Erigha Onome Tina Portfolio

Data Scientist skilled in Sql, Tableau, PowerBi, Python, Keras@erighaonome

BANK CHURN PREDICTION

In this project we perform churn prediction analysis on a bank dataset using various machine learning models. Churn prediction is a vital task for businesses aiming to understand and manage customer retention. The analysis involves preprocessing data, training classification models, evaluating their performance, and visualizing the results.

UDEMY-COURSRS-ANALYSIS-AND-PREDICTION

In this project, I conducted a comprehensive analysis of a dataset containing information about Udemy courses. The goal was to gain insights into various aspects of the courses and build a classification model to predict whether a course is paid or free. The dataset included details such as course titles, subject, level, number of subscribers, number of lectures, content duration, and more.

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    • UDEMY DASHBOARD

    Discover my Udemy Analysis Dashboard – a tool that shows you course details. See how many people join "Paid" and "Free" courses, compare payments, and learn about different subjects. Find out how many lessons are in courses and their prices. See which courses are famous at each level. Understand when courses were made and what subjects they cover. Use these insights to choose courses wisely and make your learning better.

    covid_19_prediction_app

    The COVID-19 prediction app showcases an interactive dashboard designed for comprehensive exploration of COVID-19 data. The app encompasses multiple sections, each tailored to a specific aspect of analysis. Users are empowered to visualize the dataset, conduct exploratory data analysis (EDA), facilitate data cleaning and transformation, and even predict COVID-19 cases. The use of intuitive tabs streamlines access to distinct insights, including correlation matrices, null value counts, and unique value counts. Data preprocessing steps include column elimination, missing value handling, datetime column conversion, and categorical data encoding. To conclude, the app leverages a Linear Regression model for predicting COVID-19 cases based on user-defined inputs, delivering predicted outcomes on demand. This project exemplifies an engaging and informative tool for data analysis and prediction.

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