House Prices Prediction with Machine Learning Algorithms

House price prediction is a general regression issue in machine learning. We assist five to seven topics for house price prediction   the reference papers that we refereed from high impact journals will also be provided. The topic solution service that we give to scholars will address a wide range of open unanswered questions. Originality and uniqueness will be assured for all types of our research work. Some of the factors that impact house prices involves are the size of the house, its location, amenities, age and the economic situations of the market. Here we had given a step-by-step guidance to improve a house price prediction system utilizing machine learning:

  1. Objective Definition

            Define the main aim: “To predict the house price on the basis of appropriate structures we enhance a machine learning method”.  

  1. Data Collection
  • Dataset: We utilize the most famous dataset for this issue is the Boston Housing Dataset, but the dataset Ames Housing dataset or the kaggle House Prices dataset are more inclusive.
  • Features: Some of the features that our paper utilizes are number of bedrooms, bathrooms, square footage, neighborhood, age of the house, proximity to amenities, etc.
  1. Data Preprocessing
  • Data Cleaning: We clean the data by managing missing values (utilizing imputations or other approaches) and eliminate outliers if essential.
  • Feature Engineering: Developing new structures that will affect house prices. For example: the amount per square foot or a pointer if the hose has a pool or not.
  • Categorical Features: We utilize the methods like one-hot encoding or original encoding to change categorical data to number patterns.
  • Scaling: To make sure that the structures are on the same scale, our work utilizes Min-Max scaling or standardization (Z- score normalization) especially significant for linear methods.
  1. Model Selection & Development
  • Linear Regression: It is an efficient method for regression issues but frequently it is simple.
  • Ridge/ Lasso Regression: We utilize Lasso/Ridge regression through regularization by alternates to linear regression.
  • Decision Trees and Random Forests: By utilizing Decision Trees and Random Forest methods, our work offers non-linear relationships and feature significances.
  • Gradient Boosted Trees (e.g., XGBoost, LightGBM): In our work we frequently produce achievements on structured data by using gradient boosted trees.
  • Neural Networks: We use neural network when the data given be too big and complicated, otherwise it cannot be necessary.
  1. Training the Model
  • Splitting the data: Our work splits the dataset into three sets namely training set, validation set and test set.
  • Cross-Validation: To evaluate the models achievement our work utilizes the method like k-fold cross validation.
  • Training: In our work we utilize the training dataset to train the model and then optimizing it for a regression metric (e.g., Mean Absolute Error or Root Mean Squared Error).
  1. Model Evaluation
  • By utilizing relevant metrics we will estimate the models achievement on validation/test dataset. The generally utilized metric is RMSE (Root Mean Squared Error) but the most explainable metric is MAE (Mean Absolute Error).
  1. Optimization & Hyperparameter Tuning (if required)
  • We need to optimize the hyperparameters based on the model we utilized. We work with the methods like Grid search or Random search; also we examine Bayesian optimization for most efficient search.
  1. Deployment
  • We implement our model as a service after we are fulfilled with its achievements. If any of them wish to acquire an evaluation of house price that will be utilized by the real estate websites and brokers.
  1. User Interface (if applicable)
  • In our work we input house structures and obtain price prediction by permitting the users on web communication.
  • To understand the significance of various structures or acquire insights from the essential data, the users can aids in visualization.
  1. Conclusions & Future Enhancements
  • At last we summarize the achievements, limitations and the knowledge we gained during our project.
  • By combining the real-time data sources (e.g., current market conditions) to examining the enhancements, we utilize more advanced methods or refining structures.


  • Feature Selection: In our work, not every character has same significance. We will choose the most significant features by utilizing the approaches like model-specific feature significance or recursive feature removal.
  • Temporal Factors: Ensure that the influence of expansion or economic conditions over time whether if we had time-based data.
  • External Data: Our work examine by combining external datasets that will affect house prices like crime rates, school ratings, or public transport accessibility.

By following this guidance our work improves a machine learning method to predict house prices, that possibly helps in correct accurate and data-driven real estate decisions.

Journal Article will be framed as per scholars’ interest we do maintain a meticulously work flow and make sure that consistency is maintained through our work. Your ideas, concepts and citations will be used as and when needed.

House Prices Prediction with Machine Learning Algorithms Projects

House Prices Prediction with Machine Learning Algorithms Thesis Topics

Our subject matter experts guide scholars for thesis ideas and thesis topics moreover we also provide thesis writing carefully as per your university guidelines. Plagiarism free paper will be provided we use leading tools to detect plagiarism so that a flawless paper can be produced. Our researchers are well trained professionals to write thesis as per your interest or we follow strict university rules. The topics that we have developed are given below.

