Wine Quality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('winequality-red.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           1599 non-null float64\n",
      "volatile acidity        1599 non-null float64\n",
      "citric acid             1599 non-null float64\n",
      "residual sugar          1599 non-null float64\n",
      "chlorides               1599 non-null float64\n",
      "free sulfur dioxide     1599 non-null float64\n",
      "total sulfur dioxide    1599 non-null float64\n",
      "density                 1599 non-null float64\n",
      "pH                      1599 non-null float64\n",
      "sulphates               1599 non-null float64\n",
      "alcohol                 1599 non-null float64\n",
      "quality                 1599 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for c in df:\n",
    "    df.hist(column=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('quality',axis=1)\n",
    "y=df['quality']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "528"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         2\n",
      "           4       0.00      0.00      0.00        19\n",
      "           5       0.74      0.76      0.75       217\n",
      "           6       0.60      0.71      0.65       213\n",
      "           7       0.51      0.37      0.43        70\n",
      "           8       0.50      0.14      0.22         7\n",
      "\n",
      "   micro avg       0.65      0.65      0.65       528\n",
      "   macro avg       0.39      0.33      0.34       528\n",
      "weighted avg       0.62      0.65      0.63       528\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "param={'n_estimators':[50,75,100,150,175,200],'min_samples_leaf':[1,2,3,4],'max_depth':[5,8,15]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf=RandomForestClassifier()\n",
    "grid=GridSearchCV(cf,param,cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [50, 75, 100, 150, 175, 200], 'min_samples_leaf': [1, 2, 3, 4], 'max_depth': [5, 8, 15]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6694677871148459"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(528,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         2\n",
      "           4       0.25      0.05      0.09        19\n",
      "           5       0.52      0.67      0.59       217\n",
      "           6       0.46      0.47      0.47       213\n",
      "           7       0.42      0.16      0.23        70\n",
      "           8       0.00      0.00      0.00         7\n",
      "\n",
      "   micro avg       0.49      0.49      0.49       528\n",
      "   macro avg       0.28      0.23      0.23       528\n",
      "weighted avg       0.46      0.49      0.46       528\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2=scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X0=pd.DataFrame(X2,columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.528360</td>\n",
       "      <td>0.961877</td>\n",
       "      <td>-1.391472</td>\n",
       "      <td>-0.453218</td>\n",
       "      <td>-0.243707</td>\n",
       "      <td>-0.466193</td>\n",
       "      <td>-0.379133</td>\n",
       "      <td>0.558274</td>\n",
       "      <td>1.288643</td>\n",
       "      <td>-0.579207</td>\n",
       "      <td>-0.960246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.298547</td>\n",
       "      <td>1.967442</td>\n",
       "      <td>-1.391472</td>\n",
       "      <td>0.043416</td>\n",
       "      <td>0.223875</td>\n",
       "      <td>0.872638</td>\n",
       "      <td>0.624363</td>\n",
       "      <td>0.028261</td>\n",
       "      <td>-0.719933</td>\n",
       "      <td>0.128950</td>\n",
       "      <td>-0.584777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.298547</td>\n",
       "      <td>1.297065</td>\n",
       "      <td>-1.186070</td>\n",
       "      <td>-0.169427</td>\n",
       "      <td>0.096353</td>\n",
       "      <td>-0.083669</td>\n",
       "      <td>0.229047</td>\n",
       "      <td>0.134264</td>\n",
       "      <td>-0.331177</td>\n",
       "      <td>-0.048089</td>\n",
       "      <td>-0.584777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.654856</td>\n",
       "      <td>-1.384443</td>\n",
       "      <td>1.484154</td>\n",
       "      <td>-0.453218</td>\n",
       "      <td>-0.264960</td>\n",
       "      <td>0.107592</td>\n",
       "      <td>0.411500</td>\n",
       "      <td>0.664277</td>\n",
       "      <td>-0.979104</td>\n",
       "      <td>-0.461180</td>\n",
       "      <td>-0.584777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.528360</td>\n",
       "      <td>0.961877</td>\n",
       "      <td>-1.391472</td>\n",
       "      <td>-0.453218</td>\n",
       "      <td>-0.243707</td>\n",
       "      <td>-0.466193</td>\n",
       "      <td>-0.379133</td>\n",
       "      <td>0.558274</td>\n",
       "      <td>1.288643</td>\n",
       "      <td>-0.579207</td>\n",
       "      <td>-0.960246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0      -0.528360          0.961877    -1.391472       -0.453218  -0.243707   \n",
       "1      -0.298547          1.967442    -1.391472        0.043416   0.223875   \n",
       "2      -0.298547          1.297065    -1.186070       -0.169427   0.096353   \n",
       "3       1.654856         -1.384443     1.484154       -0.453218  -0.264960   \n",
       "4      -0.528360          0.961877    -1.391472       -0.453218  -0.243707   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
       "0            -0.466193             -0.379133  0.558274  1.288643  -0.579207   \n",
       "1             0.872638              0.624363  0.028261 -0.719933   0.128950   \n",
       "2            -0.083669              0.229047  0.134264 -0.331177  -0.048089   \n",
       "3             0.107592              0.411500  0.664277 -0.979104  -0.461180   \n",
