wine.ipynb

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   "source": [
    "# Prediction of Wine Quality\n",
    "Following is a detailed notebook that explores the [Wine Quality Dataset](!https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009).\n",
    "We employ the help of libraries like:\n",
    "- Pandas\n",
    "- Seaborn\n",
    "- Matplotlib\n",
    "- sklearn<br>\n",
    "## Content\n",
    "- Imports and Dataloading\n",
    "- Data Exploration\n",
    "- Training of the Prediction Model\n",
    "- Optimizing for best results"
   ],
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   "metadata": {}
  },
  {
   "source": [
    "## Imports and Dataloading\n",
    "We import the requirements to run the model and exploring the dataset.\n",
    "Once, the imports are made we load the dataset. The dataset <b>Wine Quality Red</b> is a rather small dataset standing at about 100kb and can be easily downloaded rather than creating an API call from the kaggle website."
   ],
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   "source": [
    "# Imports\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 confusion_matrix, classification_report\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading dataset\n",
    "wine = pd.read_csv('data/winequality-red.csv')"
   ]
  },
  {
   "source": [
    "## Data Exploration\n",
    "We use visualization libraries like Seaborn and Matplotlib to properly get familiar to our dataset.<br>\n",
    "The dataset itself contains over 1600 rows with columns describing various aspects of Wine Quality such as:\n",
    "- Fixed Acidity\n",
    "- Volatile Acidity\n",
    "- Citric Acid\n",
    "- Residual Sugar\n",
    "- Chlorides\n",
    "- Free Sulfer Dioxide\n",
    "- Total Sulfer Dioxide\n",
    "- Density\n",
    "- pH\n",
    "- Sulphates\n",
    "- ALcohol\n",
    "- Quality"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1599 entries, 0 to 1598\nData 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  \ndtypes: float64(11), int64(1)\nmemory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "# Information about the columns\n",
    "wine.info()"
   ]
  },
  {
   "source": [
    "### Let us see how different factors seem to affect the quality"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='fixed acidity'>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Fixed Acidity\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'fixed acidity', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='volatile acidity'>"
      ]
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Volatile Acidity\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'volatile acidity', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='citric acid'>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Citric Acid\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'citric acid', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='residual sugar'>"
      ]
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Residual Sugar\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'residual sugar', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='chlorides'>"
      ]
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Chlorides\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'chlorides', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='free sulfur dioxide'>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Free Sulfur Dioxide\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'free sulfur dioxide', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='total sulfur dioxide'>"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Total Sulfur Dioxide\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'total sulfur dioxide', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='density'>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Density\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'density', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='pH'>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# pH\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'pH', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='sulphates'>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Sulphates\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'sulphates', data = wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='alcohol'>"
      ]
     },
     "metadata": {},
     "execution_count": 14
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Alcohol\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'alcohol', data = wine)"
   ]
  },
  {
   "source": [
    "## Training\n",
    "Coming to training the model for <b>Wine Quality Prediction</b> we will be first pre-processing the data and then running 3 different models on the dataset to single out the best result.\n",
    "The models are as follows:\n",
    "- Random Forest Classifier\n",
    "- Stochastic Gradient Descent Classifier\n",
    "- Support Vector Classifier"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Pre-Processing"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    1382\n",
       "1     217\n",
       "Name: quality, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# Firstly we will replace the variables \"good\" and \"bad\" with 0 and 1 in the last column\n",
    "# Dividing the good and bad quality rows\n",
    "bins = (2, 6.5, 8)\n",
    "group_names = ['bad', 'good']\n",
    "wine['quality'] = pd.cut(wine['quality'], bins = bins, labels = group_names)\n",
    "label_quality = LabelEncoder()\n",
    "wine['quality'] = label_quality.fit_transform(wine['quality'])\n",
