ML Lab Internal
Department of Computer Science & Engineering (AI
& ML, DS)
III B. Tech, I SEM
Machine Learning
Internal Lab Examination
List of programs as
follows
1. Using matplotlib and seaborn to perform data
visualization on the standard dataset
a.
Find missing
values
b.
Print the no of
rows and columns
c.
Plot box plot
d.
Heat map
e.
Scatter plot
f.
Bubble chart
g.
Area chart
2. Build a Linear Regression model using python for a
particular data set by
a.
Finding missing
data
b.
Splitting
training and test data
c.
Evaluate the
model (intercept and slope)
d.
Visualize the
training set and testing set
e.
Predict the test
set result
f.
Compare actual
output value with predicted values
3. Build a Linear Regression model using Gradient
Descent methods in python for a particular data set by
a.
Finding missing
data
b.
Splitting
training and test data
c.
Evaluate the
model (intercept and slope)
d.
Visualize the
training set and testing set
e.
Predict the test
set result
f.
Compare actual
output value with predicted values
4. Build a Linear Regression model using Ordinary least
squared model in python for a particular data set by
a.
Finding missing
data
b.
Splitting
training and test data
c.
Evaluate the
model (intercept and slope)
d.
Visualize the
training set and testing set
e.
Predict the test
set result
f.
Compare actual
output value with predicted values
5. Implement quadratic Regression
a.
Finding missing
data
b.
Splitting
training and test data
c. Evaluate the model
d.
Visualize the
training set and testing set
e.
Predict the test
set result
f.
Compare actual
output value with predicted values
6. Implement Logistic Regression
a.
Finding missing
data
b.
Splitting
training and test data
c.
Evaluate the
model (intercept and slope)
d.
Visualize the
training set and testing set
e.
Predict the test
set result
f.
Compare actual
output value with predicted values
7. Implement classification using SVM
a.
Finding missing
data
b.
Splitting
training and test data
c. Evaluate the model
d.
Visualize the
training set and testing set
e.
Predict the test
set result
f.
Compare actual
output value with predicted values
8. Implement Decision-tree learning
a.
Create the
sample dataset and save that file
b.
Read that saved
CSV file
c.
Splitting
training and test data
d. Evaluate the model
e. Calculate the Entropy
f.
Calculate the Information Gain
g. Display the Tree
9. Implement Bagging using Random Forests
a.
Finding missing
data
b.
Splitting
training and test data
c.
Evaluate the
model (intercept and slope)
d.
Visualize the
training set and testing set
e.
Predict the test
set result
f.
Compare actual
output value with predicted values
10. Implement K-means Clustering
a.
Create the
sample dataset and save that file
b.
Read that saved
CSV file
c.
Splitting
training and test data
d.
Evaluate the
model (intercept and slope)
e.
Visualize the
training set and testing set
f.
Predict the test
set result
g.
Compare actual
output value with predicted values
11. Implement DBSCAN clustering
a.
Create the
sample dataset and save that file
b.
Read that saved
CSV file
c.
Splitting
training and test data
d.
Evaluate the
model (intercept and slope)
e.
Visualize the
training set and testing set
f.
Predict the test
set result
g.
Compare actual
output value with predicted values
12. Implement the Gaussian Mixture Model
a.
Create the
sample dataset and save that file
b.
Read that saved
CSV file
c.
Splitting
training and test data
d.
Evaluate the
model (intercept and slope)
e.
Visualize the
training set and testing set
f.
Predict the test
set result
g.
Compare actual
output value with predicted values
Data sets
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