House Price Prediction Project Using Machine Learning

House price prediction is a famous regression issue in machine learning (ML). By detecting house rates, shareholders make meaningful decisions on buying and selling properties. We provide you with novel ideas and framed methodologies for all types of machine learning projects. As per customers’ requirements we offer editing services at any part of your research work. House Price Prediction Project Ideas and topics will be referred from reputed journals we strictly complete your work before delivery period as it is the major ethics that we follow.

 Here is a step-by-step process which we develop a house price detection project by using ML:

  1. Define Objective:
  • We focus on detecting the price of a house with a set of characteristics.
  • When we work on particular types of properties such as apartment and detached homes we choose the common model.
  1. Data Gathering:
  • Datasets: There are several publicly accessible datasets like the Boston Housing Dataset and the Kaggle House Prices: Advanced Regression Techniques dataset which we make use of.
  • Features: The general features that we involve house size (square footage), number of bedrooms, number of bathrooms, location, age of the house, adjacency to important facilities and others.
  1. Pre-processing the Data:
  • Handle Missing Values: Identify when we should suggest using techniques like mean, median, mode and more advanced methods and removing the lost values.
  • Categorical Variables: We transform categorical variables into numerical patterns by one-hot encoding and label encoding.
  • Feature Scaling: For approaches that are susceptible to measures such as linear regression with regularization, SVM, and k-NN we perform normalization and standardization.
  • Data Splitting: To train, evaluate and test sets we segment the data.
  1. Feature Engineering:
  • We obtain the latest features and convert traditional ones to enhance model efficiency. For example, the entire area is a sum of the basement, first floor and second floor areas.
  • For communication terms we capture essential effects. For instance, multiplying the number of bedrooms by bathrooms leads to a luxury score.
  1. Framework Selection and Training:
  • Existing ML frameworks: Linear Regression with and without regularization such as Ridge and Lasso, Decision Trees, Random Forests, Gradient Boosting machines, Support Vector Machines and so on help our project.
  • Deep Learning (DL): When there is a huge amount of data we employ neural networks.
  • Training: We instruct models on the training set and implement the validation set to adjust hyperparameters.
  1. Evaluation:
  • Metrics: For regression tasks we utilize metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R-squared.
  • Cross-Validation: To obtain a more powerful estimate of framework efficiency we incorporate k-fold cross-validation.
  1. Deployment:
  • After our model is trained and validated we apply it for shareholders to employ it.
  • We design a web and mobile application which allows the users to input property characteristics and attain a detected price.

Project Extensions:

  • Time Series Research: When we get time-stamped data, we understand the detecting rate of house as a time sequence by taking temporal directions and seasonality.
  • Geospatial Analysis: For better capturing the fine location we employ geographical data.
  • Image Data: To retrieve features from the pictures for assisting our prediction we utilize Convolutional Neural Networks (CNNs) when we get images of the properties.


  • Feature Importance: In more complicated models we understand the essentialness of features that are difficult. The methods such as permutation importance and SHAP help us.
  • Overfitting: We make sure our model generalizes better to both fresh and hidden data as well as memorize the training data.
  • Temporal Dynamics: Real estate business often modifies due to several factors and we update our model periodically with new data which is useful.

       We know that the actual estate market is altered by infinite factors in which few of them are not added in our project dataset. By integrating with field professionals like real estate experts, we offer crucial understanding and improve our model’s performance.     

Get your synopsis done on House Price Prediction, here we aid you with introduction, literature review and the methodology we are going to implement. Being an eminent concern, we run for more than  18+ years and have assisted more than 6000+ scholars and have gained trust globally.

House Price Prediction Project Using Machine Learning Ideas

House Price Prediction Project Using Machine Learning Thesis Ideas

Are you struggling with your thesis on House Price Prediction? Our custom thesis writing team consists of well qualified writers who are thorough with machine learning ideas and topic. Scholars can contact us to get valuable thesis ideas, topics and writing services from us. We also provide thesis editing support, if you are locked up in your research, we help you with proper explanation.

The thesis topics that we have worked are as follows.

  1. House Price Prediction using Machine Learning Algorithm


Radio frequency, Histograms, Machine learning algorithms, Linear regression, Predictive models, Prediction algorithms, Boosting

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.  

2. 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


Visualization, Forestry, Communications technology

            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. 

8. 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.     

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