KNN.ipynb

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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import neighbors\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>uniform_cell_size</th>\n",
       "      <th>uniform_cell_shape</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>single_epith_cell_size</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>Bland_chromatin</th>\n",
       "      <th>Normal_nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1000025</td>\n",
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       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1018099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <th>7</th>\n",
       "      <td>1018561</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "        id  clump_thickness  uniform_cell_size  uniform_cell_shape  \\\n",
       "0  1000025                5                  1                   1   \n",
       "1  1002945                5                  4                   4   \n",
       "2  1015425                3                  1                   1   \n",
       "3  1016277                6                  8                   8   \n",
       "4  1017023                4                  1                   1   \n",
       "5  1017122                8                 10                  10   \n",
       "6  1018099                1                  1                   1   \n",
       "7  1018561                2                  1                   2   \n",
       "8  1033078                2                  1                   1   \n",
       "9  1033078                4                  2                   1   \n",
       "\n",
       "   marginal_adhesion  single_epith_cell_size bare_nuclei  Bland_chromatin  \\\n",
       "0                  1                       2           1                3   \n",
       "1                  5                       7          10                3   \n",
       "2                  1                       2           2                3   \n",
       "3                  1                       3           4                3   \n",
       "4                  3                       2           1                3   \n",
       "5                  8                       7          10                9   \n",
       "6                  1                       2          10                3   \n",
       "7                  1                       2           1                3   \n",
       "8                  1                       2           1                1   \n",
       "9                  1                       2           1                2   \n",
       "\n",
       "   Normal_nucleoli  Mitoses  class  \n",
       "0                1        1      2  \n",
       "1                2        1      2  \n",
       "2                1        1      2  \n",
       "3                7        1      2  \n",
       "4                1        1      2  \n",
       "5                7        1      4  \n",
       "6                1        1      2  \n",
       "7                1        1      2  \n",
       "8                1        5      2  \n",
       "9                1        1      2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('breast-cancer-wisconsin.txt')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'clump_thickness', 'uniform_cell_size', 'uniform_cell_shape',\n",
       "       'marginal_adhesion', 'single_epith_cell_size', 'bare_nuclei',\n",
       "       'Bland_chromatin', 'Normal_nucleoli', 'Mitoses', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        0\n",
       "clump_thickness           0\n",
       "uniform_cell_size         0\n",
       "uniform_cell_shape        0\n",
       "marginal_adhesion         0\n",
       "single_epith_cell_size    0\n",
       "bare_nuclei               0\n",
       "Bland_chromatin           0\n",
       "Normal_nucleoli           0\n",
       "Mitoses                   0\n",
       "class                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "df.replace('?',-99999,inplace=True)\n",
    "df.drop(['id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>uniform_cell_size</th>\n",
       "      <th>uniform_cell_shape</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>single_epith_cell_size</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>Bland_chromatin</th>\n",
       "      <th>Normal_nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>class</th>\n",
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       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "   clump_thickness  uniform_cell_size  uniform_cell_shape  marginal_adhesion  \\\n",
       "0                5                  1                   1                  1   \n",
       "1                5                  4                   4                  5   \n",
       "2                3                  1                   1                  1   \n",
       "3                6                  8                   8                  1   \n",
       "4                4                  1                   1                  3   \n",
       "\n",
       "   single_epith_cell_size bare_nuclei  Bland_chromatin  Normal_nucleoli  \\\n",
       "0                       2           1                3                1   \n",
       "1                       7          10                3                2   \n",
       "2                       2           2                3                1   \n",
       "3                       3           4                3                7   \n",
       "4                       2           1                3                1   \n",
       "\n",
       "   Mitoses  class  \n",
       "0        1      2  \n",
       "1        1      2  \n",
       "2        1      2  \n",
       "3        1      2  \n",
       "4        1      2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "clump_thickness           0\n",
       "uniform_cell_size         0\n",
       "uniform_cell_shape        0\n",
       "marginal_adhesion         0\n",
       "single_epith_cell_size    0\n",
       "bare_nuclei               0\n",
       "Bland_chromatin           0\n",
       "Normal_nucleoli           0\n",
       "Mitoses                   0\n",
       "class                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
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   "execution_count": 9,
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df[df.columns[1:11]].corr(), annot=True, square=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "X = np.array(df.drop(['class'], axis=1))\n",
    "y = np.array(df['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9714285714285714\n"
     ]
    }
   ],
   "source": [
    "\n",
    "clf = neighbors.KNeighborsClassifier(n_jobs=-1, n_neighbors=7)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "score = clf.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    }
   ],
   "source": [
    "predict_data = np.array([(2, 1, 2, 1, 2, 1, 3, 1, 1)])\n",
    "\n",
    "predict_data = predict_data.reshape(len(predict_data), -1)\n",
    "result = clf.predict(predict_data)\n",
    "\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 4, 4, 2, 4, 4, 2, 4, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 4,\n",
       "       2, 2, 4, 2, 4, 2, 2, 2, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4,\n",
       "       4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 4, 2, 4,\n",
       "       4, 2, 2, 2, 4, 2, 4, 2, 4, 4, 2, 2, 2, 4, 2, 2, 2, 2, 4, 2, 2, 2,\n",
       "       4, 2, 2, 2, 2, 4, 4, 2, 2, 2, 2, 4, 4, 2, 4, 2, 2, 4, 2, 4, 4, 2,\n",
       "       4, 4, 2, 2, 2, 2, 4, 2, 2, 4, 2, 2, 4, 4, 2, 4, 4, 2, 2, 2, 2, 4,\n",
       "       2, 4, 2, 2, 4, 2, 4, 2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = clf.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[88,  2],\n",
       "       [ 2, 48]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "confusion_matrix(pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       0.98      0.98      0.98        90\n",
      "           4       0.96      0.96      0.96        50\n",
      "\n",
      "    accuracy                           0.97       140\n",
      "   macro avg       0.97      0.97      0.97       140\n",
      "weighted avg       0.97      0.97      0.97       140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "                         ---Predicted Class---\n",
    "     |                   Yes.                    No.\n",
    "Actual Class    Yes. |  True Positive           False Positive\n",
    "     |          No.  |  False Positive          True Positive\n",
    "                "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Precision: Correctly predicted positive observations to the total                    predicted positive observations\n",
    "           Precision: Tp/Tp+Fp\n",
    "          \n",
    "Recall: Ratio of correctly predicted positive observations to the all             observations in actual class    \n",
    "        Recall: Tp/Tp+FN\n",
    "        \n",
    "F1-score: Weighted average of Precision ,this score takes both false               positives and false negatives into account   \n",
    "          F1-score: 2(recall*precision)/(Recall+precision) \n",
    "          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chossing K Value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = {}\n",
    "\n",
    "for i in range(1,41):\n",
    "    clf = neighbors.KNeighborsClassifier(n_neighbors=i)\n",
    "    clf.fit(X_train,y_train)\n",
    "    pred = clf.predict(X_test)\n",
    "    mean = np.mean(pred!=y_test)\n",
    "    error_rate[i] = mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'ErrorRate')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,10))\n",
    "plt.plot(list(error_rate.keys()), list(error_rate.values()), linestyle='dashed', marker='o', markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate with K')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('ErrorRate')"
   ]
  },
  {
   "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": "3.7.4"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}