Welcome to the Data Science and Analysis Projects Repository! This repository contains a collection of projects covering various topics in data analysis, machine learning, and Power BI reporting. Each project focuses on a unique dataset and explores different methodologies for data wrangling, visualization, and prediction.
Analyze a large dataset containing over 9000+ movies, focusing on various attributes such as ratings, genres, and box office performance. This project provides insights into trends within the movie industry, exploring factors that correlate with movie success.
This Power BI project involves building a comprehensive report for the company "Waggle". It covers various aspects of their business data, utilizing the visualization power of Power BI to present actionable insights for stakeholders.
A follow-up project to the Movies Dataset Analysis, where we delve deeper into various advanced analytical techniques. It covers preprocessing, feature extraction, and advanced data manipulation to uncover hidden patterns in movie data.
This project focuses on data visualization with a dataset containing diamond characteristics. The goal is to use visual tools to uncover trends and distributions in diamond attributes such as carat, cut, color, clarity, and price.
In this project, clinical data from Phase 2 trials is wrangled and analyzed. The project explores preprocessing techniques to clean and structure the dataset for meaningful insights regarding clinical trial results.
This project handles the wrangling and analysis of a Twitter dataset involving WeRateDogs. The project includes data collection, cleaning, and transformation to prepare it for meaningful analysis of user engagement and dog rating patterns.
A machine learning project focused on predicting the stock prices of banks using decision trees and random forest algorithms. This project provides insights into the stock market and the impact of various factors on bank stock prices.
This project analyzes bank data from a financial crisis period. It includes statistical analysis and visualizations to understand how the financial crisis impacted the banking sector and stock prices during that time.
This project applies the K-Nearest Neighbors (KNN) algorithm to an anonymized dataset. The goal is to predict certain outcomes based on various features within the dataset, using the KNN algorithm to classify data points accurately.
A machine learning project that uses Logistic Regression to predict whether a credit card application will be approved based on various customer data points. This project highlights the use of logistic regression for binary classification problems.
A Power BI project that analyzes data from the U.S. healthcare industry. This report focuses on various healthcare metrics and provides insights into industry trends, performance, and areas of concern.
This repository is licensed under the MIT License. See the LICENSE file for more details.
We welcome contributions! Please see the CONTRIBUTING file for guidelines on how to contribute to this repository.
Feel free to explore the projects and modify them as needed. If you have any questions, feel free to raise an issue or contribute to the repository!