Model Training and Evaluation
As this is a Classification problem then we will be using the below models for training the data.
And for evaluation, we will be using Accuracy Score.
Python3
from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from sklearn.linear_model import LogisticRegression from sklearn import metrics knn = KNeighborsClassifier(n_neighbors=3) rfc = RandomForestClassifier(n_estimators = 7, criterion = 'entropy', random_state =7) svc = SVC() lc = LogisticRegression() # making predictions on the training set for clf in (rfc, knn, svc,lc): clf.fit(x_train, y_train) y_pred = clf.predict(x_test) print("Accuracy score of ",clf.__class__.__name__,"=", 100*metrics.accuracy_score(y_test, y_pred)) |
Output :
Accuracy score of RandomForestClassifier = 84.5 Accuracy score of KNeighborsClassifier = 82.5 Accuracy score of SVC = 86.15 Accuracy score of LogisticRegression = 80.75
Data Preprocessing, Analysis, and Visualization for building a Machine learning model
In this article, we are going to see the concept of Data Preprocessing, Analysis, and Visualization for building a Machine learning model. Business owners and organizations use Machine Learning models to predict their Business growth. But before applying machine learning models, the dataset needs to be preprocessed.
So, let’s import the data and start exploring it.
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