WineNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine Dataset: PCA & Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1 = pd.read_csv('./Metodi numerici per la DS/Dataset/Wine-Dataset/winequality-red.csv',delimiter=';')\n",
    "dataset1[\"Class\"] = 0\n",
    "dataset2 = pd.read_csv('./Metodi numerici per la DS/Dataset/Wine-Dataset/winequality-white.csv',delimiter=';')\n",
    "dataset2[\"Class\"] = 1\n",
    "dataset = pd.concat([dataset1, dataset2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Class</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>0</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>0</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>0</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>0</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>0</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  Class  \n",
       "0      9.4        5      0  \n",
       "1      9.8        5      0  \n",
       "2      9.8        5      0  \n",
       "3      9.8        6      0  \n",
       "4      9.4        5      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribuzione dei dati per feature \"quality\"\n",
    "Da quanto segue si evince che il numero di vini è diverso per ogni qualità, pertanto si può affermare che il dataset è sbilanciato: ci sono molti vini di qualità 5, 6 e 7, e pochi vini di qualità 3, 4, 5, 8, 9.\n",
    "Pertanto si prevede che la retta approssimerà meglio la qualità dei vini 5, 6 e 7, perchè è lì che è presente la maggior parte dei dati."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    681\n",
       "6    638\n",
       "7    199\n",
       "4     53\n",
       "8     18\n",
       "3     10\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot dei vini per classe\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data to plot\n",
    "labels = ['Red', 'White']\n",
    "sizes = [len(dataset1), len(dataset2)]\n",
    "colors = ['red', 'gold']\n",
    "#explode = (0.1, 0, 0, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(sizes, labels=labels, colors=colors,autopct='%1.1f%%', shadow=True, startangle=140, textprops={'color':\"w\"}) #Con tema bianco, rimuovere l'attributo textprops\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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"
    }
   ],
   "source": [
    "ax = dataset['quality'].plot.hist(bins=6, alpha=0.5)\n",
    "ax = dataset2['quality'].plot.hist(bins=6, alpha=0.5)\n",
    "ax = dataset1['quality'].plot.hist(bins=5, alpha=0.5)\n",
    "ax.legend([\"Total Wine\", \"White Wine\", 'Red Whine']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
      "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
      "       'pH', 'sulphates', 'alcohol', 'Class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# test_size: what proportion of original data is used for test set\n",
    "x = dataset.drop(['quality'],axis=1)\n",
    "y = dataset['quality'].values\n",
    "x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.3, random_state=1, shuffle=True)\n",
    "\n",
    "print(x_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "# Fit on training set only.\n",
    "scaler.fit(x_train)\n",
    "# Apply transform to both the training set and the test set.\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# Make an instance of the Model\n",
    "#pca = PCA(0.80) #Si imposta il modello fermandosi quando si ha il 95% della varianza spiegata\n",
    "pca = PCA()\n",
    "pca.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = pca.explained_variance_ratio_\n",
    "x = np.arange(len(y)) + 1\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y, \"o-\")\n",
    "plt.title(\"Variance for each component\")\n",
    "plt.xticks(x, [\"Comp.\"+str(i) for i in x], rotation=90)\n",
    "plt.ylabel(\"Variance\")\n",
    "\n",
    "y2 = np.cumsum((pca.explained_variance_ratio_))\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, y2, \"o-\")\n",
    "plt.xticks(x, [\"Comp.\"+str(i) for i in x], rotation=90)\n",
    "plt.title(\"Cumulative Variance\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.24793237  0.35865268 -0.12292603 -0.24574352  0.2908113  -0.34232161\n",
      "  -0.41333815  0.14026993  0.19418423  0.28528493  0.02494674 -0.47159346]\n",
      " [ 0.27899132  0.04314754  0.20445899  0.39748782  0.24972249  0.16040544\n",
      "   0.19191568  0.55832588 -0.20314256  0.12742053 -0.46950214 -0.07691746]\n",
      " [ 0.45317741 -0.29051596  0.59064288 -0.18301375  0.00487614 -0.15759546\n",
      "  -0.12684385 -0.16098756 -0.42666955  0.10602437  0.25691363  0.04145509]\n",
      " [-0.16701457 -0.19160741  0.25498404 -0.14146275  0.22885918  0.39200672\n",
      "   0.21374524 -0.06955815  0.41986973  0.63328366  0.12739328 -0.01500562]\n",
      " [ 0.15999533 -0.12595807  0.15921678  0.35903779 -0.64788914 -0.15621677\n",
      "  -0.12513323  0.30179692  0.43927377  0.09018652  0.21411963 -0.08019944]\n",
      " [ 0.27310678  0.51695337 -0.17002794  0.13147141 -0.14675653  0.45764157\n",
      "   0.19573579 -0.04323431 -0.25146863  0.0873562   0.50587665 -0.11850699]\n",
