{"id":26264377,"url":"https://github.com/shruti-h/sales_data_analysis","last_synced_at":"2026-04-30T02:31:36.717Z","repository":{"id":279968296,"uuid":"940592920","full_name":"Shruti-H/Sales_Data_Analysis","owner":"Shruti-H","description":"Sales Data Analysis | Pandas \u0026 Matplotlib","archived":false,"fork":false,"pushed_at":"2025-02-28T13:23:00.000Z","size":386,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-29T22:38:43.308Z","etag":null,"topics":["data-analysis","data-science","data-vi","matplotlib","pandas-library","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/Shruti-H.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,"zenodo":null}},"created_at":"2025-02-28T12:59:47.000Z","updated_at":"2025-02-28T13:27:19.000Z","dependencies_parsed_at":null,"dependency_job_id":"6ca74029-f3f6-48c4-94db-bb5282ee43dc","html_url":"https://github.com/Shruti-H/Sales_Data_Analysis","commit_stats":null,"previous_names":["shruti-h/sales_data_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Shruti-H/Sales_Data_Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shruti-H%2FSales_Data_Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shruti-H%2FSales_Data_Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shruti-H%2FSales_Data_Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shruti-H%2FSales_Data_Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Shruti-H","download_url":"https://codeload.github.com/Shruti-H/Sales_Data_Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shruti-H%2FSales_Data_Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32452257,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-29T22:27:22.272Z","status":"online","status_checked_at":"2026-04-30T02:00:05.929Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["data-analysis","data-science","data-vi","matplotlib","pandas-library","python"],"created_at":"2025-03-14T02:14:59.741Z","updated_at":"2026-04-30T02:31:36.712Z","avatar_url":"https://github.com/Shruti-H.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sales Data Analysis - Mini Project\n\n## 📌 Overview\nThis repository contains a **Sales Data Analysis** project that explores and answers business-related questions using **Python, Pandas, and Matplotlib**. The dataset consists of **12 months of sales data** from an electronics store, containing information on order ID, products, quantity ordered, price, order data and purchase address.\n\nThe project includes data cleaning, exploratory data analysis (EDA), and visualization of insights to make data-driven business decisions.\n\n---\n\n## 📊 Data Cleaning \u0026 Preparation\nBefore diving into analysis, data cleaning was performed to ensure accuracy and consistency. Tasks included:\n\n✔ **Dropping NaN values** from the DataFrame.   \n✔ **Converting data types** \n✔ **Extracting useful columns** (e.g., hour from timestamp, city from address).  \n\n---\n\n## 🔍 Business Questions Answered\nUsing **Pandas \u0026 Matplotlib**, the following **key business questions** were explored:\n\n1️⃣ **What was the best month for sales?** 🏆 How much revenue was generated that month?  \n2️⃣ **Which city had the highest sales?** 📍 Understanding regional demand.  \n3️⃣ **What time should advertisements be displayed?** ⏰ Maximizing customer purchase likelihood.  \n4️⃣ **Which products are most often sold together?** 🔗 Product bundling insights.  \n5️⃣ **What product sold the most?** 📦 Why might it have been the top seller?  \n\nEach question was answered using **data aggregation, groupby operations, and visualizations**\n\n---\n\n## 🔧 Methods \u0026 Techniques Used\nThroughout this analysis, the following **Pandas \u0026 Matplotlib techniques** were leveraged:\n\n✔ **Merging \u0026 Concatenating multiple CSV files** to create a unified dataset (`pd.concat`).  \n✔ **Adding new calculated columns** (e.g., sales, hour,city etc).  \n✔ **String parsing operations** (`.str.split()`, `.apply()` functions).  \n✔ **Using `groupby` for aggregate analysis**.  \n✔ **Visualizing insights** using vertival and horizontal bar charts and line graphs \n✔ **Labeling and formatting graphs** for better readability.  \n\n---\n## 🏆 Credits\nThis project was inspired by **real-world business problems** and implemented using Python's powerful data analysis tools.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshruti-h%2Fsales_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshruti-h%2Fsales_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshruti-h%2Fsales_data_analysis/lists"}