diff --git a/al_projects.csv b/al_projects.csv
new file mode 100644
index 0000000..2d284b7
--- /dev/null
+++ b/al_projects.csv
@@ -0,0 +1,12 @@
+title,team,hour,xp
+ft_linear_regression,Solo,49,4200
+dslr,Group,98,6000
+Leaffliction,Group,294,15750
+Multilayer_Perceptron,Solo,98,9450
+Total_perspective_vortex,Solo,98,9450
+lem-in,Group,98,9450
+n-puzzle,Group,98,9450
+rubik,Group,98,9450
+expert-system,Group,98,9450
+Gomoku,Group,196,25200
+krpsim,Group,49,9450
diff --git a/al_projects.py b/al_projects.py
new file mode 100644
index 0000000..14f43c5
--- /dev/null
+++ b/al_projects.py
@@ -0,0 +1,44 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import sys
+
+def load_data(path):
+	try:
+		data = pd.read_csv(path)
+	except:
+		print("Invalid file error.")
+		sys.exit()
+	#print("data shape:", data.shape)
+	columns = data.columns.tolist()
+
+	return (data.values, columns)
+
+def normalization(data):
+	data_min = np.min(data, axis=0)
+	data_max = np.max(data, axis=0)
+	normalized_data = (data - data_min) / (data_max - data_min)
+	return normalized_data, data_min, data_max
+
+def denormalization(normalized_data, data_min, data_max):
+	x = normalized_data * (data_max - data_min)
+	denormalized_data = normalized_data * (data_max - data_min) + data_min
+	return denormalized_data
+
+def plot_scatter(data, feature, target):
+	plt.scatter(data[:, 0], data[:, 1])
+	plt.title(f'Scatter Plot: {feature} vs {target}')
+	plt.xlabel(feature)
+	plt.ylabel(target)
+	plt.show()
+
+if __name__ == "__main__":
+	# Load the data
+	data, columns = load_data("al_projects.csv")
+	print(data[:,2:])
+	print(columns)
+
+	# Normalization
+	normalized_data, data_min, data_max = normalization(data[:,2:])
+	print(normalized_data)
+	plot_scatter(normalized_data, columns[2], columns[3])