10 Classification_ Social_Network_Ads.ipynb

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   "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": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YvGPUQaHhXfL"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K1VMqkGvhc3-"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M52QDmyzhh9s"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>19</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EstimatedSalary  Purchased\n",
       "0     19            19000          0\n",
       "1     35            20000          0\n",
       "2     26            43000          0\n",
       "3     27            57000          0\n",
       "4     19            76000          0\n",
       "..   ...              ...        ...\n",
       "395   46            41000          1\n",
       "396   51            23000          1\n",
       "397   50            20000          1\n",
       "398   36            33000          0\n",
       "399   49            36000          1\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "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": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AVzJWAXIhxoC"
   },
   "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": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2163,
     "status": "ok",
     "timestamp": 1588265315502,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
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    "id": "P3nS3-6r1i2B",
    "outputId": "75d6e0cf-d13b-42cf-a353-888682415d37"
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    {
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     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2158,
     "status": "ok",
     "timestamp": 1588265315502,
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     },
     "user_tz": -240
    },
    "id": "8dpDLojm1mVG",
    "outputId": "7ae11087-76ab-4027-c94e-9eb18f7573bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1\n",
      " 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1\n",
      " 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0\n",
      " 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0\n",
      " 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0\n",
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      " 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2154,
     "status": "ok",
     "timestamp": 1588265315503,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "qbb7i0DH1qui",
    "outputId": "94e717d7-fa9a-4e22-9c68-d43421cbfb92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [    52  90000]\n",
      " [    26  30000]\n",
      " [    49  86000]\n",
      " [    57 122000]\n",
      " [    34  25000]\n",
      " [    35  57000]\n",
      " [    34 115000]\n",
      " [    59  88000]\n",
      " [    45  32000]\n",
      " [    29  83000]\n",
      " [    26  80000]\n",
      " [    49  28000]\n",
      " [    23  20000]\n",
      " [    32  18000]\n",
      " [    60  42000]\n",
      " [    19  76000]\n",
      " [    36  99000]\n",
      " [    19  26000]\n",
      " [    60  83000]\n",
      " [    24  89000]\n",
      " [    27  58000]\n",
      " [    40  47000]\n",
      " [    42  70000]\n",
      " [    32 150000]\n",
      " [    35  77000]\n",
      " [    22  63000]\n",
      " [    45  22000]\n",
      " [    27  89000]\n",
      " [    18  82000]\n",
      " [    42  79000]\n",
      " [    40  60000]\n",
      " [    53  34000]\n",
      " [    47 107000]\n",
      " [    58 144000]\n",
      " [    59  83000]\n",
      " [    24  55000]\n",
      " [    26  35000]\n",
      " [    58  38000]\n",
      " [    42  80000]\n",
      " [    40  75000]\n",
      " [    59 130000]\n",
      " [    46  41000]\n",
      " [    41  60000]\n",
      " [    42  64000]\n",
      " [    37 146000]\n",
      " [    23  48000]\n",
      " [    25  33000]\n",
      " [    24  84000]\n",
      " [    27  96000]\n",
      " [    23  63000]\n",
      " [    48  33000]\n",
      " [    48  90000]\n",
      " [    42 104000]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2149,
     "status": "ok",
     "timestamp": 1588265315503,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "kj1hnFAR1s5w",
    "outputId": "ec9924c4-6d8a-4f20-dbf9-272fbaa24f92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0\n",
      " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
      " 0 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kW3c7UYih0hT"
   },
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9fQlDPKCh8sc"
   },
   "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": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2139,
     "status": "ok",
     "timestamp": 1588265315503,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "syrnD1Op2BSR",
