Dataset

This dataset was created by Kesar_vani

Released under CC0: Public Domain

Contents

The data set contains all data used to generate the figures included in the publication, Release and transformation of nanoparticle additives from surface coatings on pristine & weathered pressure treated lumber surfaces1. The data is arranged by figures and the excel spreadsheet tabs indicate the figure the data is from. All the data presented in the excel file is clearly labeled.

1. Thornton, S.B.; Boggins, S.J.; Peloquin, D.M.; Luxton, T.P. and Clar, J.G. (2020). Release and transformation of nanoparticle additives from surface coatings on pristine & weathered pressure treated lumber. Science of the Total Environment 737: 7.

This dataset is associated with the following publication: Thorton, S.B., S.J. Boggins, D.M. Peloquin, T.P. Luxton, and J.G. Clar. Release and transformation of nanoparticle additives from surface coatings on pristine & weathered pressure treated lumber. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 737: 139451, (2020).

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Project Description:

Title: Pandas Data Manipulation and File Conversion

Overview: This project aims to demonstrate the basic functionalities of Pandas, a powerful data manipulation library in Python. In this project, we will create a DataFrame, perform some data manipulation operations using Pandas, and then convert the DataFrame into both Excel and CSV formats.

Key Objectives:

1. DataFrame Creation: Utilize Pandas to create a DataFrame with sample data.
2. Data Manipulation: Perform basic data manipulation tasks such as adding columns, filtering data, and performing calculations.
3. File Conversion: Convert the DataFrame into Excel (.xlsx) and CSV (.csv) file formats.

Tools and Libraries Used:

• Python
• Pandas

Project Implementation:

1. DataFrame Creation:

   • Import the Pandas library.
   • Create a DataFrame using either a dictionary, a list of dictionaries, or by reading data from an external source like a CSV file.
   • Populate the DataFrame with sample data representing various data types (e.g., integer, float, string, datetime).

2. Data Manipulation:

   • Add new columns to the DataFrame representing derived data or computations based on existing columns.
   • Filter the DataFrame to include only specific rows based on certain conditions.
   • Perform basic calculations or transformations on the data, such as aggregation functions or arithmetic operations.

3. File Conversion:

   • Utilize Pandas to convert the DataFrame into an Excel (.xlsx) file using the to_excel() function.
   • Convert the DataFrame into a CSV (.csv) file using the to_csv() function.
   • Save the generated files to the local file system for further analysis or sharing.

Expected Outcome:

Upon completion of this project, you will have gained a fundamental understanding of how to work with Pandas DataFrames, perform basic data manipulation tasks, and convert DataFrames into different file formats. This knowledge will be valuable for data analysis, preprocessing, and data export tasks in various data science and analytics projects.

Conclusion:

The Pandas library offers powerful tools for data manipulation and file conversion in Python. By completing this project, you will have acquired essential skills that are widely applicable in the field of data science and analytics. You can further extend this project by exploring more advanced Pandas functionalities or integrating it into larger data processing pipelines.in this data we add number of data and make that data a data frame.and save in single excel file as different sheet name and then convert that excel file in csv file .

Contains monthly data from the Assuring Transformation dataset. Data is available in Excel or CSV format.

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

It contains all the necessary data to replicate the statistical and regression results presented in this paper. (XLSX)

This data package contains supplemental material data for the under review TSE submission: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis. The restricted access requirement will be lifted upon approval of the manuscript.

The comprehensive explanation of this dataset can be found at: https://sailuh.github.io/causal_commit_flow_docs

The following briefly describes the contents of the folders. The analysis presented in the manuscript requires the following:

Git Log

Mailing List

Software Vulnerabilities (NVD Feed)

This data is provided to a mining software repository tool, Kaiaulu. The data specifications and configuration parameters are defined in the OpenSSL project configuration file (.yml), also included in this package.

An R notebook in Kaiaulu, taking the dataset above + project configuration file, can then perform the first analysis step:

https://github.com/sailuh/kaiaulu/blob/master/vignettes/issue_social_smell_showcase.Rmd

The file 1_openssl_social_smells_timeline.csv is generated as an output of this R Notebook, and included in the causal_model folder of this package. The following files in this folder numbered 2 through 16, describe transformation steps using Excel, Python scripts, and Tetrad (also an open source tool). These are described conceptually in the manuscript, but in more detail in the comprehensive explanation of this dataset linked at the start.

