added my code in 'machine_learning' and 'neural_network' folder

machine_learning/decision_tree_churn/churn_cal.py

@@ -0,0 +1,48 @@
+# Predicting Customer Churn rate using Decision Tree
+# Dataset from Kaggle: https://www.kaggle.com/code/korfanakis/predicting-customer-churn-with-machine-learning/input
+import pandas as pd
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+
+
+df = pd.read_csv("churn_modelling.csv")
+
+# prints the first 5 rows of the dataset
+# print(df.head())
+
+# Sorting the dependent and independent values
+x = df[
+ [
+ "CreditScore",
+ "Age",
+ "Tenure",
+ "Balance",
+ "NumOfProducts",
+ "HasCrCard",
+ "IsActiveMember",
+ "EstimatedSalary",
+ ]
+].values
+y = df["Exited"]
+
+# Splitting the dataset into training and testing data
+x_test, x_train, y_test, y_train = train_test_split(x, y, test_size=0.3, random_state=3)
+
+# Creating the decision tree classifier
+decision_tree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
+decision_tree.fit(x_train, y_train)
+
+# Predicting the values
+pred_tree = decision_tree.predict(x_test)
+print("Accuracy of the model: ", metrics.accuracy_score(y_test, pred_tree))
+
+# Comparing the first five predicted and actual values
+print("Predicted values: ", pred_tree[0:5])
+print("Actual values: \n", y_test[0:5])
+
+# Error rate
+print("Error rate: ", metrics.mean_squared_error(y_test, pred_tree))
+# Visualizing the decision tree
+# tree.plot_tree(decision_tree)
+# plt.show()