8

 import numpy as np 

import matplotlib.pyplot as plt 

from sklearn.datasets import load_breast_cancer 

from sklearn.model_selection import train_test_split 

from sklearn.tree import DecisionTreeClassifier, plot_tree 

from sklearn.metrics import accuracy_score 

# Load dataset 

data = load_breast_cancer() 

X, y = data.data, data.target 

# Train-test split 

X_train, X_test, y_train, y_test = train_test_split(X, y, 

test_size=0.2, random_state=42) 

# Train Decision Tree 

clf = DecisionTreeClassifier(random_state=42) 

clf.fit(X_train, y_train) 

# Accuracy 

y_pred = clf.predict(X_test) 

print(f"Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%") 

# Predict one sample 

sample = X_test[0] 

predicted_label = clf.predict([sample])[0] 

print("Predicted Class:", "Benign" if predicted_label == 1 else 

"Malignant") 

# Plot tree 

plt.figure(figsize=(12, 8)) 

plot_tree(clf, filled=True, feature_names=data.feature_names, 

class_names=data.target_names) 

plt.title("Decision Tree - Breast Cancer") 

plt.show()

Comments

Post a Comment

Popular posts from this blog

2

import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from sklearn.datasets import fetch_california_housing # Load dataset and convert to DataFrame data = fetch_california_housing() df = pd.DataFrame(data.data, columns=data.feature_names) df['MedHouseVal'] = data.target # Add target column # 1. Correlation Matrix correlation_matrix = df.corr() print("\nCorrelation Matrix:") print(correlation_matrix) # 2. Heatmap of Correlation Matrix plt.figure(figsize=(10, 8)) sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5) plt.title('Correlation Matrix of California Housing Features') plt.tight_layout() plt.show() # 3. Pair Plot for Selected Features selected_features = ['MedInc', 'HouseAge', 'AveRooms', 'AveOccup', 'MedHouseVal'] sns.pairplot(df[selected_features], diag_kind='kde', plot_kws={'alpha': ...
Read more

1

 import numpy as np  import pandas as pd  import matplotlib.pyplot as plt  import seaborn as sns  from sklearn.datasets import fetch_california_housing  # Load California housing dataset  housing_data = fetch_california_housing()  df = pd.DataFrame(housing_data.data,  columns=housing_data.feature_names)  # 1. Display First Five Rows  print("First five rows of the dataset:")  print(df.head())  # 2. Dataset Summary  print("\nDataset Summary:")  print(df.describe())  # 3. Histograms for All Features  df.hist(figsize=(12, 8), bins=30, edgecolor='black')  plt.suptitle("Histograms for All Numerical Features", fontsize=16)  plt.show()  # 4. Boxplots for All Features  plt.figure(figsize=(12, 6))  df.boxplot(rot=45)  plt.title("Box Plots for All Numerical Features", fontsize=16)  plt.show()  # 5. Outlier Detection using IQR  Q1 = df.quantile(0.25)  Q3 = df.quanti...
Read more

3

 import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.decomposition import PCA # Load the Iris dataset iris = load_iris() data = iris.data labels = iris.target label_names = iris.target_names # Reduce dimensionality to 2 using PCA pca = PCA(n_components=2) reduced_data = pca.fit_transform(data) # Create DataFrame for visualization df_pca = pd.DataFrame(reduced_data, columns=['PC1', 'PC2']) df_pca['Label'] = labels # Plot PCA result plt.figure(figsize=(8, 6)) colors = ['r', 'g', 'b'] for i, label in enumerate(np.unique(labels)): subset = df_pca[df_pca['Label'] == label] plt.scatter(subset['PC1'], subset['PC2'], color=colors[i], label=label_names[label], alpha=0.7) plt.title('PCA on Iris Dataset') plt.xlabel('Principal Component 1') plt.ylabel('Principal Component 2') plt.legend() plt.grid(True) p...
Read more