Machine Learning A-Z (Codes and Datasets)/Part 3 - Classification/Section 14 - Logistic Regression/Python/logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0MRC0e0KhQ0S"
   },
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LWd1UlMnhT2s"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K1VMqkGvhc3-"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:,:-1].values\n",
    "y = dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YvxIPVyMhmKp"
   },
   "source": [
    "## Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kW3c7UYih0hT"
   },
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [ 1.37390747 -1.40858358]\n",
      " [ 1.47293972  0.99784738]\n",
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      " [ 2.1661655   1.11381995]\n",
      " [-1.79512465  0.35999821]\n",
      " [ 1.86906873  0.12805305]\n",
      " [ 0.38358493 -0.13288524]\n",
      " [-1.20093113  0.30201192]\n",
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      " [ 0.97777845 -1.06066585]\n",
      " [ 0.28455268  0.30201192]\n",
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      " [ 0.77971394 -1.3505973 ]\n",
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      " [-0.11157634  0.04107362]\n",
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      " [ 0.97777845 -1.00267957]\n",
      " [ 1.07681071 -1.20563157]\n",
      " [-0.01254409 -0.13288524]\n",
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      " [ 0.77971394 -1.20563157]\n",
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      " [ 0.77971394 -1.089659  ]\n",
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      " [-1.79512465  0.12805305]\n",
      " [-0.90383437  0.1570462 ]\n",
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      " [ 0.8787462  -1.29261101]\n",
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      " [ 0.38358493  0.1570462 ]\n",
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      " [ 0.08648817  0.1570462 ]\n",
      " [-1.89415691 -1.29261101]\n",
      " [-0.11157634  0.30201192]\n",
      " [-0.21060859 -0.27785096]\n",
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      " [-0.21060859  1.6067034 ]\n",
      " [ 0.97777845 -1.17663843]\n",
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      " [ 1.27487521  1.8676417 ]\n",
      " [-1.10189888 -0.3648304 ]\n",
      " [-0.01254409  0.04107362]\n",
      " [ 0.08648817 -0.24885782]\n",
      " [-1.59706014 -1.23462472]\n",
      " [-0.50770535 -0.27785096]\n",
      " [ 0.97777845  0.12805305]\n",
      " [ 1.96810099 -1.3505973 ]\n",
      " [ 1.47293972  0.07006676]\n",
      " [-0.60673761  1.37475825]\n",
      " [ 1.57197197  0.01208048]\n",
      " [-0.80480212  0.30201192]\n",
      " [ 1.96810099  0.73690908]\n",
      " [-1.20093113 -0.50979612]\n",
      " [ 0.68068169  0.27301877]\n",
      " [-1.39899564 -0.42281668]\n",
      " [ 0.18552042  0.1570462 ]\n",
      " [-0.50770535 -1.20563157]\n",
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      " [-1.59706014 -1.49556302]\n",
      " [-0.50770535 -0.53878926]\n",
      " [ 0.48261718  1.83864855]\n",
      " [-1.39899564 -1.089659  ]\n",
      " [ 0.77971394 -1.37959044]\n",
      " [-0.30964085 -0.42281668]\n",
      " [ 1.57197197  0.99784738]\n",
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      " [-1.49802789 -0.1038921 ]\n",
      " [-0.11157634  1.95462113]\n",