🚖 Rapido Ride Data — July 2025 📘 Overview

This dataset contains simulated Rapido ride data for July 2025, designed for data analysis, business intelligence, and machine learning use cases. It represents daily ride operations including customer bookings, driver performance, revenue generation, and service quality insights.

🎯 Purpose

The goal of this dataset is to help analysts and learners explore real-world mobility analytics. You can use it to:

Build interactive dashboards (Power BI, Tableau, Excel)

Perform exploratory data analysis (EDA)

Create KPI reports and trend visualizations

Train models for demand forecasting or cancellation prediction

📂 Dataset Details

The dataset includes realistic, time-based entries covering one month of operations.

Column Name Description ride_id Unique ID for each ride ride_date Date of the ride (July 2025) pickup_time Ride start time drop_time Ride end time ride_duration Duration of the ride (minutes) distance_km Distance travelled (in kilometers) fare_amount Fare charged to customer payment_mode Type of payment (Cash, UPI, Card) driver_id Unique driver identifier customer_id Unique customer identifier driver_rating Rating given by customer customer_rating Rating given by driver ride_status Completed, Cancelled by Driver, Cancelled by Customer city City where ride took place ride_type Bike, Auto, or Cab waiting_time Waiting time before ride started promo_used Yes/No for discount applied cancellation_reason Reason if ride cancelled revenue Net revenue earned per ride 📊 Key Insights You Can Explore

🕒 Ride demand patterns by day & hour

📅 Cancellations by weekday/weekend

🚦 Driver performance & customer satisfaction

💰 Revenue trends and top-performing drivers

🌆 City-wise ride distribution

🧠 Suitable For

Data cleaning & transformation practice

Power BI / Excel dashboard building

SQL analysis & reporting

Predictive modeling (e.g., cancellation prediction, fare forecasting)

⚙️ Tools You Can Use

Power BI – For KPI dashboards & visuals

Excel – For pivot tables & charts

Python / Pandas – For EDA and ML

SQL – For query-based insights

💡 Acknowledgment

This dataset is synthetically generated for educational and analytical purposes. It does not represent actual Rapido data.

We sought to assess the measurement properties of items aimed at operationalizing participation in care by next of kin, applied in nursing homes.

Data file 1: SPSS file (.sav) with sex, age category, contact person, 37 items aimed to capture participationin care. Data file 2: Excel (.xlsx) transformation tables from raw total scores, to linear logits, to linearised total scors with the same range as the original scores. Data file 3: File (.rum) for Rasch model analysis, prepared for RUMM2030.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

The rapid advancement of additive manufacturing (AM) requires researchers to keep up with these advancements by continually improving the AM processes. Improving manufacturing processes involves evaluating the process outputs and their conformity to the required specifications. Process capability indices, calculated using critical quality characteristics (QCs), have long been used in the evaluation process due to their proven effectiveness. AM processes typically involve multi-correlated critical QCs, indicating the need to develop a multivariate process capability index (MPCI) rather than a univariate capability index, which may lead to misleading results. In this regard, this study proposes a general methodological framework for evaluating AM processes using MPCI. The proposed framework starts by identifying the AM process and product design. Fused Deposition Modeling (FDM) is chosen for this investigation. Then, the specification limits associated with critical QCs are established. To ensure that the MPCI assumptions are met, the critical QCs data are examined for normality, stability, and correlation. Additionally, the MPCI is estimated by simulating a large sample using the properties of the collected QCs data and determining the percent of nonconforming (PNC). Furthermore, the FDM process and its capable tolerance limits are then assessed using the proposed MPCI. Finally, the study presents a sensitivity analysis of the FDM process and suggestions for improvement based on the analysis of assignable causes of variation. The results revealed that the considered process mean is shifted for all QCs, and the most variation is associated with part diameter data. Moreover, the process data are not normally distributed, and the proposed transformation algorithm performs well in reducing data skewness. Also, the performance of the FDM process according to different designations of specification limits was estimated. The results showed that the FDM process is incapable of different designs except with very coarse specifications.

