{"id":8143,"date":"2021-03-08T04:35:56","date_gmt":"2021-03-08T04:35:56","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2021\/03\/08\/house-price-prediction-using-machine-learning\/"},"modified":"2021-03-08T04:35:56","modified_gmt":"2021-03-08T04:35:56","slug":"house-price-prediction-using-machine-learning","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2021\/03\/08\/house-price-prediction-using-machine-learning\/","title":{"rendered":"House Price Prediction using Machine Learning"},"content":{"rendered":"
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Introduction<\/h2>\n

Purchasing a new house is always a big decision ! Is it the location ? Is it the overall quality of the house ? Is it the size ? Could it be sold at a good price in future ? Moreover, economic growth plays an important role as housing demand is often seen as elastic in terms of income, leading to an increase in revenues for households. Undoubtedly it is a \u00a0tough call to consider which features should be of most importance. To ease this decision-making, in today\u2019s world, Machine Learning allows an entrepreneur \u00a0on forecasting the house price with a maximum accuracy of market trend and building model of the historical dataset on \u2018what happened and why\u2019 to predict \u2018What is going to happen\u2019.<\/p>\n

In this blog, a step-by-step technical approach have\u00a0 been described for predicting house sale price for a Kaggle data-set of Ames, Iowa<\/a>. The project endeavors to extensive data analysis and implementation of different machine learning techniques in python for having the best model with most important features of a house on insight of both business value and realistic perspective. The dataset consists of 79 different features for 1460 houses in Ames which can be used as training data to predict the sale price of another 1459 test data set of machine learning model.<\/p>\n

Process Steps<\/h2>\n
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The entire process of machine learning can be divided in 4 main steps to get the desired prediction.<\/p>\n