SVM_employee_churn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "87539237",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import sklearn.preprocessing\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import ConfusionMatrixDisplay , classification_report , accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "17208d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/Tedi/Downloads/HR.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cd7844fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_evaluation          0\n",
       "number_project           0\n",
       "average_montly_hours     0\n",
       "time_spend_company       0\n",
       "Work_accident            0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "sales                    0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6ea63450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       14999\n",
       "last_evaluation          14999\n",
       "number_project           14999\n",
       "average_montly_hours     14999\n",
       "time_spend_company       14999\n",
       "Work_accident            14999\n",
       "left                     14999\n",
       "promotion_last_5years    14999\n",
       "sales                    14999\n",
       "salary                   14999\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe5a7bd",
   "metadata": {},
   "source": [
    "Pentru a putea folosi un Support Vector Machine este nevoie sa codificam variabilele categoriale. Mai intai vom verifica valorile luate de fiecare variabila de acest tip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cb6ce29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sales', 'accounting', 'hr', 'technical', 'support', 'management',\n",
       "       'IT', 'product_mng', 'marketing', 'RandD'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sales'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "80e663a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['low', 'medium', 'high'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "04a82a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder() #transformam variabilele categoriale in forma numerica\n",
    "df[\"sales\"]=le.fit_transform(df[\"sales\"])\n",
    "df[\"salary\"]=le.fit_transform(df[\"salary\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "439dd76e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
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       "      <th>salary</th>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0      7   \n",
       "1                   6              0     1                      0      7   \n",
       "2                   4              0     1                      0      7   \n",
       "3                   5              0     1                      0      7   \n",
       "4                   3              0     1                      0      7   \n",
       "\n",
       "   salary  \n",
       "0       1  \n",
       "1       2  \n",
       "2       2  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #verificam daca codificarea a functionat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e33519",
   "metadata": {},
   "source": [
    "Vom nota cu X variabilele explicative si cu y variabila dependenta, apoi vom imparti setul de date in parti pentru testare si pentru antrenare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6529a546",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('left', axis = 1).copy()\n",
    "y = df['left'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "508d8e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bd685a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler() #folosim standard scaler pentru a standardiza variabilele\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "461ddbcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['CLASA'] = np.where(df['left'] > 0, \"Plecat\", \"Ramas\")\n",
    "df['Color'] = np.where(df['left'] > 0, \"sandybrown\", \"lightskyblue\")\n",
    "plt.scatter(df['satisfaction_level'], df['last_evaluation'], linewidth=1, color=df['Color'])\n",
    "plt.xlabel('satisfaction_level')\n",
    "plt.ylabel('last_evaluation')\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2eeae2",
   "metadata": {},
   "source": [
    "Asa cum am vazut si mai sus, datele nu sunt separabile in mod liniar, deci vom folosi kerneluri de tip radial, polinomial si sigmoid. Valoarea default a parametrului de ajustare este de 1.0. Dupa identificarea celui mai eficient model, vom utiliza metoda grid search pentru a identifica cea mai buna combinatie de parametrii. Intai vom incerca diferite functii polinomiale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f27a0288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(degree=2, kernel=&#x27;poly&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(degree=2, kernel=&#x27;poly&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(degree=2, kernel='poly', random_state=0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_poly_2 = SVC(kernel = 'poly', degree = 2, random_state = 0)\n",
    "model_svm_poly_2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "52697c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "yp2_pred = model_svm_poly_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9d81965f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.938\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_poly_2 = accuracy_score(y_test, yp2_pred)\n",
    "print(accuracy_svm_poly_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1c08027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(degree=4, kernel=&#x27;poly&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(degree=4, kernel=&#x27;poly&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(degree=4, kernel='poly', random_state=0)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_poly_4 = SVC(kernel = 'poly', degree = 4, random_state = 0)\n",
