This repository contains various projects in the field of data science. Each project focuses on a specific aspect of data analysis and machine learning. Below is a brief overview of the projects available in this repository:
Predicting customer churn is crucial for businesses to retain customers and improve customer satisfaction. This project aims to build predictive model to forecast customer churn based on historical data.
Diabetes is a prevalent health condition worldwide, and early detection is essential for effective management. This project involves building classification model to predict whether a patient has diabetes based on various health indicators.
Understanding sales data is crucial for businesses to make informed decisions and optimize their strategies. This project involves analyzing sales data to identify patterns, trends, and insights that can help improve sales performance.
Analyzing the distribution of chronic diseases in US states over a certain period. The project aims to understand the prevalence and distribution of chronic diseases, explore stratification processes used to group patients, and analyze how each state is affected by different diseases or topics.
In this project, we conduct an analysis to understand the factors influencing on-time delivery in e-commerce operations. We explore relationships between various aspects such as product properties, logistics operations, and customer experiences, and their impact on delivery performance. Our aim is to build predictive models using machine learning techniques to forecast delivery timelines accurately.
This project analyzes healthcare accessibility challenges in Nairobi, focusing on the uneven distribution of healthcare facilities, demographic disparities, and service gaps. Using data on healthcare infrastructure and population demographics, the study provides insights into key disparities and suggests targeted recommendations in line with Sustainable Development Goal 3 (SDG 3), aiming to improve access to healthcare for all Nairobi residents.
Each project directory contains the necessary files, including datasets, code scripts, and documentation, to facilitate understanding and reproduction of the analysis. Feel free to explore each project for more details and insights.