diff --git a/Clustering/Hierarchical Clustering/hierarchical_clustering.ipynb b/Clustering/Hierarchical Clustering/hierarchical_clustering.ipynb
new file mode 100644
index 0000000..fd031f1
--- /dev/null
+++ b/Clustering/Hierarchical Clustering/hierarchical_clustering.ipynb	
@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "JKkbeQi2Mzug"
+   },
+   "source": [
+    "# Hierarchical Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "TaQI437hM1Ho"
+   },
+   "source": [
+    "## Importing the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "2UW48DgcM4YS"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "gFeTEtDxM7K4"
+   },
+   "source": [
+    "## Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "4fS2J3HGM99q"
+   },
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('../../datasets/Mall_Customers.csv')\n",
+    "X = dataset.iloc[:, [3, 4]].values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "czYMlG7cNBsu"
+   },
+   "source": [
+    "## Using the dendrogram to find the optimal number of clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 4948,
+     "status": "ok",
+     "timestamp": 1588363683148,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "RDQODpAFNILO",
+    "outputId": "8743058d-09a8-43f5-892d-6b1c140792a0"
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.cluster.hierarchy as sch\n",
+    "dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))\n",
+    "plt.title('Dendrogram')\n",
+    "plt.xlabel('Customers')\n",
+    "plt.ylabel('Euclidean distances')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "KDbXbo9INLF6"
+   },
+   "source": [
+    "## Training the Hierarchical Clustering model on the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "IoH3zs2KNSw6"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import AgglomerativeClustering\n",
+    "hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')\n",
+    "y_hc = hc.fit_predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "X-SYG7l9NVmU"
+   },
+   "source": [
+    "## Visualising the clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 1290,
+     "status": "ok",
+     "timestamp": 1588363703003,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "-91tDJrnNY2p",
+    "outputId": "fc9652fa-6e3f-4b68-c4ff-e6fd6b4bce7d"
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(X[y_hc == 0, 0], X[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
+    "plt.scatter(X[y_hc == 1, 0], X[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
+    "plt.scatter(X[y_hc == 2, 0], X[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
+    "plt.scatter(X[y_hc == 3, 0], X[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
+    "plt.scatter(X[y_hc == 4, 0], X[y_hc == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
+    "plt.title('Clusters of customers')\n",
+    "plt.xlabel('Annual Income (k$)')\n",
+    "plt.ylabel('Spending Score (1-100)')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyOE/Ghkv22sqrXHjexUJwPA",
+   "collapsed_sections": [],
+   "name": "hierarchical_clustering.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Clustering/K-Means Clustering/k_means_clustering.ipynb b/Clustering/K-Means Clustering/k_means_clustering.ipynb
new file mode 100644
index 0000000..0ae0f1c
--- /dev/null
+++ b/Clustering/K-Means Clustering/k_means_clustering.ipynb	
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "i_paGd_yLbgH"
+   },
+   "source": [
+    "# K-Means Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "nAuqPwTnLipr"
+   },
+   "source": [
