Hierarchical Clustering Day 02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mall Dataset\n",
    "\n",
    "https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python\n",
    "\n",
    "### Context\n",
    "You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. \n",
    "\n",
    "Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data. \n",
    "\n",
    "### Problem Statement \n",
    "\n",
    "You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score, homogeneity_score\n",
    "from sklearn.cluster import KMeans, AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer = pd.read_csv(\"./Downloads/Mall Customers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot of Annual Income and Spending Score\n",
    "plt.scatter(customer[\"Annual Income (k$)\"], \n",
    "            customer[\"Spending Score (1-100)\"])\n",
    "plt.xlabel(\"Annual Income\")\n",
    "plt.ylabel(\"Spending Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mall = customer.drop([\"CustomerID\",\"Genre\",\"Age\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining K Mean\n",
    "k =KMeans()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding WCSS\n",
    "wcss = []\n",
    "cluster_size = range(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cluster_size:\n",
    "    k = KMeans(n_clusters=i)\n",
    "    model = k.fit(mall)\n",
    "    wcss.append(k.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cluster_size, wcss, \"-o\", markerfacecolor=\"r\", markersize = 8)\n",
    "plt.xlabel(\"# Clusters\")\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.title(\"WCSS PLOT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since n_clusters = 5, hence we will fit the clustering\n",
    "k = KMeans(n_clusters=5)\n",
    "labels = k.fit(mall).predict(mall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4,\n",
       "       2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 3,\n",
       "       2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 1, 0, 3, 0, 1, 0, 1, 0,\n",
       "       3, 0, 1, 0, 1, 0, 1, 0, 1, 0, 3, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.553931997444648"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(mall, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting Silhoutte\n",
    "sil=[]\n",
    "silhoutte=[]\n",
    "for i in cluster_size:\n",
    "    k = KMeans(n_clusters=i)\n",
    "    model = k.fit(mall).predict(mall)\n",
    "    sil = silhouette_score(mall, model)\n",
    "    silhoutte.append(sil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,4])\n",
    "plt.subplot(121)\n",
    "plt.plot(cluster_size, wcss, \"-o\", markerfacecolor=\"r\", markersize = 8)\n",
    "plt.xlabel(\"# Clusters\")\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.title(\"WCSS PLOT\")\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(cluster_size, silhoutte, \"-o\", markerfacecolor=\"r\", markersize = 8)\n",
    "plt.xlabel(\"# Clusters\")\n",
    "plt.ylabel(\"Silhoutte\")\n",
    "plt.title(\"Silhoutte Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25d45d7ef28>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mall[\"Annual Income (k$)\"], mall[\"Spending Score (1-100)\"],\n",
    "           c =labels, s = 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Dendrogram')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,15])\n",
    "dend = sch.dendrogram(sch.linkage(mall, method=\"complete\"))\n",
    "plt.xlabel(\"Income\")\n",
    "plt.ylabel(\"Spending Score\")\n",
    "plt.title(\"Dendrogram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Agglomerative Clustering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_agg = agg.fit_predict(mall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
       "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1,\n",
       "       4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 0, 2, 0, 2,\n",
       "       1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_agg = pd.DataFrame(pred_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_agg.columns=[\"Labels\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "newmall = pd.concat([mall, pred_agg], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Annual Income (k$)  Spending Score (1-100)  Labels\n",
       "0                  15                      39       4\n",
       "1                  15                      81       3\n",
       "2                  16                       6       4\n",
       "3                  16                      77       3\n",
       "4                  17                      40       4"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newmall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "plt.subplot(221)\n",
    "plt.scatter(mall[\"Annual Income (k$)\"], mall[\"Spending Score (1-100)\"],\n",
    "           c =labels, s = 80)\n",
    "plt.xlabel(\"Income\")\n",