  1. The Comparison Study of Regression Models (Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression) for House Price Prediction in West Nusa Tenggara


Comparison Study of Models, Regression, House Price Prediction, Accuracy Testing

            Our paper compares five regression models like Multiple Linear Regression, Ridge, Lasso, RF and polynomial regression to predict house price in west Nusa Tenggara. We have to gather the data from public data and can be gathered by utilizing a web scraping method and we process it by ML. We use the metrics R-squared, RMSE and Cross validation to calculate the accuracy of the model. In our study R-squared and RMSE displays that Multiple Linear Regression and Lasso Regression are best methods. 

  1. A Housing Price Prediction Method Based on Stacking Ensemble Learning Optimization Method


D-Stacking, Ensemble learning, BP neural network, Machine learning, Diversity of learners

            We proposed a novel stacking ensemble learning method (D Stacking) that can be based on the diversity of learning with XGBoost and BP neural network.  D stacking method can effectively predict the possibility of hose price. We can collect the data then the result specifies that the diversity of base learner can affect the predictive power of D-stacking method. Our proposed D-stacking method gives the best outcome.  

  1. A Comparative Study of Regression algorithms on House Sales Price Prediction


House Sales Price Prediction, Linear Regression, Random Forest, Gradient Boosting, Extra-Trees

            We offer a study that examines the performance of different regression methods namely Linear Regression, Random Forest, Gradient Boosting and Extra Trees to predict the house sale price. By investigate the wide range of regression methods we find the most effective method to accurately predict the house price. Our Extra Trees can perform the best when compare with other regression methods. 

  1. Random Forest-based House Price Prediction


Lasso Regression, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE),  R-squared Error

            Our study concentrates on utilizing the Random Forest method to accurately predict the house price. We can gather the dataset and then investigate the steps of data conditioning, designing features, instruction of models, and oversight. Lasso Regression can be used to find the key feature that significantly affect house price. Our study explains the precision of Random Forest and Linear Regression- based ML assertions to make the accurate choice. 

  1. Effective House Price Prediction Using Machine Learning


Regression algorithm

            We concentrate on constructing a successful model to predict the house price. As price is a continuous variable, it is convenient to utilize regression methods. We utilize some regression methods like LR, Random Forest Regressor, Extreme Gradient Boosting Regressor, SVM regressor; KNN and Linear Regression were employed. Random Forest Regressor is the best model to predict house price. 

  1. Comparing Machine Learning Techniques for House Price Prediction


Real Estate, House Prices

            Our study offers a comparison of different machine learning methods to predict the house price. We can compare the regression methods like SVM, Kernel Ridge, Gradient Boosting, Lasso, Random Forest, XGB, LGBM, Average and Voting Regressor. Our comparison displays that the Voting Regressor as the best method for R squared and RMSLE method.

  1. A Comparative Study of Machine Learning Models for House Price Prediction and Analysis in Smart Cities


Multicollinearity, bagging and boosting techniques, Random Forest Regression, Repo rates, XG Boost Regression, Voting technique, Gradient Descent

            The goal of our paper is to offer digital- based solutions to real estate prices to improve the growth of online platform that offer virtual tour. We recognize the pertinent attributes and the efficient method to construct the forecast of estimated price. The result of our analysis verified the use of methods like LR, RF regression, XGBoost and Voting Regressor as efficient method. Our Random Forest method will gives the best result.

  1. Data Mining Approach in Predicting House Price for Automated Property Appraiser Systems


Multiple linear regression (MLR), Decision tree regression (DTR)

            Our goal is to construct a model to predict the house price based on house price feature dataset. Then our dataset can run through ML methods to predict new house price. We utilized cross-industry standard process for data mining (CRISP-DM) method for our paper. We also used DTR, LR, MLR and RFR. Our outcome displays that the RFR method is the most appropriate method.

  1. Cloud-Based House Price Predictor App Using Machine Learning


            We can predict the house price by utilizing some machine learning methods. At first we can examine the data analysis of dataset. Later we have to train and test the model by utilizing the methods like LR, DT, RF, Extra Trees Regressor and Extreme Gradient Boosting Regressor. Finally, XGBoost regressor method can be utilized to improve and launch a cloud-based application to predict the house price. 

  1. Machine Learning based House Price Prediction Model


Neural Network

            The aim of our paper is to predict the house price for non-owner of the house based on their financial resources and aspirations. We utilized the tools like ML, ANN and Chabot to evaluate the estimated price. Our research will specifically conduct multiple researches on the inexpensiveness of houses present within Malaysia. The purpose of our work is to construct a prediction model to aid the process of house price prediction to support both buyer and seller to have a common view of current market price and trend.


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