+    "## Importing the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "yUXGcC4KLmcL"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "5LciKOr8Lo5O"
+   },
+   "source": [
+    "## Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9RlmPzZGLtGi"
+   },
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('../../datasets/Mall_Customers.csv')\n",
+    "X = dataset.iloc[:, [3, 4]].values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "nWC2EWp2Lx5G"
+   },
+   "source": [
+    "## Using the elbow method to find the optimal number of clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 1431,
+     "status": "ok",
+     "timestamp": 1588239707642,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "zWs6ciOoL1b3",
+    "outputId": "bd3740ef-650e-4ae3-da64-821d7df9c7d0"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)\n",
+    "plt.plot(range(1, 11), wcss)\n",
+    "plt.title('The Elbow Method')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "VgT0mANLL4Nz"
+   },
+   "source": [
+    "## Training the K-Means model on the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "cjEfU6ZSMAPl"
+   },
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
+    "y_kmeans = kmeans.fit_predict(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "e7YrS1JAMFnm"
+   },
+   "source": [
+    "## Visualising the clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 1085,
+     "status": "ok",
+     "timestamp": 1588239716139,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "d0ZYecccMHNx",
+    "outputId": "2db95eb9-0c99-4718-f28c-1d3155258041"
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
+    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
+    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
+    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
+    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
+    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
+    "plt.title('Clusters of customers')\n",
+    "plt.xlabel('Annual Income (k$)')\n",
+    "plt.ylabel('Spending Score (1-100)')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyN979dFKn9B6Ro9v0hJ4uqU",
+   "name": "k_means_clustering.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/README.md b/README.md
index d741f57..e94e0c7 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,9 @@ The repository is composed of the following sections:
   * Naive Bayes
   * Decision Tree Classification
   * Random Forest Classification
+* `Clustering` : This folder contain Jupyter notebooks for the following Clustering Models:
+  * K-Means Clustering
+  * Hierarchical Clustering
 
 
 
diff --git a/datasets/Mall_Customers.csv b/datasets/Mall_Customers.csv
new file mode 100644
index 0000000..b324941
--- /dev/null
+++ b/datasets/Mall_Customers.csv
@@ -0,0 +1,201 @@
+CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
+0001,Male,19,15,39
+0002,Male,21,15,81
+0003,Female,20,16,6
+0004,Female,23,16,77
+0005,Female,31,17,40
+0006,Female,22,17,76
+0007,Female,35,18,6
+0008,Female,23,18,94
+0009,Male,64,19,3
+0010,Female,30,19,72
+0011,Male,67,19,14
+0012,Female,35,19,99
+0013,Female,58,20,15
+0014,Female,24,20,77
+0015,Male,37,20,13
+0016,Male,22,20,79
+0017,Female,35,21,35
+0018,Male,20,21,66
+0019,Male,52,23,29
+0020,Female,35,23,98
+0021,Male,35,24,35
+0022,Male,25,24,73
+0023,Female,46,25,5
+0024,Male,31,25,73
+0025,Female,54,28,14
+0026,Male,29,28,82
+0027,Female,45,28,32
+0028,Male,35,28,61
+0029,Female,40,29,31
+0030,Female,23,29,87
+0031,Male,60,30,4
+0032,Female,21,30,73
+0033,Male,53,33,4
+0034,Male,18,33,92