    "model_svm_poly_4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f6ef07c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "yp4_pred = model_svm_poly_4.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "df673295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9535555555555556\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_poly_4 = accuracy_score(y_test, yp4_pred)\n",
    "print(accuracy_svm_poly_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1d41d7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(degree=6, kernel=&#x27;poly&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(degree=6, kernel=&#x27;poly&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(degree=6, kernel='poly', random_state=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_poly_6 = SVC(kernel = 'poly', degree = 6, random_state = 0)\n",
    "model_svm_poly_6.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "330c0994",
   "metadata": {},
   "outputs": [],
   "source": [
    "yp6_pred = model_svm_poly_6.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d93b5675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9531111111111111\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_poly_6= accuracy_score(y_test, yp6_pred)\n",
    "print(accuracy_svm_poly_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9ec2c9f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(degree=8, kernel=&#x27;poly&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(degree=8, kernel=&#x27;poly&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(degree=8, kernel='poly', random_state=0)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_poly_8 = SVC(kernel = 'poly', degree = 8, random_state = 0)\n",
    "model_svm_poly_8.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "da2a92a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "yp8_pred = model_svm_poly_8.predict(X_test) # performantele scad odata cu cresterea gradului functiei mai mult de 4-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d8a520a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9464444444444444\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_poly_8= accuracy_score(y_test, yp8_pred)\n",
    "print(accuracy_svm_poly_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7aee6ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(degree=12, kernel=&#x27;poly&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(degree=12, kernel=&#x27;poly&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(degree=12, kernel='poly', random_state=0)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_poly_12 = SVC(kernel = 'poly', degree = 12, random_state = 0)\n",
    "model_svm_poly_12.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "661f833c",
   "metadata": {},
   "outputs": [],
   "source": [
    "yp12_pred = model_svm_poly_12.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "af18db7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9282222222222222\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_poly_12= accuracy_score(y_test, yp12_pred) # pe baza rezultatelor de pana acum vom incerca cu gradul 5\n",
    "print(accuracy_svm_poly_12) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "be1409fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(degree=5, kernel=&#x27;poly&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(degree=5, kernel=&#x27;poly&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(degree=5, kernel='poly', random_state=0)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_poly_5 = SVC(kernel = 'poly', degree = 5, random_state = 0)\n",
    "model_svm_poly_5.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8ea1ccef",
   "metadata": {},
   "outputs": [],
   "source": [
    "yp5_pred = model_svm_poly_5.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8a6ea734",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9595555555555556\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_poly_5= accuracy_score(y_test, yp5_pred) # acesta este cel mai eficient model\n",
    "print(accuracy_svm_poly_5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6213d80d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3372   62]\n",
      " [ 120  946]]\n"
     ]
    }
   ],
   "source": [
    "conf_matrix_svm_poly5 = confusion_matrix(y_test, yp5_pred) #I N T E RPRETARE\n",
    "print(conf_matrix_svm_poly5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9753d42c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.97      3434\n",
      "           1       0.94      0.89      0.91      1066\n",
      "\n",
      "    accuracy                           0.96      4500\n",
      "   macro avg       0.95      0.93      0.94      4500\n",
      "weighted avg       0.96      0.96      0.96      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_rep_svm_poly5= classification_report(y_test, yp5_pred)\n",
    "print(classification_rep_svm_poly5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "abc6a418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_rbf = SVC(kernel = 'rbf', random_state = 0) \n",
    "model_svm_rbf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cc7f9ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "yrbf_pred = model_svm_rbf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "11925a02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3345   89]\n",
      " [ 100  966]]\n"
     ]
    }
   ],
   "source": [
    "conf_matrix_svm_rbf = confusion_matrix(y_test, yrbf_pred) #I N T E RPRETARE\n",
    "print(conf_matrix_svm_rbf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "783fbb2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.958\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_rbf = accuracy_score(y_test, yrbf_pred)\n",
    "print(accuracy_svm_rbf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "aa3332a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97      3434\n",
      "           1       0.92      0.91      0.91      1066\n",
      "\n",
      "    accuracy                           0.96      4500\n",
      "   macro avg       0.94      0.94      0.94      4500\n",
      "weighted avg       0.96      0.96      0.96      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_rep_svm_rbf= classification_report(y_test, yrbf_pred)\n",
