{"id":20260138,"url":"https://github.com/iguptashubham/customer-intent-prediction","last_synced_at":"2026-05-14T23:37:03.613Z","repository":{"id":245406690,"uuid":"818159155","full_name":"iguptashubham/customer-intent-prediction","owner":"iguptashubham","description":"Logistic regression is a binary classification technique used to predict outcomes like customer churn or purchase intent. It models the probability of an event happening (e.g., a customer making a purchase) based on input features.","archived":false,"fork":false,"pushed_at":"2024-07-21T12:45:59.000Z","size":142,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-14T04:13:41.352Z","etag":null,"topics":["data-science","data-science-projects","logistic-regression","machine-learning","machine-learning-projects","machinelearning","project","sklearn"],"latest_commit_sha":null,"homepage":"https://customer-intent-prediction.streamlit.app/","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/iguptashubham.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":"2024-06-21T08:18:20.000Z","updated_at":"2024-07-21T12:46:02.000Z","dependencies_parsed_at":"2024-06-22T01:42:13.646Z","dependency_job_id":"d24f8d0c-13e2-4716-8365-de7201ac7597","html_url":"https://github.com/iguptashubham/customer-intent-prediction","commit_stats":null,"previous_names":["iguptashubham/customer-intent-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iguptashubham%2Fcustomer-intent-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iguptashubham%2Fcustomer-intent-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iguptashubham%2Fcustomer-intent-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iguptashubham%2Fcustomer-intent-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iguptashubham","download_url":"https://codeload.github.com/iguptashubham/customer-intent-prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241720406,"owners_count":20008973,"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-science","data-science-projects","logistic-regression","machine-learning","machine-learning-projects","machinelearning","project","sklearn"],"created_at":"2024-11-14T11:18:08.666Z","updated_at":"2026-05-14T23:36:58.591Z","avatar_url":"https://github.com/iguptashubham.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer-intent-prediction\nLogistic regression is a binary classification technique used to predict outcomes like customer churn or purchase intent. It models the probability of an event happening (e.g., a customer making a purchase) based on input features.\n\n# Pipeline of Model\n\nCertainly! Let's delve into the model pipeline I created. This pipeline combines several essential steps to build a predictive model:\n\n![Screenshot 2024-06-21 175623](https://github.com/iguptashubham/customer-intent-prediction/assets/140319219/360d04bd-3619-47df-bb6e-4ee368e5e999)\n\n\n1. **Data Preprocessing**:\n   - The pipeline begins by handling raw data. It performs transformations to make the data suitable for modeling.\n   - Specifically, it:\n     - **One-Hot Encodes** categorical features (like product category and brand) to convert them into numerical representations.\n     - **Standardizes** numerical features (such as customer age, purchase frequency, and satisfaction) to have zero mean and unit variance.\n     - Applies a **Power Transformation** (Box-Cox) to the product price feature to improve its distribution.\n\n2. **Model Selection and Training**:\n   - After preprocessing, the pipeline feeds the transformed data into a **Logistic Regression** model.\n   - Logistic Regression is a binary classification algorithm that predicts the probability of an event (e.g., customer making a purchase).\n   - The model learns from historical data to make predictions based on the input features.\n\n3. **Hyperparameter Tuning**:\n   - Although not explicitly mentioned, hyperparameters (like solver, maximum iterations, and regularization) can be fine-tuned to optimize model performance.\n   - The choice of hyperparameters affects the model's accuracy and generalization ability.\n\n4. **Model Evaluation**:\n   - It's crucial to assess how well the model performs. Metrics like accuracy, precision, recall, or F1-score help evaluate its effectiveness.\n   - Iteratively refine the model based on evaluation results.\n\n5. **Deployment**:\n   - Once satisfied with the model's performance, deploy it in real-world scenarios (e.g., an application or website).\n   - Continuously monitor and maintain the model as new data becomes available.\n\nIn summary, this pipeline transforms data, trains a logistic regression model, and prepares it for practical use. 🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figuptashubham%2Fcustomer-intent-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Figuptashubham%2Fcustomer-intent-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figuptashubham%2Fcustomer-intent-prediction/lists"}