International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
House Price Prediction Using Machine Learning Aditi Shahasane1, Mayuri Gosavi2, Ayushi Bhagat3, Nandini Mishra4, Amit Nerurkar5 1, 2, 3, 4Students, Dept. of Computer Engineering, Vidyalankar Institute of Technology, Wadala, Mumbai, India 5Professor, Dept. of Computer Engineering, Vidyalankar Institute of Technology, Wadala, Mumbai, India
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Abstract - Real Estate industry is dynamic in terms of the
style of construction, balcony space, condition of building, price per square foot etc. The proposed model aims to create an accurate result by taking into consideration all different factors. For House price prediction one can use various prediction models (Machine Learning Models) like support vector regression, Support vector machine (SVM), Logistic regression, k-means, artificial neural network etc. Housepricing model is beneficial for the buyers, property investors, and house builders. This model will be informative and knowledgeable for the entities related to the real estate and all the stakeholders to evaluate the current market trends and budget friendly properties. Studies initially concentrated on analysis of the attributes which influence prices of the houses based on which model of ML is used and still this article brings together both predicting house price and attributes together.
prices being fluctuated regularly. It’s one of the main area to apply the machine learning concepts to predict the prices of real estate depending upon the current situations and make out maximum accuracy for the same. The research paper mainly focus on to predicting the real valued prices for the places and the houses by applying the appropriate ML algorithms. The proposed article considers some essential aspects and parameters for calculating the prices of real estate property Also some more geographical and statistical techniques will be needed to predict the price of a house. The paper consist how the house pricing model works after using some machine learning techniques and algorithms. The use of the dataset in the proposed system from the reputed website helps to get the detailed analysis of the data points. Algorithms like Linear regression and sklearn are used to effectively increase the accuracy. During model structure nearly all data similarities and cleaning, outlier removal and feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tuning, k fold cross-validation, etc. are covered.
For this paper, Bangalore city is taken as an example because it is Asia's fastest-growing city. The city's growth has already slowed its own economic growth rate and it has gone through various changes that have contributed to its growth over the last few decades, one of which is the IT industry. Bangalore has an excellent social infrastructure, also excellent educational institutions and a rapidly changing physical infrastructure. These factors have led to an increase in migration from other states to Bangalore, but the cost of living as increased, making it difficult for or people to manage their households effectively [5]. The model building starts with the dataset from a reliable source that is simple to use. For a dataset was chosen for our house price prediction, which contains 13320 records of data and 9 features for training our model. There are various machine learning procedures that can be used to forecast future values. In any case, it is required a model that can forecast future property estimations with greater accuracy and less error. With a specific end goal of preparing the model, a significant amount of memorable dataset is required .Generally one wants to create a framework because there is little research on forecasting land property in India. This can forecast the cost of a property by taking into account the various parameters that influence the target value. In addition, the prediction accuracy is measured by taking into account various error metrics [5].
Key Words: Linear regression model, Python, Machine Learning, House Price, Decision Tree, Lasso.
1. INTRODUCTION The proposed research paper refers to the predictions on the recent trends and for the plans of economy. The main drive behind the article is prediction of the real estate prices to build best of the house price prediction systems using the machine learning algorithms with maximum accuracy. Under the domain of ML and Data Science the designing of the real estate price prediction along with the full-fledged website is done. According to the census of 2011 only 80 percent of people own their houses. And only people based in rural areas own maximum houses but people in urban sector only about 69 % own a house. This is due to the raising prices of the properties and vague house prices. The main aim to design and develop this model is to produce price prediction system along with a user-friendly front end that will facilitate the users to choose the desired destination and get an idea about the price rates. The Analysis that has been made in the paper is mainly using the dataset from the trusted website that gives ample of sample points for better analysis. One must be aware of the exact price of house before concluding the deal. As the price of house depends on many factors like Area, location, population, size and number of bedrooms & bathrooms given, parking space, elevator,
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2. LITERATURE SURVEY Every common man's first desire and need is for real estate property. Investing in the real estate appears to be very profitable as the property rates do not fall steeply. Investing
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