      " [-0.2881057  -0.14791403 -0.31247782  0.41460467  0.04396416 -0.30732542\n",
      "  -0.13138375 -0.0206199  -0.40204529  0.54951544  0.17101453  0.13658289]\n",
      " [-0.29552962  0.23405226  0.41578572  0.41303308  0.4550527  -0.15055203\n",
      "  -0.06233293  0.00992797  0.16165373 -0.31619659  0.39085752  0.04246239]]\n",
      "[3.81801279 2.55298781 1.53822889 0.96709133 0.7273649  0.62356778\n",
      " 0.53238464 0.49331644]\n",
      "[0.31809776 0.2127022  0.12815755 0.08057322 0.06060041 0.05195255\n",
      " 0.04435563 0.04110066]\n",
      "[131.74477661 107.73060187  83.62289484  66.30533316  57.50305061\n",
      "  53.24226806  49.19573728  47.35627254]\n"
     ]
    }
   ],
   "source": [
    "#Ottenuto il grafico, richiamo PCA sul numero delle componenti prefissate dall'utente e ne eseguo il fit\n",
    "pca = PCA(n_components=8)\n",
    "pca.fit(x_train)\n",
    "x_train = pca.transform(x_train)\n",
    "x_test = pca.transform(x_test)\n",
    "\n",
    "#Stampa dei valori della PCA\n",
    "print(pca.components_)\n",
    "print(pca.explained_variance_)\n",
    "print(pca.explained_variance_ratio_)\n",
    "print(pca.singular_values_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressione Logistica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all parameters not specified are set to their defaults\n",
    "# default solver is incredibly slow which is why it was changed to 'lbfgs'\n",
    "logisticRegr = LogisticRegression(solver = 'lbfgs', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=-1, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict for more observation\n",
    "y_predicted = logisticRegr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score:  0.5261538461538462\n"
     ]
    }
   ],
   "source": [
    "print(\"Score: \",logisticRegr.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.0707186   0.04937831  2.49838416  3.04209319  1.63154443 -0.44623462\n",
      " -4.70444686]\n",
      "COEFFICIENTI: \n",
      " [[ 2.03242964e-01  3.72969694e-01 -2.56536968e-01 -7.86862686e-02\n",
      "  -6.40776415e-01  5.05308498e-01 -3.99469525e-01  4.25047573e-01]\n",
      " [ 2.33757506e-01  1.71419317e-01 -3.33349942e-01 -6.97829428e-01\n",
      "  -6.76225784e-01 -6.17650116e-01 -2.45407610e-01  2.24249901e-01]\n",
      " [ 1.80986499e-01  5.33871802e-01 -3.21201865e-01 -1.66780873e-01\n",
      "  -6.42975842e-01 -5.95407503e-01 -2.16647825e-01 -3.12843173e-01]\n",
      " [ 7.38084804e-02  1.44292893e-01  2.13572716e-03  1.42146268e-01\n",
      "  -2.46454320e-01 -3.18811973e-01  2.97855423e-01 -3.44451904e-02]\n",
      " [-7.04453521e-02 -2.44035689e-01  2.88263150e-01  3.07036965e-01\n",
      "   4.39745503e-01  2.55247716e-04  4.83473171e-01 -2.05229614e-01]\n",
      " [-2.78976335e-01 -2.90580750e-01  2.61832646e-01  4.45263285e-01\n",
      "   3.41182157e-01  4.51828200e-01  5.60804384e-01  2.53468587e-01]\n",
      " [-3.42373762e-01 -6.87937266e-01  3.58857252e-01  4.88500512e-02\n",
      "   1.42550470e+00  5.74477646e-01 -4.80608018e-01 -3.50248083e-01]]\n"
     ]
    }
   ],
   "source": [
    "#(X_test, logisticRegr.coef_ * X_test + logisticRegr.intercept_)\n",
    "print(logisticRegr.intercept_)\n",
    "print(\"COEFFICIENTI: \\n\",logisticRegr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisi dei Residui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.6748717948717948\n",
      "R score: 0.10011856048425705\n",
      "Mean Absolute Error: 0.5384615384615384\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predicted)\n",
    "r = r2_score(y_test, y_predicted)\n",
    "mae = mean_absolute_error(y_test,y_predicted)\n",
    "print(\"Mean Squared Error:\",mse)\n",
    "print(\"R score:\",r)\n",
    "print(\"Mean Absolute Error:\",mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisi Performance\n",
    "Dal codice seguente si evince che i valori predetti ricadono all'interno di un intervallo discreto compreso tra 5 e 7, a differenza dei valori presenti nel test set, che ha valori in un intervallo compreso tra 3 e 9.\n",
    "Questo accade per le motivazioni esposte nel paragrafo \"Distribuzione dei dati per feature quality\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.809744</td>\n",
       "      <td>5.735897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.866222</td>\n",
       "      <td>0.566278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual    Predicted\n",
       "count  1950.000000  1950.000000\n",
       "mean      5.809744     5.735897\n",
       "std       0.866222     0.566278\n",
       "min       3.000000     4.000000\n",
       "25%       5.000000     5.000000\n",
       "50%       6.000000     6.000000\n",
       "75%       6.000000     6.000000\n",
       "max       9.000000     7.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_predicted})\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si riporta inoltre un istogramma per confrontare i primi 40 valori reali con quelli predetti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.head(40)\n",
    "df.plot(kind='bar',figsize=(15,8))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}