BreastCancerClassification.py.ipynb

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   "source": [
    "In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points) we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A."
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   "source": [
    "In this sense, it is important to consider the value of k. But hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the 'K' Nearest Neighbors (points) when it predicts the classification of the test point."
   ]
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   "source": [
    "Lets load required libraries"
   ]
  },
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   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 33 columns</p>\n",
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      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/breast-cancer-wisconsin-data/data.csv')\n",
    "\n",
    "df.head()"
   ]
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   "source": [
    "<div id=\"visualization_analysis\">\n",
    "    <h2>Data Visualization and Analysis</h2> \n",
    "</div>\n"
   ]
  },
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   "cell_type": "markdown",
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   },
   "source": [
    "#### Let’s see how many of each class is in our data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 33)\n"
     ]
    }
   ],
   "source": [
    "df['radius_mean'].value_counts()\n",
    "\n",
    "print(df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diagnosis_value(diagnosis): \n",
    "    if diagnosis == 'M': \n",
    "        return 1\n",
    "    else: \n",
    "        return 0\n",
    "  \n",
    "    df['diagnosis'] = df['diagnosis'].apply(diagnosis_value)\n",
    "    df.drop(['Unnamed: 32','id'],axis=1)"
   ]
  },
  {
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   "source": [
    "### Feature set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets define feature sets, X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 19.,  79., 185., 129.,  55.,  50.,  38.,   7.,   4.,   3.]),\n",
       " array([ 6.981 ,  9.0939, 11.2068, 13.3197, 15.4326, 17.5455, 19.6584,\n",
       "        21.7713, 23.8842, 25.9971, 28.11  ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0OXSSY7MNzx/AcJekJjksI0kNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg/4f527i4AWwWnMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['radius_mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "X = df[[ 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
    "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
    "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
    "       'radius_se', 'texture_se', 'area_se', 'smoothness_se',\n",
    "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
    "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
    "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
    "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
    "       'symmetry_worst', 'fractal_dimension_worst']] .values  #.astype(float)\n",
    "X[0:4]\n",
    "y= np.array(df['diagnosis'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our labels?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization give data zero mean and unit variance, it is good practice, especially for algorithms such as KNN which is based on distance of cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398e+00, -2.07333501e+00,  1.26993369e+00,\n",
       "         9.84374905e-01,  1.56846633e+00,  3.28351467e+00,\n",
       "         2.65287398e+00,  2.53247522e+00,  2.21751501e+00,\n",
       "         2.25574689e+00,  2.48973393e+00, -5.65265059e-01,\n",
       "         2.48757756e+00, -2.14001647e-01,  1.31686157e+00,\n",
       "         7.24026158e-01,  6.60819941e-01,  1.14875667e+00,\n",
       "         9.07083081e-01,  1.88668963e+00, -1.35929347e+00,\n",
       "         2.30360062e+00,  2.00123749e+00,  1.30768627e+00,\n",
       "         2.61666502e+00,  2.10952635e+00,  2.29607613e+00,\n",
       "         2.75062224e+00,  1.93701461e+00],\n",
       "       [ 1.82982061e+00, -3.53632408e-01,  1.68595471e+00,\n",
       "         1.90870825e+00, -8.26962447e-01, -4.87071673e-01,\n",
       "        -2.38458552e-02,  5.48144156e-01,  1.39236330e-03,\n",
       "        -8.68652457e-01,  4.99254601e-01, -8.76243603e-01,\n",
       "         7.42401948e-01, -6.05350847e-01, -6.92926270e-01,\n",
       "        -4.40780058e-01,  2.60162067e-01, -8.05450380e-01,\n",
       "        -9.94437403e-02,  1.80592744e+00, -3.69203222e-01,\n",
       "         1.53512599e+00,  1.89048899e+00, -3.75611957e-01,\n",
       "        -4.30444219e-01, -1.46748968e-01,  1.08708430e+00,\n",
       "        -2.43889668e-01,  2.81189987e-01],\n",
       "       [ 1.57988811e+00,  4.56186952e-01,  1.56650313e+00,\n",
       "         1.55888363e+00,  9.42210440e-01,  1.05292554e+00,\n",
       "         1.36347845e+00,  2.03723076e+00,  9.39684817e-01,\n",
       "        -3.98007910e-01,  1.22867595e+00, -7.80083377e-01,\n",