      " [-0.70576986 -0.33583725]\n",
      " [-0.50770535 -0.8287207 ]\n",
      " [ 0.68068169 -1.37959044]\n",
      " [-0.80480212 -1.58254245]\n",
      " [-1.89415691 -1.46656987]\n",
      " [ 1.07681071  0.12805305]\n",
      " [ 0.08648817  1.51972397]\n",
      " [-0.30964085  0.09905991]\n",
      " [ 0.08648817  0.04107362]\n",
      " [-1.39899564 -1.3505973 ]\n",
      " [ 0.28455268  0.07006676]\n",
      " [-0.90383437  0.38899135]\n",
      " [ 1.57197197 -1.26361786]\n",
      " [-0.30964085 -0.74174127]\n",
      " [-0.11157634  0.1570462 ]\n",
      " [-0.90383437 -0.65476184]\n",
      " [-0.70576986 -0.04590581]\n",
      " [ 0.38358493 -0.45180983]\n",
      " [-0.80480212  1.89663484]\n",
      " [ 1.37390747  1.28777882]\n",
      " [ 1.17584296 -0.97368642]\n",
      " [ 1.77003648  1.83864855]\n",
      " [-0.90383437 -0.24885782]\n",
      " [-0.80480212  0.56295021]\n",
      " [-1.20093113 -1.5535493 ]\n",
      " [-0.50770535 -1.11865214]\n",
      " [ 0.28455268  0.07006676]\n",
      " [-0.21060859 -1.06066585]\n",
      " [ 1.67100423  1.6067034 ]\n",
      " [ 0.97777845  1.78066227]\n",
      " [ 0.28455268  0.04107362]\n",
      " [-0.80480212 -0.21986468]\n",
      " [-0.11157634  0.07006676]\n",
      " [ 0.28455268 -0.19087153]\n",
      " [ 1.96810099 -0.65476184]\n",
      " [-0.80480212  1.3457651 ]\n",
      " [-1.79512465 -0.59677555]\n",
      " [-0.11157634  0.12805305]\n",
      " [ 0.28455268 -0.30684411]\n",
      " [ 1.07681071  0.56295021]\n",
      " [-1.00286662  0.27301877]\n",
      " [ 1.47293972  0.35999821]\n",
      " [ 0.18552042 -0.3648304 ]\n",
      " [ 2.1661655  -1.03167271]\n",
      " [-0.30964085  1.11381995]\n",
      " [-1.6960924   0.07006676]\n",
      " [-0.01254409  0.04107362]\n",
      " [ 0.08648817  1.05583366]\n",
      " [-0.11157634 -0.3648304 ]\n",
      " [-1.20093113  0.07006676]\n",
      " [-0.30964085 -1.3505973 ]\n",
      " [ 1.57197197  1.11381995]\n",
      " [-0.80480212 -1.52455616]\n",
      " [ 0.08648817  1.8676417 ]\n",
      " [-0.90383437 -0.77073441]\n",
      " [-0.50770535 -0.77073441]\n",
      " [-0.30964085 -0.91570013]\n",
      " [ 0.28455268 -0.71274813]\n",
      " [ 0.28455268  0.07006676]\n",
      " [ 0.08648817  1.8676417 ]\n",
      " [-1.10189888  1.95462113]\n",
      " [-1.6960924  -1.5535493 ]\n",
      " [-1.20093113 -1.089659  ]\n",
      " [-0.70576986 -0.1038921 ]\n",
      " [ 0.08648817  0.09905991]\n",
      " [ 0.28455268  0.27301877]\n",
      " [ 0.8787462  -0.5677824 ]\n",
      " [ 0.28455268 -1.14764529]\n",
      " [-0.11157634  0.67892279]\n",
      " [ 2.1661655  -0.68375498]\n",
      " [-1.29996338 -1.37959044]\n",
      " [-1.00286662 -0.94469328]\n",
      " [-0.01254409 -0.42281668]\n",
      " [-0.21060859 -0.45180983]\n",
      " [-1.79512465 -0.97368642]\n",
      " [ 1.77003648  0.99784738]\n",
      " [ 0.18552042 -0.3648304 ]\n",
      " [ 0.38358493  1.11381995]\n",
      " [-1.79512465 -1.3505973 ]\n",
      " [ 0.18552042 -0.13288524]\n",
      " [ 0.8787462  -1.43757673]\n",
      " [-1.99318916  0.47597078]\n",
      " [-0.30964085  0.27301877]\n",
      " [ 1.86906873 -1.06066585]\n",
      " [-0.4086731   0.07006676]\n",
      " [ 1.07681071 -0.88670699]\n",
      " [-1.10189888 -1.11865214]\n",
      " [-1.89415691  0.01208048]\n",
      " [ 0.08648817  0.27301877]\n",
