Hierarchical Clustering.ipynb

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   "cell_type": "code",
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   "source": [
    "# import hierarchical clustering libraries\n",
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Univ = pd.read_csv(\"Universities.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization function \n",
    "def norm_func(i):\n",
    "    x = (i-i.min())/(i.max()-i.min())\n",
    "    return (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalized data frame (considering the numerical part of data)\n",
    "df_norm = norm_func(Univ.iloc[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dendrogram\n",
    "dendrogram = sch.dendrogram(sch.linkage(df_norm, method='average'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create clusters\n",
    "hc = AgglomerativeClustering(n_clusters=4, affinity = 'euclidean', linkage = 'single')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save clusters for chart\n",
    "y_hc = hc.fit_predict(df_norm)\n",
    "Clusters=pd.DataFrame(y_hc,columns=['Clusters'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Univ['h_clusterid'] = hc.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Univ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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