    "wine['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/aryan/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  warnings.warn(\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='count'>"
      ]
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Visualizing the binary data\n",
    "sns.countplot(wine['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will seperate the quality column from the whole data\n",
    "X = wine.drop('quality',axis = 1)\n",
    "y = wine['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into Test and Train\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying Standard scaling to get optimized result\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "source": [
    "### Training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### Random Forest Classifier\n",
    "Random forest classifier creates a set of decision trees from randomly selected subset of training set. It then aggregates the votes from different decision trees to decide the final class of the test object"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200)\n",
    "rfc.fit(X_train, y_train)\n",
    "pred_rfc = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.90      0.97      0.93       273\n           1       0.68      0.36      0.47        47\n\n    accuracy                           0.88       320\n   macro avg       0.79      0.67      0.70       320\nweighted avg       0.87      0.88      0.87       320\n\n"
     ]
    }
   ],
   "source": [
    "#Let's see how our model performed\n",
    "print(classification_report(y_test, pred_rfc))"
   ]
  },
  {
   "source": [
    "#### Stochastic Gradient Descent Classifier\n",
    "Stochastic Gradient Descent (SGD) is a simple yet efficient optimization algorithm used to find the values of parameters/coefficients of functions that minimize a cost function. In other words, it is used for discriminative learning of linear classifiers under convex loss functions such as SVM and Logistic regression."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGDClassifier(penalty=None)\n",
    "sgd.fit(X_train, y_train)\n",
    "pred_sgd = sgd.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.87      0.94      0.91       273\n           1       0.38      0.21      0.27        47\n\n    accuracy                           0.83       320\n   macro avg       0.63      0.58      0.59       320\nweighted avg       0.80      0.83      0.81       320\n\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_sgd))"
   ]
  },
  {
   "source": [
    "#### Support Vector Classifier\n",
    "A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "pred_svc = svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.88      0.98      0.93       273\n           1       0.71      0.26      0.37        47\n\n    accuracy                           0.88       320\n   macro avg       0.80      0.62      0.65       320\nweighted avg       0.86      0.88      0.85       320\n\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_svc))"
   ]
  },
  {
   "source": [
    "### Optimizing for Best Resuls\n",
    "Since Support Vector Classifier fetched us the best results we are going to be using methods to try to get the best results"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### Grid Search\n",
    "Grid search is a process that searches exhaustively through a manually specified subset of the hyperparameter space of the targeted algorithm. Random search, on the other hand, selects a value for each hyperparameter independently using a probability distribution."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding best parameters for our SVC model\n",
    "param = {\n",
    "    'C': [0.1,0.8,0.9,1,1.1,1.2,1.3,1.4],\n",
    "    'kernel':['linear', 'rbf'],\n",
    "    'gamma' :[0.1,0.8,0.9,1,1.1,1.2,1.3,1.4]\n",
    "}\n",
    "grid_svc = GridSearchCV(svc, param_grid=param, scoring='accuracy', cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
       "                         'gamma': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
       "                         'kernel': ['linear', 'rbf']},\n",
       "             scoring='accuracy')"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "grid_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'C': 1.2, 'gamma': 0.9, 'kernel': 'rbf'}"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "#Best parameters for our svc model\n",
    "grid_svc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.90      0.99      0.94       273\n           1       0.89      0.34      0.49        47\n\n    accuracy                           0.90       320\n   macro avg       0.89      0.67      0.72       320\nweighted avg       0.90      0.90      0.88       320\n\n"
     ]
    }
   ],
   "source": [
    "#Let's run our SVC again with the best parameters.\n",
    "svc2 = SVC(C = 1.2, gamma =  0.9, kernel= 'rbf')\n",
    "svc2.fit(X_train, y_train)\n",
    "pred_svc2 = svc2.predict(X_test)\n",
    "print(classification_report(y_test, pred_svc2))"
   ]
  },
  {
   "source": [
    "#### Cross Validation\n",
    "We will be sing Cross Validation for Random Forest results.<br>\n",
    "Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9140194389763779"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "#Now lets try to do some evaluation for random forest model using cross validation.\n",
    "rfc_eval = cross_val_score(estimator = rfc, X = X_train, y = y_train, cv = 10)\n",
    "rfc_eval.mean()"
   ]
  },
  {
   "source": [
    "## Conclusion\n",
    "We observe that Random Forest Classifier with Cross Validation is able to give us 91.4% accuracy that puts it as the best approach for the prompt."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}