diff --git a/ch02/perceptron.py b/ch02/perceptron.py
new file mode 100644
index 00000000..2eb03990
--- /dev/null
+++ b/ch02/perceptron.py
@@ -0,0 +1,37 @@
+###
+# page 025
+##
+import numpy as np
+
+
+class Perceptron:
+    def __init__(self, eta=0.01, n_iter=50, random_state=1):
+        self.error_ = None
+        self.w_ = None
+        self.eta = max(0.0, min(eta, 1.0))
+        self.n_iter = n_iter
+        self.random_state = random_state
+
+    def fit(self, X, y):
+        rgen = np.random.RandomState(self.random_state)
+        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])
+        self.error_ = []
+
+        for _ in range(self.n_iter):
+            errors = 0
+            zip_data = zip(X, y)
+            for xi, target in zip_data:
+                update = self.eta * (target - self.predict(xi))
+                self.w_[1:] += update * xi
+                self.w_[0] += update
+                errors += int(update != 0)
+            self.error_.append(errors)
+        return self
+
+    def net_input(self, X):
+        res = np.dot(X, self.w_[1:]) + self.w_[0]
+        return res
+
+    def predict(self, X):
+        res = np.where(self.net_input(X) >= 0.0, 1, -1)
+        return res
diff --git a/ch02/test.py b/ch02/test.py
new file mode 100644
index 00000000..63e4f3ea
--- /dev/null
+++ b/ch02/test.py
@@ -0,0 +1,99 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import requests
+import io
+import os
+from perceptron import Perceptron
+PATH = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+
+with requests.get(PATH) as response:
+    raw_data = response.text
+
+# s = os.path.join("https://archive.ics.uci.edu", "ml",
+#                  "machine-learning-databases",
+#                  "iris", "iris.data")
+s = os.path.join(PATH)
+
+df = pd.read_csv(s)
+# print(df.tail())
+# string_data = io.StringIO(raw_data)
+#
+# df = pd.read_csv(string_data, header=None, encoding="utf-8")
+#
+# print(df.tail())
+
+import matplotlib.pyplot as plot
+import numpy as np
+
+y = df.iloc[:100, 4].values
+y = np.where(y == "Iris-setosa", -1, 1)
+
+X = df.iloc[:100, [0, 2]].values
+
+# temp_X = df.iloc[:, [0, 2, 4]].values
+# setosa_array = temp_X[(temp_X[:, 2] == "Iris-setosa")]
+# virginica_array = temp_X[(temp_X[:, 2] == "Iris-virginica")]
+# versicolor_array = temp_X[(temp_X[:, 2] == "Iris-versicolor")]
+#
+# plt.scatter(setosa_array[:, 0], setosa_array[:, 1],
+#             color="red", marker="o", label="setosa")
+# plt.scatter(virginica_array[:, 0], virginica_array[:, 1],
+#             color="blue", marker="x", label="virginica")
+# plt.scatter(versicolor_array[:, 0], versicolor_array[:, 1],
+#             color="green", marker="^", label="versicolor")
+#
+# plt.xlabel("sepal length [cm]")
+# plt.ylabel("petal length [cm]")
+# plt.legend(loc="upper left")
+# plt.show()
+
+ppn = Perceptron(eta=0.1, n_iter=10)
+ppn.fit(X, y)
+# plt.plot(range(1, len(ppn.error_) + 1),
+#          ppn.error_, marker="o")
+# plt.xlabel("Epochs")
+# plt.xlabel("Number of updates")
+# plt.show()
+
+from matplotlib.colors import ListedColormap
+
+# for idx, cl in enumerate(np.unique(y)):
+#     print(f"index : {idx}, cl {cl}")
+#     print(X[y == cl, 0])
+
+def plot_decision_regions(X, y, classifier: Perceptron, resolution=0.02):
+    markers = ("s", "x", "o", "^", "v")
+    colors = ("red", "blue", "lightgreen", "gray", "cyan")
+    unique_len = len(np.unique(y))
+    cmap = ListedColormap(colors[:unique_len])
+
+    x1_min = X[:, 0].min() - 1
+    x1_max = X[:, 0].max() + 1
+
+    x2_min = X[:, 1].min() - 1
+    x2_max = X[:, 1].max() + 1
+
+    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
+                           np.arange(x2_min, x2_max, resolution))
+
+    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
+    Z = Z.reshape(xx1.shape)
+    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
+    plt.xlim(xx1.min(), xx1.max())
+    plt.ylim(xx2.min(), xx2.max())
+
+    for idx, cl in enumerate(np.unique(y)):
+        plt.scatter(x=X[y == cl, 0],
+                    y=X[y == cl, 1],
+                    alpha=0.8,
+                    c=cmap.colors[idx],
+                    marker=markers[idx],
+                    label=cl,
+                    edgecolors="black")
+
+
+plot_decision_regions(X, y, classifier=ppn)
+plt.xlabel("sepal length [cm]")
+plt.ylabel("petal length [cm]")
+plt.legend(loc="upper left")
+plt.show()
\ No newline at end of file
diff --git a/ch03/scikit_learb_test.py b/ch03/scikit_learb_test.py
new file mode 100644
index 00000000..75eb985d
--- /dev/null
+++ b/ch03/scikit_learb_test.py
@@ -0,0 +1,16 @@
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import numpy as np
+
+iris = datasets.load_iris()
+X = iris.data[:, [2, 3]]
+y = iris.target
+print(f"Class labels : {np.unique(y)}")
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
+
+sc = StandardScaler()
+sc.fit(X_train)
+X_train_std = sc.transform(X_train)
+X_test_std = sc.transform(X_test)
diff --git a/ch03/sigmoid_test.py b/ch03/sigmoid_test.py
new file mode 100644
index 00000000..d59021ed
--- /dev/null
+++ b/ch03/sigmoid_test.py
@@ -0,0 +1,21 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def sigmoid(z):
+    return 1.0 / (1.0 + np.exp(-z))
+
+
+z = np.arange(-7, 7, 0.1)
+phi_z = sigmoid(z)
+plt.plot(z, phi_z)
+plt.axvline(0.0, color="k")
+plt.ylim(-0.1, 1.1)
+plt.xlabel("z")
+plt.ylabel("$\phi (z)$")
+plt.yticks([0.0, 0.5, 1.0])
+ax = plt.gca()
+ax.yaxis.grid(True)
+plt.tight_layout()
+
+plt.show()