Machine Learning with Python

Assignment 1

.Download the titanic data set given below in the link, passenger survival data for the Titanic

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https://www.kaggle.com/hesh97/titanicdataset-traincsv/download

2. Perform Random forest, Decision trees and Gradient boost classification on the above dataset

3. To calculate this, generate a random 80/20 split (using dataset. Split (0.8)) train the model

on the 80% fraction and then evaluate the accuracy on the 20% fraction.

4. What is the accuracy of your decision tree classifier on the Titanic data set with unlimited

depth. (Repeat this 100 times and average the result (hint: do the repetition in code :).

5. What is the best depth limit to use for this data? To answer this, do the same calculations

as above (average 100 experiments), but do it for increasing depth limits, specifically 0, 1, 2,

…, 10. Show all of your results.

6. Do we see overfitting with this data set? Repeat the experiment from question 3 with in-

creasing depth (0, 1, …, 10) and calculate the accuracy this time on both the testing data

(like before) and the training data.

7.What is the accuracy of the random forest and gradient boost classifier on the Titanic data set.

8. Create a graph with matplotlib library with these results and then provide a 1-2 sentence answer describing the graph.

9. preprocess the data using Standard Scalar or MinMax Scalar for Random Forest and Gradient Boost.

10. Evaluate the model and plot the confusion matrices. Compare the Performance without preprocessing and with preprocessing and Tabulate your results and provide a justification which approach is better.

11. Prepare a neat report discussing all the above tasks

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Assignment 2

 

Download the Novel Coronavirus dataset or titanic data set given below in the link, passenger ?patients survival data for the Titanic/Coronavirus

https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset

or

https://www.kaggle.com/hesh97/titanicdataset-traincsv/download

Perform the below Tasks using DNN or CNN or RNN

  • Run a Deep Neural Network (feed forward neural network) with two hidden layers and ReLU activation function on the titanic dataset using the keras(10 Marks)
  • For the above Deep Learning Model, preprocess the data using Standard Scalar or MinMax Scalar, repeat the steps (b to k), Compare the Performance without preprocessing and with preprocessing and Tabulate your results and provide a justification which approach is better.
  • Evaluate the Models using 10 folds Cross validation technique and compute the Accuracy of the model. Compare the results with train-test split and Cross validation, discuss which provides best performance(4 Marks)
  • Prepare the Report discussing all the above tasks(2 Mark)
  • Load the data from the csv file
  • Build the Neural Network Classifier model using Keras
  • Compile the model
  • Setup Division of Data for Training, and Testing- 70% for Training and 30% for Testing.
  • Train the Model
  • Evaluate the Performance in terms of accuracy
  • Make Prediction on Train and Test data
  • Plot the accuracy results for training and validation(test) data
  • Plot the loss for training and validation loss using Matplotlib
  • shows the percentages of correct and incorrect classifications using confusion matrix.
  • Plot the confusion Matrix.
  • For the above Deep Learning Model, preprocess the data using Standard Scalar or MinMax Scalar, repeat the steps (b to k), Compare the Performance without preprocessing and with preprocessing and Tabulate your results and provide a justification which approach is better..
  • Evaluate the Models using 10 folds Cross validation technique and compute the Accuracy of the model. Compare the results with train-test split and Cross validation, discuss which provides best performance
  • Prepare the Report discussing all the above tasks

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