    "outputId": "711172c9-25a5-4c03-e835-6ab94766bdc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.58164944 -0.88670699]\n",
      " [-0.60673761  1.46173768]\n",
      " [-0.01254409 -0.5677824 ]\n",
      " [-0.60673761  1.89663484]\n",
      " [ 1.37390747 -1.40858358]\n",
      " [ 1.47293972  0.99784738]\n",
      " [ 0.08648817 -0.79972756]\n",
      " [-0.01254409 -0.24885782]\n",
      " [-0.21060859 -0.5677824 ]\n",
      " [-0.21060859 -0.19087153]\n",
      " [-0.30964085 -1.29261101]\n",
      " [-0.30964085 -0.5677824 ]\n",
      " [ 0.38358493  0.09905991]\n",
      " [ 0.8787462  -0.59677555]\n",
      " [ 2.06713324 -1.17663843]\n",
      " [ 1.07681071 -0.13288524]\n",
      " [ 0.68068169  1.78066227]\n",
      " [-0.70576986  0.56295021]\n",
      " [ 0.77971394  0.35999821]\n",
      " [ 0.8787462  -0.53878926]\n",
      " [-1.20093113 -1.58254245]\n",
      " [ 2.1661655   0.93986109]\n",
      " [-0.01254409  1.22979253]\n",
      " [ 0.18552042  1.08482681]\n",
      " [ 0.38358493 -0.48080297]\n",
      " [-0.30964085 -0.30684411]\n",
      " [ 0.97777845 -0.8287207 ]\n",
      " [ 0.97777845  1.8676417 ]\n",
      " [-0.01254409  1.25878567]\n",
      " [-0.90383437  2.27354572]\n",
      " [-1.20093113 -1.58254245]\n",
      " [ 2.1661655  -0.79972756]\n",
      " [-1.39899564 -1.46656987]\n",
      " [ 0.38358493  2.30253886]\n",
      " [ 0.77971394  0.76590222]\n",
      " [-1.00286662 -0.30684411]\n",
      " [ 0.08648817  0.76590222]\n",
      " [-1.00286662  0.56295021]\n",
      " [ 0.28455268  0.07006676]\n",
      " [ 0.68068169 -1.26361786]\n",
      " [-0.50770535 -0.01691267]\n",
      " [-1.79512465  0.35999821]\n",
      " [-0.70576986  0.12805305]\n",
      " [ 0.38358493  0.30201192]\n",
      " [-0.30964085  0.07006676]\n",
      " [-0.50770535  2.30253886]\n",
      " [ 0.18552042  0.04107362]\n",
      " [ 1.27487521  2.21555943]\n",
      " [ 0.77971394  0.27301877]\n",
      " [-0.30964085  0.1570462 ]\n",
      " [-0.01254409 -0.53878926]\n",
      " [-0.21060859  0.1570462 ]\n",
      " [-0.11157634  0.24402563]\n",
      " [-0.01254409 -0.24885782]\n",
      " [ 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",
      " [ 0.77971394  1.37475825]\n",
      " [-0.30964085 -0.24885782]\n",
      " [-1.6960924  -0.04590581]\n",
      " [-1.00286662 -0.74174127]\n",
      " [ 0.28455268  0.50496393]\n",
      " [-0.11157634 -1.06066585]\n",
      " [-1.10189888  0.59194336]\n",
      " [ 0.08648817 -0.79972756]\n",
      " [-1.00286662  1.54871711]\n",
      " [-0.70576986  1.40375139]\n",
      " [-1.29996338  0.50496393]\n",
      " [-0.30964085  0.04107362]\n",
      " [-0.11157634  0.01208048]\n",
      " [-0.30964085 -0.88670699]\n",
      " [ 0.8787462  -1.3505973 ]\n",
      " [-0.30964085  2.24455257]\n",
      " [ 0.97777845  1.98361427]\n",
      " [-1.20093113  0.47597078]\n",
      " [-1.29996338  0.27301877]\n",
      " [ 1.37390747  1.98361427]\n",
      " [ 1.27487521 -1.3505973 ]\n",
      " [-0.30964085 -0.27785096]\n",
      " [-0.50770535  1.25878567]\n",
      " [-0.80480212  1.08482681]\n",
      " [ 0.97777845 -1.06066585]\n",
      " [ 0.28455268  0.30201192]\n",
      " [ 0.97777845  0.76590222]\n",
      " [-0.70576986 -1.49556302]\n",
      " [-0.70576986  0.04107362]\n",
      " [ 0.48261718  1.72267598]\n",
      " [ 2.06713324  0.18603934]\n",
      " [-1.99318916 -0.74174127]\n",
      " [-0.21060859  1.40375139]\n",
      " [ 0.38358493  0.59194336]\n",
      " [ 0.8787462  -1.14764529]\n",
      " [-1.20093113 -0.77073441]\n",
      " [ 0.18552042  0.24402563]\n",
      " [ 0.77971394 -0.30684411]\n",
      " [ 2.06713324 -0.79972756]\n",
      " [ 0.77971394  0.12805305]\n",
      " [-0.30964085  0.6209365 ]\n",
      " [-1.00286662 -0.30684411]\n",
      " [ 0.18552042 -0.3648304 ]\n",
      " [ 2.06713324  2.12857999]\n",
      " [ 1.86906873 -1.26361786]\n",
      " [ 1.37390747 -0.91570013]\n",
      " [ 0.8787462   1.25878567]\n",
      " [ 1.47293972  2.12857999]\n",
      " [-0.30964085 -1.23462472]\n",
      " [ 1.96810099  0.91086794]\n",
      " [ 0.68068169 -0.71274813]\n",
      " [-1.49802789  0.35999821]\n",
      " [ 0.77971394 -1.3505973 ]\n",
      " [ 0.38358493 -0.13288524]\n",
      " [-1.00286662  0.41798449]\n",
      " [-0.01254409 -0.30684411]\n",
      " [-1.20093113  0.41798449]\n",
      " [-0.90383437 -1.20563157]\n",
      " [-0.11157634  0.04107362]\n",
      " [-1.59706014 -0.42281668]\n",
      " [ 0.97777845 -1.00267957]\n",
      " [ 1.07681071 -1.20563157]\n",
      " [-0.01254409 -0.13288524]\n",
      " [-1.10189888 -1.52455616]\n",
      " [ 0.77971394 -1.20563157]\n",
      " [ 0.97777845  2.07059371]\n",
      " [-1.20093113 -1.52455616]\n",
      " [-0.30964085  0.79489537]\n",
