{"id":18510421,"url":"https://github.com/rakibhhridoy/appliedmachinelearninghousing-regression","last_synced_at":"2026-04-05T23:04:32.338Z","repository":{"id":131513614,"uuid":"275152399","full_name":"rakibhhridoy/AppliedMachineLearningHousing-Regression","owner":"rakibhhridoy","description":"Let's take the Housing dataset which contains information about different houses in Boston. This data was originally a part of UCI Machine Learning Repository and has been removed now. We can also access this data from the scikit-learn library. The objective is to predict the value of prices of the house using the given features.","archived":false,"fork":false,"pushed_at":"2020-08-18T05:03:47.000Z","size":1830,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-05-14T11:18:06.016Z","etag":null,"topics":["deep-learning","housing-market","housing-prices","machine-learning","numpy","pandas","python","real-estate","regression","scikit-learn"],"latest_commit_sha":null,"homepage":"https://rakibhhridoy.github.io","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/rakibhhridoy.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":"2020-06-26T12:36:54.000Z","updated_at":"2020-08-18T05:03:49.000Z","dependencies_parsed_at":"2023-04-15T09:32:58.998Z","dependency_job_id":null,"html_url":"https://github.com/rakibhhridoy/AppliedMachineLearningHousing-Regression","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rakibhhridoy/AppliedMachineLearningHousing-Regression","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rakibhhridoy%2FAppliedMachineLearningHousing-Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rakibhhridoy%2FAppliedMachineLearningHousing-Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rakibhhridoy%2FAppliedMachineLearningHousing-Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rakibhhridoy%2FAppliedMachineLearningHousing-Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rakibhhridoy","download_url":"https://codeload.github.com/rakibhhridoy/AppliedMachineLearningHousing-Regression/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rakibhhridoy%2FAppliedMachineLearningHousing-Regression/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261528619,"owners_count":23172748,"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":["deep-learning","housing-market","housing-prices","machine-learning","numpy","pandas","python","real-estate","regression","scikit-learn"],"created_at":"2024-11-06T15:23:15.928Z","updated_at":"2025-12-30T22:27:02.246Z","avatar_url":"https://github.com/rakibhhridoy.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# *Applied Machine Learning Housing- Regression*\n\n![img0](https://miro.medium.com/max/566/1*Zm2Hu724W6UQCVWWQe7afg.jpeg)\n\n\n\u003e1.Understand the requirements of the business\n\nWe are enthusiastic data scientists and before starting we need to ask some fundamental questions\n\n 1. Why does our organisation need this predictive model?\n\n    * possibly we are a real-estate firm and interested in investing in California\n    * the organisation will use this data to feed another machine learning model\n    * current process is good but manual and time consuming\n    * our organisation wants an edge over competition\n    * we are a consulting firm in the real-estate business and this data is valuable\n\n2. We need to understand what are we doing at the root level\n\n    * We’ll train our model on existing data so we are doing supervised learning\n    * Since we need to predict housing prices we are doing regression\n    * Output depends on many parameters so we are doing multivariate-regression\n\n\u003e2. Acquire the dataset\nGet the dataset in CSV format here and store it in a folder. We prepare a virtual environment, activate it, install the dependencies\nStart Jupyter notebook and do the basic imports\n```python\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport pandas as pdhousing = pd.read_csv('./housing.csv')\nhousing.head(5)\n```\n\nThis data has metrics such as the ```population, median income, median housing price```, and so on for each block group in California.\n\n```python\nhousing.info()\n\u003cclass 'pandas.core.frame.DataFrame'\u003e\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 10 columns):\nlongitude             20640 non-null float64\nlatitude              20640 non-null float64\nhousing_median_age    20640 non-null float64\ntotal_rooms           20640 non-null float64\ntotal_bedrooms        20433 non-null float64\npopulation            20640 non-null float64\nhouseholds            20640 non-null float64\nmedian_income         20640 non-null float64\nmedian_house_value    20640 non-null float64\nocean_proximity       20640 non-null object\ndtypes: float64(9), object(1)\nmemory usage: 1.6+ MB\n```\n[Source](https://medium.com/@gurupratap.matharu/end-to-end-machine-learning-project-on-predicting-housing-prices-using-regression-7ab7832840ab)\n\nnext you will find in the notebook. \n\n\n### *Get Touch With Me*\nConnect- [Linkedin](https://linkedin.com/in/rakibhhridoy) \u003cbr\u003e\nWebsite- [RakibHHridoy](https://rakibhhridoy.github.io)\n\n\n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frakibhhridoy%2Fappliedmachinelearninghousing-regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frakibhhridoy%2Fappliedmachinelearninghousing-regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frakibhhridoy%2Fappliedmachinelearninghousing-regression/lists"}