BDA-PPT Final
BDA-PPT Final
BDA-PPT Final
GROUP 3 Surabhi
Shubham
Shinjan
Saket
Ujjwal
Lohith
WHAT IS MACHINE
LEARNING?
Machine learning is an application of artificial intelligence
(AI) that provides systems the ability to automatically
learn and improve from experience without being
explicitly programmed.
Machine learning focuses on the development of
computer programs that can access data and use it learn
for themselves.
TYPES OF MACHINE
LEARNING
• A supervised learning algorithm learns from labeled training
data, helps you to predict outcomes for unforeseen data.
Supervised • E.g.: Predicting the time you will reach home depends on
Learning Weather conditions, time of day, route chosen, etc.
Multinomia
l Naïve Logistic Decision
Regression Tree
Bayes
WHAT IS THE PROJECT
ABOUT?
• Classify the data set using various models
• Compare the score of the classification of various
models
• Extract the model that gives the best score
SUPPORT VECTOR MACHINE
Key Concept: Training data enters optimization problem in the form of dot
products of pairs of points.
• support vectors
weights associated with data points are zero except for those points nearest the separator
(i.e., the support vectors)
• Main aim is that every pair of features being classified is independent of each other.
• To predict the likihood that an event will occur given evidence that present in your data.
• Assumes that the presence (or absence) of a particular feature of a class is unrelated to
• Used when features are categorical or continuous and describes discrete frequency counts.
• simple Naive Bayes would model a document as the presence and absence of particular
words, Multinomial Naive Bayes explicitly models the word counts and adjusts the
underlying calculations to deal with in
GAUSSIAN
12
MULTIPLE LINEAR REGRESSION
13
LINEAR REGRESSION – MACHINE LEARNING
MULTIPLE LINEAR REGRESSION – MACHINE
LEARNING
THE LOGISITIC REGRESSION MODEL
Let p denote P[y = 1] = P[Success].
p
The ratio: is called the odds ratio
1 p
This quantity will also increase with the value of
x, ranging from zero to infinity.
p
The quantity: ln
1 p
is called the log odds ratio
EXAMPLE: ODDS RATIO, LOG ODDS
RATIO
Suppose a die is rolled:
Success = “roll a six”, p = 1/6
p 1 1
1
The odds ratio 61 6
1 p 1 6 5
6 5
Sepal /Petal
Iris setosa Iris versicolor Iris virginica
OUTPUT
model best_score best_params
4 naive_bayes_multinomial 0.953333 {}