{"id":25027140,"url":"https://github.com/nurfakhri/e-commerce-data-analyst","last_synced_at":"2026-02-10T16:31:35.568Z","repository":{"id":272901813,"uuid":"918114025","full_name":"NurFakhri/E-commerce-Data-Analyst","owner":"NurFakhri","description":"E-commerce data analysis supported by data wrangling, EDA, and web dashboard","archived":false,"fork":false,"pushed_at":"2025-01-17T11:53:33.000Z","size":67687,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-30T15:19:09.287Z","etag":null,"topics":["dashboard","data-analysis","e-commerce","flask-application","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/NurFakhri.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":"2025-01-17T09:25:04.000Z","updated_at":"2025-01-22T14:00:07.000Z","dependencies_parsed_at":"2025-02-05T18:52:10.367Z","dependency_job_id":null,"html_url":"https://github.com/NurFakhri/E-commerce-Data-Analyst","commit_stats":null,"previous_names":["nurfakhri/e-commerce-data-analyst"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/NurFakhri/E-commerce-Data-Analyst","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NurFakhri%2FE-commerce-Data-Analyst","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NurFakhri%2FE-commerce-Data-Analyst/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NurFakhri%2FE-commerce-Data-Analyst/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NurFakhri%2FE-commerce-Data-Analyst/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NurFakhri","download_url":"https://codeload.github.com/NurFakhri/E-commerce-Data-Analyst/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NurFakhri%2FE-commerce-Data-Analyst/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29307904,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-10T16:09:25.305Z","status":"ssl_error","status_checked_at":"2026-02-10T16:08:52.170Z","response_time":65,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["dashboard","data-analysis","e-commerce","flask-application","python"],"created_at":"2025-02-05T18:40:45.741Z","updated_at":"2026-02-10T16:31:35.553Z","avatar_url":"https://github.com/NurFakhri.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# E-commerce Data Analyst 🛒\n\n## Project Overview\nThis project focuses on analyzing e-commerce data, starting from formulating questions to answering them using exploratory data analysis (EDA). Some of the key questions addressed in this analysis include:  \n- Which products receive the most positive and negative reviews?  \n- Are certain products more popular or frequently purchased in specific regions?  \n- What is the success rate of product deliveries?  \n\nThe project was part of the tasks in a **Dicoding** course, where we explore and apply data analysis techniques in real-world scenarios.\n\n## Dataset\nThe dataset used in this analysis contains various details related to e-commerce transactions, including product reviews, purchase information, customer demographics, and delivery success. This data allows for deep insights into customer behavior, product performance, and regional differences in purchasing preferences.\n\n## Dependencies\nThe following dependencies were used in this project:\n- **Streamlit**: A framework for building interactive web applications. It is used to create a simple web interface for visualizing and interacting with the analysis results.\n- **Pandas**: A powerful data manipulation and analysis library. It is used for handling and processing the dataset, including cleaning, transforming, and analyzing the data.\n- **Matplotlib**: A plotting library for creating static, animated, and interactive visualizations. It is used to generate various types of charts and graphs to visualize data patterns.\n- **Seaborn**: A statistical data visualization library based on Matplotlib. It is used for creating informative and attractive statistical graphics, such as heatmaps and distribution plots.\n- **WordCloud**: A tool to generate word clouds from text data. It is used to visualize the most frequent words or terms in the product reviews for sentiment analysis.\n\n## Methodology\nThe following steps were taken in this analysis:\n1. **Import Libraries**: Import necessary Python libraries for data processing and analysis.\n2. **Data Wrangling**: Clean and prepare the data by handling missing values, correcting data types, and merging relevant data sources.\n3. **Exploratory Data Analysis (EDA)**: Perform EDA to uncover insights related to product reviews, popularity across regions, and delivery success.\n4. **Web Application**: Build a simple web application using **Streamlit** to visualize and interact with the analysis results.\n\n## Web Installation\nTo run the dashboard web-based application using **Streamlit**, follow these steps:\n\n### 1. **Clone the Repository**\n   First, clone the repository to your local machine:\n\n   ```bash\n   git clone https://github.com/NurFakhri/E-commerce-Data-Analyst.git\n   cd dashboard.py\n   ```\n\n### 2. **Create and Activate a Virtual Environment**\n   - **For Windows**:\n     ```bash\n     python -m venv venv\n     venv\\Scripts\\activate\n     ```\n\n   - **For macOS/Linux**:\n     ```bash\n     python3 -m venv venv\n     source venv/bin/activate\n     ```\n\n### 3. **Install the Required Dependencies**\n   Once the virtual environment is activated, install all necessary dependencies by running:\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n   This will install **Streamlit** along with other dependencies required for the project. \n\n### 4. **Run the Streamlit Application**\n   After the installation is complete, you can start the Streamlit web application with the following command:\n\n   ```bash\n   streamlit run dashboard.py\n   ```\n\n   The application will start, and you can view it by navigating to the URL provided in the terminal.\n\n## Result\n### Conclusion for Problem 1:\nThe product that receives the most positive and negative reviews is the same item. This product is also the highest-selling one by far. Based on this, we recommend using **Natural Language Processing (NLP)** techniques to analyze customer feedback in the comment section to gain deeper insights into customer sentiment.\n\n### Conclusion for Problem 2:\nProduct popularity varies significantly by region. Certain products contribute up to 20-30% of sales in specific regions. Based on this, marketing and inventory stocking strategies should be tailored to match each region's purchasing preferences for better optimization.\n\n### Conclusion for Problem 3:\nThe majority of recorded items have been successfully delivered, indicating that the overall delivery process is functioning well. However, further analysis could identify potential areas for improvement in delivery efficiency.\n\n## Website\n- Dashboard\n  \n![Dashboard](dashboard/dashboard.png)\n\n- Dashboard Diagram\n  \n![Image Description](dashboard/All%20diagram.png)\n\n## Author\nMuhammad Hadi Nur Fakhri\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurfakhri%2Fe-commerce-data-analyst","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnurfakhri%2Fe-commerce-data-analyst","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurfakhri%2Fe-commerce-data-analyst/lists"}