38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import numpy as np
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import pandas as pd
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import joblib
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import matplotlib.pyplot as plt
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.naive_bayes import MultinomialNB, GaussianNB
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from sklearn import svm
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from sklearn.model_selection import GridSearchCV
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# Loading the Data - as different payloads got many special chars csv is loaded using predefined amount of cols to avoid problems with delimiter
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data = pd.read_csv('../datasets/out.csv',
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usecols=range(2),
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lineterminator='\n',
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header=None)
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y_data=data[0]
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x_data=data[1]
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split =(int)(0.8*data.shape[0])
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x_train=x_data[:split]
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x_test=x_data[split:]
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y_train=y_data[:split]
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y_test=y_data[split:]
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# Extracting Features
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count_vector = CountVectorizer()
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extracted_features = count_vector.fit_transform(x_train)
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# Building and Training the Model
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tuned_parameters = {'kernel': ['rbf','linear'], 'gamma': [1e-3, 1e-4],'C': [1, 10, 100, 1000]}
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model = GridSearchCV(svm.SVC(), tuned_parameters)
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model.fit(extracted_features,y_train)
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print("Model Trained Successfully!")
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print("Accuracy of the model is: ",model.score(count_vector.transform(x_test),y_test)*100)
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joblib.dump(model, "nb.joblib")
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joblib.dump(count_vector, "vec.joblib") |