      " [-1.20093113  0.33100506]\n",
      " [-1.29996338  0.30201192]\n",
      " [-1.00286662  0.44697764]\n",
      " [ 1.67100423 -0.88670699]\n",
      " [ 1.17584296  0.53395707]\n",
      " [ 1.07681071  0.53395707]\n",
      " [ 1.37390747  2.331532  ]\n",
      " [-0.30964085 -0.13288524]\n",
      " [ 0.38358493 -0.45180983]\n",
      " [-0.4086731  -0.77073441]\n",
      " [-0.11157634 -0.50979612]\n",
      " [ 0.97777845 -1.14764529]\n",
      " [-0.90383437 -0.77073441]\n",
      " [-0.21060859 -0.50979612]\n",
      " [-1.10189888 -0.45180983]\n",
      " [-1.20093113  1.40375139]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.80480212  0.50496393]\n",
      " [-0.01254409 -0.5677824 ]\n",
      " [-0.30964085  0.1570462 ]\n",
      " [-0.80480212  0.27301877]\n",
      " [-0.30964085 -0.5677824 ]\n",
      " [-1.10189888 -1.43757673]\n",
      " [-0.70576986 -1.58254245]\n",
      " [-0.21060859  2.15757314]\n",
      " [-1.99318916 -0.04590581]\n",
      " [ 0.8787462  -0.77073441]\n",
      " [-0.80480212 -0.59677555]\n",
      " [-1.00286662 -0.42281668]\n",
      " [-0.11157634 -0.42281668]\n",
      " [ 0.08648817  0.21503249]\n",
      " [-1.79512465  0.47597078]\n",
      " [-0.60673761  1.37475825]\n",
      " [-0.11157634  0.21503249]\n",
      " [-1.89415691  0.44697764]\n",
      " [ 1.67100423  1.75166912]\n",
      " [-0.30964085 -1.37959044]\n",
      " [-0.30964085 -0.65476184]\n",
      " [ 0.8787462   2.15757314]\n",
      " [ 0.28455268 -0.53878926]\n",
      " [ 0.8787462   1.02684052]\n",
      " [-1.49802789 -1.20563157]\n",
      " [ 1.07681071  2.07059371]\n",
      " [-1.00286662  0.50496393]\n",
      " [-0.90383437  0.30201192]\n",
      " [-0.11157634 -0.21986468]\n",
      " [-0.60673761  0.47597078]\n",
      " [-1.6960924   0.53395707]\n",
      " [-0.11157634  0.27301877]\n",
      " [ 1.86906873 -0.27785096]\n",
      " [-0.11157634 -0.48080297]\n",
      " [-1.39899564 -0.33583725]\n",
      " [-1.99318916 -0.50979612]\n",
      " [-1.59706014  0.33100506]\n",
      " [-0.4086731  -0.77073441]\n",
      " [-0.70576986 -1.03167271]\n",
      " [ 1.07681071 -0.97368642]\n",
      " [-1.10189888  0.53395707]\n",
      " [ 0.28455268 -0.50979612]\n",
      " [-1.10189888  0.41798449]\n",
      " [-0.30964085 -1.43757673]\n",
      " [ 0.48261718  1.22979253]\n",
      " [-1.10189888 -0.33583725]\n",
      " [-0.11157634  0.30201192]\n",
      " [ 1.37390747  0.59194336]\n",
      " [-1.20093113 -1.14764529]\n",
      " [ 1.07681071  0.47597078]\n",
      " [ 1.86906873  1.51972397]\n",
      " [-0.4086731  -1.29261101]\n",
      " [-0.30964085 -0.3648304 ]\n",
      " [-0.4086731   1.31677196]\n",
      " [ 2.06713324  0.53395707]\n",
      " [ 0.68068169 -1.089659  ]\n",
      " [-0.90383437  0.38899135]\n",
      " [-1.20093113  0.30201192]\n",
      " [ 1.07681071 -1.20563157]\n",
      " [-1.49802789 -1.43757673]\n",
      " [-0.60673761 -1.49556302]\n",
      " [ 2.1661655  -0.79972756]\n",
      " [-1.89415691  0.18603934]\n",
      " [-0.21060859  0.85288166]\n",
      " [-1.89415691 -1.26361786]\n",
      " [ 2.1661655   0.38899135]\n",
      " [-1.39899564  0.56295021]\n",
      " [-1.10189888 -0.33583725]\n",
      " [ 0.18552042 -0.65476184]\n",
      " [ 0.38358493  0.01208048]\n",
      " [-0.60673761  2.331532  ]\n",
      " [-0.30964085  0.21503249]\n",
      " [-1.59706014 -0.19087153]\n",
