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Welcome to my data science repository! Here you will find a collection of resources and examples for exploring, analyzing, and manipulating data using Python. The repository includes code templates, case studies, and exercises to help you learn and practice data science concepts and techniques. The topics covered include data exploration, data visu
Leveraging advanced data cleaning techniques and feature engineering, a robust food delivery prediction model was developed using regression algorithms.
Comprehensive object detection using YOLOv5, trained from scratch. Includes data preparation, YOLOv5 training on 20 labels, and testing on images/videos. Utilizes Google Colab's V100 GPU for robust detection.
"Predicting a Greener Future 🌾📊 Delve into the world of agriculture and data science with our Yield Prediction project. We harness machine learning and weather data to forecast crop yields accurately. Join us in cultivating smarter farming practices for a sustainable tomorrow."
This project involves analyzing real-world medical appointment data through Time Series Analysis. The tasks include dataset cleaning, comprehensive analysis, and extracting insights using Python and MySQL.
Explore my solo Customer Segmentation Project, diving into data analysis, clustering, and visualization. Uncover distinct customer segments for tailored marketing strategies and enhanced engagement. Discover the power of data-driven insights in this independent project.
In this project conducted in RStudio using the R programming language, we delved into Spotify data to unravel patterns and insights contributing to music's popularity. Employing statistical techniques and leveraging the power of ggplot2 for visualizations, our analysis aimed to provide a comprehensive understanding of what drives music preferences.
Explore NYC Green Taxi data, predicting fares and optimizing pickup locations using machine learning. Regression models uncover travel patterns and enhance taxi services for an efficient urban transport experience.
This project is an EDA endeavor that delves into the world of Google Play Store data. This project uncovers valuable trends, patterns, and statistics within the Play Store ecosystem. From app categories and user reviews to pricing and app sizes, the project offers a comprehensive analysis of this dynamic and ever-expanding marketplace.
Welcome to the FIFA Dataset Data Cleaning and Transformation project! This initiative focuses on refining and enhancing the FIFA dataset to ensure it is well-prepared for in-depth analysis. The project involves a comprehensive data cleaning process and transformation of key features to improve data quality and usability.