Programming Assignment – 3 Classification using Perceptron

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CS:5007 Deep Learning
Programming Assignment – 3
Classification using Perceptron
1. (5 points) Implement Perceptron model from scratch and compare its performance with Logistic Regression and
Naive Bayes Classifier for the task of binary classification on the given three datasets. You are NOT allowed to
use in-built function for implementing Perceptron. For each of the three datasets, You are given two separate
files, for example, data1 train.csv and data1 test.csv, having comma separated values. For data1 train.csv, you
are given the features and the label for each datapoint. For data1 test.csv, you are only given the features.
You will train your model on the training dataset and use the trained Perceptron model to predict labels for
the datapoints in the test data given in data1 test.csv. You have to use cross validation (CV) to find best
generalization accuracy. You are free to use in-built CV function from sklearn. The evaluation will be on
following points:
(a) (1 point) Correct (error free) code that we are able to run.
(b) (1 point) Submit the predicted labels for all three datasets as follows:
For example, for data1, the predicted labels must be put as a separate column titled ‘Predictions’ beside
the test datapoints in data1 test.csv. Submit the modified .csv file containing the predictions. Do this
for all three datasets, i.e., your submission must include three .csv files data test.csv, data2 test.csv and
data3 test.csv.
(c) (3 points) Write a report such that:
For each dataset, include the following in the report:
i. Comparison between the performance (use metric ‘test accuracy’) of Logistic Regression, Naive Bayes
Classifier and Perceptron.
ii. Plot the decision boundary plots for all the three models, i.e., Logistic Regression, Naive Bayes Classifier
and Perceptron, and write down your observations comparing and contrasting between the three plots.
NOTE:You need NOT do this for dataset 3 as it contains more than 3 features.
iii. Mention whether the data is linearly separable or not linearly separable using the Perceptron model.
NOTE: You must submit the report in PDF format with the name of the file as ‘Report.pdf’.


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