    "plt.ylabel(\"Spending Score\")\n",
    "plt.title(\"K-Means Clustering\")\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.scatter(newmall[newmall.Labels==0][\"Annual Income (k$)\"],\n",
    "            newmall[newmall.Labels==0][\"Spending Score (1-100)\"], \n",
    "            label = \"Sensible\")\n",
    "plt.scatter(newmall[newmall.Labels==1][\"Annual Income (k$)\"],\n",
    "            newmall[newmall.Labels==1][\"Spending Score (1-100)\"], \n",
    "            label = \"Standard\")\n",
    "plt.scatter(newmall[newmall.Labels==2][\"Annual Income (k$)\"],\n",
    "            newmall[newmall.Labels==2][\"Spending Score (1-100)\"], \n",
    "            label = \"Target\")\n",
    "plt.scatter(newmall[newmall.Labels==3][\"Annual Income (k$)\"],\n",
    "            newmall[newmall.Labels==3][\"Spending Score (1-100)\"], \n",
    "            label = \"Careless\")\n",
    "plt.scatter(newmall[newmall.Labels==4][\"Annual Income (k$)\"],\n",
    "            newmall[newmall.Labels==4][\"Spending Score (1-100)\"], \n",
    "            label = \"Careful\")\n",
    "plt.xlabel(\"Income\")\n",
    "plt.ylabel(\"Spending Score\")\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9510419321635281"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homogeneity_score(newmall.Labels, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IRIS Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "ir = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.DataFrame(ir.data,columns=ir.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=3)\n",
    "pred_iris = agg.fit_predict(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "dendogram = sch.dendrogram(sch.linkage(iris, method=\"weighted\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "irispred = pd.DataFrame(pred_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "irispred.columns=[\"Species\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "newiris = pd.concat([iris, irispred], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7608008469718723"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homogeneity_score(ir.target, newiris.Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5540972907866913"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(iris, newiris.Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(newiris[newiris.Species==0][\"petal length (cm)\"],\n",
    "            newiris[newiris.Species==0][\"petal width (cm)\"])\n",
    "plt.scatter(newiris[newiris.Species==1][\"petal length (cm)\"],\n",
    "            newiris[newiris.Species==1][\"petal width (cm)\"])\n",
    "plt.scatter(newiris[newiris.Species==2][\"petal length (cm)\"],\n",
    "            newiris[newiris.Species==2][\"petal width (cm)\"])\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.title(\"Agglomerative Clustering - IRIS Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
       "       'petal width (cm)', 'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newiris.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "college = pd.read_csv(\"./Downloads/College Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adrian College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Unnamed: 0 Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "0  Abilene Christian University     Yes  1660    1232     721         23   \n",
       "1            Adelphi University     Yes  2186    1924     512         16   \n",
       "2                Adrian College     Yes  1428    1097     336         22   \n",
       "3           Agnes Scott College     Yes   417     349     137         60   \n",
       "4     Alaska Pacific University     Yes   193     146      55         16   \n",
       "\n",
       "   Top25perc  F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  Personal  \\\n",
       "0         52         2885          537      7440        3300    450      2200   \n",
       "1         29         2683         1227     12280        6450    750      1500   \n",
       "2         50         1036           99     11250        3750    400      1165   \n",
       "3         89          510           63     12960        5450    450       875   \n",
       "4         44          249          869      7560        4120    800      1500   \n",
       "\n",
       "   PhD  Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0   70        78       18.1           12    7041         60  \n",
       "1   29        30       12.2           16   10527         56  \n",
       "2   53        66       12.9           30    8735         54  \n",
       "3   92        97        7.7           37   19016         59  \n",
       "4   76        72       11.9            2   10922         15  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    565\n",
       "No     212\n",
