{"id":21486296,"url":"https://github.com/anjupriya-v/sales-prediction-analysis","last_synced_at":"2025-04-23T05:20:29.951Z","repository":{"id":139557575,"uuid":"568405659","full_name":"anjupriya-v/sales-prediction-analysis","owner":"anjupriya-v","description":"Developed using Angular.js, Flask and MongoDB","archived":false,"fork":false,"pushed_at":"2023-03-30T08:50:20.000Z","size":3296,"stargazers_count":8,"open_issues_count":0,"forks_count":4,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-29T22:23:39.929Z","etag":null,"topics":["angularjs","emailjs","flask","mongodb"],"latest_commit_sha":null,"homepage":"","language":"TypeScript","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/anjupriya-v.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":"2022-11-20T12:44:20.000Z","updated_at":"2025-01-16T08:30:09.000Z","dependencies_parsed_at":null,"dependency_job_id":"e45e0a22-5b7c-4f44-8a83-bf2b982b7719","html_url":"https://github.com/anjupriya-v/sales-prediction-analysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anjupriya-v%2Fsales-prediction-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anjupriya-v%2Fsales-prediction-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anjupriya-v%2Fsales-prediction-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anjupriya-v%2Fsales-prediction-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/anjupriya-v","download_url":"https://codeload.github.com/anjupriya-v/sales-prediction-analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250373924,"owners_count":21419899,"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":["angularjs","emailjs","flask","mongodb"],"created_at":"2024-11-23T13:20:15.322Z","updated_at":"2025-04-23T05:20:29.942Z","avatar_url":"https://github.com/anjupriya-v.png","language":"TypeScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sales Prediction Analysis\n\n## Tech Stacks Used:\n\n- Angular.js\n- Python (Flask)\n- MongoDB\n- Machine learning model (SARIMAX Model)\n- Email.js \n\n## Demo Video 👇\n\nhttps://user-images.githubusercontent.com/84177086/211177742-99782e71-a60e-4591-9aa6-98ce188b4723.mp4\n\n\n## :point_down:Steps to initialize the project:\n\n- Clone the repository\n\n```\n$ git clone https://github.com/anjupriya-v/sales-prediction-analysis.git\n```\n\n- Redirect to the cloned repo directory\n\n- Open up the terminal and redirect to client directory.\n\n- Install the dependencies\n\n```\nnpm install\n```\n\n- create the mongoDB account in the mongoDB atlas and create the cluster\n\n- Note: A guide to create the mongoDB account and mongoDB URL\n  https://www.youtube.com/watch?v=oVHQXwkdS6w\n\n\n- click on connect and select connect your application.\n\n- select python as Driver and select version as per the version that you have installed in your PC and get the MONGO DB url from it\n\n- Then create the database user by clicking the database access from the mongoDB atlas menu and click on `Add New Database User`. Then provide the username and password and set the built-in role as `read and write to any database` and click on Add user.\n\n- Replace the DB user name and password in the MongoDB URL.\n\n- Paste the MongoDB URL in app.py file `/server/app.py`\n\n![image](https://user-images.githubusercontent.com/84177086/211177985-2f65f5ac-bf7a-436a-9470-e381841e6fdc.png)\n\n- To create the database, click the database from the mongoDB atlas menu. Then click `Browse Collections` and click `Create Database`\n\n- Note: The database should be named as `SalesPrediction` and  the collection should be named as `account` .\n\n- Create the Secret key typing the following command in the terminal.\n\n```\npython -c 'import os; print(os.urandom(24))';\n```\n\n- Secret key will be generated and paste it in app.py file `/server/app.py`\n\n![image](https://user-images.githubusercontent.com/84177086/211178109-65428e1a-c945-4033-b28f-87ea1ffe7f58.png)\n\n- Use email.js for sending the contact form data to your email inbox\n\n- Create the email.js account in `https://www.emailjs.com/` and paste the service id, template id and user id in `/client/src/app/components/contact-us/contact-us.component.ts`\n\n![image](https://user-images.githubusercontent.com/84177086/211178227-ee5ff76c-3bd4-4408-af31-bb505fd8a228.png)\n\n- A guide to Email.js\n\n  https://www.youtube.com/watch?v=dgcYOm8n8ME\n\n### Email.js Content template screenshot 👇\n\n![image](https://user-images.githubusercontent.com/84177086/211178340-f9ddba76-db86-4405-bfef-d2a66d6043d3.png)\n\n### Email.js Auto reply template screenshot 👇\n\n![image](https://user-images.githubusercontent.com/84177086/211178388-403e071b-5ca8-4261-9aec-944d9193afac.png)\n\n\n- For starting the client, type the following in the command prompt\n\n```\ncd client \n```\n```\nng serve -o\n```\n\n- For starting the server, type the following in the new command prompt\n\n```\ncd server\n```\n```\nflask --app app --debug run\n```\n\n- Note: This Application will be worked only for the following single dataset.\n\n[sales_data_sample.csv](https://github.com/anjupriya-v/sales-prediction-analysis/files/10367624/sales_data_sample.csv)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanjupriya-v%2Fsales-prediction-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanjupriya-v%2Fsales-prediction-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanjupriya-v%2Fsales-prediction-analysis/lists"}