diff --git a/Logistica.py b/Logistica.py
new file mode 100644
index 0000000..bf9cb86
--- /dev/null
+++ b/Logistica.py
@@ -0,0 +1,78 @@
+from sklearn import datasets
+from sklearn.linear_model import LogisticRegression
+import numpy as np
+import matplotlib.pyplot as plt
+
+iris = datasets.load_iris()
+list(iris.keys())
+print(iris.DESCR)
+X = iris["data"][:, 3:]  # petal width
+y = (iris["target"] == 2).astype(np.int)  # 1 if Iris-Virginica, else 0
+
+
+
+log_reg = LogisticRegression(solver="liblinear", random_state=42)
+log_reg.fit(X, y)
+
+
+
+
+X_new = np.linspace(0, 3, 1000).reshape(-1, 1)
+y_proba = log_reg.predict_proba(X_new)
+decision_boundary = X_new[y_proba[:, 1] >= 0.5][0]
+
+plt.figure(figsize=(8, 3))
+plt.plot(X[y==0], y[y==0], "bs")
+plt.plot(X[y==1], y[y==1], "g^")
+plt.plot([decision_boundary, decision_boundary], [-1, 2], "k:", linewidth=2)
+plt.plot(X_new, y_proba[:, 1], "g-", linewidth=2, label="Iris-Virginica")
+plt.plot(X_new, y_proba[:, 0], "b--", linewidth=2, label="Not Iris-Virginica")
+plt.text(decision_boundary+0.02, 0.15, "Decision  boundary", fontsize=14, color="k", ha="center")
+plt.arrow(decision_boundary, 0.08, -0.3, 0, head_width=0.05, head_length=0.1, fc='b', ec='b')
+plt.arrow(decision_boundary, 0.92, 0.3, 0, head_width=0.05, head_length=0.1, fc='g', ec='g')
+plt.xlabel("Ancho de petalo (cm)", fontsize=14)
+plt.ylabel("Probabilidad", fontsize=14)
+plt.legend(loc="center left", fontsize=14)
+plt.axis([0, 3, -0.02, 1.02])
+
+
+
+X = iris["data"][:, (2, 3)]  # petal length, petal width
+y = iris["target"]
+
+softmax_reg = LogisticRegression(multi_class="multinomial",solver="lbfgs", C=10, random_state=42)
+softmax_reg.fit(X, y)
+
+x0, x1 = np.meshgrid(
+        np.linspace(0, 8, 500).reshape(-1, 1),
+        np.linspace(0, 3.5, 200).reshape(-1, 1),
+    )
+X_new = np.c_[x0.ravel(), x1.ravel()]
+
+
+y_proba = softmax_reg.predict_proba(X_new)
+y_predict = softmax_reg.predict(X_new)
+
+zz1 = y_proba[:, 1].reshape(x0.shape)
+zz = y_predict.reshape(x0.shape)
+
+plt.figure(figsize=(10, 4))
+plt.plot(X[y==2, 0], X[y==2, 1], "g^", label="Iris-Virginica")
+plt.plot(X[y==1, 0], X[y==1, 1], "bs", label="Iris-Versicolor")
+plt.plot(X[y==0, 0], X[y==0, 1], "yo", label="Iris-Setosa")
+
+from matplotlib.colors import ListedColormap
+custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
+
+plt.contourf(x0, x1, zz, cmap=custom_cmap)
+contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)
+plt.clabel(contour, inline=1, fontsize=12)
+plt.xlabel("Largo de petalo", fontsize=14)
+plt.ylabel("ancho de petalo", fontsize=14)
+plt.legend(loc="center left", fontsize=14)
+plt.axis([0, 7, 0, 3.5])
+
+
+
+plt.show()
+