       "Name: Private, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.Private.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(777, 19)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Private', 'Apps', 'Accept', 'Enroll', 'Top10perc',\n",
       "       'Top25perc', 'F.Undergrad', 'P.Undergrad', 'Outstate', 'Room.Board',\n",
       "       'Books', 'Personal', 'PhD', 'Terminal', 'S.F.Ratio', 'perc.alumni',\n",
       "       'Expend', 'Grad.Rate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "coll_new = college.drop(['Unnamed: 0', 'Private'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = range(1,10)\n",
    "wcss = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in clusters:\n",
    "    k = KMeans(n_clusters=i)\n",
    "    model = k.fit(coll_new)\n",
    "    wcss.append(k.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhoutte = []\n",
    "for i in range(2,10):\n",
    "    k = KMeans(n_clusters=i)\n",
    "    model = k.fit(coll_new).predict(coll_new)\n",
    "    silhoutte.append(silhouette_score(coll_new, model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "plt.subplot(221)\n",
    "plt.plot(clusters, wcss, \"-o\", markerfacecolor = \"red\")\n",
    "plt.title(\"WCSS Plot\")\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(range(2,10), silhoutte, \"-o\", \n",
    "         markerfacecolor = \"brown\", linestyle=\"dashed\")\n",
    "plt.title(\"Silhoutte Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clusters =5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Apps', 'Accept', 'Enroll', 'Top10perc', 'Top25perc', 'F.Undergrad',\n",
       "       'P.Undergrad', 'Outstate', 'Room.Board', 'Books', 'Personal', 'PhD',\n",
       "       'Terminal', 'S.F.Ratio', 'perc.alumni', 'Expend', 'Grad.Rate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coll_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25d48237cc0>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(coll_new.Accept, coll_new.Enroll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AgglomerativeClustering\n",
    "agg = AgglomerativeClustering(n_clusters=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_college = agg.fit_predict(coll_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "college_agg = pd.DataFrame(pred_college)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "college_agg.columns=[\"College_Labels\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "newcollege = pd.concat([coll_new, college_agg], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(newcollege[newcollege.College_Labels==0][\"Accept\"],\n",
    "newcollege[newcollege.College_Labels==0][\"Enroll\"])\n",
    "plt.scatter(newcollege[newcollege.College_Labels==1][\"Accept\"],\n",
    "newcollege[newcollege.College_Labels==1][\"Enroll\"])\n",
    "plt.scatter(newcollege[newcollege.College_Labels==2][\"Accept\"],\n",
    "newcollege[newcollege.College_Labels==2][\"Enroll\"])\n",
    "plt.scatter(newcollege[newcollege.College_Labels==3][\"Accept\"],\n",
    "newcollege[newcollege.College_Labels==3][\"Enroll\"])\n",
    "plt.scatter(newcollege[newcollege.College_Labels==4][\"Accept\"],\n",
    "newcollege[newcollege.College_Labels==4][\"Enroll\"])\n",
    "plt.xlabel(\"Acceptance\")\n",
    "plt.ylabel(\"Enrollment\")\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Findings\n",
    "\n",
    "1. Cluster 0 and Cluster 4 appears to be similar.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "d0 = newcollege[newcollege.College_Labels==0]\n",
    "d1 = newcollege[newcollege.College_Labels==1]\n",
    "d2 = newcollege[newcollege.College_Labels==2]\n",
    "d3 = newcollege[newcollege.College_Labels==3]\n",
    "d4 = newcollege[newcollege.College_Labels==4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "      <th>College_Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>587</td>\n",
       "      <td>479</td>\n",
       "      <td>158</td>\n",
       "      <td>38</td>\n",
       "      <td>62</td>\n",
       "      <td>678</td>\n",
       "      <td>41</td>\n",
       "      <td>13500</td>\n",
       "      <td>3335</td>\n",
       "      <td>500</td>\n",
       "      <td>675</td>\n",
       "      <td>67</td>\n",
       "      <td>73</td>\n",
       "      <td>9.4</td>\n",
       "      <td>11</td>\n",
       "      <td>9727</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Apps  Accept  Enroll  Top10perc  Top25perc  F.Undergrad  P.Undergrad  \\\n",
       "0  1660    1232     721         23         52         2885          537   \n",
       "1  2186    1924     512         16         29         2683         1227   \n",
       "2  1428    1097     336         22         50         1036           99   \n",
       "4   193     146      55         16         44          249          869   \n",
       "5   587     479     158         38         62          678           41   \n",
       "\n",
       "   Outstate  Room.Board  Books  Personal  PhD  Terminal  S.F.Ratio  \\\n",
