Mtech_seed_Kmeans and HCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kmeans and Hierarchical Clustering on SEED dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Demonstrating seed dataset on various techniques'''\n",
    "# Importing library\n",
    "# Adding Preliminary Libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>A</th>\n",
       "      <th>P</th>\n",
       "      <th>C</th>\n",
       "      <th>LK</th>\n",
       "      <th>WK</th>\n",
       "      <th>A_Coef</th>\n",
       "      <th>LKG</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.26</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>5.763</td>\n",
       "      <td>3.312</td>\n",
       "      <td>2.221</td>\n",
       "      <td>5.220</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.88</td>\n",
       "      <td>14.57</td>\n",
       "      <td>0.8811</td>\n",
       "      <td>5.554</td>\n",
       "      <td>3.333</td>\n",
       "      <td>1.018</td>\n",
       "      <td>4.956</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.29</td>\n",
       "      <td>14.09</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>5.291</td>\n",
       "      <td>3.337</td>\n",
       "      <td>2.699</td>\n",
       "      <td>4.825</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.84</td>\n",
       "      <td>13.94</td>\n",
       "      <td>0.8955</td>\n",
       "      <td>5.324</td>\n",
       "      <td>3.379</td>\n",
       "      <td>2.259</td>\n",
       "      <td>4.805</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.14</td>\n",
       "      <td>14.99</td>\n",
       "      <td>0.9034</td>\n",
       "      <td>5.658</td>\n",
       "      <td>3.562</td>\n",
       "      <td>1.355</td>\n",
       "      <td>5.175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>12.19</td>\n",
       "      <td>13.20</td>\n",
       "      <td>0.8783</td>\n",
       "      <td>5.137</td>\n",
       "      <td>2.981</td>\n",
       "      <td>3.631</td>\n",
       "      <td>4.870</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>11.23</td>\n",
       "      <td>12.88</td>\n",
       "      <td>0.8511</td>\n",
       "      <td>5.140</td>\n",
       "      <td>2.795</td>\n",
       "      <td>4.325</td>\n",
       "      <td>5.003</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>13.20</td>\n",
       "      <td>13.66</td>\n",
       "      <td>0.8883</td>\n",
       "      <td>5.236</td>\n",
       "      <td>3.232</td>\n",
       "      <td>8.315</td>\n",
       "      <td>5.056</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>11.84</td>\n",
       "      <td>13.21</td>\n",
       "      <td>0.8521</td>\n",
       "      <td>5.175</td>\n",
       "      <td>2.836</td>\n",
       "      <td>3.598</td>\n",
       "      <td>5.044</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>12.30</td>\n",
       "      <td>13.34</td>\n",
       "      <td>0.8684</td>\n",
       "      <td>5.243</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.637</td>\n",
       "      <td>5.063</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         A      P       C     LK     WK  A_Coef    LKG  target\n",
       "0    15.26  14.84  0.8710  5.763  3.312   2.221  5.220       0\n",
       "1    14.88  14.57  0.8811  5.554  3.333   1.018  4.956       0\n",
       "2    14.29  14.09  0.9050  5.291  3.337   2.699  4.825       0\n",
       "3    13.84  13.94  0.8955  5.324  3.379   2.259  4.805       0\n",
       "4    16.14  14.99  0.9034  5.658  3.562   1.355  5.175       0\n",
       "..     ...    ...     ...    ...    ...     ...    ...     ...\n",
       "205  12.19  13.20  0.8783  5.137  2.981   3.631  4.870       2\n",
       "206  11.23  12.88  0.8511  5.140  2.795   4.325  5.003       2\n",
       "207  13.20  13.66  0.8883  5.236  3.232   8.315  5.056       2\n",
       "208  11.84  13.21  0.8521  5.175  2.836   3.598  5.044       2\n",
       "209  12.30  13.34  0.8684  5.243  2.974   5.637  5.063       2\n",
       "\n",
       "[210 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing Dataset\n",
    "#To demonstrate various clustering algorithms in python, the Iris dataset will be used which has three classes\n",
    "# in the dependent variable (three type of Iris flowers) and using this dataset clusters will be formed.\n",
    "seed = pd.read_csv('Seed_data.csv')\n",
    "seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing Data\n",
    "#Here we have the target variable ‘Type’. We need to remove the target variable so that this dataset can be used to work in an unsupervised learning environment. The iloc function is used to get the features we require. We also use .values function to get an array of the dataset.\n",
    "#(Note that we transformed the dataset to an array so that we can plot the graphs of the clusters).\n",
    "\n",
    "Y = seed['target']          # Split off classifications\n",
    "X = seed.iloc[:, [0, 1, 2, 3, 4, 5, 6]].values # Split off features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will separate the target variable from the original dataset and again convert it to an array by using numpy.\n",
    "\n",
    "Y = seed['target']\n",
    "Y = np.array(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seed dataset clustering plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c90cbbfef0>"
      ]
     },
     "execution_count": 7,
     "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": [
    "# Visualise Classes\n",
    "# seed dataset has three classes in target\n",
    "\n",
    "plt.scatter(X[Y == 0, 0], X[Y == 0, 6], s =80, c = 'orange', label = 'Target 0')\n",
    "plt.scatter(X[Y == 1, 0], X[Y == 1, 6], s =80,  c = 'yellow', label = 'Target 1')\n",
    "plt.scatter(X[Y == 2, 0], X[Y == 2, 6], s =80,  c = 'green', label = 'Target 2')\n",
    "plt.title('Seed dataset plot')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kmeans Clustering for Seed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Kmeans is a kind of Unsupervised type of Clustering . It basically takes input from Dataset and predicts the clusters \n",
    "accordingly'''\n",
    "\n",
    "# Wine dataset for KMeans\n",
    "\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating WCSS (within-cluster sums of squares) \n",
    "\n",
    "wcss=[]\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elbow plot (Kmeans) for SEED dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WCSS')"
