House Price Forecast
A house value is simply more than location and square footage. Like the features that make up a person, an educated party would want to know all aspects that give a house its value. The client wants to take advantage of the features that influence a house price the most. They typically want to buy a house at a low price and invest on the features that will give the highest return. For example, buying a house at a good location but small square footage. The client will invest on making rooms at a small cost to get a large return.
Identifying the housing markets’ leading indicators.
Clean up and preparation of huge amount of Data.
Identification of neighbourhood sale trends and assessment sale trends.
High-performance python toolkits an Exploratory Data Analysis (EDA) was performed on the dataset. With over 40 different parameters, a correlation study helped to identify the market indicators.
EDA helped in gathering insights about data. Features are categorised into Categorical and Continuous. This helped in data filtering and cleanup.
Clustering helped in grouping similar properties based on location. A complex pipeline of filtering addresses, reverse geocoding and custom clustering helped to get neighbourhood sale trends.
Advanced Regression and Deep learning techniques applied on strongest market indicators helped to forecast sale prices of the properties. Accuracy of 95% with error of 10K price.
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