       "         1.18133606e+00, -2.97005012e-01,  8.14973504e-01,\n",
       "         2.13076435e-01,  1.42482747e+00,  2.37035535e-01,\n",
       "         2.93559404e-01,  1.51187025e+00, -2.39743838e-02,\n",
       "         1.34747521e+00,  1.45628455e+00,  5.27407405e-01,\n",
       "         1.08293217e+00,  8.54973944e-01,  1.95500035e+00,\n",
       "         1.15225500e+00,  2.01391209e-01],\n",
       "       [-7.68909287e-01,  2.53732112e-01, -5.92687167e-01,\n",
       "        -7.64463792e-01,  3.28355348e+00,  3.40290899e+00,\n",
       "         1.91589718e+00,  1.45170736e+00,  2.86738293e+00,\n",
       "         4.91091929e+00,  3.26373441e-01, -1.10409044e-01,\n",
       "        -2.88378148e-01,  6.89701660e-01,  2.74428041e+00,\n",
       "         8.19518384e-01,  1.11500701e+00,  4.73268037e+00,\n",
       "         2.04751088e+00, -2.81464464e-01,  1.33984094e-01,\n",
       "        -2.49939304e-01, -5.50021228e-01,  3.39427470e+00,\n",
       "         3.89339743e+00,  1.98958826e+00,  2.17578601e+00,\n",
       "         6.04604135e+00,  4.93501034e+00],\n",
       "       [ 1.75029663e+00, -1.15181643e+00,  1.77657315e+00,\n",
       "         1.82622928e+00,  2.80371830e-01,  5.39340452e-01,\n",
       "         1.37101143e+00,  1.42849277e+00, -9.56046689e-03,\n",
       "        -5.62449981e-01,  1.27054278e+00, -7.90243702e-01,\n",
       "         1.19035676e+00,  1.48306716e+00, -4.85198799e-02,\n",
       "         8.28470780e-01,  1.14420474e+00, -3.61092272e-01,\n",
       "         4.99328134e-01,  1.29857524e+00, -1.46677038e+00,\n",
       "         1.33853946e+00,  1.22072425e+00,  2.20556166e-01,\n",
       "        -3.13394511e-01,  6.13178758e-01,  7.29259257e-01,\n",
       "        -8.68352984e-01, -3.97099619e-01]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Train Test Split  \n",
    "Out of Sample Accuracy is the percentage of correct predictions that the model makes on data that that the model has NOT been trained on. Doing a train and test on the same dataset will most likely have low out-of-sample accuracy, due to the likelihood of being over-fit.\n",
    "\n",
    "It is important that our models have a high, out-of-sample accuracy, because the purpose of any model, of course, is to make correct predictions on unknown data. So how can we improve out-of-sample accuracy? One way is to use an evaluation approach called Train/Test Split.\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
    "\n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (455, 29) (455,)\n",
      "Test set: (114, 29) (114,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"classification\">\n",
    "    <h2>Classification</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h3>K nearest neighbor (KNN)</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Import library "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Classifier implementing the k-nearest neighbors vote."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Training\n",
    "\n",
    "Lets start the algorithm with k=4 for now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
      "           weights='uniform')\n"
     ]
    }
   ],
   "source": [
    "k = 4\n",
    "#Train Model and Predict  \n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "print(neigh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Predicting\n",
    "we can use the model to predict the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'B', 'B', 'B', 'M'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = neigh.predict(X_test)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Accuracy evaluation\n",
    "In multilabel classification, __accuracy classification score__ is a function that computes subset accuracy. This function is equal to the jaccard_similarity_score function. Essentially, it calculates how closely the actual labels and predicted labels are matched in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.9714285714285714\n",
      "Test set Accuracy:  0.9473684210526315\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
      "           weights='uniform')\n",
      "Train Accuracy is :  0.9714285714285714\n",
      "test accuracy is :  0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "# write your code here\n",
    "k=6\n",
    "neigh1=KNeighborsClassifier(n_neighbors=k).fit(X_train,y_train)\n",
    "print(neigh1)\n",
    "#predicting\n",
    "yhat6=neigh1.predict(X_test)\n",
    "print('Train Accuracy is : ',metrics.accuracy_score(y_train, neigh1.predict(X_train)))\n",
    "print('test accuracy is : ', metrics.accuracy_score(y_test, yhat6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95614035, 0.94736842, 0.95614035, 0.94736842, 0.96491228,\n",
       "       0.96491228, 0.96491228, 0.96491228, 0.96491228])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Plot  model accuracy  for Different number of Neighbors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of estimators (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.9649122807017544 with k= 5\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}