      ]
     },
     "execution_count": 10,
     "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": [
    "# Plot the WCSS\n",
    "\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The elbow method for Seed dataset')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0,\n",
       "       0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0,\n",
       "       0, 0, 0, 2, 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, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running K-Means Model\n",
    "\n",
    "cluster_Kmeans = KMeans(n_clusters=3)\n",
    "model_kmeans = cluster_Kmeans.fit(X)\n",
    "pred_kmeans = model_kmeans.labels_\n",
    "pred_kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kmeans Clustering plot for Seed dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c90d918b70>"
      ]
     },
     "execution_count": 37,
     "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": [
    "# Visualizing Output\n",
    "# In the above output we got value labels: ‘0’, ‘1’  and ‘2’. For a better understanding, we can visualize these clusters.\n",
    "\n",
    "plt.scatter(X[pred_kmeans == 0, 5], X[pred_kmeans == 0, 0], s = 80, c = 'orange', label = 'Target 0')\n",
    "plt.scatter(X[pred_kmeans == 1, 0], X[pred_kmeans == 1, 5], s = 80, c = 'yellow', label = 'Target 1')\n",
    "plt.scatter(X[pred_kmeans == 2, 0], X[pred_kmeans == 2, 5], s = 80, c = 'green', label = 'Target 2')\n",
    "\n",
    "plt.title('Kmeans Plot for Seed dataset')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KNN accuracy\n",
    "\n",
    "seed=pd.read_csv('Seed_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=seed.iloc[:,:-1].values\n",
    "y=seed.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9811320754716981\n",
      "Confusion matrix:\n",
      "[[13  0  1]\n",
      " [ 0 24  0]\n",
      " [ 0  0 15]]\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.93      0.96        14\n",
      "           1       1.00      1.00      1.00        24\n",
      "           2       0.94      1.00      0.97        15\n",
      "\n",
      "    accuracy                           0.98        53\n",
      "   macro avg       0.98      0.98      0.98        53\n",
      "weighted avg       0.98      0.98      0.98        53\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculating Accuracy score, Confusion matrix, Classification report.\n",
    "\n",
    "from sklearn import neighbors, datasets, preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "X=seed.iloc[:,:-1].values\n",
    "y=seed.iloc[:,-1].values\n",
    "\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y)\n",
    "scaler = preprocessing.StandardScaler().fit(Xtrain)\n",
    "Xtrain = scaler.transform(Xtrain)\n",
    "Xtest = scaler.transform(Xtest)\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=14)\n",
    "knn.fit(Xtrain, y_train)\n",
    "y_pred = knn.predict(Xtest)\n",
    "\n",
    "print('Accuracy score:', accuracy_score(y_test, y_pred))\n",
    "print('Confusion matrix:')\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('Classification report:')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9707344008834898"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression :\n",
      "Accuracy =  0.9285714285714286\n",
      "[[12  1  0]\n",
      " [ 2 14  0]\n",
      " [ 0  0 13]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ayantika\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Ayantika\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"Logistic Regression :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8925831202046036"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Nearest Neighbors Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K Nearest Neighbors :\n",
      "Accuracy =  0.8571428571428571\n",
      "[[11  1  1]\n",
      " [ 4 12  0]\n",
      " [ 0  0 13]]\n"
     ]
    }
   ],
   "source": [
    "#K Nearest Neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "classifier = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"K Nearest Neighbors :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7862595419847328"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine:\n",
      "Accuracy =  0.8888888888888888\n",
      "[[16  1  2]\n",
      " [ 4 23  0]\n",
      " [ 0  0 17]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ayantika\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Support Vector Machine\n",
    "from sklearn.svm import SVC\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "classifier = SVC()\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"Support Vector Machine:\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8315508021390374"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes :\n",
      "Accuracy =  0.8809523809523809\n",
      "[[22  2  3]\n",
      " [ 5 28  0]\n",
      " [ 0  0 24]]\n"
     ]
    }
   ],
   "source": [
    "#Gaussian Naive Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"Gaussian Naive Bayes :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for GNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8208573256557902"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier :\n",
      "Accuracy =  0.8809523809523809\n",
      "[[22  1  4]\n",
      " [ 2 31  0]\n",
      " [ 0  0 24]]\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree Classifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier as DT\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "classifier = DT(criterion='entropy', random_state=0)\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(\"Decision Tree Classifier :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for DTC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8743589743589744"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier :\n",
      "Accuracy =  0.8809523809523809\n",
      "[[13  0  0]\n",
      " [ 2 14  0]\n",
      " [ 0  0 13]]\n"
     ]
    }
   ],
   "source": [
    "#Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier as RF\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "classifier = RF(n_estimators=10, criterion='entropy', random_state=0)\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(\"Random Forest Classifier :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for RFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9285714285714286"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical clustering Analysis (HCA) for Seed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Library for Hierarchical clustering\n",
