House Price Prediction using Linear Regression and Python

Predictive modeling is a field which has immense growth in line in due years to come due to the definite explosion of data that we are noticing. In the year 2017, it was forecasted by IBM that the demand for data scientists and analytical professionals will grow by 15% in the year 2020. Many companies have realized the importance of using predictive modeling for their business but currently, there is a shortage of skilled professionals. A substantial amount of salaries is offered to people with this skillset because of the nature of the job. The demand for qualified candidates is increasing at a significant rate. It is the right time to invest in learning for such a niche skill as the market for predictive analytics is not coming down any sooner.

It is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur. Python is used for predictive modeling because Python–based frameworks give us results faster and also help in the planning of the next steps based on the results.