ST.
ANN’S COLLEGE OF ENGINEERING & TECHNOLOGY: CHIRALA
(AUTONOMOUS)
CSE - UG – R22
Year III Year – II Semester
&Sem
Course L T P C
Code
22UCS48
0 0 3 1.5
Course MACHINE LEARNING USING PYTHON LAB
Name
Course Objectives:
This course will enable students to learn and understand different Data sets in implementing the
machine learning algorithms.
Course Outcomes (Cos): At the end of the course, student will be able to
Implement procedures for the machine learning algorithms
Design and Develop Python programs for various Learning algorithms
Apply appropriate data sets to the Machine Learning algorithms
Develop Machine Learning algorithms to solve real world problems
Requirements: Develop the following program using Anaconda/ Jupiter/ Spider and evaluate
ML models.
Experiment-1:
Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based
on a given set of training data samples. Read the training data from a .CSV file.
Experiment-2:
For a given set of training data examples stored in a .CSV file, implement and demonstrate the
Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with
the training examples.
Experiment-3:
Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an
appropriate data set for building the decision tree and apply this knowledge to classify a new
sample.
Experiment-4:
Exercises to solve the real-world problems using the following machine learning methods: a) Linear
Regression b) Logistic Regression c) Binary Classifier
Experiment-5: Develop a program for Bias, Variance, Remove duplicates , Cross Validation
Experiment-6: Write a program to implement Categorical Encoding, One-hot Encoding
Experiment-7:
Build an Artificial Neural Network by implementing the Back propagation algorithm and test the
same using appropriate data sets.
Experiment-8:
Write a program to implement k-Nearest Neighbor algorithm to classify the iris data set. Print both
correct and wrong predictions.
Experiment-9: Implement the non-parametric Locally Weighted Regression algorithm in order to
fit data points. Select appropriate data set for your experiment and draw graphs.
Experiment-10:
ST.ANN’S COLLEGE OF ENGINEERING & TECHNOLOGY: CHIRALA
(AUTONOMOUS)
CSE - UG – R22
Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to
perform this task. Built-in Java classes/API can be used to write the program. Calculate the
accuracy, precision, and recall for your data set.
Experiment-11: Apply EM algorithm to cluster a Heart Disease Data Set. Use the same data set for
clustering using k-Means algorithm. Compare the results of these two algorithms and comment on
the quality of clustering. You can add Java/Python ML library classes/API in the program.
Experiment-12: Exploratory Data Analysis for Classification using Pandas or Matplotlib.
Experiment-13:
Write a Python program to construct a Bayesian network considering medical data. Use this model
to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set
Experiment-14:
Write a program to Implement Support Vector Machines and Principle Component Analysis
Experiment-15:
Write a program to Implement Principle Component Analysis
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