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ML Exp 6 - Pastebin.com
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- import pandas as pd
- from sklearn.model_selection import train_test_split
- from sklearn.preprocessing import StandardScaler, LabelEncoder
- from sklearn.tree import DecisionTreeClassifier, plot_tree
- from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
- import matplotlib.pyplot as plt
- data = pd.read_csv("Social_Network_Ads.csv")
- le = LabelEncoder()
- data['Gender'] = le.fit_transform(data['Gender']) # Male=1, Female=0
- X = data[['Gender', 'Age', 'EstimatedSalary']]
- y = data['Purchased']
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
- scaler = StandardScaler()
- X_train = scaler.fit_transform(X_train)
- X_test = scaler.transform(X_test)
- id3 = DecisionTreeClassifier(criterion='entropy', random_state=42)
- id3.fit(X_train, y_train)
- # Predictions
- y_pred = id3.predict(X_test)
- print("Accuracy:", accuracy_score(y_test, y_pred))
- print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))
- print("\nClassification Report:\n", classification_report(y_test, y_pred))
- plt.figure(figsize=(20,12))
- plot_tree(
- id3,
- feature_names=['Gender','Age','EstimatedSalary'],
- class_names=['Not Purchased','Purchased'],
- fontsize=15,
- filled=True
- )
- plt.show()
- new_sample = pd.DataFrame({'Gender':[1], 'Age':[30], 'EstimatedSalary':[100000]})
- new_sample_scaled = scaler.transform(new_sample)
- prediction = id3.predict(new_sample_scaled)
- predicted_class = 'Purchased' if prediction[0]==1 else 'Not Purchased'
- print(f"The predicted purchase status for the new sample is {predicted_class}")
- https://jumpshare.com/s/qM5yLBS964qeCHauD2d2
Tags:
ML
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