Red-Wine-Quality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ce4ffc5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Importing required packages\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3521dc30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3935f5e7",
   "metadata": {},
   "source": [
    "# Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c71f179",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('winequality-red.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "63f8ae32",
   "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",
       "      <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,
   "id": "c5af5003",
   "metadata": {
    "scrolled": true
   },
   "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",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "900633d0",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### Ploting multiple barplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b5719b2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axs = plt.subplots(4, 3, figsize=(7, 12))\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"fixed acidity\"], ax=axs[0][0])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"volatile acidity\"], ax=axs[0][1])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"citric acid\"], ax=axs[0][2])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"residual sugar\"], ax=axs[1][0])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"chlorides\"], ax=axs[1][1])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"free sulfur dioxide\"], ax=axs[1][2])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"total sulfur dioxide\"], ax=axs[2][0])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"density\"], ax=axs[2][1])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"pH\"], ax=axs[2][2])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"sulphates\"], ax=axs[3][0])\n",
    "sns.barplot(x=df[\"quality\"], y=df[\"alcohol\"], ax=axs[3][1])\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb10213",
   "metadata": {},
   "source": [
    "#### If one observe density vs quality plot, one can observe that density can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375c5118",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f37e3d11",
   "metadata": {},
   "source": [
    "# Preprocessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfcca73a",
   "metadata": {},
   "source": [
    "#### We will re-label values in 'quality' column as either good (value>6) or bad (value<=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c483f4ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 8\n"
     ]
    }
   ],
   "source": [
    "print(df['quality'].min(), df['quality'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ceae6a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = (2, 6, 8)\n",
    "labels = ['bad', 'good']\n",
    "df['quality'] = pd.cut(df['quality'], bins=bins, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6cb45878",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "df['quality'] = label_encoder.fit_transform(df['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ffce3aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1382\n",
       "1     217\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c6a8cdc",
   "metadata": {},
   "source": [
    "#### Creating training and testing sets after droping 'quality' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f69245f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['quality', 'density'], axis=1)\n",
    "y = df['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "619daeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f72073",
   "metadata": {},
   "source": [
    "#### Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8336a07f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "\n",
    "X_train_sc = sc.fit_transform(X_train)\n",
    "X_test_sc = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e06587f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e69ba3f3",
   "metadata": {},
   "source": [
    "### Trying RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f519edd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.97      0.94       273\n",
      "           1       0.69      0.43      0.53        47\n",
      "\n",
      "    accuracy                           0.89       320\n",
      "   macro avg       0.80      0.70      0.73       320\n",
      "weighted avg       0.88      0.89      0.88       320\n",
      "\n",
      "Accuracy is:  0.8875\n"
     ]
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(n_estimators=200)\n",
    "rf_clf.fit(X_train_sc, y_train)\n",
    "y_pred_rf_clf = rf_clf.predict(X_test_sc)\n",
    "print(classification_report(y_test, y_pred_rf_clf))\n",
    "print(\"Accuracy is: \", accuracy_score(y_test, y_pred_rf_clf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a9c226c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.915581938976378"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf_eval = cross_val_score(estimator=rf_clf, X = X_train_sc, y = y_train, cv=10)\n",
    "rf_clf_eval.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f6a64b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f4340b00",
   "metadata": {},
   "source": [
    "### Trying SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "05b6e56e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.97      0.92       273\n",
      "           1       0.50      0.15      0.23        47\n",
      "\n",
      "    accuracy                           0.85       320\n",
      "   macro avg       0.68      0.56      0.57       320\n",
      "weighted avg       0.82      0.85      0.82       320\n",
      "\n",
      "Accuracy is:  0.853125\n"
     ]
    }
   ],
   "source": [
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train_sc, y_train)\n",
    "y_pred_sgd_clf = sgd_clf.predict(X_test_sc)\n",
    "print(classification_report(y_test, y_pred_sgd_clf))\n",
    "print(\"Accuracy is: \", accuracy_score(y_test, y_pred_sgd_clf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "544341c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8639517716535433"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf_eval = cross_val_score(estimator=sgd_clf, X = X_train_sc, y = y_train, cv=10)\n",
    "sgd_clf_eval.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e00ea21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "15f9dc16",
   "metadata": {},
   "source": [
    "### Trying SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c503c7a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.99      0.93       273\n",
      "           1       0.80      0.26      0.39        47\n",
      "\n",
      "    accuracy                           0.88       320\n",
      "   macro avg       0.84      0.62      0.66       320\n",
      "weighted avg       0.87      0.88      0.85       320\n",
      "\n",
      "Accuracy is:  0.88125\n"
     ]
    }
   ],
   "source": [
    "svc_clf = SVC(random_state=42)\n",
    "svc_clf.fit(X_train_sc, y_train)\n",
    "y_pred_svc_clf = svc_clf.predict(X_test_sc)\n",
    "print(classification_report(y_test, y_pred_svc_clf))\n",
    "print(\"Accuracy is: \", accuracy_score(y_test, y_pred_svc_clf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622d610c",
   "metadata": {},
   "source": [
    "#### Using GridSearchCV to get best params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c5fb709",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_svc = {\n",
    "    'C' : [0.1, 0.8, 0.9, 1, 1.1, 1.2],\n",
    "    'kernel' : ['poly', 'rbf', 'linear']\n",
    "}\n",
    "svc_clf2 = GridSearchCV(svc_clf, param_grid=param_svc, scoring='accuracy', cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1386cc84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(random_state=42),\n",
       "             param_grid={'C': [0.1, 0.8, 0.9, 1, 1.1, 1.2],\n",
       "                         'kernel': ['poly', 'rbf', 'linear']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_clf2.fit(X_train_sc, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "16518132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_clf2.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "465a4dbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is:  0.853125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Karan\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but SVC was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "svc_final = SVC(C=1.1, kernel='rbf')\n",
    "svc_final.fit(X_train_sc, y_train)\n",
    "y_pred_svc_final = svc_final.predict(X_test)\n",
    "print(\"Accuracy is: \", accuracy_score(y_test, y_pred_svc_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99dda22a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "801a4ee2",
   "metadata": {},
   "source": [
    "##### Thus RandomForestClassifier gives the highest accuracy"
   ]
  }
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