{"id":24915257,"url":"https://github.com/mohamed15058/amazon-sales","last_synced_at":"2026-04-13T21:03:56.271Z","repository":{"id":245692986,"uuid":"818981579","full_name":"Mohamed15058/amazon-sales","owner":"Mohamed15058","description":"amazon-sales","archived":false,"fork":false,"pushed_at":"2025-03-01T23:24:04.000Z","size":1407,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-02T00:23:22.396Z","etag":null,"topics":["dashboard","excel","machine-learning-algorithms","matplotlib","numpy","pandas","powerbi","report","seaborn"],"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/Mohamed15058.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":"2024-06-23T12:41:07.000Z","updated_at":"2025-03-01T23:24:08.000Z","dependencies_parsed_at":"2024-06-23T13:53:30.642Z","dependency_job_id":"f967c14d-42d2-4b8d-9538-3ec988d58e09","html_url":"https://github.com/Mohamed15058/amazon-sales","commit_stats":null,"previous_names":["mohamed15058/amazon-sales-report-"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohamed15058%2Famazon-sales","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohamed15058%2Famazon-sales/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohamed15058%2Famazon-sales/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohamed15058%2Famazon-sales/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mohamed15058","download_url":"https://codeload.github.com/Mohamed15058/amazon-sales/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245980487,"owners_count":20704131,"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":["dashboard","excel","machine-learning-algorithms","matplotlib","numpy","pandas","powerbi","report","seaborn"],"created_at":"2025-02-02T07:17:12.358Z","updated_at":"2026-04-13T21:03:56.229Z","avatar_url":"https://github.com/Mohamed15058.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# amazon-sales\nObjective:\n Analyze the Amazon sales report dataset attached in the mail to extract meaningful insights,\n preprocess the data, create visualizations using Python libraries (matplotlib and seaborn),\n build predictive models, and develop a dashboard for comprehensive data presentation.\n Detailed Task Breakdown\n Step 1: Exploratory Data Analysis (EDA)\n 1. Data Inspection:\n ○ Loadthe dataset and inspect the first few rows to understand its structure.\n ○ Checkthe data types of each column and identify any potential issues.\n 2. Summary Statistics:\n ○ Generate summary statistics for numerical and categorical variables.\n ○ Visualize the distribution of key features to identify trends and patterns.\n Step 2: Data Preprocessing\n 1. Handling Missing Values:\n ○ Identify columns with missing values and decide on appropriate strategies to\n handle them (e.g., imputation, removal).\n 2. Data Type Conversion:\n ○ Convert relevant columns to appropriate data types (e.g., converting Date\n column to datetime format).\n 3. Outlier Detection and Treatment:\n ○ Identify and treat outliers in numerical columns to ensure data quality.\n Step 3: Data Visualization\n 1. Using Matplotlib and Seaborn:\n ○ Create visualizations to understand data distributions and relationships.\n ○ Examples include histograms, bar plots, line plots, and heatmaps.\n 2. Visual Analysis:\n ○ Visualize sales trends over time (e.g., monthly sales trends).\n ○ Identify top-selling products and categories using bar plots.\n ○ Analyze regional sales distributions using geographical visualizations.\n Step 4: Predictive Modeling\n 1. Building Predictive Models:\n ○ Develop models to predict the order status (Shipped, Canceled, etc.).\n ○ Useclassification algorithms such as logistic regression, decision trees, or\n random forests.\n2. Model Evaluation:\n ○ Evaluate the models using appropriate metrics (e.g., accuracy, precision,\n recall).\n ○ Perform cross-validation to ensure model robustness.\n Step 5: Dashboard Development\n 1. Dashboard Design:\n ○ Create an interactive dashboard to present key insights and visualizations.\n ○ Ensure the dashboard is user-friendly and provides actionable insights at a\n glance.\n 2. Tools:\n ○ UsePython libraries like Dash, Plotly, or Streamlite to build the dashboard.\n ○ Integrate visualizations created using matplotlib and seaborn into the\n dashboard    \n 12- Some analysis by excel    \n 13 - Dashboard and report by power bi   \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohamed15058%2Famazon-sales","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmohamed15058%2Famazon-sales","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohamed15058%2Famazon-sales/lists"}