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:
- Objective Definition
Define the main aim: “To predict the house price on the basis of appropriate structures we enhance a machine learning method”.
- 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.
- 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.
- 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.
- 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).
- 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).
- 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.
- 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.
- 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.
- 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.
Instructions:
- 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 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.
- The Comparison Study of Regression Models (Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression) for House Price Prediction in West Nusa Tenggara
Keywords:
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.
- A Housing Price Prediction Method Based on Stacking Ensemble Learning Optimization Method
Keywords:
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.
- A Comparative Study of Regression algorithms on House Sales Price Prediction
Keywords:
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.
- Random Forest-based House Price Prediction
Keywords:
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.
- Effective House Price Prediction Using Machine Learning
Keywords:
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.
- Comparing Machine Learning Techniques for House Price Prediction
Keywords:
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.
- A Comparative Study of Machine Learning Models for House Price Prediction and Analysis in Smart Cities
Keywords:
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.
- Data Mining Approach in Predicting House Price for Automated Property Appraiser Systems
Keywords:
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.
- Cloud-Based House Price Predictor App Using Machine Learning
Keywords:
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.
- Machine Learning based House Price Prediction Model
Keywords:
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.
Milestones
MILESTONE 1: Research Proposal
Finalize Journal (Indexing)
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
Research Subject Selection
As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.
Research Topic Selection
We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other
Literature Survey Writing
To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)
Case Study Writing
After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.
Problem Statement
Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.
Writing Research Proposal
Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)
MILESTONE 2: System Development
Fix Implementation Plan
We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.
Tools/Plan Approval
We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.
Pseudocode Description
Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.
Develop Proposal Idea
We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.
Comparison/Experiments
We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.
Graphs, Results, Analysis Table
We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.
Project Deliverables
For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.
MILESTONE 3: Paper Writing
Choosing Right Format
We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.
Collecting Reliable Resources
Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.
Writing Rough Draft
We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources
Proofreading & Formatting
We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on
Native English Writing
We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.
Scrutinizing Paper Quality
We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).
Plagiarism Checking
We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.
MILESTONE 4: Paper Publication
Finding Apt Journal
We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.
Lay Paper to Submit
We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.
Paper Submission
We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.
Paper Status Tracking
We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.
Revising Paper Precisely
When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.
Get Accept & e-Proofing
We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.
Publishing Paper
Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
MILESTONE 5: Thesis Writing
Identifying University Format
We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.
Gathering Adequate Resources
We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.
Writing Thesis (Preliminary)
We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.
Skimming & Reading
Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.
Fixing Crosscutting Issues
This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.
Organize Thesis Chapters
We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.
Writing Thesis (Final Version)
We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.
How PhDservices.org deal with significant issues ?
1. Novel Ideas
Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.
2. Plagiarism-Free
To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.
3. Confidential Info
We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.
- Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
- Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.
CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.
4. Publication
Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.
5. No Duplication
After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.
I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it.
- Aaron
I had wishes to complete implementation using latest software/tools and I had no idea of where to order it. My friend suggested this place and it delivers what I expect.
- Aiza
It really good platform to get all PhD services and I have used it many times because of reasonable price, best customer services, and high quality.
- Amreen
My colleague recommended this service to me and I’m delighted their services. They guide me a lot and given worthy contents for my research paper.
- Andrew
I’m never disappointed at any kind of service. Till I’m work with professional writers and getting lot of opportunities.
- Christopher
Once I am entered this organization I was just felt relax because lots of my colleagues and family relations were suggested to use this service and I received best thesis writing.
- Daniel
I recommend phdservices.org. They have professional writers for all type of writing (proposal, paper, thesis, assignment) support at affordable price.
- David
You guys did a great job saved more money and time. I will keep working with you and I recommend to others also.
- Henry
These experts are fast, knowledgeable, and dedicated to work under a short deadline. I had get good conference paper in short span.
- Jacob
Guys! You are the great and real experts for paper writing since it exactly matches with my demand. I will approach again.
- Michael
I am fully satisfied with thesis writing. Thank you for your faultless service and soon I come back again.
- Samuel
Trusted customer service that you offer for me. I don’t have any cons to say.
- Thomas
I was at the edge of my doctorate graduation since my thesis is totally unconnected chapters. You people did a magic and I get my complete thesis!!!
- Abdul Mohammed
Good family environment with collaboration, and lot of hardworking team who actually share their knowledge by offering PhD Services.
- Usman
I enjoyed huge when working with PhD services. I was asked several questions about my system development and I had wondered of smooth, dedication and caring.
- Imran
I had not provided any specific requirements for my proposal work, but you guys are very awesome because I’m received proper proposal. Thank you!
- Bhanuprasad
I was read my entire research proposal and I liked concept suits for my research issues. Thank you so much for your efforts.
- Ghulam Nabi
I am extremely happy with your project development support and source codes are easily understanding and executed.
- Harjeet
Hi!!! You guys supported me a lot. Thank you and I am 100% satisfied with publication service.
- Abhimanyu
I had found this as a wonderful platform for scholars so I highly recommend this service to all. I ordered thesis proposal and they covered everything. Thank you so much!!!
- Gupta