{"id":25641944,"url":"https://github.com/nambukeerthi/flatprice_project","last_synced_at":"2026-04-09T18:50:10.159Z","repository":{"id":269865817,"uuid":"908240099","full_name":"Nambukeerthi/flatprice_project","owner":"Nambukeerthi","description":"This project helps both buyers and sellers in Singapore’s housing market:  Buyers – Estimate resale prices for better decisions. Sellers – Understand their flat’s market value. Practical Use of Machine Learning – Applies ML in real estate and web development.","archived":false,"fork":false,"pushed_at":"2025-02-21T18:51:24.000Z","size":10211,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-21T19:38:35.589Z","etag":null,"topics":["eda","machine-learning","pandas","python","sklearn","streamlit"],"latest_commit_sha":null,"homepage":"https://flatpriceproject-jripvgug8b43r2dwz8d2ta.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/Nambukeerthi.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":"2024-12-25T14:46:34.000Z","updated_at":"2025-02-21T18:51:28.000Z","dependencies_parsed_at":"2024-12-26T19:18:01.552Z","dependency_job_id":"bd3341ba-5303-4d1e-b2d3-9f5e143780ad","html_url":"https://github.com/Nambukeerthi/flatprice_project","commit_stats":null,"previous_names":["nambukeerthi/flatprice_project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nambukeerthi%2Fflatprice_project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nambukeerthi%2Fflatprice_project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nambukeerthi%2Fflatprice_project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nambukeerthi%2Fflatprice_project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Nambukeerthi","download_url":"https://codeload.github.com/Nambukeerthi/flatprice_project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240271533,"owners_count":19774859,"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":["eda","machine-learning","pandas","python","sklearn","streamlit"],"created_at":"2025-02-23T05:16:34.968Z","updated_at":"2026-04-09T18:50:10.149Z","avatar_url":"https://github.com/Nambukeerthi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1\u003e Singapore_Resale_Flat_Prices_Predicting \u003c/h1\u003e\n\n\n\u003ch1 align=\"center\"\u003e\n  \u003cbr\u003e\n  \u003ca href=\"\"\u003e\u003cimg src=\"app/flat_pic.jpg\" alt=\"\" width=\"400\"\u003e\u003c/a\u003e\n  \u003ch2  align=\"center\"\u003e\n  Link: https://flatpriceproject-jripvgug8b43r2dwz8d2ta.streamlit.app/\n  \u003c/h2\u003e  \n  \u003cbr\u003e\n \n  \u003cbr\u003e\n\u003c/h1\u003e\n\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"#Introduction\"\u003e\u003c/a\u003e\n  \u003ca href=\"#Technologies Applied\"\u003e\u003c/a\u003e  \n\u003c/p\u003e\n\nVideo Link: [Linked-IN Video](https://www.linkedin.com/posts/nambu-keerthi-r-9b8839283_project-name-singapore-resale-flat-prices-activity-7296605369458405377-C2ID?utm_source=share\u0026utm_medium=member_android\u0026rcm=ACoAABMFg5wB3AA0b9CHRbG1E_77kFaZB8cVz7c)\n\nPortfolio: [Nambu Keerthi](https://portfolio-b5zieg8xn5nhwau5b4bhp8.streamlit.app/)\n\n## Introduction \nThis project will develop a machine learning model to predict the resale values of apartments in Singapore. The model will be implemented as a user-friendly online application to help buyers and sellers estimate a flat's worth based on past transactions.\n\n**Domain** : *Real Estate*\n\n## Technologies Applied\n* python\n* streamlit \n* Pandas \n* Numpy\n* Skikit-Learn \n\n\n## Project Setup\n1. Firstly install all the required extensions in the requirements.txt\n```\npip install -r requirements.txt\n```\n\n2. Second get the Data from the Data source and Load the data for Data cleaning and Pre Processing. Then finding the outliers for removing then make it visible the dataset columns by using matplotlib, plotly_express and seaborn.\n```\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport plotly.io as pio\n```\n\n3. After that should make heatmap visualization to knowning how data values are spreaded there.\n```\nplt.figure(figsize=(15,10))\nsns.heatmap(corrm, annot=True,cmap='coolwarm',fmt='.2f')\nplt.show()     \n```\n\n4. Then split the dataset as well as train and test data for creating ML models. Save the models in \".pkl\" file.\n```\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import mean_absolute_error,mean_squared_error, r2_score\n```\n\n5. To run the application\n```\nstreamlit run main.py\n```\n\n   \n## Project Methodology\n\n**Resale Price Prediction**\n\n1. Select the \"Predictions\" option menu.\n\n2. Fill in the following required informations.\n\n3. Click the \"RESALE PRICE\" button.\n\n4. The app will display the predicted selling price based on the provided information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnambukeerthi%2Fflatprice_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnambukeerthi%2Fflatprice_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnambukeerthi%2Fflatprice_project/lists"}