{"id":22561985,"url":"https://github.com/pradipece/weather_forecast_data_analysis","last_synced_at":"2025-03-28T12:42:04.515Z","repository":{"id":264502576,"uuid":"893550269","full_name":"pradipece/Weather_forecast_data_analysis","owner":"pradipece","description":"Using decision trees and random forest algorithms to solve real-world data analysis. \"sklearn_decision_trees_random_forests\"","archived":false,"fork":false,"pushed_at":"2024-11-30T15:35:23.000Z","size":1596,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-02T13:15:42.619Z","etag":null,"topics":["data-analysis","data-science","data-visualization","git","github","python","python3"],"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/pradipece.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":"2024-11-24T18:19:20.000Z","updated_at":"2024-11-30T16:08:31.000Z","dependencies_parsed_at":null,"dependency_job_id":"ef0cebff-0d97-431e-819a-d104f3daca29","html_url":"https://github.com/pradipece/Weather_forecast_data_analysis","commit_stats":null,"previous_names":["pradipece/weather_forecast_data_analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradipece%2FWeather_forecast_data_analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradipece%2FWeather_forecast_data_analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradipece%2FWeather_forecast_data_analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradipece%2FWeather_forecast_data_analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pradipece","download_url":"https://codeload.github.com/pradipece/Weather_forecast_data_analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246034231,"owners_count":20712851,"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","data-visualization","git","github","python","python3"],"created_at":"2024-12-07T22:11:08.578Z","updated_at":"2025-03-28T12:42:04.491Z","avatar_url":"https://github.com/pradipece.png","language":"Jupyter Notebook","readme":"## Weather_forecast_data_analysis\nUsing decision trees and random forest algorithms to solve real-world data analysis. \"sklearn_decision_trees_random_forests\"\n\n### Problem Statement\n\nThis project coding-focused approach how to use `decision trees and random forests` to solve a real-world problem from [Kaggle](https://kaggle.com/datasets):\n\n\u003e **QUESTION**: The [dataset](https://kaggle.com/jsphyg/weather-dataset-rattle-package) contains about 10 years of daily weather observations from numerous Au weather stations. Here's a small sample from the dataset:\n\u003e \n\u003e ![](https://i.imgur.com/5QNJvir.png)\n\u003e\n\u003e As a data scientist at the Bureau of Meteorology, you are tasked with creating a fully automated system that can use today's weather data for a given location to predict whether it will rain at the location. \n\u003e\n\u003e\n\u003e ![](https://i.imgur.com/KWfcpcO.png)\n\n### Overview \n\nPerform the following steps to prepare the dataset for training:\n\n1. Create a train/test/validation split\n2. Identify input and target columns\n3. Identify numeric and categorical columns\n4. Impute (fill) missing numeric values\n5. Scale numeric values to the $(0, 1)$ range\n6. Encode categorical columns to one-hot vectors\n\n### Training and Visualizing Decision Trees\n\nA decision tree in general parlance represents a hierarchical series of binary decisions:\n\n\u003cimg src=\"https://i.imgur.com/qSH4lqz.png\"\u003e\n\nA decision tree in machine learning works in the same way except that we let the computer figure out the optimal structure hierarchy of decisions, following the instruction of criteria.\n\n### Summary \n\nThe following topics were covered in this tutorial:\n\n- Downloading a real-world dataset\n- Preparing a dataset for training\n- Training and interpreting decision trees\n- Training and interpreting random forests\n- Overfitting, hyperparameter tuning \u0026 regularization\n- Making predictions on single inputs\n\n\nIntroduced the following terms:\n\n* Decision tree\n* Random forest\n* Overfitting\n* Hyperparameter\n* Hyperparameter tuning\n* Regularization\n* Ensembling\n* Generalization\n* Bootstrapping\n\n### References\nCheck out the following resources to learn more: \n\n- https://scikit-learn.org/stable/modules/tree.html\n- https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n- https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction\n- https://www.kaggle.com/willkoehrsen/introduction-to-manual-feature-engineering\n- https://www.kaggle.com/willkoehrsen/intro-to-model-tuning-grid-and-random-search\n- https://www.kaggle.com/c/home-credit-default-risk/discussion/64821\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpradipece%2Fweather_forecast_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpradipece%2Fweather_forecast_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpradipece%2Fweather_forecast_data_analysis/lists"}