Chukwunonso David Omoko's Projects
This repository contains a data analysis project focused on crime data in Los Angeles, completed as part of a DataCamp certification. The goal is to identify patterns in criminal behavior to help the LAPD allocate resources more effectively. The dataset includes information on crime types, dates, times, locations, and victim demographics.
Config files for my GitHub profile.
This repository contains the SQL code and documentation for a project completed as part of the DataCamp certification as an Associate Data Analyst in SQL. The project revolves around analyzing sales data for a company specializing in motorcycle parts.
This project delves into the factors influencing mental well-being among students, utilizing PostgreSQL to analyze survey data from a Japanese international university.
High-Growth Companies Analysis Project on DataCamp: Explore trends in high-growth companies using SQL queries on a unicorns database. Identify top-performing industries and valuation dynamics for investment insights. Contains queries, results, presentation, and instructions.
Explore trends in New York City (NYC) school SAT performance with this Python project. Completed as part of a Datacamp data analysis course, this project utilizes the pandas library to analyze a dataset containing SAT scores and school information for NYC public schools.
Analyze trends in Netflix movie durations with this Python project, completed as part of the Datacamp Data Analysis in Python course. Utilizing pandas and matplotlib, explore the dataset to determine if movie lengths are decreasing over time. Clone the repository to replicate the analysis and gain insights into the entertainment industry.
This repository contains a machine learning project for predicting DVD rental durations. Using Lasso regression for feature selection and Random Forest for final prediction, we achieved an MSE of 2.16. The project includes data exploration, feature engineering, and model optimization. Ideal for improving inventory planning efficiency.
Harness the power of machine learning to help farmers optimize their crop selection. This project predicts the best crop type based on soil metrics like nitrogen, phosphorous, potassium levels, and pH value. Using logistic regression models, we identify the most predictive soil feature to maximize crop yield efficiently.
Explore the history of Nobel Prize winners through data visualization and analysis. Analyze trends, gender distribution, and top-performing countries using pandas, seaborn, and numpy.