The "Sales Analysis Portfolio Project - Part 2/3" continues our journey into the world of sales data analysis with Python, focusing on the Pandas library. In this second part of the three-part series, you'll delve even deeper into the dataset, building on the foundational knowledge gained in Part 1.
This project takes your skills to the next level by introducing more advanced data analysis techniques. You'll explore topics like predictive modeling, forecasting, and identifying key performance indicators (KPIs) to help make data-driven business decisions. Part 2 equips you with the tools to extract actionable insights from the sales data and enhance your understanding of the business's performance.
Chapters:
0:30 - Group category and sub-category for the quantity column with groupby method
1:50 - Define the result to the newly created dataframe
3:50 - Visualize the data to the histogram with the seaborn library using FacetGrid
6:20 - Converting the datetime datatyoe to the string and extract the month to a new column
9:50 - Groupby the dataframe by year and month
10:44 - Visualize the data by year and sales
#python #ai #bigdata #dataanalysis #datascience #project #pandas #datavisualization
Source:
https://github.com/Rruhid/data/blob/m...
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Data Analysis
Sales Data
Python
Pandas
Data Cleaning
Data Visualization
Data Exploration
Sales Trends
Data Analytics
Portfolio Project
Part 2 of 3
Real-World Data
Python Data Analysis
Data Insights
Sales Patterns
Data From Zero to Hero Sales analysis in pandas
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