Given above data build a machine learning model that can predict home prices based on square feet area You can represent values in above table as a scatter plot (values are shown in red markers). After that one can draw a straight line that best fits values on chart.You might remember about linear equation from your high school days math class. Home prices can be presented as following equation, home price = m * (area) + b

Predict canada's per capita income in year 2020. There is an exercise folder here on github at same level as this notebook, download that and you will find canada_per_capita_income.csv file. Using this build a regression model and predict the per capita income fo canadian citizens in year 2020

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Given table containing home prices in monroe twp, NJ. Here price depends on area (square feet), bed rooms and age of the home (in years). Given these prices we have to predict prices of new homes based on area, bed rooms and age.Given these home prices find out price of a home that has, 3000 sqr ft area, 3 bedrooms, 40 year old 2500 sqr ft area, 4 bedrooms, 5 year old We will use regression with multiple variables.

In exercise folder (same level as this notebook on github) there is hiring.csv. This file contains hiring statics for a firm such as experience of candidate, his written test score and personal interview score. Based on these 3 factors, HR will decide the salary. Given this data, you need to build a machine learning model for HR department that can help them decide salaries for future candidates. Using this predict salaries for following candidates, 2 yr experience, 9 test score, 6 interview score 12 yr experience, 10 test score, 10 interview score

53713.86 and 93747.79