Customer Segmentation Analysis 📌 Project Overview This project aims to analyze sales and customer segmentation data using an interactive Power BI dashboard. The analysis provides insights into total sales, customer distribution, education levels, language preferences, and state-wise segmentation, helping businesses make data-driven decisions.

📊 Dashboard Features The project includes two key dashboards:

1️⃣ Sales Segmentation Analysis Total Sales: 162M | Total Customers: 40K Sales Distribution by Gender, Year, and Preferred Language Education Level vs. Sales Contribution State-wise Total Sales and Performance Trends

2️⃣ Customer Segmentation Analysis Total Customer Distribution across Different Metrics Customer Demographics: Gender, Education Level, and Language Preference State-wise Customer Count

🛠️ Tech Stack & Tools Used Power BI – Data visualization and interactive dashboard creation Excel / CSV Dataset – Raw data source DAX & Power Query – Data transformation and calculated measures

📈 Key Insights & Findings Top-performing languages: Customers who speak German (26.95%) and French (24.9%) contribute the most to sales. Education Level Impact: Associate Degree holders generate the highest sales and customer count. State-wise performance: Lakshadweep, Himachal Pradesh, and Bihar are the top-performing states.

This document compiles raw data used in the aerosol to vesicle transformation study carried out by Serge Nader et al. For detailed information and context, refer to the main article and its supplementary material published in ACS Earth and Space Chemistry.

The Excel file contains data relevant to each figure in the main article and supporting information. The additional compressed file contains raw Transmission Electron Microscopy (TEM) photographs.

Data Import and Table Selection: Import Excel data into Power BI. Select specific tables (Calendar, Customer, Product, Sales, Terriority). Data Modeling: Design star schema architecture in Model view. Establish relationships between tables. Data Transformation: Filter Calendar table for years 2017 and 2018. Remove unnecessary columns from the Calendar table. Utilize Power Query Editor for data manipulation. DAX Measures: Create measures for analyzing sales data. Use DAX functions to calculate total sales, tax amount, total orders, distinct product count, etc. Add comments to DAX measures for clarity. Visualization: Create matrices to display summarized data. Format measures (e.g., change to currency). Utilize visual elements like icons and tooltips for better understanding. Drill-Down Analysis: Implement drill-down functionality to explore data hierarchically. Additional Measures: Calculate total customers and percentage of distinct customers. Analyze product-related metrics (e.g., max price, weight values). Data Quality Analysis: Identify and analyze empty cells in specific columns. Multiple Sheets and Visuals: Create multiple sheets with different matrix tables. Utilize slicers for interactive filtering. Implement visual filters for dynamic data exploration. Advanced DAX Functions: Utilize SUMX function for calculating total sales including tax. Calculate dealer margin using SUMX function. Conclusion: Summarize the project and its focus on measures, matrix tables, and advanced DAX functions. Overall, your project plan covers various aspects of data analysis and visualization in Power BI, from data import to advanced calculations and visualization techniques, providing a comprehensive guide for analysis and decision-making.

This repository includes the data and the source code used to obtain the output reported in the paper 'Does monetary policy influence euro area fiscal sustainability?' by Afonso and Gomes-Pereira (2025). The data is in .xlsx format (data_for_replication.xlsx) and the source code is an RMarkdown file in R language (Replication Code.Rmd). The source code file should be run in RStudio. More details regarding the source and transformation of the variables is included in the excel file’s sheet ‘Variable Descriptions’.

Contains monthly data from the Assuring Transformation dataset. Data is available in Excel or CSV format.

🚘 Electric Vehicle (EV) Sales Analysis & Forecasting

The Electric Vehicle (EV) Sales Analysis & Forecasting project explores India’s EV adoption trends from 2014 to 2024, combining BigQuery for data analytics and Power BI for visualization. This end-to-end data project demonstrates expertise in SQL-based data analysis, time-series forecasting using BigQuery ML (ARIMA+), and interactive dashboard design. This repository presents an in-depth data analysis and visualization of Electric Vehicle (EV) sales trends across India using Microsoft Excel, SQL, Python and Power BI.

