Algorithms

Question 1

Linear Regression?
Example.

Answer 1

LR is a supervised ML model that finds the best fit linear relationship line between independent variables and dependent variables.
Forecasting, will customer would buy in the future

Question 2

Logistic Regression?
Example.

Answer 2

Logistic Regression is a classification algorithm used to predict a binary outcome for a given set of independent variables.
Customer’s interest in buying/continuing services

Question 3

Clustering?
Example.

Answer 3

Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.
Market research, customer base

Question 4

SVM?
Example

Answer 4

SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs.
Predicting customers churn in telecommunication

Question 5

Decision Tree?
Example

Answer 5

A decision tree builds classification (or regression) models as a tree structure, with datasets broken up into ever-smaller subsets while developing the decision tree, literally in a tree-like way with branches and nodes. Decision trees can handle both categorical and numerical data.
Expansion and growth

Question 6

Naive Bayes?
Example

Answer 6

Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.
Spam filtering, recommendation systems

Question 7

ML LifeCycle?

Answer 7

Data Gathering -> Data Prep -> Selecting right model -> Training model -> Testing model -> Tuning the parameters -> Making predictions

Question 8

K-means vs KNN?

Answer 8

K-means is unsupervised algorithm else.
It is a clustering algorithm, KNN classification
KNN uses proximity to make classification/predictions about the grouping of an individual data point.
K in KNN means number or nearest neighbors