{"id":22061907,"url":"https://github.com/sermonzagoto/data_cleansing_in_telco","last_synced_at":"2025-03-23T17:28:36.701Z","repository":{"id":207131659,"uuid":"279531342","full_name":"sermonzagoto/Data_Cleansing_in_Telco","owner":"sermonzagoto","description":"Data Cleansing in Python","archived":false,"fork":false,"pushed_at":"2020-07-14T08:52:18.000Z","size":113,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-01-28T23:29:39.315Z","etag":null,"topics":["data-analysis","data-science","machine-learning","matplotlib-figures","pandas-python","seaborn-plots"],"latest_commit_sha":null,"homepage":"https://github.com/sermonzagoto/Data_Cleansing_in_Telco","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sermonzagoto.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}},"created_at":"2020-07-14T08:45:46.000Z","updated_at":"2020-07-14T14:24:54.000Z","dependencies_parsed_at":"2023-11-14T09:27:57.352Z","dependency_job_id":"be38027e-a5bd-4db0-bb43-efd80c6bf460","html_url":"https://github.com/sermonzagoto/Data_Cleansing_in_Telco","commit_stats":null,"previous_names":["sermonzagoto/data_cleansing_in_telco"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FData_Cleansing_in_Telco","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FData_Cleansing_in_Telco/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FData_Cleansing_in_Telco/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FData_Cleansing_in_Telco/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sermonzagoto","download_url":"https://codeload.github.com/sermonzagoto/Data_Cleansing_in_Telco/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245139482,"owners_count":20567189,"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":["data-analysis","data-science","machine-learning","matplotlib-figures","pandas-python","seaborn-plots"],"created_at":"2024-11-30T18:16:17.211Z","updated_at":"2025-03-23T17:28:36.677Z","avatar_url":"https://github.com/sermonzagoto.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Science in Telco: Data Cleansing\nDQLab Telco merupakan perusahaan Telco yang sudah mempunyai banyak cabang tersebar dimana-mana. Sejak berdiri pada tahun 2019, DQLab Telco konsisten untuk memperhatikan customer experience nya sehingga tidak akan di tinggalkan pelanggan.\n\nWalaupun baru berumur 1 tahun lebih sedikit, DQLab Telco sudah mempunyai banyak pelanggan yang beralih langganan ke kompetitior. Pihak management ingin mengurangi jumlah pelanggan yang beralih (churn) dengan menggunakan machine learning.\n\nOleh karena itu, tim Data Scientist dimimta untuk mempersiapkan data sekaligus membuat model prediksi yang tepat untuk menentukan pelanggan akan berhenti berlangganan (churn) atau tidak.\n\nPada project kali ini saya menyelesaikan module dari DQLab yaitu Data Science in Telco: Data Cleansing. \n\nLangkah-langkah yang saya lakukan untuk melakukan Data Cleansing pada Dataset yaitu :\n1. Mencari ID pelanggan (Nomor telphone) yang valid\n2. Mengatasi data-data yang masih kosong (Missing Values)\n3. Mengatasi Nilai-Nilai Pencilan (Outlier) dari setiap Variable\n4. Menstandarisasi Nilai dari Variable\n\nUntuk mengunduh hasil project saya di folder ini, Anda dapat menjalankan perintah berikut di terminal Anda:\ngit clone https://github.com/sermonzagoto/Data_Cleansing_in_Telco.git\nAtau meng-klik tombol hijau bertuliskan \"Code\" dan \"Download ZIP\" di bagian kanan atas halaman ini.\n\n# ⚙️ Instalasi\nUntuk menggunakan ekstensi **.ipynb** project saya, pastikan perangkat anda baik itu Laptop atau PC sudah terinstall jupyter notebook. Kebetulan saya menggunakan Google Collaboratory untuk mengerjakan project ini\n\n# ⚖️ Lisensi\nKarya ini sudah memiliki lisensi MIT License https://opensource.org/licenses/MIT. Untuk mengetahui lebih lanjut, silahkan kunjungi https://opensource.org/licenses/MIT\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsermonzagoto%2Fdata_cleansing_in_telco","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsermonzagoto%2Fdata_cleansing_in_telco","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsermonzagoto%2Fdata_cleansing_in_telco/lists"}