Data Analysis is like a puzzle, and sometimes the best way to solve it is to take a step back and look at the big picture. - Peter Ibeabuchi
This project demonstrates the use of UMAP and PCA techniques for dimensionality reduction in Python using Jupyter Notebook. It focuses on visualizing high-dimensional data, showcasing how these methods can simplify complex datasets for easier analysis and interpretation
https://github.com/PE-Ibeabcuhi/Dimensionality-ReductionThis Python-based application is designed to simplify the transcription process, making it efficient for user to generate call transcripts. The Audio Transcriber project converts audio files to text using Assembly AI and OpenAI, and it provides accurate transcriptions in various languages
https://github.com/PE-Ibeabcuhi/Audio-TranscriberThe App Analyser Analysis project is a web application that performs sentiment analysis on app reviews from the App Store and Play Store. Built using Streamlit and leveraging Hugging Face's Transformer and BERT models, it provides insights into user feedback, helping developers understand user sentiments towards their apps.
https://app-review-analyser.streamlit.appThis project is a Streamlit web application for classifying images of white and dark chocolate using a Convolutional Neural Network (CNN) model. Users can upload images in various formats (JPEG, JPG, PNG, WEBP) and receive predictions about whether the image contains white or dark chocolate.
https://chocolate-image-classification.streamlit.appThis project focuses on analyzing Amazon's historical stock data and developing ARIMA models to predict future stock prices. The project utilizes R packages like quantmod, forecast, tseries, and TTR for comprehensive time series analysis and forecasting.
https://github.com/PE-Ibeabcuhi/R-Stock-ForecastingThis project predicts smartphone prices in India using machine learning models. The project utilizes a dataset sourced from Kaggle and employs libraries like NumPy, Pandas, Seaborn, Matplotlib, and Scikit-learn for analysis and prediction.
https://github.com/PE-Ibeabcuhi/Smart-Phone-Price-AnalysisThis project provides a comprehensive statistical analysis of a dataset. It includes exploratory data analysis (EDA) for understanding data structure and characteristics, outlier detection using Chebyshev's rule and box plots, exploration of probability distributions, and hypothesis testing to validate assumptions and make inferences.
https://github.com/PE-Ibeabcuhi/R-Statistical-AnalysisThis project uses machine learning to forecast rental prices in Lagos State based on features like location, number of bedrooms, and amenities. The project's dataset is sourced from property listings in Lagos.
https://github.com/PE-Ibeabcuhi/Lagos-Rent-PredictionThis project uses various classification models to analyze factors associated with strokes and predict their occurrence.
This project is a web scraper that extracts details from the Amazon web store using the Selenium library, allowing developers to retrieve data for purposes such as data analysis, price comparison, or monitoring product availability.
https://github.com/PE-Ibeabcuhi/Amazon-web-ScrappingThis project analyzes a sample database provided by MySQL, which consists of 8 tables: Customers, Products, ProductLines, Orders, OrderDetails, Payments, Employees, and Offices. The project aims to answer key business questions, such as identifying top products, employees, and customers, as well as determining the best-performing branch and month.
https://github.com/PE-Ibeabcuhi/Classic-model-AnalysisThis project, uncovers insights and trends in a company's sales performance using SQL. It analyzes a dataset of 186,862 rows and 6 columns, to answer key business questions such as total revenue, monthly revenue, top-selling products, highest price point, and cities with the highest number of deliveries