Manan

  Descriptive - Describe or Summarize the data Exploratory - To find relations in data Inferential  - To use small sample of data and give properties of whole of data (sample survey) Predictive - Tu use current/existing data and predict future output Causal - TO find out the causes of output Mechanistic - To know how much change in which variable will change output to what extent 1. Descriptive analysis The goal of descriptive analysis is to describe or summarize a set of data. Whenever you get a new dataset to examine, this is usually the first kind of analysis you will perform. Descriptive analysis will generate simple summaries about the samples and their measurements. You may be familiar with common descriptive statistics: measures of central tendency (eg: mean, median, mode) or measures of variability (eg: range, standard deviations or variance). This type of analysis is aimed at summarizing your sample – not for generalizing the results of the analysis to a larger

  1. Linear Regression If you want to start machine learning, Linear regression is the best place to start. Linear Regression is a regression model, meaning, it’ll take features and predict a continuous output, eg : stock price,salary etc. Linear regression as the name says, finds a linear curve solution to every problem.       It is one of the most-used regression algorithms in Machine Learning. A significant variable from the data set is chosen to predict the output variables (future values). Linear regression algorithm is used if the labels are continuous, like the number of flights daily from an airport, etc. The representation of linear regression is y = b*x + c. In the above representation, ‘y’ is the independent variable, whereas ‘x’ is the dependent variable. When you plot the linear regression, then the slope of the line that provides us the output variables is termed ‘b’, and ‘c’ is its intercept. The linear regression algorithms assume that there is a linear