diff --git a/10_Dimensionality_Reduction/t_SNE/Wine.csv b/10_Dimensionality_Reduction/t_SNE/Wine.csv
new file mode 100644
index 0000000..579a256
--- /dev/null
+++ b/10_Dimensionality_Reduction/t_SNE/Wine.csv
@@ -0,0 +1,179 @@
+Alcohol,Malic_Acid,Ash,Ash_Alcanity,Magnesium,Total_Phenols,Flavanoids,Nonflavanoid_Phenols,Proanthocyanins,Color_Intensity,Hue,OD280,Proline,Customer_Segment
+14.23,1.71,2.43,15.6,127,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065,1
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+14.37,1.95,2.5,16.8,113,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480,1
+13.24,2.59,2.87,21,118,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735,1
+14.2,1.76,2.45,15.2,112,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450,1
+14.39,1.87,2.45,14.6,96,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290,1
+14.06,2.15,2.61,17.6,121,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295,1
+14.83,1.64,2.17,14,97,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045,1
+13.86,1.35,2.27,16,98,2.98,3.15,0.22,1.85,7.22,1.01,3.55,1045,1
+14.1,2.16,2.3,18,105,2.95,3.32,0.22,2.38,5.75,1.25,3.17,1510,1
+14.12,1.48,2.32,16.8,95,2.2,2.43,0.26,1.57,5,1.17,2.82,1280,1
+13.75,1.73,2.41,16,89,2.6,2.76,0.29,1.81,5.6,1.15,2.9,1320,1
+14.75,1.73,2.39,11.4,91,3.1,3.69,0.43,2.81,5.4,1.25,2.73,1150,1
+14.38,1.87,2.38,12,102,3.3,3.64,0.29,2.96,7.5,1.2,3,1547,1
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+13.39,1.77,2.62,16.1,93,2.85,2.94,0.34,1.45,4.8,0.92,3.22,1195,1
+13.3,1.72,2.14,17,94,2.4,2.19,0.27,1.35,3.95,1.02,2.77,1285,1
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+13.29,1.97,2.68,16.8,102,3,3.23,0.31,1.66,6,1.07,2.84,1270,1
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+12.37,0.94,1.36,10.6,88,1.98,0.57,0.28,0.42,1.95,1.05,1.82,520,2
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+12.64,1.36,2.02,16.8,100,2.02,1.41,0.53,0.62,5.75,0.98,1.59,450,2
+13.67,1.25,1.92,18,94,2.1,1.79,0.32,0.73,3.8,1.23,2.46,630,2
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+12.99,1.67,2.6,30,139,3.3,2.89,0.21,1.96,3.35,1.31,3.5,985,2
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+13.84,4.12,2.38,19.5,89,1.8,0.83,0.48,1.56,9.01,0.57,1.64,480,3
+12.45,3.03,2.64,27,97,1.9,0.58,0.63,1.14,7.5,0.67,1.73,880,3
+14.34,1.68,2.7,25,98,2.8,1.31,0.53,2.7,13,0.57,1.96,660,3
+13.48,1.67,2.64,22.5,89,2.6,1.1,0.52,2.29,11.75,0.57,1.78,620,3
+12.36,3.83,2.38,21,88,2.3,0.92,0.5,1.04,7.65,0.56,1.58,520,3
+13.69,3.26,2.54,20,107,1.83,0.56,0.5,0.8,5.88,0.96,1.82,680,3
+12.85,3.27,2.58,22,106,1.65,0.6,0.6,0.96,5.58,0.87,2.11,570,3
+12.96,3.45,2.35,18.5,106,1.39,0.7,0.4,0.94,5.28,0.68,1.75,675,3
+13.78,2.76,2.3,22,90,1.35,0.68,0.41,1.03,9.58,0.7,1.68,615,3
+13.73,4.36,2.26,22.5,88,1.28,0.47,0.52,1.15,6.62,0.78,1.75,520,3
+13.45,3.7,2.6,23,111,1.7,0.92,0.43,1.46,10.68,0.85,1.56,695,3
+12.82,3.37,2.3,19.5,88,1.48,0.66,0.4,0.97,10.26,0.72,1.75,685,3
+13.58,2.58,2.69,24.5,105,1.55,0.84,0.39,1.54,8.66,0.74,1.8,750,3
+13.4,4.6,2.86,25,112,1.98,0.96,0.27,1.11,8.5,0.67,1.92,630,3
+12.2,3.03,2.32,19,96,1.25,0.49,0.4,0.73,5.5,0.66,1.83,510,3
+12.77,2.39,2.28,19.5,86,1.39,0.51,0.48,0.64,9.899999,0.57,1.63,470,3
+14.16,2.51,2.48,20,91,1.68,0.7,0.44,1.24,9.7,0.62,1.71,660,3
+13.71,5.65,2.45,20.5,95,1.68,0.61,0.52,1.06,7.7,0.64,1.74,740,3
+13.4,3.91,2.48,23,102,1.8,0.75,0.43,1.41,7.3,0.7,1.56,750,3
+13.27,4.28,2.26,20,120,1.59,0.69,0.43,1.35,10.2,0.59,1.56,835,3
+13.17,2.59,2.37,20,120,1.65,0.68,0.53,1.46,9.3,0.6,1.62,840,3
+14.13,4.1,2.74,24.5,96,2.05,0.76,0.56,1.35,9.2,0.61,1.6,560,3
\ No newline at end of file
diff --git a/10_Dimensionality_Reduction/t_SNE/t_SNE.py b/10_Dimensionality_Reduction/t_SNE/t_SNE.py
new file mode 100644
index 0000000..f39362c
--- /dev/null
+++ b/10_Dimensionality_Reduction/t_SNE/t_SNE.py
@@ -0,0 +1,38 @@
+""" T-distributed Stochastic Neighbor Embedding (t-SNE)
+"""
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.manifold import TSNE
+
+def main():
+
+    # Importing the dataset
+    dataset = pd.read_csv('Wine.csv')
+    X = dataset.iloc[:, 0:13].values
+    y = dataset.iloc[:, 13].values
+
+    # Feature Scaling
+    sc = StandardScaler()
+    X = sc.fit_transform(X)
+    # Applying t-SNE
+    tsne = TSNE(n_components=2)
+    X_tsne = tsne.fit_transform(X, y)
+
+    # Visualising the embeddings distribution
+    x_axis = X_tsne[:, 0]
+    y_axis = X_tsne[:, 1]
+    
+    plt.figure(figsize=(20, 8))
+    sc = plt.scatter(x_axis, y_axis, c = y, alpha=.4)
+    plt.title('t-SNE visualization')
+    plt.xlabel('x axis')
+    plt.ylabel('y axis')
+    plt.colorbar(sc)
+    plt.show()
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file