diff --git a/Decision_Tree.png b/Decision_Tree.png
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diff --git a/KDE.py b/KDE.py
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+import numpy as np
+import pandas as pd
+from numpy.random import randn
+from scipy import stats
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+#1.manually creat KDE by summing the gaussian distribution
+ds = randn(100)
+sns.rugplot(ds)
+plt.hist(ds,alpha=0.5)
+plt.savefig('image1.png')
+
+x_axes = np.linspace(ds.min()-1,ds.max()+1,50)
+
+#bandwith creation
+bandwith = ((4*ds.std()**5)/(3*len(ds))) ** 0.2
+
+kernels =[]
+
+for point in ds:
+    kernel = stats.norm(point,bandwith).pdf(x_axes)
+    kernels.append(kernel)
+
+    kernel = kernel/ kernel.max()
+    kernel = kernel * 0.6
+
+    plt.plot(x_axes,kernel,alpha=0.5,color="red")
+
+
+plt.savefig('image6.png')
+
+kde =np.sum(kernels,axis=0)
+kde_fig = plt.plot(x_axes,kde,color='green')
+sns.rugplot(ds)
+plt.suptitle('KDE Plot')
+plt.savefig('image7.png')
+
+
+
+
+
+#2. using seaborn - shortcut
+kdefig = sns.kdeplot(ds)
+fig = kdefig.get_figure()
+fig.savefig('image8.png')