{"id":25489864,"url":"https://github.com/shrish01/kaggle-competition-notebooks","last_synced_at":"2026-05-02T10:42:54.085Z","repository":{"id":266559673,"uuid":"889981510","full_name":"SHRISH01/Kaggle-Competition-Notebooks","owner":"SHRISH01","description":"Kaggle Competition Notebook","archived":false,"fork":false,"pushed_at":"2025-01-09T14:54:04.000Z","size":3104,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-09T15:43:05.463Z","etag":null,"topics":["data-science","kaggle"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/shrishh","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SHRISH01.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":"2024-11-17T18:11:40.000Z","updated_at":"2025-01-09T14:54:07.000Z","dependencies_parsed_at":"2025-01-09T15:32:54.529Z","dependency_job_id":"3cb79e8d-ac15-4882-a628-9eb1a9bb8002","html_url":"https://github.com/SHRISH01/Kaggle-Competition-Notebooks","commit_stats":null,"previous_names":["shrish01/kaggle-competition-notebooks"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SHRISH01%2FKaggle-Competition-Notebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SHRISH01%2FKaggle-Competition-Notebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SHRISH01%2FKaggle-Competition-Notebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SHRISH01%2FKaggle-Competition-Notebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SHRISH01","download_url":"https://codeload.github.com/SHRISH01/Kaggle-Competition-Notebooks/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239550268,"owners_count":19657541,"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","kaggle"],"created_at":"2025-02-18T21:18:11.897Z","updated_at":"2026-05-02T10:42:54.041Z","avatar_url":"https://github.com/SHRISH01.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Kaggle Competition Notebooks \n\nWelcome to my collection of Kaggle competition notebooks! In this repository, I share my solutions, insights, and techniques for tackling various real-world data science challenges through Kaggle competitions.\n\n## Overview \n\nThis repository contains notebooks for several Kaggle competitions I've participated in. The notebooks include detailed explanations, step-by-step approaches, and code implementations that showcase different machine learning techniques, data wrangling methods, and model optimization strategies.\n\n### Key Areas of Focus:\n- **Predictive Modeling**: Building models to predict outcomes based on historical data.\n- **Data Preprocessing**: Cleaning and transforming data for optimal model performance.\n- **Feature Engineering**: Creating new features to improve model accuracy.\n- **Model Optimization**: Tuning models to achieve competitive results.\n- **Visualization**: Using data visualizations to uncover patterns and insights.\n\n## Notebooks \n\nYou can explore the following notebooks within this repository:\n\n1. **Dog Vs Cat Classification - `Dog_Vs_Cat.ipynb`**\n   - Description: A basic deep learning model using TensorFlow-Keras to classify images of dogs and cats.\n   - Techniques used: Convolutional Neural Networks (CNN), data augmentation, and transfer learning.\n\n2. **Mental Health Prediction using H2O.ai - `mental-health-data-using-h2o-ai.ipynb`**\n   - Description: Predicting mental health outcomes using a dataset and H2O.ai's machine learning capabilities.\n   - Techniques used: H2O.ai AutoML, data preprocessing, and model evaluation.\n\n3. **Child Mind Institute Data - `child-mind-institute-l-gbm-h2o-ai.ipynb`**\n   - Description: A model for predicting outcomes related to child mental health using LightGBM and H2O.ai.\n   - Techniques used: Gradient Boosting Machine (GBM), feature selection, and hyperparameter tuning.\n\n4. **CatBoost for Classification - `cibmtr-catboost.ipynb`**\n   - Description: A classification model using CatBoost for predicting outcomes based on categorical features.\n   - Techniques used: CatBoost, feature engineering, and model interpretation.\n\n5. **Deep Learning for Classification - `classification-using-dl-basic.ipynb`**\n   - Description: A simple deep learning model to classify data with a basic architecture.\n   - Techniques used: Deep Learning (DL), activation functions, and backpropagation.\n\n6. **Prediction with H2O.ai - `prediction-using-h2o-ai (1).ipynb`**\n   - Description: A predictive modeling approach using H2O.ai, focusing on automating the machine learning pipeline.\n   - Techniques used: H2O.ai AutoML, model stacking, and model evaluation.\n\n## Installation\n\nTo run these notebooks, you'll need to set up the required Python environment. You can use the following steps:\n\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/SHRISH01/Kaggle-Competition-Notebooks.git\n   cd Kaggle-Competition-Notebooks\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshrish01%2Fkaggle-competition-notebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshrish01%2Fkaggle-competition-notebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshrish01%2Fkaggle-competition-notebooks/lists"}