+0035,Female,49,33,14
+0036,Female,21,33,81
+0037,Female,42,34,17
+0038,Female,30,34,73
+0039,Female,36,37,26
+0040,Female,20,37,75
+0041,Female,65,38,35
+0042,Male,24,38,92
+0043,Male,48,39,36
+0044,Female,31,39,61
+0045,Female,49,39,28
+0046,Female,24,39,65
+0047,Female,50,40,55
+0048,Female,27,40,47
+0049,Female,29,40,42
+0050,Female,31,40,42
+0051,Female,49,42,52
+0052,Male,33,42,60
+0053,Female,31,43,54
+0054,Male,59,43,60
+0055,Female,50,43,45
+0056,Male,47,43,41
+0057,Female,51,44,50
+0058,Male,69,44,46
+0059,Female,27,46,51
+0060,Male,53,46,46
+0061,Male,70,46,56
+0062,Male,19,46,55
+0063,Female,67,47,52
+0064,Female,54,47,59
+0065,Male,63,48,51
+0066,Male,18,48,59
+0067,Female,43,48,50
+0068,Female,68,48,48
+0069,Male,19,48,59
+0070,Female,32,48,47
+0071,Male,70,49,55
+0072,Female,47,49,42
+0073,Female,60,50,49
+0074,Female,60,50,56
+0075,Male,59,54,47
+0076,Male,26,54,54
+0077,Female,45,54,53
+0078,Male,40,54,48
+0079,Female,23,54,52
+0080,Female,49,54,42
+0081,Male,57,54,51
+0082,Male,38,54,55
+0083,Male,67,54,41
+0084,Female,46,54,44
+0085,Female,21,54,57
+0086,Male,48,54,46
+0087,Female,55,57,58
+0088,Female,22,57,55
+0089,Female,34,58,60
+0090,Female,50,58,46
+0091,Female,68,59,55
+0092,Male,18,59,41
+0093,Male,48,60,49
+0094,Female,40,60,40
+0095,Female,32,60,42
+0096,Male,24,60,52
+0097,Female,47,60,47
+0098,Female,27,60,50
+0099,Male,48,61,42
+0100,Male,20,61,49
+0101,Female,23,62,41
+0102,Female,49,62,48
+0103,Male,67,62,59
+0104,Male,26,62,55
+0105,Male,49,62,56
+0106,Female,21,62,42
+0107,Female,66,63,50
+0108,Male,54,63,46
+0109,Male,68,63,43
+0110,Male,66,63,48
+0111,Male,65,63,52
+0112,Female,19,63,54
+0113,Female,38,64,42
+0114,Male,19,64,46
+0115,Female,18,65,48
+0116,Female,19,65,50
+0117,Female,63,65,43
+0118,Female,49,65,59
+0119,Female,51,67,43
+0120,Female,50,67,57
+0121,Male,27,67,56
+0122,Female,38,67,40
+0123,Female,40,69,58
+0124,Male,39,69,91
+0125,Female,23,70,29
+0126,Female,31,70,77
+0127,Male,43,71,35
+0128,Male,40,71,95
+0129,Male,59,71,11
+0130,Male,38,71,75
+0131,Male,47,71,9
+0132,Male,39,71,75
+0133,Female,25,72,34
+0134,Female,31,72,71
+0135,Male,20,73,5
+0136,Female,29,73,88
+0137,Female,44,73,7
+0138,Male,32,73,73
+0139,Male,19,74,10
+0140,Female,35,74,72
+0141,Female,57,75,5
+0142,Male,32,75,93
+0143,Female,28,76,40
+0144,Female,32,76,87
+0145,Male,25,77,12
+0146,Male,28,77,97
+0147,Male,48,77,36
+0148,Female,32,77,74
+0149,Female,34,78,22
+0150,Male,34,78,90
+0151,Male,43,78,17
+0152,Male,39,78,88
+0153,Female,44,78,20
+0154,Female,38,78,76
+0155,Female,47,78,16
+0156,Female,27,78,89
+0157,Male,37,78,1
+0158,Female,30,78,78
+0159,Male,34,78,1
+0160,Female,30,78,73
+0161,Female,56,79,35
+0162,Female,29,79,83
+0163,Male,19,81,5
+0164,Female,31,81,93
+0165,Male,50,85,26
+0166,Female,36,85,75
+0167,Male,42,86,20
+0168,Female,33,86,95
+0169,Female,36,87,27
+0170,Male,32,87,63
+0171,Male,40,87,13
+0172,Male,28,87,75
+0173,Male,36,87,10
+0174,Male,36,87,92
+0175,Female,52,88,13
+0176,Female,30,88,86
+0177,Male,58,88,15
+0178,Male,27,88,69
+0179,Male,59,93,14
+0180,Male,35,93,90
+0181,Female,37,97,32
+0182,Female,32,97,86
+0183,Male,46,98,15
+0184,Female,29,98,88
+0185,Female,41,99,39
+0186,Male,30,99,97
+0187,Female,54,101,24
+0188,Male,28,101,68
+0189,Female,41,103,17
+0190,Female,36,103,85
+0191,Female,34,103,23
+0192,Female,32,103,69
+0193,Male,33,113,8
+0194,Female,38,113,91
+0195,Female,47,120,16
+0196,Female,35,120,79
+0197,Female,45,126,28
+0198,Male,32,126,74
+0199,Male,32,137,18
+0200,Male,30,137,83
\ No newline at end of file