Wine Quality Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wine quality analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as mpt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Wine_Quality_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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",
       "      <th>color</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",
       "      <td>red</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",
       "      <td>red</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",
       "      <td>red</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",
       "      <td>red</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",
       "      <td>red</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 color  \n",
       "0      9.4        5   red  \n",
       "1      9.8        5   red  \n",
       "2      9.8        5   red  \n",
       "3      9.8        6   red  \n",
       "4      9.4        5   red  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar',\n",
       "       'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality', 'color'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['red', 'white'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['color'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_changer = {\n",
    "    'red': 0,\n",
    "    'white':1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['color'] = data['color'].map(color_changer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def qualityconv(x):\n",
    "#     x=float(x)\n",
    "#     return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['quality'] = data['quality'].apply(qualityconv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed_acidity           float64\n",
       "volatile_acidity        float64\n",
       "citric_acid             float64\n",
       "residual_sugar          float64\n",
       "chlorides               float64\n",
       "free_sulfur_dioxide     float64\n",
       "total_sulfur_dioxide    float64\n",
       "density                 float64\n",
       "pH                      float64\n",
       "sulphates               float64\n",
       "alcohol                 float64\n",
       "quality                   int64\n",
       "color                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observing heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ec8950f978>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpt.figure(figsize=(12,8))\n",
    "sb.heatmap(data.corr(), annot=True, linewidth=1, linecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = data[['alcohol', 'density', 'volatile_acidity', 'chlorides']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_data = data['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, x_test, Y, y_test = train_test_split(training_data, output_data, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing logistic regression algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\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\\ishit\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "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)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = linear_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0,   0,   0,   0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0],\n",
       "       [  3,  47, 381, 273,  28,   3,   0],\n",
       "       [  6,  22, 230, 612, 275,  44,   1],\n",
       "       [  0,   0,   2,   9,  12,   2,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(predictions, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5153846153846153"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(predictions, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Through KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_model = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_predictions = knn_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49538461538461537"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_score = accuracy_score(knn_predictions, y_test)\n",
    "knn_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "para = {'n_neighbors':[5,10, 13, 15]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_model = GridSearchCV(KNeighborsClassifier(), param_grid=para)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', 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': [5, 10, 13, 15]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 15}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5306795689465582"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_model=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, 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, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_model_prediction = tree_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_score = accuracy_score(tree_model_prediction, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5723076923076923"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'features' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-9a750579f0f2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdot_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m tree.export_graphviz(tree_model, out_file=dot_data,  \n\u001b[1;32m----> 7\u001b[1;33m                          feature_names=features)  \n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mgraph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpydotplus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_from_dot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdot_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m#graph.write_png(\"Hiring-Decision.png\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'features' is not defined"
     ]
    }
   ],
   "source": [
    "from IPython.display import Image  \n",
    "from sklearn.externals.six import StringIO  \n",
    "import pydotplus\n",
    "\n",
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(tree_model, out_file=dot_data,  \n",
    "                         feature_names=features)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "#graph.write_png(\"Hiring-Decision.png\")\n",
    "#Image(graph.create_png())  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing ensemble learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\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": [
       "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)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_predictions = svm_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5256410256410257"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_score = accuracy_score(svm_predictions, y_test)\n",
    "svm_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_parameters = {\n",
    "    'C':[0.01, 0.1, 1, 10, 100 ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_svm = GridSearchCV(SVC(kernel='rbf'), param_grid=svm_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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\\ishit\\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": [
       "GridSearchCV(cv='warn', 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': [0.01, 0.1, 1, 10, 100]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score='warn', scoring=None, verbose=0)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_svm.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_svm.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 52,
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   "outputs": [],
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