the two count variablesregistered and casualshow obvious strong correlation to total_count similarlytemp and atemp show high correlation wind_speed and humidity have slight negative correlation overallnone of the attributes show high correlational statistics regression analysis regression analysis is statistical modeling technique used by statisticians and data scientists alike it is the process of investigating relationships between dependent and independent variables regression itself includes variety of techniques for modeling and analyzing relationships between variables it is widely used for predictive analysisforecastingand time series analysis the dependent or target variable is estimated as function of independent or predictor variables the estimation function is called the regression function ##note in very abstract senseregression is referred to estimation of continuous response/target variables as opposed to classificationwhich estimates discrete targets the height-weight relationship is classic example to get started with regression analysis the example states that weight of person is dependent on his/her height thuswe can formulate regression function to estimate the weight (dependent variablegiven height (independent variableof personprovided we have enough training examples we discuss more on this in the coming section regression analysis models the relationship between dependent and independent variables it should be kept in mind that correlation between dependent and independent variables does not imply causationtypes of regression there are multiple techniques that have evolved over the years and that help us perform regression analysis in generalall regression modeling techniques involve the followingthe independent variable the dependent or target variable unknown parameter( )denoted as thusa regression function relates these entities asy , the function (needs to be specified or learned from the dataset available depending upon the data and use case at handthe following are commonly used regression techniqueslinear regressionas the name suggestsit maps linear relationships between dependent and independent variables the regression line is straight line in this technique the aim here is to minimize the error (sum of squared error for instancelogistic regressionin cases where the dependent variable is binary ( / or yes/no)this technique is utilized it helps us determine the probability of the binary target variable it derives its name from the logit function used by this technique the aim here is to maximize the likelihood of observed values this technique has more in common with classification techniques than regression