{"id":22061888,"url":"https://github.com/sermonzagoto/customer_churn_prediction","last_synced_at":"2025-10-07T06:56:16.372Z","repository":{"id":207131658,"uuid":"282625114","full_name":"sermonzagoto/Customer_Churn_Prediction","owner":"sermonzagoto","description":"Customer Churn Prediction using Machine Learning","archived":false,"fork":false,"pushed_at":"2020-07-26T10:28:13.000Z","size":483,"stargazers_count":1,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-28T23:29:34.746Z","etag":null,"topics":["gradient-boosting-classifier","jupyter-notebook","logistic-regression-classifier","machine-learning","python3","random-forest-classifier"],"latest_commit_sha":null,"homepage":"https://github.com/sermonzagoto/Customer_Churn_Prediction","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/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":"2020-07-26T10:19:40.000Z","updated_at":"2020-07-26T10:30:51.000Z","dependencies_parsed_at":"2023-11-14T09:38:03.122Z","dependency_job_id":null,"html_url":"https://github.com/sermonzagoto/Customer_Churn_Prediction","commit_stats":null,"previous_names":["sermonzagoto/customer_churn_prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FCustomer_Churn_Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FCustomer_Churn_Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FCustomer_Churn_Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2FCustomer_Churn_Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sermonzagoto","download_url":"https://codeload.github.com/sermonzagoto/Customer_Churn_Prediction/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245139458,"owners_count":20567184,"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":["gradient-boosting-classifier","jupyter-notebook","logistic-regression-classifier","machine-learning","python3","random-forest-classifier"],"created_at":"2024-11-30T18:16:05.807Z","updated_at":"2025-10-07T06:56:11.341Z","avatar_url":"https://github.com/sermonzagoto.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Churn Prediction Using Machine Learning\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\nSetelah kemarin kita mempersiapkan data sekaligus melakukan Cleansing, maka sekarang saatnya kita untuk membuat model yang tepat untuk memprediksi churn pelanggan.\n\nPada project part 1 kemarin kita telah melakukan Cleansing Data. Sekarang, sebagai data scientist kamu diminta untuk membuat model yang tepat.\n\nPada tugas kali ini, kamu akan melakukan Pemodelan Machine Learning dengan menggunakan data bulan lalu, yakni Juni 2020.\n\nLangkah yang akan dilakukan adalah,\n\n1. Melakukan Exploratory Data Analysis\n2. Melakukan Data Pre-Processing\n3. Melakukan Pemodelan Machine Learning\n4. Menentukan Model Terbaik\n\nUntuk mengunduh hasil project saya di folder ini, Anda dapat menjalankan perintah berikut di terminal Anda:\ngit clone https://github.com/sermonzagoto/Customer_Churn_Prediction.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%2Fcustomer_churn_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsermonzagoto%2Fcustomer_churn_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsermonzagoto%2Fcustomer_churn_prediction/lists"}