{"id":21281251,"url":"https://github.com/m-rishab/housing-price-prediction","last_synced_at":"2026-04-11T09:37:13.451Z","repository":{"id":200333957,"uuid":"705291865","full_name":"m-rishab/Housing-price-Prediction","owner":"m-rishab","description":"The Housing Price Prediction Accuracy Improvement project is a data-driven initiative focused on enhancing the precision and reliability of housing price predictions. 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This project encompasses a multidisciplinary approach, combining data science, machine learning, and real estate insights to optimize the accuracy of forecasts in the housing market.\n\n## Project Objectives:\n\nImprove the precision of housing price predictions by leveraging advanced machine learning algorithms.\nAddress data preprocessing challenges, including scaled and capped attributes, differing scales, and skewed distributions.\nInvestigate the impact of capped values on the accuracy of predictions, seeking solutions in collaboration with the client team.\nDevelop and apply feature engineering techniques to refine the dataset and enhance model performance.\nImplement feature scaling strategies, such as standardization, to create a uniform data environment for machine learning models.\nExperiment with different regression models, including Random Forest and Linear Regression, to find the most suitable predictive approach.\nEmploy cross-validation techniques to assess model performance and ensure robust and reliable predictions.\nExplore outlier handling methods to mitigate the influence of extreme values on forecasts.\n\n## Piepline\n![house-price-prediction-6-2048](https://github.com/m-rishab/Housing-price-Prediction/assets/113618652/c14fce1e-2c44-4847-9ac6-df29a208866a)\n\n## Key Achievements:\n\nEnhanced accuracy and reliability in predicting housing prices.\nSuccessfully addressed challenges related to scaled and capped attributes.\nDeveloped a comprehensive data preprocessing pipeline to create a standardized dataset.\nCollaborated with the client team to determine the most appropriate approach for handling capped values.\nImproved the distribution of attributes through feature engineering and transformation.\nEmployed advanced machine learning models for housing price predictions.\n\n## Impact:\nThe Housing Price Prediction Accuracy Improvement project has a significant impact on the real estate sector and the clients who rely on accurate housing price predictions. By optimizing the accuracy of these predictions, the project facilitates better decision-making for homeowners, investors, and real estate professionals. It also sets a benchmark for data-driven approaches in the field of housing market analysis.\n\n\n## Reference Book: \n\n-----\u003e  https://drive.google.com/file/d/12qvNbJAAafbhjzvBqCK_Wnl1oYhMUruC/view?usp=sharing\n\nYou can download the book for the description and steps performed in this project!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm-rishab%2Fhousing-price-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fm-rishab%2Fhousing-price-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm-rishab%2Fhousing-price-prediction/lists"}