PCA-ML-File.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. Import the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. Import the dataset\n",
    "dataset = pd.read_csv('Wine.csv')\n",
    "X = dataset.iloc[: , :-1].values\n",
    "y = dataset.iloc[ : ,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. Split the dataset into test set and training set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train , X_test , y_train , y_test = train_test_split(X, y , test_size = 0.2 ,random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2, 1, 2, 2, 1, 3, 2, 2, 3, 3, 1, 2, 3, 2, 1, 1, 2, 1, 2, 1,\n",
       "       1, 2, 2, 2, 2, 2, 2, 3, 1, 1, 2, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#4. Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#5. Applying PCA\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components = 2)\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6 Train Logistic Regression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state= 0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#7. Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = classifier.predict(X_test)\n",
    "cm = confusion_matrix(y_test , y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  0,  0],\n",
       "       [ 1, 15,  0],\n",
       "       [ 0,  0,  6]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9722222222222222"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#8 Accuracy of the model\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test , y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#9 Visualizing Training set result\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set , y_set = X_train , y_train\n",
    "X1,X2= np.meshgrid(np.arange(start = X_set[: ,0].min() -1,\n",
    "                             stop = X_set[: , 0].max()+1,step =0.01 ),\n",
    "                   np.arange(start = X_set[: , 1].min()-1 ,\n",
    "                             stop = X_set[: ,1].max()+1 ,step =0.01))\n",
    "plt.contourf(X1 ,X2 ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),\n",
