rakshureddy1308 / outlierdetection

OutlierDetection

Load the dataset

from sklearn.datasets import load_diabetes a = load_diabetes() X = a.data

Import the required libraries

from sklearn.ensemble import IsolationForest

clf = IsolationForest(n_estimators=100, contamination=0.1, random_state=42) clf.fit(X)

y_pred = clf.predict(X)

import matplotlib.pyplot as plt

plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap='viridis') plt.title("Isolation Forest Outlier Detection on DIABETES Dataset") plt.xlabel("Sepal Length") plt.ylabel("Sepal Width") plt.show()

from sklearn.neighbors import LocalOutlierFactor

clf = LocalOutlierFactor(n_neighbors=20, contamination=0.1) y_pred = clf.fit_predict(X)

plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap='viridis') plt.title("Local Outlier Factor Outlier Detection on DIABETES Dataset") plt.xlabel("Sepal Length") plt.ylabel("Sepal Width") plt.show()

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.ensemble import IsolationForest from sklearn.neighbors import LocalOutlierFactor

Load the IRIS dataset

a = load_diabetes() X = a.data y = a.target

Fit Isolation Forest model

clf_iso = IsolationForest(contamination=0.1, random_state=42) y_pred_iso = clf_iso.fit_predict(X)

Fit Local Outlier Factor model

clf_lof = LocalOutlierFactor(contamination=0.1) y_pred_lof = clf_lof.fit_predict(X)

Plot Isolation Forest outliers

plt.scatter(X[:, 0], X[:, 1], c=np.where(y_pred_iso == -1, 'red', 'blue'), label='Isolation Forest') plt.title("Outlier Detection using Isolation Forest on DIABETES Dataset") plt.xlabel("Sepal Length") plt.ylabel("Sepal Width") plt.legend() plt.show()

Plot Local Outlier Factor outliers

plt.scatter(X[:, 0], X[:, 1], c=np.where(y_pred_lof == -1, 'red', 'blue'), label='Local Outlier Factor') plt.title("Outlier Detection using Local Outlier Factor on DIABETES Dataset") plt.xlabel("Sepal Length") plt.ylabel("Sepal Width") plt.legend() plt.show()