Dr. Sharmila Sharad More discusses regression analysis using Python. Regression analysis seeks relationships between variables, such as how employee salaries relate to experience, education, role, and city. Logistic regression specifically is used for binary classification problems to predict whether an event occurs or not based on independent variables. Examples include predicting email spam, fraudulent transactions, and whether tumors are malignant or benign. Logistic regression calculates the probability of a binary event and deals with classification issues.

1. Dr Sharmila Sharad More,
Assistant Professor ,
Dept Of Science and Computer Science,
MIT, ACSC,Alandi, Pune
Regression Analysis Using Python

2. WHAT IS REGRESSION ANALYSIS?
 Regression analysis is one of the most important fields in statistics and machine learning.
 There are many regression methods available. Linear regression, Logistic regression.
 Regression searches for relationships among variables.
 Example :-
Employees of some company and try to understand how their salaries depend on
the features, such as experience, level of education, role, city they work in, and so on.
This is a regression problem where data related to each employee represent
one observation. The presumption is that the experience, education, role, and city are
the independent features, while the salary depends on them.

3. WHAT IS LOGISTIC REGRESSION?
• Logistic regression is one of the most common and useful classification algorithms in machine learning.
• It is used to predict a binary outcome based on a set of independent variables.
• So what is binary outcome & independent variables.
• A binary outcome is one where there are only two possibilities, either the event happens (1) or it does not happen (0).
• Independent variables are those variables or factors which may influence the outcome (or dependent variable).

4. Example:-
• Logistic regression is the correct type of analysis to use when you’re working
with binary data.
• You know you’re dealing with binary data when the output or dependent
variable is dichotomous or categorical in nature; in other words, if it fits into one
of two categories (such as “yes” or “no”, “pass” or “fail”, and so on).
• Classification problems are Email spam or not spam, Online transactions Fraud
or not Fraud, Tumor Malignant or Benign.
• What are the types of logistic regression
1.Binary (eg. Tumor Malignant or Benign)
2.Multi-linear functions failsClass (eg. Cats, dogs or Sheep's)

5. Binary Logistic Regression

7. WHAT IS LOGISTIC REGRESSION USED FOR?
• Logistic regression is used to calculate the probability of a binary event occurring, and to deal
with issues of classification.
• For example, predicting if an incoming email is spam or not spam, or predicting if a credit card
transaction is fraudulent or not fraudulent. In a medical context, logistic regression may be used
to predict whether a tumor is benign or malignant.
• In marketing, it may be used to predict if a given user (or group of users) will buy a certain
product or not. An online education company might use logistic regression to predict whether a
student will complete their course on time or not.