    "            alpha = 0.75, cmap = ListedColormap(('red' ,'green','blue')))\n",
    "\n",
    "for i , j in enumerate (np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j , 0] ,X_set[y_set == j ,1] ,\n",
    "                c = ListedColormap(('red' ,'green','blue'))(i),label = j )\n",
    "    \n",
    "plt.title(\"Logistic Regression after PCA\")\n",
    "plt.xlabel(\"PC1\")\n",
    "plt.ylabel(\"PC2\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.84416995, -4.83416995, -4.82416995, ...,  5.32583005,\n",
       "         5.33583005,  5.34583005],\n",
       "       [-4.84416995, -4.83416995, -4.82416995, ...,  5.32583005,\n",
       "         5.33583005,  5.34583005],\n",
       "       [-4.84416995, -4.83416995, -4.82416995, ...,  5.32583005,\n",
       "         5.33583005,  5.34583005],\n",
       "       ...,\n",
       "       [-4.84416995, -4.83416995, -4.82416995, ...,  5.32583005,\n",
       "         5.33583005,  5.34583005],\n",
       "       [-4.84416995, -4.83416995, -4.82416995, ...,  5.32583005,\n",
       "         5.33583005,  5.34583005],\n",
       "       [-4.84416995, -4.83416995, -4.82416995, ...,  5.32583005,\n",
       "         5.33583005,  5.34583005]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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FV3/2atqntVe1LpUARJpcrp5fvYDqo6ev8LTPxZaXY2BggCs+cAWLLlzEWeeeVfX6lABEMmBW+yyd8OtkdvtsNvVtKri8Gu7Opz7+KQ474jA+8GcfqGpdOaoCEhFJ0NITlzJl8ujpoKdMnsLSE6ubDvoXP/sF3//O91mzag3nLzyf8xeezwP3PVDVOlUCEBFJUK6hd/nq5fT09TC7fTZLT1xadQPwcSccxxNbnkgixBHBEoCZTQEeBNriOG5z90+HikdEJCmLjliUWI+fWgpZAugHTnf3PjNrBVaZ2Q/dfU3AmEREMiNYAnB3B/rip63xw0PFIyKSNUEbgc2sxczWAZuB+9z9ZwXes8TM1prZ2oHtA3WPUUSkWQVNAO4+5O6dwBzgeDM7qsB7Vrj7fHef3zqtte4xiog0q1R0A3X3rUAXcHbYSEREsiNkL6CZwIC7bzWzqcCZwD+EikdEJM36d/Zzybsv4fXXX2docIizFp3FFVddUdU6Q/YCmg3cbGYtRCWR77j73QHjERFJrT3a9uCm229ir/a9GBgY4OJzL+bUM06lc35nxesM2QvoUeDYUNsXEamVu26bxvLrZ9Lz3GRmHzTI0mVbWHTh9qrWaWbs1b4XAIMDgwwODGJmVa1TI4FFRBJ0123TuPbKA9i5I2pi3bSxlWuvPACg6iQwNDTEe854D88+/Szv++D7NB20iEiaLL9+5sjJP2fnjkksv35m1etuaWnhzq476Xq0i0d/8ShPPv5kVetTAhARSVDPc4UrVootr8Te++zN8Scfz0M/faiq9SgBiIgkaPZBg2UtL9VLL7zEtle2AbBzx05WP7CajjdVd1