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https://github.com/m-rishab/housing-price-prediction

The Housing Price Prediction Accuracy Improvement project is a data-driven initiative focused on enhancing the precision and reliability of housing price predictions. 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.
https://github.com/m-rishab/housing-price-prediction

gridsearchcv hyperparameter-tuning matplotlib matplotlib-pyplot numpy pandas pickle python3 randomforestregressor scipy sklearn

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The Housing Price Prediction Accuracy Improvement project is a data-driven initiative focused on enhancing the precision and reliability of housing price predictions. 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.

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# Housing-price-Prediction

The Housing Price Prediction Accuracy Improvement project is a data-driven initiative focused on enhancing the precision and reliability of housing price predictions. 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.



## Project Objectives:



Improve the precision of housing price predictions by leveraging advanced machine learning algorithms.

Address data preprocessing challenges, including scaled and capped attributes, differing scales, and skewed distributions.

Investigate the impact of capped values on the accuracy of predictions, seeking solutions in collaboration with the client team.

Develop and apply feature engineering techniques to refine the dataset and enhance model performance.

Implement feature scaling strategies, such as standardization, to create a uniform data environment for machine learning models.

Experiment with different regression models, including Random Forest and Linear Regression, to find the most suitable predictive approach.

Employ cross-validation techniques to assess model performance and ensure robust and reliable predictions.

Explore outlier handling methods to mitigate the influence of extreme values on forecasts.



## Piepline

![house-price-prediction-6-2048](https://github.com/m-rishab/Housing-price-Prediction/assets/113618652/c14fce1e-2c44-4847-9ac6-df29a208866a)



## Key Achievements:



Enhanced accuracy and reliability in predicting housing prices.

Successfully addressed challenges related to scaled and capped attributes.

Developed a comprehensive data preprocessing pipeline to create a standardized dataset.

Collaborated with the client team to determine the most appropriate approach for handling capped values.

Improved the distribution of attributes through feature engineering and transformation.

Employed advanced machine learning models for housing price predictions.



## Impact:

The 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.



## Reference Book: 



----->  https://drive.google.com/file/d/12qvNbJAAafbhjzvBqCK_Wnl1oYhMUruC/view?usp=sharing



You can download the book for the description and steps performed in this project!