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A complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level.

The primary objective of this project is to develop a cutting-edge forecasting model utilizing advanced machine-learning algorithms and sophisticated time-series analysis techniques. The model aims to deliver precise predictions of future sales across diverse retail outlets.

This project aims to analyze and visualize the sales data for Retail and Food Services in the U.S.A. The data is sourced from the U.S. government website and has been processed using SQL to create a database for easy management and analysis.

It is challenging to build useful forecasts for sparse demand products. If the forecast is lower than the actual demand, it can lead to poor assortment and replenishment decisions, and customers will not be able to get the products they want when they need them. If the forecast is higher than the actual demand, the unsold products will occupy inventory shelves, and if the products are perishable, they will have to be liquidated at low costs to prevent spoilage. The overall objective of the model is to use the retail data which provides us with historic sales across various countries and products for a firm. We use this information given, and make use of FM’ s to predict the sparse demand with missing transactions. The above step then enhances the overall demand forecast achieved with LSTM analysis. As part of the this project we answered the following questions: How well does matrix factorization perform at predicting intermittent demand How does matrix factorization approach improve the overall time-series forecasting

A data analysis project exploring consumer behavior and sales trends through EDA using Python. Includes visualizations and insights derived from retail shopping data.

RetailTS is a data visualization and exploratory analytics project focused on uncovering trends, patterns, and seasonal behaviors in retail sales using time series analysis techniques.

A complete end-to-end MySQL project analyzing grocery store operations through database design, complex SQL queries, and business insights. Includes ERD modeling, customer & sales analysis, supplier performance evaluation, and data-driven recommendations for improving retail decision-making.

This project focuses on analyzing retail sales data using SQL to extract meaningful business insights. The database sql_project_p2 was created to store transaction details such as sales date, time, customer demographics, product categories, quantity, cost, and total sales.

Retail Sales Forecasting and Monitoring project offers real-time analysis and forecasts for retail sales.

SQL Project on Retail Sales Analysis

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The superstore data analysis project aims to gain meaningful insights from a large dataset related to a retail superstore's sales and profit. The dataset contains several attributes, including sales, profit, order date, ship date, and more.

This project include Exploratory Data Analysis, Time Series Data Analysis, Forecasting, and Data Visualization (Dashboard) for Retail Sales Data

This project analysis the sales database for a retail company

Retail data analysis project with Python: data cleaning, sales trends analysis, customer segmentation, and data visualization

In this project, we explore the sales data for a retail company and generate various analytics and insights from customer's past purchase behavior. I used SQL to analyze sales revenue. We also perform customer segmentation analysis using the RFM technique.

The project to make analysis of various retail business. The goal is to find business insight and make prediction models for sales.

Discover ML projects with Scala & Python. Explore data analysis, MLflow integration, regression, decision tree classification, Spark DataFrame manipulation, flight & retail sales analysis, and statistical utilities. Includes datasets like forestfires and online shoppers intention for practical learning.

Target Store Sales Prediction – Objective& Deliverables Content: You are provided with historical sales data for 45 stores located in different region search store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Objective & Deliverables Problem description: One challenge of modeling retail data is the need to make decisions based on limited history. Holidays and select major events come once a year, and so does the chance to see how strategic decisions impacted the bottom line. In addition, markdowns are known to affect sales the challenge is to predict which departments will be affected and to what extent. Recommended Project Steps & Guidelines: 1. Understand the data variables properly. Check the variable description to understand the data properly. 2. Clean the data: Clean the data, that is, fill the missing values (if any), treat the outliers (or odd values), etc. Ensure each variable’s data is as per the nature of the variable (e.g. – Date field should contain only date values – can extract year, month and day of the week, and numeric column should be formatted as numeric, etc.). 3. Conduct EDA (Exploratory Data Analysis) on the cleaned Data: Summarize, explore the data and then decide your strategy. Make note of any important assumptions that you make. 4. Uni-variate and Bi-variate Analysis: Check the distribution of independent variables and also compare them with the dependent variable. 5. Feature Engineering: Create new meaningful features based on the existing features by applying some aggregation functions on them. 6. Hypothesis Testing: Hypothesis testing in statistics is a way for you to test the results of a survey or experiment to see if you have meaningful results. You should give a brief summary of the data and a summary of the results of your statistical test. In the discussion, you can discuss whether your initial hypothesis was supported or refuted. TARGET STORE SALES PREDICTION 7. Identify the most important variables (or data parameters) that affect the final decision: Identify the impact of each variable on the final result graphically (correlation / scatter plots, regression plots, etc.). Keep those variables that affect the final outcome. 8. Develop and Validate Samples: Divide samples into 2 parts: Development Sample (70%) & Validation Sample (30%). Build your analysis model using the Development Sample, and validate it on the validation sample and then predict on test sample. 9. Model Building: Analyze the dependent variable and decide which technique out of regression or classification to use and hence build the model. 10. Improving model accuracy: We know that machine learning algorithms are driven by parameters. These parameters majorly influence the outcome of learning process. So, find the optimum value for each parameter to improve the accuracy of the model and repeat this process with a number of well performing models. 11. Model Comparison: Comparing the each model with other similar models and then choose that model which give highest accuracy. But it is not necessary that higher accuracy models always perform better (for unseen data points). So, find the right accuracy of the model, you must use cross validation technique before finalizing the model.

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Power BI Project about Internet Sales Analysis for a Retail Company.

Online retail sales analysis project showcasing SQL queries, revenue insights and customer behaviour analysis.

Retail sales data analysis project using SQL for insights and performance tracking.

A comprehensive SQL project focused on retail sales data analysis, featuring database setup, data cleaning, exploratory analysis, and insightful business-driven queries to uncover sales trends and customer behavior.

SQL project analyzing online retail sales data. Covering database creation, data seeding, EER diagram and detailed analysis with MySQL.

Comprehensive retail sales data analysis and customer segmentation project with Python, featuring exploratory data analysis, customer behavior insights, and machine learning-based segmentation

Retail Sales Analysis SQL project demonstrating data cleaning, EDA, and business insights. Built a p1_retail_db database, cleaned raw data, ran SQL queries to analyze sales trends, customer behavior, and product performance, and generated reports to support data-driven retail decisions.