Metrics

output

precision recall f1-score support 0 0.783 0.837 0.809 43 1 0.562 0.474 0.514 19 accuracy 0.726 62 macro avg 0.673 0.655 0.662 62 weighted avg 0.715 0.726 0.719 62

code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from io import StringIO
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.decomposition import PCA

# ===== 1) Carrega o CSV =====
df = pd.read_csv('https://raw.githubusercontent.com/marcelademartini/Machine-Learning-1/refs/heads/main/Testing.csv')

# Define a coluna alvo 
target = 'Outcome' if 'Outcome' in df.columns else df.columns[-1]

# X e y (dummies para categóricas)
X_raw = df.drop(columns=[target])
X = pd.get_dummies(X_raw, drop_first=True)
y = df[target]

# Codifica alvo não numérico
if not np.issubdtype(y.dtype, np.number):
    y = pd.factorize(y)[0]

# Trata NaN
X = X.fillna(X.median(numeric_only=True))

# ===== 2) Split + escala =====
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y if len(np.unique(y)) > 1 else None
)

scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train)
X_test_s  = scaler.transform(X_test)

# ===== 3) Treina KNN =====
k = 3
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train_s, y_train)
y_pred = knn.predict(X_test_s)

# ===== 4) Métricas =====
acc = accuracy_score(y_test, y_pred)
print(classification_report(y_test, y_pred, digits=3))


# ===== Helper: imprimir figura como SVG  =====
def print_svg_current_fig():
    buf = StringIO()
    plt.savefig(buf, format="svg", transparent=True, bbox_inches="tight")
    print(buf.getvalue())
    plt.close()

# ===== 5) Matriz de confusão  =====
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(5,4), dpi=120)
plt.imshow(cm, interpolation='nearest')
plt.title("Matriz de Confusão (teste)")
plt.xlabel("Predito")
plt.ylabel("Real")
for i in range(cm.shape[0]):
    for j in range(cm.shape[1]):
        plt.text(j, i, str(cm[i, j]), ha="center", va="center")
plt.colorbar()
print_svg_current_fig()

# ===== 6) Visualização 2D (PCA) da fronteira de decisão  =====
if X_train.shape[1] >= 2:
    pca = PCA(n_components=2, random_state=42)
    X_train_2d = pca.fit_transform(X_train_s)
    X_test_2d  = pca.transform(X_test_s)

    knn_viz = KNeighborsClassifier(n_neighbors=k).fit(X_train_2d, y_train)

    h = 0.05
    x_min, x_max = X_train_2d[:, 0].min() - 0.5, X_train_2d[:, 0].max() + 0.5
    y_min, y_max = X_train_2d[:, 1].min() - 0.5, X_train_2d[:, 1].max() + 0.5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = knn_viz.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)

    plt.figure(figsize=(6,5), dpi=120)
    plt.contourf(xx, yy, Z, alpha=0.30)
    plt.scatter(X_train_2d[:,0], X_train_2d[:,1], c=y_train, s=20, marker='o', label='treino')
    plt.scatter(X_test_2d[:,0],  X_test_2d[:,1],  c=y_test,  s=40, marker='x', label='teste')
    plt.title(f"Fronteira de Decisão (PCA 2D) — KNN k={k}")
    plt.xlabel("PC1")
    plt.ylabel("PC2")
    plt.legend(loc="best")
    print_svg_current_fig()

output

code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from io import StringIO
from sklearn.cluster import KMeans

# Usa o CSV como base (duas primeiras colunas numéricas, para manter o mesmo plot)
df = pd.read_csv('https://raw.githubusercontent.com/marcelademartini/Machine-Learning-1/refs/heads/main/Testing.csv')
X_num = df.select_dtypes(include=[np.number]).dropna()

if X_num.shape[1] >= 2:
    X = X_num.iloc[:, :2].to_numpy()
else:
    # Se só houver 1 coluna numérica, duplica para conseguir plotar em 2D
    col = X_num.iloc[:, 0].to_numpy().reshape(-1, 1)
    X = np.hstack([col, col])


plt.figure(figsize=(12, 10))


# Run K-Means
kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=100, random_state=42)
labels = kmeans.fit_predict(X)

# Plot results
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=50)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], 
           c='red', marker='*', s=200, label='Centroids')
plt.title('K-Means Clustering Results')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.legend()

# # Print centroids and inertia
# print("Final centroids:", kmeans.cluster_centers_)
# print("Inertia (WCSS):", kmeans.inertia_)

# # Display the plot
buffer = StringIO()
plt.savefig(buffer, format="svg", transparent=True)
print(buffer.getvalue())