diff --git a/DecisionTree/decision_tree_classification.R b/DecisionTree/decision_tree_classification.R
new file mode 100644
index 0000000..2f95b99
--- /dev/null
+++ b/DecisionTree/decision_tree_classification.R
@@ -0,0 +1,67 @@
+# Decision Tree Classification
+
+# Importing the dataset
+dataset = read.csv('Social_Network_Ads.csv')
+dataset = dataset[3:5]
+
+# Encoding the target feature as factor
+dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1))
+
+# Splitting the dataset into the Training set and Test set
+# install.packages('caTools')
+library(caTools)
+set.seed(123)
+split = sample.split(dataset$Purchased, SplitRatio = 0.75)
+training_set = subset(dataset, split == TRUE)
+test_set = subset(dataset, split == FALSE)
+
+# Feature Scaling
+training_set[-3] = scale(training_set[-3])
+test_set[-3] = scale(test_set[-3])
+
+# Fitting Decision Tree Classification to the Training set
+# install.packages('rpart')
+library(rpart)
+classifier = rpart(formula = Purchased ~ .,
+                   data = training_set)
+
+# Predicting the Test set results
+y_pred = predict(classifier, newdata = test_set[-3], type = 'class')
+
+# Making the Confusion Matrix
+cm = table(test_set[, 3], y_pred)
+
+# Visualising the Training set results
+library(ElemStatLearn)
+set = training_set
+X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
+X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
+grid_set = expand.grid(X1, X2)
+colnames(grid_set) = c('Age', 'EstimatedSalary')
+y_grid = predict(classifier, newdata = grid_set, type = 'class')
+plot(set[, -3],
+     main = 'Decision Tree Classification (Training set)',
+     xlab = 'Age', ylab = 'Estimated Salary',
+     xlim = range(X1), ylim = range(X2))
+contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)
+points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
+points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))
+
+# Visualising the Test set results
+library(ElemStatLearn)
+set = test_set
+X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
+X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
+grid_set = expand.grid(X1, X2)
+colnames(grid_set) = c('Age', 'EstimatedSalary')
+y_grid = predict(classifier, newdata = grid_set, type = 'class')
+plot(set[, -3], main = 'Decision Tree Classification (Test set)',
+     xlab = 'Age', ylab = 'Estimated Salary',
+     xlim = range(X1), ylim = range(X2))
+contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)
+points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
+points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))
+
+# Plotting the tree
+plot(classifier)
+text(classifier)
\ No newline at end of file
diff --git a/logistic regression/use Python and NumPy/logistic_regression2.py b/logistic regression/use Python and NumPy/logistic_regression2.py
new file mode 100644
index 0000000..45e7457
--- /dev/null
+++ b/logistic regression/use Python and NumPy/logistic_regression2.py	
@@ -0,0 +1,70 @@
+# Logistic Regression
+
+# Importing the libraries
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Social_Network_Ads.csv')
+X = dataset.iloc[:, [2, 3]].values
+y = dataset.iloc[:, 4].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.cross_validation import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
+
+# Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Fitting Logistic Regression to the Training set
+from sklearn.linear_model import LogisticRegression
+classifier = LogisticRegression(random_state = 0)
+classifier.fit(X_train, y_train)
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+# Visualising the Training set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_train, y_train
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Logistic Regression (Training set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
+
+# Visualising the Test set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_test, y_test
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Logistic Regression (Test set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
\ No newline at end of file