrape data from the Jumia website to create a comprehensive list of all products currently offered at a discount.
Ppius6
Config files for my GitHub profile.
citi-bikes-nyc-EDA
This is an exploratory data analysis of ride trips data from citi-bikes, a New York Company. The data used is from February 2021 to April 2024.
fetching-yahoo-stock-data
This stock tracking app uses Confluent Kafka and Yahoo Finance for real-time data. It has a Producer fetching stock prices, publishing to Kafka, and a Consumer for updates. A Flask web app displays live and historical charts, aiding informed investment decisions.
Titanic-Analysis
An end to end data science project to predict the survival rate of the passengers aboard the Titanic in 1912.
Fake-News-Detection-Model
This project is dedicated to building a fake news detection model using Logistic Regression
Spotify-Playlist-Analysis
This project involves analysing a dataset of tracks from Spotify's Top Hits playlist from the years 2010 to 2022. The goal is to develop machine learning models to predict the popularity of a track based on various features . The analysis is comprehensive, including data cleaning, exploratory data analysis, and predictive modelling.
Player-Market-Valuation-Analysis
The Football Player Valuation Prediction Model on GitHub utilizes machine learning to forecast football player market values. Leveraging extensive datasets of player attributes and market trends, it aims to be a vital tool for clubs, analysts, and enthusiasts in making informed decisions during transfer seasons.
Job-Market-Analysis
This project delves into the dynamics of the data science job market . Using a rich dataset of job postings, this repository offers deep insights into trends, patterns, and demands in various data-centric job roles across different industries. It identifies popular job titles to understanding the skill demands and relevant pay scales.
Google-Capstone-R-Analysis
This project utilizes R programming language to analyze Cyclistic bike-share data to identify trends in usage patterns between annual members and casual riders. The objective is to understand the differences in usage between the two segments and identify strategies to convert casual riders into annual members.
LinkedIn-Data-Analytics-Roles-Scraping
The data is collected using web scraping techniques and processed using the Python programming language, with results presented in a structured format, such as a CSV file. The goal of this project is to analyze the key skills, educational background, and locations with the highest number of hirings for data analyst positions.
Electronics-E-Commerce-scraping
This web scraping project involves extracting data on electronic products for sale from an e-commerce website using Beautiful Soup and Requests in Python. The extracted data, including product name, product price, review rating, review count, relative URL, and product description, is then converted into a Pandas Data Frame for further analysis.
Cars-dealership-Webscraping
This web scraping project involves extracting data on cars for sale from a website using Beautiful Soup and Requests in Python. The extracted data, including car name, mileage, rating, rating count, price, and dealer name, is then converted into a Pandas dataframe for further analysis. A loop is used to automate the process of extracting data.
Investigating-Food-Claims
The project involved investigating food claims at a restaurant by analyzing a dataset to answer several questions related to the claims, such as the number of claims per location, the distribution of time taken to settle claims, and the relationship between time to close a case and amount paid.
Fetching-users
The project involved fetching users from https://randomuser.me/api and listing the top 100 male users. The result included their name, email, and phone.
SQL-Projects---Learning
This project contains a collection of SQL learning files that cover intermediate SQL concepts such as counting, having, like, like note, round, sorting, and where in. Additionally, there are files that cover the basics of joining data, including the various types of joins, UNION, UNION ALL, INTERSECT, & EXCEPT.