{"id":22601101,"url":"https://github.com/devrihan/eda-diwali-sales","last_synced_at":"2026-04-28T08:38:53.995Z","repository":{"id":253164498,"uuid":"842669703","full_name":"devrihan/EDA-Diwali-Sales","owner":"devrihan","description":"Analysis of Diwali sales data reveals key purchase trends among specific demographics to improve targeting and sales.","archived":false,"fork":false,"pushed_at":"2024-08-14T20:33:52.000Z","size":447,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-03T04:28:56.581Z","etag":null,"topics":["analytics","data-science","jupyter-notebook","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/devrihan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-14T20:25:29.000Z","updated_at":"2024-08-14T20:35:06.000Z","dependencies_parsed_at":"2024-08-14T22:32:32.170Z","dependency_job_id":"8eb9a8b6-f879-4460-807e-90cd01b66bc9","html_url":"https://github.com/devrihan/EDA-Diwali-Sales","commit_stats":null,"previous_names":["devrihan/eda-diwali-sales"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devrihan%2FEDA-Diwali-Sales","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devrihan%2FEDA-Diwali-Sales/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devrihan%2FEDA-Diwali-Sales/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devrihan%2FEDA-Diwali-Sales/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/devrihan","download_url":"https://codeload.github.com/devrihan/EDA-Diwali-Sales/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246094163,"owners_count":20722561,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analytics","data-science","jupyter-notebook","python"],"created_at":"2024-12-08T12:13:31.741Z","updated_at":"2026-04-28T08:38:48.955Z","avatar_url":"https://github.com/devrihan.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Diwali Sales Data Analysis\n## Project Overview\n\nThis project involves an Exploratory Data Analysis (EDA) of Diwali sales data, focusing on identifying key customer segments and product preferences to improve customer experience and sales strategies. The analysis provides actionable insights into the purchasing behaviors of specific demographics during the Diwali season.\n\n## Table of Contents\n\n- [Project Overview](#project-overview)\n- [Data Description](#data-description)\n- [Objectives](#objectives)\n- [Analysis Approach](#analysis-approach)\n- [Key Insights](#key-insights)\n- [Technologies Used](#technologies-used)\n- [Conclusion](#conclusion)\n- [Future Work](#future-work)\n\n## Data Description\n\nThe dataset used for this analysis includes transaction records from Diwali sales over multiple years. Key features include:\n\n- **Customer ID**: Unique identifier for each customer.\n- **Gender**: Gender of the customer.\n- **Age Group**: Age category of the customer.\n- **Marital Status**: Whether the customer is married or single.\n- **State**: The location of the customer.\n- **Profession**: The profession of the customer.\n- **Product Category**: The category of the product purchased.\n- **Amount**: The transaction amount.\n\n## Objectives\n\n1. **Understand Customer Demographics**: Analyze customer segments based on age, gender, marital status, location, and profession.\n2. **Identify Popular Product Categories**: Determine which product categories are most popular among different customer segments.\n3. **Analyze Purchase Trends**: Understand purchasing patterns among various demographics.\n4. **Enhance Sales Strategies**: Use insights to propose strategies for improving customer targeting and sales during Diwali.\n\n## Analysis Approach\n\n1. **Data Cleaning**: Handling missing values, outliers, and data inconsistencies.\n2. **Exploratory Data Analysis**:\n   - Descriptive statistics to summarize the data.\n   - Visualizations to uncover trends and patterns.\n3. **Customer Segmentation**: Grouping customers based on demographic factors and purchase behavior.\n4. **Trend Analysis**: Studying sales trends over time, identifying key customer segments and product preferences.\n5. **Correlation Analysis**: Identifying relationships between different variables (e.g., profession and product category).\n\n## Key Insights\n\n- **Demographic Focus**: Married women aged 26-35 years from Uttar Pradesh, Maharashtra, and Karnataka, working in IT, Healthcare, and Aviation, are more likely to purchase products from the Food, Clothing, and Electronics categories.\n- **Location Trends**: Customers from urban areas in these states show a strong preference for these product categories during Diwali.\n- **Product Preferences**: The Food, Clothing, and Electronics categories are the top choices among this demographic, suggesting targeted marketing opportunities.\n\n## Technologies Used\n\n- **Python**: For data manipulation and analysis.\n- **Pandas \u0026 NumPy**: For data cleaning and processing.\n- **Matplotlib \u0026 Seaborn**: For data visualization.\n- **Jupyter Notebook**: For interactive analysis and code documentation.\n\n## Conclusion\n\nThe analysis identifies a key customer segment—married women aged 26-35 years from specific states working in IT, Healthcare, and Aviation—who are more likely to buy Food, Clothing, and Electronics during the Diwali season. These insights can be used to tailor marketing strategies, optimize inventory, and enhance customer engagement.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevrihan%2Feda-diwali-sales","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdevrihan%2Feda-diwali-sales","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevrihan%2Feda-diwali-sales/lists"}