       "4            -0.466193             -0.379133  0.558274  1.288643  -0.579207   \n",
       "\n",
       "    alcohol  \n",
       "0 -0.960246  \n",
       "1 -0.584777  \n",
       "2 -0.584777  \n",
       "3 -0.584777  \n",
       "4 -0.960246  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         2\n",
      "           4       0.25      0.05      0.09        19\n",
      "           5       0.52      0.67      0.59       217\n",
      "           6       0.46      0.47      0.47       213\n",
      "           7       0.42      0.16      0.23        70\n",
      "           8       0.00      0.00      0.00         7\n",
      "\n",
      "   micro avg       0.49      0.49      0.49       528\n",
      "   macro avg       0.28      0.23      0.23       528\n",
      "weighted avg       0.46      0.49      0.46       528\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('winequality-red.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21534ac9ef0>"
      ]
     },
     "execution_count": 59,
     "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": [
    "sns.barplot(x='quality',y='fixed acidity',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21533503128>"
      ]
     },
     "execution_count": 60,
     "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": [
    "sns.barplot(x='quality',y='volatile acidity',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27196acedd8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('winequality-red.csv')\n",
    "df['quality']=df['quality'].map({3:0,4:0,5:0,6:0,7:1,8:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_quality = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1382\n",
       "1     217\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('quality',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.96      0.94       346\n",
      "           1       0.65      0.48      0.55        54\n",
      "\n",
      "   micro avg       0.90      0.90      0.90       400\n",
      "   macro avg       0.79      0.72      0.75       400\n",
      "weighted avg       0.89      0.90      0.89       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test,y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[332,  14],\n",
       "       [ 28,  26]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "param={'n_estimators':[70,100,110,120],'max_depth':[5,10,15,20]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid=GridSearchCV(cf,param_grid=param,cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [70, 100, 110, 120], 'max_depth': [5, 10, 15, 20]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9074228523769808"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "param=grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95       346\n",
      "           1       1.00      0.26      0.41        54\n",
      "\n",
      "   micro avg       0.90      0.90      0.90       400\n",
      "   macro avg       0.95      0.63      0.68       400\n",
      "weighted avg       0.91      0.90      0.87       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[346,   0],\n",
       "       [ 40,  14]], dtype=int64)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=SVC(C=1.0,gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8725"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "param={'C':[1,0.1,0.01,10,100],'gamma':[1,0.1,0.01,0.001,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid=GridSearchCV(cf,param_grid=param,cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'C': [1, 0.1, 0.01, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8849040867389492"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=10, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95       346\n",
      "           1       1.00      0.26      0.41        54\n",
      "\n",
      "   micro avg       0.90      0.90      0.90       400\n",
      "   macro avg       0.95      0.63      0.68       400\n",
      "weighted avg       0.91      0.90      0.87       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[346,   0],\n",
       "       [ 40,  14]], dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for c in df:\n",
    "    df.hist(column=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "models=[LogisticRegression(),SVC(),KNeighborsClassifier(),\n",
    "        RandomForestClassifier(),DecisionTreeClassifier()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name=['LogisticRegression','SVC','KNN','RandomForest','DecisionTree']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=[]\n",
    "d={}\n",
    "X=df.drop('quality',axis=1)\n",
    "y=df['quality']\n",
    "from sklearn.metrics import accuracy_score\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=0.25, random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: 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",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "for model in range(len(models)):\n",
    "    clf=models[model]\n",
    "    clf.fit(X_train,y_train)\n",
    "    pred=clf.predict(X_test)\n",
    "    acc.append(accuracy_score(pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={'Modelling Algo':model_name,'Accuracy':acc}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Modelling Algo</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.8550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.8975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.8625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Modelling Algo  Accuracy\n",
       "0  LogisticRegression    0.8550\n",
       "1                 SVC    0.8700\n",
       "2                 KNN    0.8625\n",
       "3        RandomForest    0.8975\n",
       "4        DecisionTree    0.8625"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_frame=pd.DataFrame(d)\n",
    "acc_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2719cf03f60>"
      ]
     },
     "execution_count": 133,
     "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": [