      " [ 0.68068169 -1.37959044]\n",
      " [-1.10189888  0.56295021]\n",
      " [-1.99318916  0.35999821]\n",
      " [ 0.38358493  0.27301877]\n",
      " [ 0.18552042 -0.27785096]\n",
      " [ 1.47293972 -1.03167271]\n",
      " [ 0.8787462   1.08482681]\n",
      " [ 1.96810099  2.15757314]\n",
      " [ 2.06713324  0.38899135]\n",
      " [-1.39899564 -0.42281668]\n",
      " [-1.20093113 -1.00267957]\n",
      " [ 1.96810099 -0.91570013]\n",
      " [ 0.38358493  0.30201192]\n",
      " [ 0.18552042  0.1570462 ]\n",
      " [ 2.06713324  1.75166912]\n",
      " [ 0.77971394 -0.8287207 ]\n",
      " [ 0.28455268 -0.27785096]\n",
      " [ 0.38358493 -0.16187839]\n",
      " [-0.11157634  2.21555943]\n",
      " [-1.49802789 -0.62576869]\n",
      " [-1.29996338 -1.06066585]\n",
      " [-1.39899564  0.41798449]\n",
      " [-1.10189888  0.76590222]\n",
      " [-1.49802789 -0.19087153]\n",
      " [ 0.97777845 -1.06066585]\n",
      " [ 0.97777845  0.59194336]\n",
      " [ 0.38358493  0.99784738]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bb6jCOCQiAmP"
   },
   "source": [
    "## Training the Logistic Regression model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yyxW5b395mR2"
   },
   "source": [
    "## Predicting a new result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[30,87000]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vKYVQH-l5NpE"
   },
   "source": [
    "## Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "num = 0\n",
    "\n",
    "for i in range(0,len(y_test)):\n",
    "    #print(classifier.predict(X_test)[i], end='')\n",
    "    #print(y_test[i])\n",
    "    if(classifier.predict(X_test)[i] == y_test[i]):\n",
    "        pass\n",
    "    else:\n",
    "        num += 1\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h4Hwj34ziWQW"
   },
   "source": [
    "## Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[65  3]\n",
      " [ 8 24]]\n",
      "0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "ac = accuracy_score(y_test,y_pred)\n",
    "print(cm)\n",
    "print(ac)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6OMC_P0diaoD"
   },
   "source": [
    "## Visualising the Training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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 a 2-D array with a single row if you really want 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 a 2-D array with a single row if you really want 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": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = sc.inverse_transform(X_train), y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
    "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Logistic Regression (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SZ-j28aPihZx"
   },
   "source": [
    "## Visualising the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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 a 2-D array with a single row if you really want 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 a 2-D array with a single row if you really want 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": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = sc.inverse_transform(X_test), y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
    "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
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