The Electric Vehicle Sales Dashboard is an interactive and visual analytics solution built using Power BI, focused on analyzing EV adoption trends across India. It includes KPIs, comparative charts, and advanced metrics to assist stakeholders in understanding EV sales performance from 2014 to 2024.

⚙️ Tech Stack

Google BigQuery → Data storage, cleaning, and analysis using SQL

BigQuery ML (ARIMA_PLUS) → Time series forecasting for future EV sales prediction

Power BI → Interactive dashboard for visualization and storytelling

Pyhton → For eda and visualization

Excel / CSV → Initial dataset preparation and transformation

🔹 Key Features: ✅ Total EV Sales Trends: Year-over-year and month-over-month change

✅ State-wise EV Performance: Compare EV adoption across Indian states

✅ Vehicle Category Breakdown: 2W, 3W, 4W, Buses, Others

✅ Dynamic Filtering: Use slicers to filter by Year, Month, State.

✅ KPI Cards: Total Sales, Average Sales

📊 Dashboard Overview Components:

Section Description 🔹 KPI Cards Highlight key performance indicators like Total Sales, Average Sales. 📈 Trend Charts Year-wise and month-wise sales volume (bar/line charts) 🗺️ Geo Analysis State-wise sales with ranking and contribution 🚗 Vehicle Type Pie Distribution of sales by 2W, 3W, 4W, Bus, Others 📅 Time Series Filtering and Interactive slicers for Year, Month, and State. 📊 YOY / MOM Change Dedicated visual section with % growth/dip over time

Dashboard using Power BI

https://github.com/user-attachments/assets/eca3b9c3-b446-4447-a38a-d61f5572a712" alt="DashBoards">

Dashboard using Excel

https://github.com/user-attachments/assets/f7cc0949-4581-4cb3-b3ab-ea9242bce803" alt="Dashboard excel">

🗓️ Year-on-Year EV Sales (2014–2024) - EV sales have grown from 2.4K in 2014 to 1.5M in 2023. - Peak growth observed in: - 2021 → 2022: +209% YoY growth - 2020 → 2021: +165% YoY growth - 2024 saw a drop (as of current data): -90.61% https://github.com/user-attachments/assets/02965465-d6cf-45bf-b127-40feb6fe9963" alt="yoy">

📆 Month-on-Month (MoM) Analysis - Highest sales months: November, December, and January - Noticeable drop in February followed by recovery in March https://github.com/user-attachments/assets/83bce2dd-b2ae-43b9-bdb2-def5b9dbe37d" alt="mom">

📍 State-wise Performance - Top Performing States: - 🥇 Uttar Pradesh: ~730K units - 🥈 Maharashtra: ~400K units - 🥉 Karnataka: ~320K units - Lower adoption in northeastern and union territory regions - https://github.com/user-attachments/assets/1ee491b5-9016-4a1e-850f-34e8b9a9aab3" alt="state wise ev">

EV State wise Sale correlation https://github.com/user-attachments/assets/4c5fc55d-389d-4bed-a9bb-eebe8ce31a11" alt="EV corr">

Top 5 State KDE Plot https://github.com/user-attachments/assets/1b9e2f69-9ea0-4ac4-8c26-0350fb75bd75" alt="EV kde plot">

🚗 EV Sales by Vehicle Type - 2-Wheelers: 50.3% of total sales - 3-Wheelers: 45.1% - 4-Wheelers and Buses: Small but growing segment https://github.com/user-attachments/assets/2fc64426-4e3d-4a14-b058-f7c67040663b" alt="vtype wise ev">

📅 Year-wise EV Sales Trend EV adoption has grown significantly from 2,392 units in 2014 to over 1.5 million units in 2023

Fastest growth period: 2020–2022, with YoY gains of 165% and 209%

2024 shows a decline (-90.6%) due to possible data incompleteness https://github.com/user-attachments/assets/d60567fb-fc61-463f-b0e8-34ddd14f1b8d" alt="Year wise ev">

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