    "\n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting of Dendrogram\n",
    "\n",
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Dendogram plot for Seed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Decide the number of clusters by using this dendrogram\n",
    "Z = sch.linkage(X, method = 'median')\n",
    "plt.figure(figsize=(20,7))\n",
    "den = sch.dendrogram(Z)\n",
    "plt.title('Dendrogram for the clustering of the dataset seed)')\n",
    "plt.xlabel('Type')\n",
    "plt.ylabel('Euclidean distance in the space with other variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building an Agglomerative Clustering Model\n",
    "\n",
    "#Initialise Model\n",
    "\n",
    "cluster_H = AgglomerativeClustering(n_clusters=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2,\n",
       "       2, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0,\n",
       "       2, 2, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 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, 2, 1, 1, 1, 1, 1, 1, 1,\n",
       "       2, 1, 2, 2, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Modelling the data\n",
    "model_clt = cluster_H.fit(X)\n",
    "model_clt\n",
    "pred1 = model_clt.labels_\n",
    "pred1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical cluster plot for Glass dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c90dbcfac8>"
      ]
     },
     "execution_count": 23,
     "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": [
    "# Plotting the HCA Cluster\n",
    "\n",
    "plt.scatter(X[pred1 == 0, 0], X[pred1 == 0, 3], s = 80, c = 'orange', label = 'Target 0')\n",
    "plt.scatter(X[pred1 == 1, 1], X[pred1 == 1, 4], s = 80, c = 'yellow', label = 'Target 1')\n",
    "plt.scatter(X[pred1 == 2, 1], X[pred1 == 2, 5], s = 80, c = 'green', label = 'Target 2')\n",
    "plt.title('Hierarchical Plot for Seed dataset')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical clustering Accuracy for Seed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3761904761904762"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import sklearn.metrics as sm\n",
    "\n",
    "target = pd.DataFrame(seed.target)\n",
    "#based on the dendrogram we have two clusetes \n",
    "k =3 \n",
    "#build the model\n",
    "HClustering = AgglomerativeClustering(n_clusters=k , affinity=\"euclidean\",linkage=\"ward\")\n",
    "#fit the model on the dataset\n",
    "HClustering.fit(X)\n",
    "#accuracy of the model\n",
    "sm.accuracy_score(target,HClustering.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Cohen Kappa Accurucy for Seed dataset (HCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9707344008834898"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression :\n",
      "Accuracy =  0.8809523809523809\n",
      "[[19  3  5]\n",
      " [ 2 31  0]\n",
      " [ 0  0 24]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ayantika\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Ayantika\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"Logistic Regression :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8201669877970457"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Nearest Neighbors Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K Nearest Neighbors :\n",
      "Accuracy =  0.8928571428571429\n",
      "[[21  1  5]\n",
      " [ 3 30  0]\n",
      " [ 0  0 24]]\n"
     ]
    }
   ],
   "source": [
    "#K Nearest Neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"K Nearest Neighbors :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8387715930902111"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine:\n",
      "Accuracy =  0.8809523809523809\n",
      "[[21  1  5]\n",
      " [ 4 29  0]\n",
      " [ 0  0 24]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ayantika\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Support Vector Machine\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC()\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"Support Vector Machine:\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8210862619808307"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes :\n",
      "Accuracy =  0.9622641509433962\n",
      "[[13  0  1]\n",
      " [ 1 19  0]\n",
      " [ 0  0 19]]\n"
     ]
    }
   ],
   "source": [
    "#Gaussian Naive Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y)\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(\"Gaussian Naive Bayes :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for GNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9427954668105775"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier :\n",
      "Accuracy =  0.9622641509433962\n",
      "[[17  1  1]\n",
      " [ 2 25  0]\n",
      " [ 0  0 17]]\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree Classifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier as DT\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "classifier = DT(criterion='entropy', random_state=0)\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(\"Decision Tree Classifier :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for DTC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9030769230769231"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier :\n",
      "Accuracy =  0.9622641509433962\n",
      "[[18  1  0]\n",
      " [ 2 25  0]\n",
      " [ 1  0 16]]\n"
     ]
    }
   ],
   "source": [
    "#Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier as RF\n",
    "Xtrain, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "classifier = RF(n_estimators=10, criterion='entropy', random_state=0)\n",
    "classifier.fit(Xtrain,y_train)\n",
    "y_pred = classifier.predict(Xtest)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(\"Random Forest Classifier :\")\n",
    "print(\"Accuracy = \", accuracy)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohen Kappa Accuracy for RFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9029275808936825"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cluster = cohen_kappa_score(y_test, y_pred)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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