MftQGIiCRo6bIto9oAAKZMHWbpsi1VrXdL7xY++dFPMjQ8hA87Z593NqeddVpV61QCEBFJUK6hN+leQHOPnMsd99+RRIgjlABERBK26MLtVZ/w60FtACIiGaUEICIygWGGiWawTzd3Z5jhkt+vBCAiMoGeHT30b+9PdRJwd/q399Ozo6fkz6gNQERkAiufXcliFjN76mwmpfS6eZhhenb0sPLZlSV/RglAJE9vXy/dL3fTP9RPW0sbHTM6mNU+K3RYEljfUB83Pn1j6DASpwQgEuvt62X9i+sZ9qgOtX+on/UvrgdQEpCmpAQgEut+uXvk5J8z7MN0v9w9YQJQyUEakRKASKx/qL+s5TkqOUijUgKQhpfU1XdbS1vBk31bS9u4n6um5CASkhKANLQkr747ZnSMWhfAJJtEx4zxJ9yqtORQCVU1SZLS2Z+piL5njggdgqTMeFff5ZrVPou5+84dueJva2lj7r5zJzzBFishTFRyKFcu2eUSSy7Z9fb1JrodyY6QN4U/GPgmcAAwDKxw9y9O9Lmuy+8FYPKhazjluutqGqOkX9JX37PaZ9Wt5FAuVTVJ0kKWAAaBv3T3ecAJwEfM7M3jfaC93Vh4aivT925l8JkT6Lr83pGEINlUr6vv8VRacihXPauaJBtC3hS+B+iJ/95uZo8DBwH/M9FnOzsBWgHoenBgJAlM/9z+dB7QWZN4JZ3qdfU9kbElh96+XlZvWJ1oXX2ljdQixaSiEdjMDgWOBX5W4LUlwBKAtrZDdvvswlOjRLBuHWy9ZjNdueXfOKsmsUq65E6qaWoYrVW30LQkO2keFnpyIzNrBx4Arnf328d777Rp833+/LUTrrPrwYGRv9VWIPWWu/Ifq62ljRMPPrGqdasXkFSi67KuR9x9/tjlQUsAZtYKfA+4daKTfzlGlQritgJQqUDqo5Z19ZU0UjcaJbn6CdkLyICvA4+7+xdqsY38toIHHhwcSQS2x6ss+OoFtdikiOrqq6BR1fUVsgRwMnAp8GszWxcvu8bd76nFxhacumtXux7ci67L71X1kNSE6uorN9G4DpUMkhWyF9AqwEJsO1dF1PXgCepBJIlLY8N0oxiv+kwlg+SlohdQKLlEsGqVehBJsrJQV18LxarPAA2Cq4FMJ4CcU04BjSsQCa9Y9dnYk3+OBsFVRwlgDI0rEAmnWPVZ7vlYalivjhJAEcVGGysRiNRWseozNawnTwmgBPltBepKKlJ/alivDSWAMoxuK9hLM5OK1JEa1pOnBFCh/FLBoEYb14VGiIokSwmgSvmlgvzRxioVJEsjREWS11B3BEu7BadO1v0KaiTJO3+JSEQlgBrQ/QqSp5uhiCRPCaDGNK4gGZpgTUDtQElTAqiTYqUCtRWURhOsidqByvTLdfDKK+O+RQkgAN2voHzN2g9cV7SlG68dSMcM6Hpgt0X337kPdHZiI3UPoykBBFTsfgUqFRTWbP3AdUVbHrUDjVHohP/MgtELOsdfhRJASoy+X0H2SgVZvBLWFW15Mt8O9NAqGBoatWi3E36ZlABSaNf9CrLRVpDVK2Fd0ZYnk+1AY67yc1U6SVECSLFcIoDmLhVk9Uo481e0ZWrWdqAR49Thj+jc7S1VUQJoEIVKBc2SCLJ6JZzJK9oqNVU7UClVOp21DSFoAjCzbwDnApvd/aiQsTSK/B5E+aOMGzkZZPVKuOmvaGW0At0yk67SKZe5e7iNm50K9AHfLCUBTJs23+fPX1v7wBpM14MDI383YiIY2wYA0ZXw3H3n6mQojauUKp06sa6uR9x9/tjlQUsA7v6gmR0aMoZm0Mj3K8j1/sk/+etKWBpSCqp0ypX6NgAzWwIsAWhrOyRwNOnWaPcrKHblr5O/NIRCVTpVdsust6BVQABxCeBuVQHVxqpVMDicziqi1RtWF637P/HgEwNEJDKBGnfLrJVUVgFJ7Y0uFaRrZtIke/9kcSCZ1FihKp1bWnL/VJHO+oaUNCWADBk1riAFM5Mm1fsnqwPJJGGlVOmcQlMJ3Q3028BCYD8z2wh82t2/HjKmrCg0rqDepYKk+sFndSCZJKBBq3SSEroX0EUhty+jexDV+34FSfWDz+pAMqnA2BN+k1XplEtVQAIUbyuodQ+iJEZ2ZnUgmUygyHz4o6p1mqxKp1xKALKbRrtfgaZUECCVI23TTglAimqU+xVoSoUMU5VOVYKPAyiHxgGkQ6NPPSEN6pfroK8v8Tnxs0DjACQxWbtfQSEad1AHqtKpOZUApGrr1sHWbdkpFWjyuhqaqEpHKlJxCcDM9gZmuvtvxyw/2t0fTTBGaVDF2gqaNRFo3EGCJppATef+mho3AZjZe4EbgM1m1gpc5u4Pxy/fBPx+TaOThpO7t3EjzkxaKo07qFAp3TKlriYqAVwDHOfuPWZ2PPAtM7vG3W8HrPbhSaMqNjNpGuYgqpbGHZRBVTqpNlECaHH3HgB3/7mZnQbcbWZzgMZpPJCgQo42rgWNOxhHoZugqEontSZKANvN7LBc/X9cElgI3AkcWdvQpNmEGm2cNI07yNMEc+Jn2bi9gMzsGOBVd39qzPJW4L3ufmuN4xtFvYCaT9Z6EDW8ia7wJZUq7QX0KjALeGrM8hOANQnFJhmW34MobfcrEHTCb3ITJYAbiBqCx9oRv7Yo4Xgkw0bNQdQEbQUNqZT72krTmCgBHFqor7+7r9XN3KVWVCqoI13hZ9pECWDKOK9NTTIQkUKarQdRcOqWKXkmSgAPm9mfuvvX8hea2QeBR2oXlsho+T2I0jwzaepopK2MY6JeQLOAO4DX2XXCnw/sAVzg7s/XPMI86gUk+dSDqAB1y5QCKuoF5O69wEnxALCj4sU/cPefJhKU2dnAF4EW4EZ3//sk1ivZ0Cj3K6ipQnX4jT5jZm8vdHdDfz+0tUFHB8zK4BiLOpioBDAF+DBwOPBr4OvuPpjIhs1agCeBtwMbgYeBi9z9f4p9RiUAKUVT36+g2Xvp9PbC+vUwnDfZ3qRJMHeukkAVKh0HcDMwADwEnAPMAz6eUEzHA0+5ezeAma0EzgOKJgCRUjTV/QqyVqXT3T365A/R8+5uJYAamCgBvNnd3wJgZl8Hfp7gtg8CNuQ93wi8beybzGwJsASgre2QBDcvza7R7m08YmxPnUav0ilHf5EZVYstl6pMlABGytLuPmiW6ASghVa2W32Uu68AVkBUBZRkAJINxdoKUjFNdaEqnbEn/E7qJ3T9e1tb4ZN9m2ZarYWJEsAxZrYt/tuAqfFzA9zd965i2xuBg/OezwE2VbE+kQnl7lcAo6eprlupoJQqnc76hLKbsfXv/f3Rc6hfEujoKNwG0KGZVmthol5ALTXc9sPAm8zs94DngMXA+2q4PZFRclVE+aWCmow2bpQqnTTUv+e2o15AdRHspvBxldJHgR8RdQP9hrs/FioeKV/o2oKk5N/FrOrRxoW6ZY4dbdtZ/mrrIi3177NmNeYPqQEFSwAA7n4PcE/IGKQyaagtSFqx+xWMWyoopVtmo4y2LaH+/db9e1nW0c2zbf0c0t/G9d0dXLy5Qb9wCZsApHGlobaglorOTPoX++9eh5/WKp1yTVD/fuv+vSyZu57XWqLXfzelnyVzo6yvJNCYlACkImmpLai13WYm/dJmvGXPxqjSKdcE9e/LOrpHTv45r7UMs6yjWwmgQSkBSEWy2Ftv4amtdD0w0NyzZ45T//5sW+HsXmy5pN+k0AFIY+roiGoH8qm3XnM7pL9wdi+2XNJPCUAqMmtWND1L7oq/rS1D07WsWxc6giCu7+5gz6HRp4w9hyZxfbeyfqNSFZBUTL31siVXz69eQM1DCaAMzdLvXapjr2zGSfl8QjVy8eZZOuE3EVUBlSjX7z3X8Jnr997bGzYuqa+FC1pDhyCSGCWAEo3X711EpBGpCqhEae/3ruopESmXSgAlKta/PQ393lU9FUBGewJJc1ECKFGa+72reqr+Wve+PXQIIlVTAihRmvu9p716qtksXNDK4IYmHg0smaE2gDKktd97FqdlkAajRqpUUgmgCaS5ekpEjVTppRJAE9BNlAJZtaqxJ4ar11V5GucOV4kEUAJoGmmtnpKUqucdfdLWSNWMdzOqkKqARCphYEOvhY6icvXsOpa2PtTqNjciSAIwsz8ys8fMbNjM5oeIQaQauTuGNax6XpWnrZEqbSWSgEJVAf0G+EPgXwNtXyTb6tl1rJJGqlrW0avb3IggCcDdHwcwsxCbF0nOunWNeT/gCe7/m7hyGqkqqaMvJ2HUe99TLPVtAGa2xMzWmtnagYEtocMR2cXgtPNfmfh9aZTmkY3l1tGX2800zfteZzUrAZjZj4EDCry0zN2/X+p63H0FsAJg2rT5nlB4IonourkfFjTovQHS2nWs3Dr6SrqZpnXf66xmCcDdz6zVukXSYOQm8ZKscuvo1ahbsdRXAYlIxpTbayht3UwbSKhuoBeY2UbgROAHZvajEHGISAqVW0eftm6mDSRUL6A7gDtCbFskaacd+gD3P7MgdBjNpZw6es2FUjFNBSFSDfVkTgc16lZEbQAiVZi+d2vUE0ikAakEIFKFzk7oeiB0FBmmWT2rogTQxPS/IU1Ns3pWTVVATUr34JCmp1k9q6YSQJNK4z04mpWZMeNtXbz8s4WhQ8mWUgeAqShclEoATUqDI+tr6z++HjqE7CllAJiKwuNSAmhSGhxZPwtOVUE6iFIGgKmaaFz65TYpzXgrDafcqppSBoCpKDwuJYAmpcGRkmpjT/b77gvPP19+j56JBoDp5i/jUgJoYhocWV9qCC5Roe6bmzbt/r4kei2oKDwutQGIJMDM1BBcqkL18sVUW1Wjm7+MSyUAkQQsOHWy7g1QqnJO6klU1agoXJRKACJSX6We1FVVU3MqAcgoGjOTYfX68ovVyx9wALz4on58daQEICM0tUr1GrYhuJ5fvrqopYYSgIzQ9BHVmdzSSt+3fwodfxM6lPLV+8tXvXwqKAHIiErHzKjaaJfBDadAI1Zba8BUJoW6J/DnzewJM3vUzO4ws+kh4pDRKpk+QlOt7HLKKaEjqILmDsmkUL2A7gOOcvejgSeBqwPFIXkqube2plppErqxeiYFSQDufq+7D8ZP1wBzQsQho1UyZkY1B01CA6YyKQ1tAJcD/1HsRTNbAiwBaGs7pF4xZVa5bXOaamV3rd3XMdCIDcFqmM2cmpUAzOzHZvabAo/z8t6zDBgEbi22Hndf4e7z3X1+a+vMWoUrFVLNwWjT92kNHYJIyWpWAnD3M8d73czeD5wLnOHuXqs4pLbUpXu06CbxDdoTSDInSBWQmZ0NXAUscPfXQsQgyVHNgUhjCtUG8GWgDbjPzADWuPuHA8UiIhLJ2KCWIAnA3Q8PsV2Repm0+naGT/zD0GFIOTI4F4pmAxVJmBqCG1QGB7UoAYjUgL/eHjoEKVcGB7UoAYgkrLMzdARSkQxOh6EEICICmRzUkoaRwCIi4WVwUIsSgEgNTN+nVT2BGlHGBrWoCkikBjo71RAs6acEICKSUUoAIiIZpQQgIpJRagQWqSF74F58wVmhw5B6a5A5hVQCEKmRhQs0JUQmNdCNspUARESS1EBzCikBiIgkqYHmFFICEBFJUgPNKaQEIFJjrd3XhQ5B6qmB5hRSAhCpoYULWhnccEroMKSeZs2CuXN3XfG3tUXPU9gLSN1ARUSS1iBzCgUpAZjZ35rZo2a2zszuNbMDQ8QhIpJloaqAPu/uR7t7J3A3oEpSEZE6C5IA3H1b3tO9AA8Rh4hIlgVrBDaz681sA3Ax45QAzGyJma01s7UDA1vqF6BIguyBe0OHILKbmiUAM/uxmf2mwOM8AHdf5u4HA7cCHy22Hndf4e7z3X1+a+vMWoUrUjOaEkLSqma9gNz9zBLf+u/AD4BP1yoWERHZXaheQG/Ke/pu4IkQcYiIZFmocQB/b2ZzgWHgd8CHA8UhUj/r1kX3ihRJiSAJwN3fE2K7IiHZK5txdG8ASQ9NBSFSB2oIljRSAhARySglABGRjFICEKmndetCRyAyQglARCSjlABE6she2Rw6BJERSgAidaKeQJI2SgAiIhmlBCAiklFKACL1tmpV6AhEACUAkbqzoddChyACKAGI1JUagiVNlABERDLK3BvndrxmtoVo+ug02w94IXQQKaLjMZqOx+50TEarxfF4o7vvdkvFhkoAjcDM1rr7/NBxpIWOx2g6HrvTMRmtnsdDVUAiIhmlBCAiklFKAMlbETqAlNHxGE3HY3c6JqPV7XioDUBEJKNUAhARySglABGRjFICqCEz+yszczPbL3QsIZnZ583sCTN71MzuMLPpoWMKwczONrP1ZvaUmX0ydDwhmdnBZna/mT1uZo+Z2cdCx5QGZtZiZr80s7vrsT0lgBoxs4OBtwPPho4lBe4DjnL3o4EngasDx1N3ZtYCfAU4B3gzcJGZvTlsVEENAn/p7vOAE4CPZPx45HwMeLxeG1MCqJ3lwCeAzLeyu/u97j4YP10DzAkZTyDHA0+5e7e7vw6sBM4LHFMw7t7j7r+I/95OdNI7KGxUYZnZHOBdwI312qYSQA2Y2buB59z9V6FjSaHLgR+GDiKAg4ANec83kvETXo6ZHQocC/wscCih3UB00Thcrw1OrteGmo2Z/Rg4oMBLy4BrgLPqG1FY4x0Pd/9+/J5lREX/W+sZW0pYgWWZLx2aWTvwPeDj7r4tdDyhmNm5wGZ3f8TMFtZru0oAFXL3MwstN7O3AL8H/MrMIKru+IWZHe/uz9cxxLoqdjxyzOz9wLnAGZ7NwScbgYPzns8BNgWKJRXMrJXo5H+ru98eOp7ATgbebWbvBKYAe5vZLe5+SS03qoFgNWZmzwDz3T2zsx2a2dnAF4AF7r4ldDwhmNlkogbwM4DngIeB97n7Y0EDC8Siq6ObgZfc/eOBw0mVuATwV+5+bq23pTYAqYcvA9OA+8xsnZl9NXRA9RY3gn8U+BFRg+d3snryj50MXAqcHv8m1sVXv1JHKgGIiGSUSgAiIhmlBCAiklFKACIiGaUEICKSUUoAIiIZpQQgUgIzG4q7Kv7GzL5rZnvGyw8ws5Vm9lsz+x8zu8fMjohf+y8z21qvmR1FyqUEIFKaHe7e6e5HAa8DH44HM90BdLn7Ye7+ZqJpQGbFn/k8UV93kVRSAhAp30PA4cBpwIC7jwxsc/d17v5Q/PdPgO1hQhSZmBKASBniKR3OAX4NHAU8EjYikcopAYiUZqqZrQPWEt3k5+thwxGpnmYDFSnNDnfvzF9gZo8BF4YJR6R6KgGIVO6nQJuZ/WlugZm91cwWBIxJpGRKACIViu9rcAHw9rgb6GPAZ4jn+Tezh4DvAmeY2UYze0ewYEUK0GygIiIZpRKAiEhGKQGIiGSUEoCISEYpAYiIZJQSgIhIRikBiIhklBKAiEhG/X8f5gNpLjSJNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#9 Visualizing Test set result\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set , y_set = X_test , y_test\n",
    "X1,X2= np.meshgrid(np.arange(start = X_set[: ,0].min() -1,\n",
    "                             stop = X_set[: , 0].max()+1,step =0.01 ),\n",
    "                   np.arange(start = X_set[: , 1].min()-1 ,\n",
    "                             stop = X_set[: ,1].max()+1 ,step =0.01))\n",
    "plt.contourf(X1 ,X2 ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),\n",
    "            alpha = 0.75, cmap = ListedColormap(('red' ,'green','blue')))\n",
    "\n",
    "for i , j in enumerate (np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j , 0] ,X_set[y_set == j ,1] ,\n",
    "                c = ListedColormap(('red' ,'green','blue'))(i),label = j )\n",
    "    \n",
    "plt.title(\"Logistic Regression after PCA(Test Dataset)\")\n",
    "plt.xlabel(\"PC1\")\n",
    "plt.ylabel(\"PC2\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}