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SartHak-0-Sach / repository
This repository contains a comprehensive suite of Python scripts and Jupyter notebooks designed for ticket analysis in the Battery Smart Driver Issues team. It includes time-based ticket tracking, category-wise issue segmentation, churn driver analysis, penalty cohort studies, and automated Slack notifications for real-time alerts.
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Amal-nellanhi /
Collaborative E-Commerce Data Analysis project for Ideathon 2025. This repository contains structured workflows, feature branches, and Jupyter Notebooks focused on visualizing insights from user behavior, sales patterns, and browsing activity.
72/100 healthdilip-026-code /
This repository contains hands-on data analysis projects built with Python and Jupyter Notebook. Each notebook focuses on real-world datasets, exploratory data analysis (EDA), data visualization, and actionable insights.
42/100 healthdarshjasani /
This repository contains Jupyter notebooks for data wrangling and analysis using various datasets. It includes cleaned data, documentation of the cleaning process, and a final report with key findings and visualizations, showcasing the complete data analysis workflow.
41/100 healthSatishY21 /
A data analysis project that explores what makes a YouTube video trend. This repository contains a Jupyter Notebook that cleans, analyzes, and visualizes data from 10 countries to uncover insights on viewer sentiment, top channels, and engagement patterns.
38/100 healthnitikshahere /
Talking about our Uber data analysis project, data storytelling is an important component of Machine Learning through which companies are able to understand the background of various operations. With the help of visualization, companies can avail the benefit of understanding the complex data and gain insights that would help them to craft decisions. You will learn how to implement the ggplot2 on the Uber Pickups dataset and at the end, master the art of data visualization in R Early in 2017, the NYC Taxi and Limousine Commission (TLC) released a dataset about Uber's ridership between September 2014 and August 2015. The data contains features distinct from those in the set previously released and thoroughly explored by FiveThirtyEight and the Kaggle community. Check the Jupyter Notebook in this repository to see the contents of the data.
27/100 health