svm classification (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eaaf9a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import svm, datasets\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f16436db",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f864dadf",
   "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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
       "145           6.7          3.0           5.2          2.3  Virginica\n",
       "146           6.3          2.5           5.0          1.9  Virginica\n",
       "147           6.5          3.0           5.2          2.0  Virginica\n",
       "148           6.2          3.4           5.4          2.3  Virginica\n",
       "149           5.9          3.0           5.1          1.8  Virginica"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87ccf990",
   "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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal.length  sepal.width  petal.length  petal.width     variety\n",
       "0            5.1          3.5           1.4          0.2      Setosa\n",
       "1            4.9          3.0           1.4          0.2      Setosa\n",
       "2            4.7          3.2           1.3          0.2      Setosa\n",
       "3            4.6          3.1           1.5          0.2      Setosa\n",
       "4            5.0          3.6           1.4          0.2      Setosa\n",
       "..           ...          ...           ...          ...         ...\n",
       "95           5.7          3.0           4.2          1.2  Versicolor\n",
       "96           5.7          2.9           4.2          1.3  Versicolor\n",
       "97           6.2          2.9           4.3          1.3  Versicolor\n",
       "98           5.1          2.5           3.0          1.1  Versicolor\n",
       "99           5.7          2.8           4.1          1.3  Versicolor\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6225cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:, :2]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9f8eec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "h = (x_max / x_min)/100\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "X_plot = np.c_[xx.ravel(), yy.ravel()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9619464",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a898867",
