Data Wrangling: From Messy Data to Meaningful Insight - delivered to my Space Science students
This Project aims at analysing 12 months’ worth of sales data containing hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc.
Utilize SQL to conduct a comprehensive analysis of pizza sales data, aiming to uncover patterns, trends, and insights that will inform strategic decision-making and enhance overall business efficiency.
Fast groupby-apply operations in python.
Perform Clustering for the crime data and identify the number of clusters formed and draw inferences. Data Description: Murder -- Muder rates in different places of United States Assualt- Assualt rate in different places of United States UrbanPop - urban population in different places of United States Rape - Rape rate in different places of Unit…
Unsupervised-ML---Hierarchical-Clustering-University Data. Import libraries, Import dataset, Create Normalized data frame (considering only the numerical part of data), Create dendrograms, Create Clusters, Plot Clusters.
Pandas groupby method used to extract data from tips dataset
Project to analyze key information from Culinary Jorney, a marketplace Company, and develop an initial strategy to lead the entire team effectively.
Understanding Pandas, Groupby Function, Filtering Function
Assignment-07-DBSCAN-Clustering-Crimes. Perform Clustering for the crime data and identify the number of clusters formed and draw inferences.
Perform DataFrame operations in Pandas for a more in-depth look at data wrangling in practice
Welcome to the Pandas Basics library! 🚀 This repository is designed to help beginners and aspiring data scientists grasp the core concepts of Pandas, one of the most powerful Python libraries for data manipulation and analysis.
Using Python and Jupyter Notebook, school district's data is cleared of inaccuracies.
Prediction of house price using Linear Regression Supervised Machine learning Algorithm
Learning "Pandas" Library
Pair Programming – NumPy & Pandas Project – collaboratively applied NumPy and Pandas for EDA, data cleaning, null handling, and transformations, including groupby/apply operations on real datasets.
Created a summary DataFrame of the ride-sharing data by city type of a Uber-like company (Pyber). Then, using Pandas and Matplotlib, I created a multiple-line graph that shows the total weekly fares for each city type. Finally, I made a written report that summarizes how the data differs by city type and how those differences can be used by deci…
A better way to summerize a table information in pandas
Created summary DataFrame of ride-sharing data by city type using Python and multiple-line graph of weekly fares by city type using Pandas and Matplotlib.
DS Foundations II | A/B Testing for ShoeFly.com
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