Prediction is about discovering hidden patterns (laws) in your data. Observing your data is as important as discovering patterns in your data. Without examining the data, your pattern detection will be imperfect and without pattern detection, you cannot draw any conclusions about your data. Hence, by examining and using a correct Machine Learning Algorithm this project is an attempt to create a daily life application in the form pf predicting house prices.
Most of the times the rates at which we buy the houses are inappropriate. We get duped often frequently we visit a builder to get the quotation of the house. Hence, we require a robust system to extract the correct rates.
• To get the exact housing price based on the features
• To not get duped by the extravagant prices of the house and thereby ensuring that we know the price beforehand
• To easily obtain prices of any house based on certain feature with the help of a computer program
• It can even help the builders to predict the proper rates of the houses on which they are working
In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine. Hence, this project is an example of one such modern application of linear regression which will reflects its power.
There is no such consolidated or formidable predictor system on which we can rely on in today’s world to predict the house prices. And here is where Machine Learning comes into play. Today the house prices are generally decided by the builder. Some builder fake the prices in order to extract more money from the people who wish to buy those houses. Considering the overall situation like the location of the project, quality of work and the appearance of the people who are wishing to buy, the rate is decided. Most of the times the rates are inappropriate. Hence, we require a robust system to extract the correct rates.
The data contains the following columns: • 'Avg. Area Income': Avg. Income of residents of the city house is located in.
• 'Avg. Area House Age': Avg Age of Houses in same city
• 'Avg. Area Number of Rooms': Avg Number of Rooms for Houses in same city
• 'Avg. Area Number of Bedrooms': Avg Number of Bedrooms for Houses in same city
• 'Area Population': Population of city house is located in
• 'Price': Price that the house sold at
• 'Address': Address for the house
Based on the data which includes above mentioned features we are going to create a model using the Linear Regression Algorithm in Machine Learning that allows to put in a few features of a house and returns back an estimate of what the house would sell for
Following are the system requirements needed to develop the project.
• Console Application
• Anaconda distribution of python with JUPYTER NOTEBOOK for better sight and data visualization