   "metadata": {},
   "outputs": [],
   "source": [
    "Svc_classifier = svm.SVC(kernel='linear', C=C).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "420923dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Support Vector Classifier with linear kernel')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = Svc_classifier.predict(X_plot)\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(121)\n",
    "plt.contourf(xx, yy, Z, cmap=plt.cm.tab10, alpha=0.3)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.title('Support Vector Classifier with linear kernel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "293642fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87c9f45f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ana\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUEAAAFZCAYAAAAGi53HAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAjZElEQVR4nO3deZgU5bn+8e8DOkIQUIZFIMCg4BKXyCImMTEeFZMYI3rczc+IiJpEkHg8MSYGcUNFgwoBJKiIGjcwEY3mRFFcEjUGUFFcIIBIWARlR4YZkef3R9Vo09MM00z31HS/9+e6+urpt96qeqaYuanlrRpzd0REQtUo6QJERJKkEBSRoCkERSRoCkERCZpCUESCphAUkaApBKXWzOxQM3vOzNaYmZvZ1XlaT/94+UflY/nFJN5Ok5Kuo5ApBAuAmX3FzH5hZn83s9Vm9pmZrTCzv8aBsUs91LAL8CegOzAUOAf4c77XmxQzK4sDxs3sye302dXMPo77LKrDuk7K138osmOmwdINm5l1A54C9gWeBZ4BPgHaAsfGr1vc/fI817EvMBe4zN1vzfO6GgO7ApXuvjWf66qhhjLgA2BzXEsnd1+e1ucU4NG4zwp3L9vJdU0CznV324l5mwCfu/tnO7NugbzvQcjOM7OmwJPA3sAp7p6+5zXCzA4DDquHcvaK31fne0Xu/jnweb7XU0t/AU4m2vO9OW3aAOAtoDGwe30VFP9cfObuW9x9c32tt1jpcLhhGwjsB4zMEIAAuPsMdx+X2hYfXr1sZhvj18tm1i99XjNbZGYvmNn+ZvaUmW0ws3Vm9qiZ7ZXS7wXgxfjjPSmHiWU1nb+Ll70ore1bZvZ/ZvaRmW02s6XxYf03UvpkXKaZtTazsWb2HzOrjN/HmllpWr+q+Y82s/81swVmVmFm88zs3EzbsQYrgb8C56Wtoz3wPeCeTDOZWR8zmxSvc1O8bV82s5PTtxFwbvy1p7z6x22T4s9tzGyima0APgW+mjLPpJTlXRy3DU1bT4f40P09M/tKltugqGlPsGE7NX6fUNsZzOznwFjgfeB6wIH+wFQzu8jd05fVEXgBeAz4JfB14CKgBXBc3Gc48DLwm7iWv8ftH2fzzZjZfsA04CNgFLCCaA/ziHi9/6xh3pbAK0A3YCLwOtAD+BlwtJn1cfcNabPdADQF/gBUxH0nmdl8d385i9InEm2/b7r7q3HbuUR7q38k+s8q3cnA/sBk4EOgNJ7nz2b2Y3d/MO43nGhn5DtEe5tVXklbXtV2uw5oBmzMVKi7jzWzo4FhZva8u//DzBrFdTYHjnX3TbX/1gPg7no10BewClifRf89iX455gMtUtpbAAuADcAeKe2LiELy9LTljI3b909pOypu65/Wt3/cflSGel4AFqV8viTu22cH30e1ZRKFhQM/T+t7cdx+XYb53wBKUto7EoXhQ7XYlmXxMsYQ7Sx8BExImf4+8Gj89ZzU7zNua5ZhmV8hOq/6blr7pOhXMWMdk+I6/rid6Q5MyvBzsAhYHH89NO43KOmf6Yb40uFww9YCWJ9F/75Eewmj3f2L+eKvf0903urYtHmWufvktLbp8Xu37MrdoXXxe7/4hH42Tiba80zfk/0D0YWik6vNAePcvbLqg7svBeYRXeGuNXffAtwPnBFfqT+C6DTFxBrm+bTq63ieUqIQnA4cYGYtsqkB+F0W9a4BzgbaA/8HDAOecPcxWa4zCArBhm090SFMbXWN39/JMG1O/L53WvvCDH1Xxe+lGabVxcNEV7h/A6w2s+lm9isz61KLebsCc+NA+kL8eS7Vvy/Y/ve2M9/XRKL/lP6b6ILIMuDp7XU2s7ZmNiHlHN4nRCH+07jLHlmuf142nd39FWAEcHi83gFZri8YCsGGbQ7Qwswy/YJnkvUQC2q+Club5dU0xmqbc87uXuHufYl+MW+M130t8H76BYMc2d73lvV2cvf3gNeIDr9PB+7z6Cp29YWbGdFQpnOB+4AzgO8T7alXnQvM6nfPszyPZ2YlRBduAFoBnbOZPyQKwYbtT/F7phPvmSyI3w/MMO1r8XumvaO6qBoy0yrDtK4Z2nD3f7n7dXEgdiPaU7p+B+tZCOyXPjA8/rwvuf++MpkIfIPotELGq8KxQ4gu9Nzk7r9098nu/rS7P0s0nCZdPgbr3gj0Bi4nOqJ42Mya5WE9BU8h2LDdRXSo97+ZhrgAmFmv+IowRFcQPwUGm1nzlD7NgcFEF02m5bjGqsO0bc41mtlZQIe0ttYZ5l9CdLiWKURTTQXaUP0/hAvi9sdqV26dPAxcAwxx95oOT6v2ELfZ4zSzg8h87nJjPH1H26BWzOwHwKXAve5+C9GFon2JLvJIGg2RacDcfZOZnUB0x8hUM3uGKMRWEf3i/xfRIc/Ncf+1ZnY50dXd11LGj/Un2uO6yN3XkUPuPtfMngUuig8D3wQOJfpln090t0WV35rZcUQDwD8gCokfEQ0lSR+InO5m4DRgrJn1JLry2wM4n+g/ih3NX2fxBaara9H1PaLzspfHY/LmEoXQRUSnOHqm9f8nMAgYZ2ZPAZ8Br7n7B9nWGI9fvBf4d7xM3P0pMxsFDDGzp9394WyXW8wUgg2cu883sx5Ev0CnAFcSHY6tBmYSnXd6MKX/ODNbTjTmb1jcPBs42d2n5qnMc4iuPv84/vrvRAF9B9FQkypTia5Yng60A8qJflkvAO6uaQXuvi6+KnsNcCLR4OUVwHhgmFcfI5gYd//czH5IdEX3XKIr9nPir79O9RB8iCjQzyQK+kZE319WIRiPB7yf6ALO99w9dSzh5cCRwB/MbKcCtljp3mERCZrOCYpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgnliZt83s7lmNt/Mrki6nmIVP2NvpZnN2XFvqQsz62Rmz8fPJHzHzIYkXVMuaIhMHlj0ePh5RPeKLgFmAGe5+7uJFlaEzOxIojsu7nP3g5Kup5jFA7Hbu/vr8V1Is4CTCv3nWnuC+dEHmO/uC+NHOT0MZLztTerG3V+iHh75L+Duy9399fjrDUR3xnRMtqq6UwjmR0fgPymfl1AEPywiVSz6Q1Q9iJ6sU9AUgvmR6VFNOu8gRcHMdid6wtEvUh/eW6gUgvmxBOiU8vmrRA/hFCloZrYrUQA+4Nv541+FRiGYHzOA7mbWNX645ZnAEwnXJFIn8VOC7gbe8zz/7en6pBDMg/iR74OIHr/+HjDZ3TM98l7qyMweAl4leuDqEjM7P+maitgRRE8JOtrM3oxfxyddVF1piIyIBE17giISNIWgiARNISgiQVMIikjQFIIiEjSFYJ6Z2YVJ1xACbef6U2zbWiGYf0X1A9OAaTvXn6La1gpBEQlaQQ2W3rNlS++4V9uky8jKmnXr2LNly6TLyFqT5i2SLiErH3/8CW3atE66jCAU4rZ++62311dUVmb8RSyoP77eca+2/Gn87UmXEYT9juybdAkiOdOqTbuV25umw2ERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkE6+DT8nJ+P+kBfvqba/jOqeew/9EncMWI26r1W/LRCvY/+oSMr9/+bnQClReHLVu2cN31N9C12740adaC/Q88mDFjx+HuSZdWdIp5W++SdAGFbM269Yy97yHalLbiwH278cI/Z9TY/5gjvsH3jjxim7bOHdvns8Si9rOLB3PX3RO5YOAA+hx2GM9Me5bBQy5l9eo1XDX0yqTLKyrFvK0VgnXQtlUrXnxkEu3atGbL559zUN9+NfbvXtaFE/v+Vz1VV9xmz36Lu+6eyKVDLuHWkbcAMPD8AZx2xlnccNMILhg4gPbt9R9MLhT7ttbhcB2UlOxKuzats5pnc0UFmysq8lRROB6ZPAWAIZcM2qZ9yOBBVFRUMPXxJ5IoqygV+7bWnmA9uu/PTzD+gUcA6NKxPT85pR8/PumEhKsqTDNnzaJdu3Z06dJlm/Y+fQ6jUaNGzHr9jYQqKz7Fvq0TDUEz+z4wCmgM3OXuNyVZT740MuObPb/OMUd8gw7t2rJy1Woe/eszXDd6PEs/WsnlPx2QdIkFZ9ny5XTs2KFae0lJCaWlpSxdujSBqopTsW/rxELQzBoDY4G+wBJghpk94e7vJlVTvnRo15Z7fjd8m7bTjj+Ocy+7kkmPTuXMH/1AF0iyVF5eTovmLTJOa9KkCeXlm+u5ouJV7Ns6yXOCfYD57r7Q3SuBh4GarywUkcaNGzPg9JPZunUrr74xO+lyCk7Tpk2pqMx8bnXz5s00bdqknisqXsW+rZMMwY7Af1I+L4nbtmFmF5rZTDObuWbdunorrj50bNcWiIbaSHY6tG/PsmXLq7VXVlayatUqOnSofvgmO6fYt3WSIWgZ2qqNvHT3Ce7e291779myZT2UVX8+XBr9YJXuUVzfV33o1bMnH330EYsXL96mfcaMmWzdupVePXskVFnxKfZtnWQILgE6pXz+KrAsoVryau36DdXaKior+cODk9mlcWOO6F3YP0RJOP20UwEY/fux27SPHjOWkpISTup3YhJlFaVi39ZJXh2eAXQ3s67AUuBM4OwE69kpf3zsL2zY+Clb49uH5i1YxB33PwzA0d86nP326cqIO+5m+cqV9Dzoa+zVpjWr1qxl6rTpfLhkGb8YcA4d4sNiqb0ePQ5lwHn9ufX2UWzYuOGLuxgmT3mUYUN/W/CHaA1JsW/rxELQ3beY2SDgaaIhMhPd/Z2k6tlZEyc/xrIVK7/4/O78Bbw7fwEA7dq0Zr99unJE7x5MfupvTH7yb6zbsJEmu+3GAd335rKB/TnuyG8lVXrBGz9uDJ07deKee+9j0r33U1bWhVG3jWTwoIuTLq3oFPO2tkK6Afqg/br7n8bfnnQZQdjvyL5JlyCSM63atJu/evWa7pmm6bY5EQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGg7ZJ0Adlo0rwF+x3ZN+kyguAbliddQhA2zXs76RKCsHXj2u1O056giARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQah2CZtbHzC5Ia+tnZm+b2VIzuyH35YmI5Fc2e4LDgBOrPphZZ+AhYC9gHfArMzsvt+WJiORXNiH4deDllM9nAgYc6u5fA54BLsxhbSIieZdNCJYCH6V8/h7wkrsvjT8/AXTPVWEiIvUhmxBcC7QDMLPdgG8AL6VMd6BpzioTEakH2fyhpTeBgWb2LHAy0AR4OmV6V2BF7koTEcm/bELwOqLzfv8iOhc4zd1npkw/AXgth7WJiORdrUPQ3V8xs55E5wLXAQ9XTTOzUqKAfCznFYqI5FFWf3fY3ecB8zK0rwIuzVVRIiL1RXeMiEjQtrsnaGbTd2J57u7H1KEeEZF6VdPh8N5Ew15ERIrWdkPQ3cvqsQ4RkUTonKCIBE0hKCJBy2qIjJntCZwPHA7sSfUQ1YURESkotQ5BM+tC9BSZDkSDpVsAq/kyDD8BPs1DjSIieZPN4fD1wB7AMURPizHgDKIwvBHYAHwnx/WJiORVNiF4DHCnuz/Pl0NnzN03ufuVwNvAiFwXKCKST9k+T3BO/PVn8Xvqo7OmAX1zUZSISH3JJgQ/BlrFX28ANgNlKdNL0PMERaTAZBOC7xA9Yh93d6JHav3czDqbWRnRo/Xfz3mFIiJ5lE0IPg5808yq9vauJbpA8gGwIP76utyWV5i2bNnCddffQNdu+9KkWQv2P/BgxowdR/R/h+TL9JdeodGeZTTas4z5CxclXU5B2ripnOF3Pshpl13LPsf/hN0P/xEXXXvbTvcrBLUOQXcf5+77uHt5/Hk68E1gFHArcKS7P5GfMgvLzy4ezFVXX0PfY49hzOjbOeTggxk85FKuu15/lTRfKisrGfTLoTRr9pWkSyloq9au58a7HuKN9xfQ84Bude5XCLIaLJ0ufrL0zB12DMjs2W9x190TuXTIJdw68hYABp4/gNPOOIsbbhrBBQMH0L59+4SrLD4jx9zJ6jXrGPiTMxl1x8SkyylYe7Vuxby/TKJD21K2bPmcPY44qU79CoFum8uxRyZPAWDIJYO2aR8yeBAVFRVMfVw7y7n24eIlDB85hhuHXU7LFs2TLqeg7VayKx3aluasXyHI5o6R2vz36u5+fhbLOwFY6e4H1baOhm7mrFm0a9eOLl26bNPep89hNGrUiFmvv5FQZcVryBVXc8iB+9P/7NO4ZsTtSZcjBSabw+H+tejjRPcW18YkYAxwXxY1NHjLli+nY8cO1dpLSkooLS1l6dKlGeaSnfXk357jyaen89pzj2NmSZcjBSibCyON0l/ArsB+wJ3AP4nuI67t8l4iuve4qJSXl7NbyW4ZpzVp0oTy8s31XFHxKi/fzJArrub8c86g16EHJ12OFKg6nRN098/d/d/ufhGwCt02R9OmTamorMg4bfPmzTRt2qSeKypew0eOYe269Qwf+sukS5EClssLI/8HnJLD5QFgZhea2Uwzm/nxx5/kevE516F9e5YtW16tvbKyklWrVtGhQ/VDZcnesuUrGDlmAhecexZr161n/sJFzF+4iNVr1gGweMkyPvjwPwlXKYWgTkNk0pQCu+dweQC4+wRgAkDv3r0a/GjjXj17Mu3Z51i8eDGdO3f+on3GjJls3bqVXj17JFhd8Vj5ySdUVFRy86jx3DxqfLXpx/Y7m5YtmrPmw7cTqE4KSZ1D0Mz2AI4l+rvDs+q6vEJ3+mmnctPNtzD692P53S1fnh0YPWYsJSUlnNTvxASrKx5dO3di8qRx1dqnTH2KKVOfYvSIa+j8Ve11y45lM0RmK9v/63NGdJHjf7JY3kPAUUBrM1sCDHP3u2s7f0PVo8ehDDivP7fePooNGzfQ57DDeGbas0ye8ijDhv5Wh8M50rJlC07td3y19jnvzQXg+8d+l257l9VzVcVh/JQnWbfhU7Zu3QrAnPmLGDHxEQB++J0+HNS9a1b9Grps9gTvo3oIOlH4zQMecvcNtV2Yu5+VxboLyvhxY+jcqRP33Hsfk+69n7KyLoy6bSSDB12cdGkiOzT6gcdYvHzlF59nz13I7LkLAejYtvSLcKttv4bOCumm/t69e/nM115Nuowg+IbqF3ck9zbN0znL+tDx2z+av7bSu2eaVuurw2Z2lZlt984OMzvQzK7amQJFRJKSzRCZq4FDaph+EDCsTtWIiNSzXI4TbAJsyeHyRETyrsYLI2bWgugvzFUpNbPOGbq2An4MaHSqiBSUHV0dvhSoOs/nwO3xKxMDLs9JVSIi9WRHIfhC/G5EYfgY8FZaHwc2Av9091dyWp2ISJ7VGILu/iLwIoCZdQHGu/tr9VGYiEh9qPVgaXc/L5+FiIgkIZtxgheb2bM1TH/GzC7KTVkiIvUjmyEy/YF/1zB9HjCgTtWIiNSzbEKwO1DTPT7vxH1ERApGNiG4K9GA6O1psoPpIiINTjYhOA/oW8P044AFdStHRKR+ZROCDwHHmdl1ZlZS1Whmu5rZNUQh+GCuCxQRyadsnid4G/AD4ErgZ2b2PtFA6QOIbpv7OzAy5xWKiORRNn9y8zOivb0rgCVAD6An0f3ClwPHEN1ZIiJSMLJ6ioy7f+buN7v7oe7eLH71AJ4HRgPL8lKliEie7PQfWjKzVsD/A84nepagEV08EREpGFk/T9DMvmdmjwBLic4TlgDXAAe7+/45rk9EJK9qtSdoZl2B84Bzga8CHwOPAmcDV7r7n/NWoYhIHtW4J2hmZ5vZc0S3y10OzAROBjoS7f3pQoiIFLQd7Qn+EVgI/AJ40N1XV00ws8L5M3UiItuxo3OClUAZ0A/4gZk1zXtFIiL1aEchuBfRXmApcD+wwszuNrMj0aGwiBSBGkPQ3de6+xh37wn0JgrCk4jGBf6D6I6RlvkuUkQkX7K5Y+R1d78Y6ACcQ/ToLIC7zOxNM/utmR2YjyJFRPIl63GC7l7h7g+6+zHAPsBwYE/gWmB2jusTEcmrOv3xdXdf5O5XEV08OR7QeEERKSg7fdtcKnd34G/xS0SkYNRpT1BEpNApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphDMgy1btnDd9TfQtdu+NGnWgv0PPJgxY8fh7kmXVtSmv/QKjfYso9GeZcxfuCjpcgrSxk3lDL/zQU677Fr2Of4n7H74j7jo2tt2ul8hUAjmwc8uHsxVV19D32OPYczo2znk4IMZPORSrrv+hqRLK1qVlZUM+uVQmjX7StKlFLRVa9dz410P8cb7C+h5QLc69ysEuyRdQLGZPfst7rp7IpcOuYRbR94CwMDzB3DaGWdxw00juGDgANq3b59wlcVn5Jg7Wb1mHQN/ciaj7piYdDkFa6/WrZj3l0l0aFvKli2fs8cRJ9WpXyHQnmCOPTJ5CgBDLhm0TfuQwYOoqKhg6uNPJFFWUftw8RKGjxzDjcMup2WL5kmXU9B2K9mVDm1Lc9avECQWgmbWycyeN7P3zOwdMxuSVC25NHPWLNq1a0eXLl22ae/T5zAaNWrErNffSKiy4jXkiqs55MD96X/2aUmXIgUoycPhLcBl7v66mTUHZpnZNHd/N8Ga6mzZ8uV07NihWntJSQmlpaUsXbo0gaqK15N/e44nn57Oa889jpklXY4UoMT2BN19ubu/Hn+9AXgP6JhUPblSXl7ObiW7ZZzWpEkTyss313NFxau8fDNDrria8885g16HHpx0OVKgGsQ5QTMrA3oAryVcSp01bdqUisqKjNM2b95M06ZN6rmi4jV85BjWrlvP8KG/TLoUKWCJXx02s92BPwG/cPf1GaZfCFwI0Llz53quLnsd2rfn7TnvVGuvrKxk1apVdOhQ/VBZsrds+QpGjpnAkJ8OYO269axdF/3orF6zDoDFS5bRuHFjunbplGSZUgASDUEz25UoAB9w9z9n6uPuE4AJAL1792rwo4179ezJtGefY/HixduE9owZM9m6dSu9evZIsLrisfKTT6ioqOTmUeO5edT4atOP7Xc2LVs0Z82HbydQnRSSxELQorPYdwPvufutSdWRa6efdio33XwLo38/lt/dMuKL9tFjxlJSUsJJ/U5MsLri0bVzJyZPGletfcrUp5gy9SlGj7iGzl/VXrfsWJJ7gkcA5wBvm9mbcdtv3P2vyZVUdz16HMqA8/pz6+2j2LBxA30OO4xnpj3L5CmPMmzob3U4nCMtW7bg1H7HV2uf895cAL5/7HfptndZPVdVHMZPeZJ1Gz5l69atAMyZv4gREx8B4Iff6cNB3btm1a+hSywE3f0fQFGOaRg/bgydO3XinnvvY9K991NW1oVRt41k8KCLky5NZIdGP/AYi5ev/OLz7LkLmT13IQAd25Z+EW617dfQWSHd1N+7dy+f+dqrSZcRBN+wPOkSgrBpns5Z1oeO3/7R/LWV3j3TtAYxREZEJCkKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIJm7p50DbVmZh8DHyZdR5ZaA58kXUQAtJ3rTyFu6y7u3ibThIIKwUJkZjPdvXfSdRQ7bef6U2zbWofDIhI0haCIBE0hmH8Tki6g0JhZmZm5mV1dU1uandrOtViuVFdUP9MKwTxz94L5gTGzo+JASH1tNLNZZjbEzBonXeP21LSd46C72swOrceSilYh/UzXxi5JFyAN0kPAXwEDOgD9gduBA4ELE6rpQ6ApsGUn5i0DhgGLgDdzuFwpAgpByeR1d/9j1QczuwN4DxhoZkPdfUX6DGbW3N035Ksgj4YxbC6U5Urh0OGw7JC7rwdeJdoz3NvMFpnZC2bWw8yeNrN1wFtV/c2su5ndb2bLzawy7n+LmTVLX7aZfdvMXjazcjNbYWZjgN0z9NvuuTszO8XMnjeztWa2yczmmtloMysxs/7A83HXe1IO81+oablmtouZ/crM3jWzzWa2ysweM7ODt1eXmZ1gZjPi/svj73mXtP4HmtkUM1tqZhVm9lFc+w9r8U8heaA9QdkhMzOgW/yxapBsZ2A6MAX4E3FwmVmvuH0t8AdgKfB14BLgCDP7rrt/Fvc9HHgW2ACMiOc5E7gvi9qGA78B3gVuA5YD+wCnAFcBLwE3xH0mAH+PZ622N5vmAeB0YBpwB7AXcDHwqpl9x93fSOt/PPBzYDwwEegH/C+wJl4/ZlZKtG2I+31INPC4N3A48FRtv2/JIXfXSy/cHeAowInCozXQBjgEuDNufzXutyj+PDDDMmYD7wPN09pPjufpn9L2ClAJ7JvSVgL8K+57dUp7WYa2PnHbdKBJ2vqML28GOCp93TtYbt+47ZGqZcTthxCdO/x7hvk/BcrS1j8HWJ7SdmLc9/Sk/631+vKlw2HJ5BrgY2AlUagNAJ4ATkrpsxq4J3Wm+FDxEOBBYDcza131Av5BFBTHxX3bAt8EHnf3eVXLcPdKoj262vhx/P5rd9/mvJ7HarmcdCfH78NTl+HubwFPAt82s/RbsKa6+6LU9RMdhu9lZlWH9+vi9x+YWYudrE1yTCEomUwg2hs6liio2rh7P9/2gsgCd/88bb4D4veqEE19rQSaAe3iPnvH7+9nWP+7tayzO9Ge1exa9q+trsBWootB6eak9Em1MEPfVfF7KYC7v0h0qN8f+CQ+F3qNmX2tzhXLTtM5Qcnk3+7+7A76bMrQZvH7SOBv25lvTVrfTHtrlqEtE9vO/HVV2/WnSv8PIePy3P1cM7uF6Bzit4HLgCvN7BfuPmYn1it1pBCUXPp3/P55LUJ0Qfx+QIZpmdoymQt8n+gQ/F819Ms2KBcA34vreCttWtVe2wdZLvPLYtznEO1R3mxmewCvATeZ2dg6HMLLTtLhsOTSG0S/3D81s73TJ8bDTloBuPtK4J9APzPbN6VPCXBpLdf3YPx+g5ntlmF9VXtgG+P3VrVc7tT4/dcpy8DMDiK6uPEPd/+4lstKraeVmW3zO+fua4kC9StAk2yXKXWnPUHJGXd3MzuH6GrtW2Y2EXiH6Be8G/DfwK+BSfEs/wO8ALxsZmP5cohMrX4u3f1fZjYC+BUwy8weAT4iOl93KtHV47VE5xg3AD83s01x20p3n76d5U4zs8lxLXua2ZN8OURmM9Fwn53xE+BSM3sMmA98BnyXaK9zsruX7+RypQ4UgpJT7v6mmfUgCrsTgZ8SBdAiovB7LqXvq2bWF7gJuAJYTzTu8A7g7Vqu7wozmw0MAi4nOrr5D9Ftf5viPuVmdiZwPdHtf7sBL/LlmL1Mfgy8TnQRYyTRle0XgaHuXqvaMngB6AGcALQnOo/4AdF4Qp0PTIgeqioiQdM5QREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgvb/AeiLasPt+PXMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# neural_network\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "classifier = MLPClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "conf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred)\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(conf_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
    "for i in range(conf_matrix.shape[0]):\n",
    "    for j in range(conf_matrix.shape[1]):\n",
    "        ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
    " \n",
    "plt.xlabel('Predictions', fontsize=18)\n",
    "plt.ylabel('Actuals', fontsize=18)\n",
    "plt.title('Confusion Matrix', fontsize=18)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "213b2a7a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        15\n",
      "           1       0.50      0.27      0.35        15\n",
      "           2       0.50      0.73      0.59        15\n",
      "\n",
      "    accuracy                           0.67        45\n",
      "   macro avg       0.67      0.67      0.65        45\n",
      "weighted avg       0.67      0.67      0.65        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# neural_network\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb5895fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.3333333333333333\n",
      "Mean Squared Error: 0.3333333333333333\n",
      "Root Mean Squared Error: 0.5773502691896257\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "874a2a3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "689dd855",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0786823",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}