       "0      7440        3300    450      2200   70        78       18.1   \n",
       "1     12280        6450    750      1500   29        30       12.2   \n",
       "2     11250        3750    400      1165   53        66       12.9   \n",
       "4      7560        4120    800      1500   76        72       11.9   \n",
       "5     13500        3335    500       675   67        73        9.4   \n",
       "\n",
       "   perc.alumni  Expend  Grad.Rate  College_Labels  \n",
       "0           12    7041         60               0  \n",
       "1           16   10527         56               0  \n",
       "2           30    8735         54               0  \n",
       "4            2   10922         15               0  \n",
       "5           11    9727         55               0  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "      <th>College_Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1899</td>\n",
       "      <td>1720</td>\n",
       "      <td>489</td>\n",
       "      <td>37</td>\n",
       "      <td>68</td>\n",
       "      <td>1594</td>\n",
       "      <td>32</td>\n",
       "      <td>13868</td>\n",
       "      <td>4826</td>\n",
       "      <td>450</td>\n",
       "      <td>850</td>\n",
       "      <td>89</td>\n",
       "      <td>100</td>\n",
       "      <td>13.7</td>\n",
       "      <td>37</td>\n",
       "      <td>11487</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1038</td>\n",
       "      <td>839</td>\n",
       "      <td>227</td>\n",
       "      <td>30</td>\n",
       "      <td>63</td>\n",
       "      <td>973</td>\n",
       "      <td>306</td>\n",
       "      <td>15595</td>\n",
       "      <td>4400</td>\n",
       "      <td>300</td>\n",
       "      <td>500</td>\n",
       "      <td>79</td>\n",
       "      <td>84</td>\n",
       "      <td>11.3</td>\n",
       "      <td>23</td>\n",
       "      <td>11644</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1732</td>\n",
       "      <td>1425</td>\n",
       "      <td>472</td>\n",
       "      <td>37</td>\n",
       "      <td>75</td>\n",
       "      <td>1830</td>\n",
       "      <td>110</td>\n",
       "      <td>16548</td>\n",
       "      <td>5406</td>\n",
       "      <td>500</td>\n",
       "      <td>600</td>\n",
       "      <td>82</td>\n",
       "      <td>88</td>\n",
       "      <td>11.3</td>\n",
       "      <td>31</td>\n",
       "      <td>10932</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2652</td>\n",
       "      <td>1900</td>\n",
       "      <td>484</td>\n",
       "      <td>44</td>\n",
       "      <td>77</td>\n",
       "      <td>1707</td>\n",
       "      <td>44</td>\n",
       "      <td>17080</td>\n",
       "      <td>4440</td>\n",
       "      <td>400</td>\n",
       "      <td>600</td>\n",
       "      <td>73</td>\n",
       "      <td>91</td>\n",
       "      <td>9.9</td>\n",
       "      <td>41</td>\n",
       "      <td>11711</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Apps  Accept  Enroll  Top10perc  Top25perc  F.Undergrad  P.Undergrad  \\\n",
       "3    417     349     137         60         89          510           63   \n",
       "7   1899    1720     489         37         68         1594           32   \n",
       "8   1038     839     227         30         63          973          306   \n",
       "10  1732    1425     472         37         75         1830          110   \n",
       "11  2652    1900     484         44         77         1707           44   \n",
       "\n",
       "    Outstate  Room.Board  Books  Personal  PhD  Terminal  S.F.Ratio  \\\n",
       "3      12960        5450    450       875   92        97        7.7   \n",
       "7      13868        4826    450       850   89       100       13.7   \n",
       "8      15595        4400    300       500   79        84       11.3   \n",
       "10     16548        5406    500       600   82        88       11.3   \n",
       "11     17080        4440    400       600   73        91        9.9   \n",
       "\n",
       "    perc.alumni  Expend  Grad.Rate  College_Labels  \n",
       "3            37   19016         59               4  \n",
       "7            37   11487         73               4  \n",
       "8            23   11644         80               4  \n",
       "10           31   10932         73               4  \n",
       "11           41   11711         76               4  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IT\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25d692cb908>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "sns.distplot(d0.Expend)\n",
    "sns.distplot(d1.Expend)\n",
    "sns.distplot(d2.Expend)\n",
    "sns.distplot(d3.Expend)\n",
    "sns.distplot(d4.Expend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}