    "print(classification_rep_svm_rbf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2ea32593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;sigmoid&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;sigmoid&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='sigmoid', random_state=0)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_sig = SVC(kernel = 'sigmoid', random_state = 0) \n",
    "model_svm_sig.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "01f02660",
   "metadata": {},
   "outputs": [],
   "source": [
    "ysig_pred = model_svm_sig.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "32bf0cc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2425 1009]\n",
      " [1019   47]]\n"
     ]
    }
   ],
   "source": [
    "conf_matrix_svm_sig = confusion_matrix(y_test, ysig_pred) #I N T E RPRETARE\n",
    "print(conf_matrix_svm_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7d3f53f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5493333333333333\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_sig = accuracy_score(y_test, ysig_pred)\n",
    "print(accuracy_svm_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "673bb4d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.71      0.71      3434\n",
      "           1       0.04      0.04      0.04      1066\n",
      "\n",
      "    accuracy                           0.55      4500\n",
      "   macro avg       0.37      0.38      0.37      4500\n",
      "weighted avg       0.55      0.55      0.55      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_rep_svm_sig= classification_report(y_test, ysig_pred)\n",
    "print(classification_rep_svm_sig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87ea413",
   "metadata": {},
   "source": [
    "Din punct de vedere al acuratetii cele mai bune performate le obtine modelul cu kernel radial, urmat de cel polinomial de grad 5. Astfel, vom utiliza grid search pentru modelul cu kernel radial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d22c1e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'C': [0.1, 0.5, 1, 2, 3, 4, 5, 10, 20],\n",
    "    'gamma': [0.1, 0.5, 0.75, 1, 2, 3, 4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "85dee65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_rbf = SVC(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b079f91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 0.5, 1, 2, 3, 4, 5, 10, 20],\n",
       "                         &#x27;gamma&#x27;: [0.1, 0.5, 0.75, 1, 2, 3, 4]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 0.5, 1, 2, 3, 4, 5, 10, 20],\n",
       "                         &#x27;gamma&#x27;: [0.1, 0.5, 0.75, 1, 2, 3, 4]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 0.5, 1, 2, 3, 4, 5, 10, 20],\n",
       "                         'gamma': [0.1, 0.5, 0.75, 1, 2, 3, 4]})"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(svm_rbf, param_grid) #numarul de validari incrucisate default este de 5\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "085a369d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cea mai buna combinatie de parametri: {'C': 2, 'gamma': 2}\n"
     ]
    }
   ],
   "source": [
    "print(\"Cea mai buna combinatie de parametri:\", grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9cb682d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cea mai mare acuratete: 0.9778070282901155\n"
     ]
    }
   ],
   "source": [
    "print(\"Cea mai mare acuratete:\", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "00c16bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=2.0, gamma=2.0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=2.0, gamma=2.0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=2.0, gamma=2.0)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svm_rbf22 = SVC(kernel = 'rbf', C = 2.0, gamma = 2.0) \n",
    "model_svm_rbf22.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b202c3e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "yrbf22_pred = model_svm_rbf22.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6126a83f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3404   30]\n",
      " [  57 1009]]\n"
     ]
    }
   ],
   "source": [
    "conf_matrix_svm_rbf22 = confusion_matrix(y_test, yrbf22_pred) #I N T E RPRETARE\n",
    "print(conf_matrix_svm_rbf22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b366ea48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9806666666666667\n"
     ]
    }
   ],
   "source": [
    "accuracy_svm_rbf22 = accuracy_score(y_test, yrbf22_pred)\n",
    "print(accuracy_svm_rbf22) #valoarea obtinuta de aceasta combinatie de parametrii se afla in jurul 0.98"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "acb2c0dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99      3434\n",
      "           1       0.97      0.95      0.96      1066\n",
      "\n",
      "    accuracy                           0.98      4500\n",
      "   macro avg       0.98      0.97      0.97      4500\n",
      "weighted avg       0.98      0.98      0.98      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_rep_svm_rbf22= classification_report(y_test, yrbf22_pred)\n",
    "print(classification_rep_svm_rbf22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b86aea03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = grid_search.cv_results_['mean_test_score'].reshape(len(param_grid['C']), len(param_grid['gamma']))\n",
    "plt.imshow(scores, interpolation='nearest', cmap=plt.cm.hot)\n",
    "plt.xlabel('Cost')\n",
    "plt.ylabel('gamma')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(len(param_grid['gamma'])), param_grid['gamma'], rotation=45)\n",
    "plt.yticks(np.arange(len(param_grid['C'])), param_grid['C'])\n",
    "plt.title('Acuratetea modelului SVM in functie de parametrii')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0922a5e",
   "metadata": {},
   "source": [
    "Putem observa ca cea mai mare acuratete se intalneste atunci cand parametrul de Cost ia valoarea 2 iar parametrul gamma ia valori cuprinse intre 2 si 10."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}