Question 1
Dataset: Pacific_train.csv and Pacific_test.csv
Dataset Description:
The NHC publishes the tropical cyclone historical database in a format known as HURDAT, short for HURricane DATabase. These databases (Atlantic HURDAT2 and NE/NC Pacific HURDAT2) contain six-hourly information on the location, maximum winds, central pressure, and (starting in 2004) size of all known tropical cyclones and subtropical cyclones.
Columns:
ID
Name
Date
Time
Event
Status
Latitude
Longitude
Maximum Wind
Minimum Pressure
Low Wind NE
Low Wind SE
Low Wind SW
Low Wind NW
Moderate Wind NE
Moderate Wind SE
Moderate Wind SW
Moderate Wind NW
High Wind NE
High Wind SE
High Wind SW
High Wind NW
Problem Statement
You are provided with two data sets “Pacific_train.csv” and “Pacific_test.csv” having hurricane and typhoon information.
***Note: No train-test splitting should be done as test data is already provided***
You are required to make a multi-class classification model where the target variable is “Status” to classify hurricanes and typhoons into the correct category.
Carry out the following tasks and select the appropriate features and make classification models using the following algorithms having a 10-fold cross validation score :
Decision Trees ( Applying different criterion and choosing the best )
Random Forest
Naive Bayes
SupportVectorClassfier
HINT: Use correlation to select the most appropriate features.
The features [Maximum Wind, Minimum Pressure, Low Wind NE] can be used for the model fitting
Write python functions for the following and compare the performance of algorithms used above:
Recall
Precision
Accuracy
The Recall, Precision is to be computed for each label and algorithm pair
1. Which is the best model?
Hint: Implement all the above-mentioned models and then calculate the value of recall, precision and accuracy of each of them to finally select the best model.
NOTE: You MUST implement all the 4 models mentioned above in the question.
NOTE: You must use the training dataset to train the model and the testing dataset to check the accuracy and confusion matrix.
NOTE: Do not use any hyperparameters of any model.
Training Data:---> res/training/Pacific_train.csv
Test Data:----> res/validation/Pacific_test.csv
Final Output Sample:
NOTE: Let's say Naive Bayes Algorithm is the best algorithm for the above scenario with accuracy 0.7, then print GaussianNB(Name of Naive Bayes function in sklearn) and its respective accuracy score in a CSV file in the above-mentioned format.
Output Format:
Perform the above operations and write your output to a file named output.csv, which should be present at the location output/output.csv
output.csv should contain the answer to each question on consecutive rows.
***Note: Write the code only inside solution() function and do not pass any additional arguments. For predefined stub refer stub.py***
Note: This question will be evaluated based on the number of test cases that your code passes.
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