    "sns.barplot(x='Modelling Algo',y='Accuracy',data=acc_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.DataFrame(x1,columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=[]\n",
    "d={}\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: 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": [
    "for model in range(len(models)):\n",
    "    clf=models[model]\n",
    "    clf.fit(X_train,y_train)\n",
    "    pred=clf.predict(X_test)\n",
    "    acc.append(accuracy_score(pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={'Modelling Algo':model_name,'Accuracy':acc}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Modelling Algo</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.843750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.862500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.852083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.887500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.843750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Modelling Algo  Accuracy\n",
       "0  LogisticRegression  0.843750\n",
       "1                 SVC  0.862500\n",
       "2                 KNN  0.852083\n",
       "3        RandomForest  0.887500\n",
       "4        DecisionTree  0.843750"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_frame=pd.DataFrame(d)\n",
    "acc_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "X=df.drop('quality',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm=MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=mm.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.DataFrame(x1,columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: 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"
     ]
    },
    {
     "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>Modelling Algo</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.839583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.858333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.864583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.881250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.860417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Modelling Algo  Accuracy\n",
       "0  LogisticRegression  0.839583\n",
       "1                 SVC  0.858333\n",
       "2                 KNN  0.864583\n",
       "3        RandomForest  0.881250\n",
       "4        DecisionTree  0.860417"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc=[]\n",
    "d={}\n",
    "for model in range(len(models)):\n",
    "    clf=models[model]\n",
    "    clf.fit(X_train,y_train)\n",
    "    pred=clf.predict(X_test)\n",
    "    acc.append(accuracy_score(pred,y_test))\n",
    "\n",
    "d={'Modelling Algo':model_name,'Accuracy':acc}\n",
    "acc_frame=pd.DataFrame(d)\n",
    "acc_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('quality',axis=1)\n",
    "y=df['quality']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.95      0.92       346\n",
      "           1       0.44      0.28      0.34        54\n",
      "\n",
      "   micro avg       0.85      0.85      0.85       400\n",
      "   macro avg       0.67      0.61      0.63       400\n",
      "weighted avg       0.83      0.85      0.84       400\n",
      "\n",
      "0.855\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "lm=LogisticRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "pred=lm.predict(X_test)\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,pred))\n",
    "print(lm.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000], 'penalty': ['l1', 'l2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_dict={'C':[0.001, 0.01, 0.1, 1, 10, 100, 1000],'penalty':['l1','l2']}\n",
    "clf_lr=GridSearchCV(estimator=LogisticRegression(),param_grid=params_dict,scoring='accuracy',cv=3)\n",
    "clf_lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8865721434528774"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_lr.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_lr.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], 'n_jobs': [-1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[i+1 for i in range(50)]\n",
    "param={'n_neighbors':l,'n_jobs':[-1]}\n",
    "grid=GridSearchCV(estimator=KNeighborsClassifier(),param_grid=param,cv=10)\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8690575479566305"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8715596330275229"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cv=3\n",
    "l=[i+1 for i in range(50)]\n",
    "param={'n_neighbors':l}\n",
    "grid=GridSearchCV(estimator=KNeighborsClassifier(),param_grid=param,cv=4)\n",
    "grid.fit(X_train,y_train)\n",
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 2}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hriti\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.87"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm=SVC()\n",
    "svm.fit(X_train,y_train)\n",
    "pred=svm.predict(X_test)\n",
    "accuracy_score(pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100], 'kernel': ['linear', 'rbf']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_dict={'C':[0.001,0.01,0.1,1,10,100],'gamma':[0.001,0.01,0.1,1,10,100],'kernel':['linear','rbf']}\n",
    "clf=GridSearchCV(estimator=SVC(),param_grid=params_dict,scoring='accuracy',cv=10)\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8949124270225187"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95       346\n",
      "           1       1.00      0.26      0.41        54\n",
      "\n",
      "   micro avg       0.90      0.90      0.90       400\n",
      "   macro avg       0.95      0.63      0.68       400\n",
      "weighted avg       0.91      0.90      0.87       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[346   0]\n",
      " [ 40  14]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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