Neerus Power BI Looker Dashboards

Power BI Sales & Inventory Forecasting Project (SARIMA)

Project Overview In this project, I built an end-to-end Business Analytics & Data Science solution using SQL, Power BI, and Python to: Analyze historical sales, profit, discounts, and units sold Build an Executive Summary Dashboard for leadership Forecast next 3 months of Sales & Units Sold Support inventory planning and business decision-making This project simulates a real-world eCommerce / Retail analytics use case , combining ETL, BI reporting, and predictive modeling in a single workflow. Business Objective Primary Goals Provide leadership with a single-source executive dashboard Identify sales, profit, and regional performance trends Predict future demand (Sales & Units Sold) for: Inventory planning Revenue forecasting Procurement & supply-chain readiness Key Questions Answered How are sales and profits trending over time? Which regions and segments drive the most value? What will be the expected sales & unit demand for the next 3 months? Architecture ...

Data Analysis and Visualization with Matplotlib and Seaborn | TOP 10 code snippets for practice

Data visualization is an essential aspect of data analysis. It enables us to better understand the underlying patterns, trends, and insights within a dataset. Two of the most popular Python libraries for data visualization are Matplotlib and Seaborn . Both libraries are highly powerful, and they can be used to create a wide variety of plots to help researchers, analysts, and data scientists present data visually. In this article, we will discuss the basics of both libraries, followed by the top 10 most used code snippets for visualization. We'll also provide links to free resources and documentation to help you dive deeper into these libraries. Matplotlib and Seaborn: A Quick Overview Matplotlib Matplotlib is a low-level plotting library in Python. It allows you to create static, animated, and interactive plots. It provides a lot of flexibility but may require more code to create complex plots compared to Seaborn. Matplotlib is especially useful when you need full control ove...

Election Data Classification Project – End-to-End Analysis

Problem Definition The objective of this project is to predict voter preference (Labour vs Conservative) using demographic, economic perception, political leadership ratings, and political awareness variables. This is a binary classification problem , where the target variable is: vote_Labour (1 = Labour, 0 = Conservative) The analysis aims to: Understand data structure and distributions Identify relationships between predictors and voting behavior Build and compare multiple classification models Select the best model based on performance metric Git Link Dataset Overview Rows: 1,525 voters Columns: 9 features + 1 target Data Types: Numerical: Age, economic conditions, leader ratings, political knowledge Categorical: Vote, Gender Missing Values: None Duplicates: 8 (not materially impactful) Target Variable Distribution Labour voters: ~70% Conservative voters: ~30% ➡️ Dataset is moderately imbalanced , which makes recall and AUC important evaluation metrics in addition to accuracy...

AI Councel Lab

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