{"id":18729472,"url":"https://github.com/hugo-hattori/customer_profile_analysis","last_synced_at":"2026-04-20T09:34:45.622Z","repository":{"id":177091904,"uuid":"656877350","full_name":"Hugo-Hattori/Customer_Profile_Analysis","owner":"Hugo-Hattori","description":"Data Analysis Project.","archived":false,"fork":false,"pushed_at":"2023-11-06T22:00:08.000Z","size":38,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-23T00:40:45.858Z","etag":null,"topics":["data-analysis","data-analysis-python","data-analytics","jupyter","jupyter-notebook","pandas","pandas-dataframe","pandas-python","plotly","plotly-express","plotly-io","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Hugo-Hattori.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2023-06-21T20:41:37.000Z","updated_at":"2024-02-05T19:43:57.000Z","dependencies_parsed_at":"2024-11-07T14:32:16.364Z","dependency_job_id":"2e934bc6-720f-4d41-9353-a5415a188673","html_url":"https://github.com/Hugo-Hattori/Customer_Profile_Analysis","commit_stats":null,"previous_names":["hugo-hattori/customer_profile_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Hugo-Hattori/Customer_Profile_Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hugo-Hattori%2FCustomer_Profile_Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hugo-Hattori%2FCustomer_Profile_Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hugo-Hattori%2FCustomer_Profile_Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hugo-Hattori%2FCustomer_Profile_Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hugo-Hattori","download_url":"https://codeload.github.com/Hugo-Hattori/Customer_Profile_Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hugo-Hattori%2FCustomer_Profile_Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32041629,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-20T00:18:06.643Z","status":"online","status_checked_at":"2026-04-20T02:00:06.527Z","response_time":94,"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-analysis-python","data-analytics","jupyter","jupyter-notebook","pandas","pandas-dataframe","pandas-python","plotly","plotly-express","plotly-io","python"],"created_at":"2024-11-07T14:27:18.542Z","updated_at":"2026-04-20T09:34:45.596Z","avatar_url":"https://github.com/Hugo-Hattori.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Profile Analysis\n\u003cp\u003eThis project's goal is to increase a company's revenue by identifying the\nIdeal Customer Profile (ICP) also known as the most valuable customer for the company.\u003c/p\u003e\n\n\u003cp\u003eTo this purpose, each client presented in the database was given a score from\n1 to 100, with 100 being the most valuable client and 1 the least valuable.\u003c/p\u003e\n\n### Packages used:\n+ pandas\n+ plotly.express\n+ plotly.io\n\n## Importing the Database\n\u003cp\u003eFirst we need to import the database, visualize and process the data using\nthe pandas package. In this scenario the .csv file contains special\ncharacters and is separated by semicolon instead of comma so the keyword\narguments \"enconding\" and \"sep\" are not default. Also, the dataframe\ncontains a column with empty values, so we need to drop it.\u003c/p\u003e\n\nhttps://github.com/Hugo-Hattori/Customer_Profile_Analysis/blob/c4ff758575da7633a1ee8035707a4df652a561c3/Customer_Profile_Analysis.py#L3-L7\n\n## Data Processing\n\u003cp\u003eUsing the DataFrame.info() method we can observe two major problems: \u003c/p\u003e\n\n\u003col\u003e\n    \u003cli\u003eThe column \"Salário Anual (R$)\" is a Dtype object and not a Dtype int64;\u003c/li\u003e\n    \u003cli\u003eThere're 35 entries where \"Profissão\" information is null, so these\nare not very useful data.\u003c/li\u003e\n\u003c/ol\u003e\n\nhttps://github.com/Hugo-Hattori/Customer_Profile_Analysis/blob/c4ff758575da7633a1ee8035707a4df652a561c3/Customer_Profile_Analysis.py#L12-L15\n\n## Data Analysis\n\u003cp\u003eBy using the DataFrame.describe() method we can see that the average\nscore achieved is around 52, so this will be our main benchmark.\u003c/p\u003e\n\n![image](https://github.com/Hugo-Hattori/Customer_Profile_Analysis/assets/136493140/6ba3b76d-cb23-4998-bbad-17d4cb81821a)\n\n\n\u003cp\u003eUsing .histogram() method from plotly.express package we can perform a\ngraphic analysis, comparing the Score with the other parameters such as\nAge (Idade) or Yearly Income (Salário Anual).\u003c/p\u003e\n\n![Captura de tela 2023-06-21 204934](https://github.com/Hugo-Hattori/Customer_Profile_Analysis/assets/136493140/f6ca3094-538c-4123-ae7c-bef6e65ef7d9)\n![Captura de tela 2023-06-21 205007](https://github.com/Hugo-Hattori/Customer_Profile_Analysis/assets/136493140/00240d36-acf2-451a-b597-88dc1849d24e)\n![Captura de tela 2023-06-21 205039](https://github.com/Hugo-Hattori/Customer_Profile_Analysis/assets/136493140/fbac0d3e-24d2-4f9b-bc0f-aa28d1766d2f)\n![Captura de tela 2023-06-21 205057](https://github.com/Hugo-Hattori/Customer_Profile_Analysis/assets/136493140/6e3784a4-381e-4854-bb11-2e0c6735d51b)\n\n## Conclusion\n\u003cp\u003eAnalysing the Age X Score, Profession X Score, Work Experience X Score\nand Family Size X Score graphics we can conclude that the ICP is above\n15 years old, works in the Entertainment Industry or is an Artist, has between\n10 to 15 years of work experience, and has a family size no larger than 7.\u003c/p\u003e\n\n\u003cp\u003e Note: this is a project developed for academic purposes, therefore the\ndata contained in \"Clientes.csv\" is fictitious and used only to learn Pandas and \nPlotly packages applications.\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhugo-hattori%2Fcustomer_profile_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhugo-hattori%2Fcustomer_profile_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhugo-hattori%2Fcustomer_profile_analysis/lists"}