House Price Prediction Using Machine Learning Project

House price forecasting is a famous regression issue in machine learning (ML). Predicting house prices accurately is useful for both sellers and buyers in the real estate business. Every member of our team works sincerely to ensure the satisfaction and quality of your house price prediction using machine learning project. Our research paper will be entirely free from all grammatical mistakes, linguistic errors and plagiarism free paper. Global support will be given for all ML project issue that you are dealing up to. Updated methodologies and techniques are used by us to complete research work without any errors.

The following is a step-by-step process which we implement in designing a house price detection project:

  1. Objective Definition:
  • We detect the selling price of a house depending on the several features.
  1. Gather Data:
  • Kaggles’s Ames Housing dataset is a suitable option and it is more literate than the most-used Boston Housing Dataset.
  • Instead of this we require to scrape and source local housing data when we are aiming in a particular urban area.
  1. Data Pre-processing:
  • Handle Missing Values: Few features contain the lost values so we decide whether to suggest, remove and replace these values.
  • Feature Encoding: For transforming variables into numerical format we utilize one-hot encoding, label encoding and relevant methods.
  • Scaling the Feature: We normalize and standardize features to maintain them on similar measurements.
  1. Feature Engineering:
  • Our project develops the latest features that play a major role in house prices like the total area of the house, presence of needed facilities and communication.
  • When necessary we execute spatiality reduction by using techniques like PCA.
  1. Framework Selection & Training:
  • Linear Regression: It is an easy baseline model which we make use of.
  • Ridge & Lasso Regression: When we deal with multicollinearity it is beneficial.
  • Neural Networks: Deep Learning is applicable but it needs a large volume of data and tuning for our project.
  • Decision Trees & Random Forest: We capture nonlinear relationships.
  • Gradient Boosting Machines (e.g., XGBoost, LightGBM): Always we produce high accuracy in regression tasks.
  1. Evaluation:
  • Mean Absolute Error (MAE): To get average of the absolute variants between detection and real values we employ this metric.
  • Mean Squared Error (MSE) and Root Mean Square Error (RMSE): By this we indicate larger errors.
  • R-Squared: It defines the difference spotted by our model.
  1. Deployment:
  • We combine our framework into a web application and a repository system where real estate experts and clients input their house properties and receive price detections.
  • Make sure that our model is simple to retrain and update.
  1. Post-Deployment Monitoring:
  • By consistently supervising our system we forecast against real sale prices.
  • Regularly we retrain our model with the latest data.


  • Temporal Factors: Due to economic, political and social factors housing business will change over time.
  • Features Selection: Some properties are highly detected in particular areas and not in others this affects our model in choosing a feature.
  • Outliers: Very high and very low house prices impact our model.

Extensions/Advanced Methods:

  • Time Series Analysis: When we get time-related data, examine incorporating time series prediction techniques.
  • Geospatial Observation: To interpret the effect of location on house prices we employ geographic data.
  • Stacking & Grouping Techniques: We integrate detections from multiple frameworks to improve accuracy.

Finally, when we scrap and source our data ensure that we get permissions to utilize and transfer entire gathered data. It is also better to experiment which offers definitions and confidence break with detection, by this user analyzing our model’s possible variability.

PhD projects on ML are provided by us under all domains with clear cut explanation, individual support will be given any types of editing services is also done by our researchers.

House Price Prediction Using Machine Learning Research Topics

House Price Prediction Using Machine Learning Thesis Ideas

Thesis ideas on all areas of House Price Prediction will be shared along with thesis topics and thesis writing. We abide by the scholar’s university rules and regulations and draft thesis writing in such a form. Moreover, thesis writing is an extensive work where the writer’s ned corrects subject knowledge and skill. Here our ML researchers finish off your work before the deadline as it is our main ethics.

  1. House Price Prediction using Machine Learning Algorithm


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In this paper we predict house prices by using ML methods. The important result of this paper is to predict the house price accurately as the requirement of the user. We have to execute different ML techniques such as Linear Regression, Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor, and Random forest regressor methods were used. At last our method gives high accuracy for predicting house price.

  1. Evaluating machine learning algorithms for predicting house prices in Saudi Arabia


Economics, Measurement, Random forests

We used ML methods to predict house price prediction. Predicting house prices in Saudi Arabia is offered in our paper. We have to use different machine learning methods namely Random Forest (RF), Decision Tree (DT) and Linear Regression (LR). Our method Random Forest gives better outcome.

  1. House Price Prediction System using Machine Learning Algorithms and Visualization


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Our paper proposes a house price prediction by using ML methods. To get accurate result we use different ML methods such as Linear Regression, Decision tree regression, Random forest regression and Artificial Neural Networks. To evaluate accuracy and effectiveness we used different metrics such as B- root mean square and e – squared score.

  1. House Price Rate Change Prediction Using Machine Learning


Pervasive computing, Correlation, Social networking (online), Urban areas, Pricing

In real-estate rate changes can decrease or increase extremely. We used machine learning methods to predict the house price. In ML mostly regression model gives the better accuracy outcome. Linear Regression Method can be utilized to predict the future house price detection.

  1. Using Machine Learning to Predict Housing Prices


Analytical models, Hospitals, Biological system modeling, Sociology

We have to identify the appropriate properties and well-organized models for predicting prices. To predict the accurate prices of the houses our paper proposes the machine learning method namely Linear Regression. The location and structural characteristics are the important features that our results recommend to predicting house prices.

  1. Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times


Mass appraisal; real estate market; partial dependence plots; COVID-19

Our aim is to predict house prices during covid19 epidemic; we have to use various ML methods. In bagging we used (Random Forest and extra trees regressor) and in Ensemble machine learning based boosting methods we used (Gradient boosting regressor, Extreme gradient boosting and light gradient boosting machine) these are the methods our paper used and compare them with Linear regression.

  1. House Price Prediction using Random Forest Machine Learning Technique


Sales forecasting, House Price Prediction

The common instrument used to evaluate the house prices is House Price Index (HPI). Our study discovers the use of Random forest a Machine Learning method to predict the house price. IN our paper we use the dataset UCI machine learning repository Boston dataset can be utilized to predict house price.

  1. Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms


Value Estimation; Spatio-Temporal Modelling

Spatio temporal non-stationary aspect can be considered on our paper and four machine learning methods are utilized to discover different features such as property attributes and neighborhood quality on house price prediction. To increase the prediction accuracy of the model our paper also used spatiotemporal lag (ST-lag).

  1. Housing Price Prediction with Machine Learning


Decision Tree.

Our paper uses different machine learning methods namely Linear Regression, Random Forest and Decision tree are utilized to predict the house price by using datasets. To discover different features on prediction our paper utilizes both Traditional and advanced machine learning methods. Our paper gives a perfect outcome for predicting hose price.

  1. House Price Prediction Based on Machine Learning: A Case of King County



Our paper concentrates on developing a feasible method to predict house price. A dataset were collected and the data were preprocessed to remove highly correlated features. CatBoost, LightGBM and XGBoost are the methods used as candiatate models. We also used several metrics as rooted mean square error, R-squared score, adjusted R-squared score and K-fold cross validation score. Our paper also finds that CatBoost method gives the best prediction outcome.