A restaurant analytical tool + sales forecasting model
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Updated
Jul 4, 2020 - Jupyter Notebook
A restaurant analytical tool + sales forecasting model
Historical Sales Using Price Elasticity to determine customer responsiveness to future price changes
Analyze retail sales data using SQL and Python. Build a SQLite database from CSV, run SQL queries for key KPIs (revenue, top products, AOV, trends), and visualize results with Matplotlib. A portfolio-ready project demonstrating SQL + data analytics + reporting automation.
End-to-end demand forecasting with Python using synthetic time-series sales data. Includes data generation, cleaning, ARIMA/SARIMA model selection by AIC, evaluation with RMSE and MAPE, and 90-day forecasts with confidence intervals. Reproducible scripts and visualizations for portfolio showcase.
Synthetic sales data analysis with Python. Generate realistic sales transactions, clean and validate data, compute KPIs, and visualize revenue trends by day, month, and category. Includes reproducible scripts and charts for portfolio demonstration.
Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.
A desktop application that uses Envato API to get sales data for Envato market Authors.
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc.
This project performs advanced analysis of sales data across different company branches. It includes data cleaning, calculation of revenue and profit metrics, and visual comparison between branches.
Dashboard interativo de vendas do Xbox Game Pass, criado no Excel para análise e visualização de dados de assinaturas.
In this project, I analyze commercial sales data using NumPy and pandas. I visualize total revenue per product using color-coded bar charts in Matplotlib. It’s a foundational step in business data analysis and project documentation.
📊 Análise Exploratória de Dados (EDA) avançada em vendas: padrões temporais, segmentação e automação com Pandas e Seaborn, preparando insights para Machine Learning.
A MySQL server data was hooked with tableau, necessary data analysis, and data cleaning was performed. In the end, all the data was used to build interactive dashboards on Tableau.
This project analyzes customer segmentation and behavior using data science and cohort analysis. Key metrics like CRR, NRR, CLR, and CLV are examined through detailed charts, including the cohort layer cake and CLR vs. CLV cost efficiency analysis. Exploratory Data Analysis and systematic data manipulation reveal actionable insights.
SQL project analyzing sales data to identify top 3 Indian cities for Monday Coffee's physical store expansion.
Sales Exploratory Data Analysis & Visualization Using Python & Tableau public.
Criação de um Data Warehouse (DW) utilizando modelagem dimensional em um esquema estrela.
Data visualization of sales data every month, highest sales, lowest sales, and average sales
Power BI multi-page report leveraging advanced data visualization for RFM analysis. Delivers deep analytical insights into customer behavior, engagement, and spending patterns, driving strategic business decisions.
FinancialTrendAnalyzer helps analyze and visualize sales data to uncover financial trends. It uses Python to calculate total sales, track changes, and generate insightful charts for better decision-making.
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