      " [ 0.08648817 -0.30684411]\n",
      " [-1.39899564 -1.23462472]\n",
      " [-0.60673761 -1.49556302]\n",
      " [ 0.77971394  0.53395707]\n",
      " [-0.30964085 -0.33583725]\n",
      " [ 1.77003648 -0.27785096]\n",
      " [ 0.8787462  -1.03167271]\n",
      " [ 0.18552042  0.07006676]\n",
      " [-0.60673761  0.8818748 ]\n",
      " [-1.89415691 -1.40858358]\n",
      " [-1.29996338  0.59194336]\n",
      " [-0.30964085  0.53395707]\n",
      " [-1.00286662 -1.089659  ]\n",
      " [ 1.17584296 -1.43757673]\n",
      " [ 0.18552042 -0.30684411]\n",
      " [ 1.17584296 -0.74174127]\n",
      " [-0.30964085  0.07006676]\n",
      " [ 0.18552042  2.09958685]\n",
      " [ 0.77971394 -1.089659  ]\n",
      " [ 0.08648817  0.04107362]\n",
      " [-1.79512465  0.12805305]\n",
      " [-0.90383437  0.1570462 ]\n",
      " [-0.70576986  0.18603934]\n",
      " [ 0.8787462  -1.29261101]\n",
      " [ 0.18552042 -0.24885782]\n",
      " [-0.4086731   1.22979253]\n",
      " [-0.01254409  0.30201192]\n",
      " [ 0.38358493  0.1570462 ]\n",
      " [ 0.8787462  -0.65476184]\n",
      " [ 0.08648817  0.1570462 ]\n",
      " [-1.89415691 -1.29261101]\n",
      " [-0.11157634  0.30201192]\n",
      " [-0.21060859 -0.27785096]\n",
      " [ 0.28455268 -0.50979612]\n",
      " [-0.21060859  1.6067034 ]\n",
      " [ 0.97777845 -1.17663843]\n",
      " [-0.21060859  1.63569655]\n",
      " [ 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",
      " [ 0.58164944  2.01260742]\n",
      " [-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",
      " [ 0.97777845  1.43274454]\n",
      " [-0.30964085 -0.48080297]\n",
      " [-0.11157634  2.15757314]\n",
      " [-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": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2133,
     "status": "ok",
     "timestamp": 1588265315504,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "JUd6iBRp2C3L",
    "outputId": "3ed7b1f1-8fb7-48bc-8df8-39b6f65081ae"
   },
   "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": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 103
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2125,
     "status": "ok",
     "timestamp": 1588265315505,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "e0pFVAmciHQs",
    "outputId": "67f64468-abdb-4fe7-cce9-de0037119610"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\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": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2118,
     "status": "ok",
     "timestamp": 1588265315505,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "f8YOXsQy58rP",
    "outputId": "2e1b0063-548e-4924-cf3a-93a79d97e35e"
   },
   "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": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2112,
     "status": "ok",
     "timestamp": 1588265315506,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "p6VMTb2O4hwM",
    "outputId": "a4f03a97-2942-45cd-f735-f4063277a96c"
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.88725351, 0.11274649],\n",
       "       [0.83324206, 0.16675794],\n",
       "       [0.80549253, 0.19450747],\n",
       "       [0.91056209, 0.08943791],\n",
       "       [0.90253903, 0.09746097],\n",
       "       [0.99212979, 0.00787021],\n",
       "       [0.9848589 , 0.0151411 ],\n",
       "       [0.26779092, 0.73220908],\n",
       "       [0.99419108, 0.00580892],\n",
       "       [0.49579544, 0.50420456],\n",
       "       [0.96395406, 0.03604594],\n",
       "       [0.97081333, 0.02918667],\n",
       "       [0.83936154, 0.16063846],\n",
       "       [0.63040685, 0.36959315],\n",
       "       [0.98450737, 0.01549263],\n",
       "       [0.66506265, 0.33493735],\n",
       "       [0.72016534, 0.27983466],\n",
       "       [0.98774666, 0.01225334],\n",
       "       [0.01139953, 0.98860047],\n",
       "       [0.95799724, 0.04200276],\n",
       "       [0.91070984, 0.08929016],\n",
       "       [0.03668135, 0.96331865],\n",
       "       [0.72304389, 0.27695611],\n",
       "       [0.11787963, 0.88212037],\n",
       "       [0.99551213, 0.00448787],\n",
       "       [0.02704431, 0.97295569],\n",
       "       [0.92232099, 0.07767901],\n",
       "       [0.92371914, 0.07628086],\n",
       "       [0.80661256, 0.19338744],\n",
       "       [0.84341507, 0.15658493],\n",
       "       [0.97980137, 0.02019863],\n",
       "       [0.70700864, 0.29299136],\n",
       "       [0.06782035, 0.93217965],\n",
       "       [0.84785394, 0.15214606],\n",
       "       [0.98566324, 0.01433676],\n",
       "       [0.99652123, 0.00347877],\n",
       "       [0.98018715, 0.01981285],\n",
       "       [0.93442624, 0.06557376],\n",
       "       [0.97244034, 0.02755966],\n",
       "       [0.44947292, 0.55052708],\n",
       "       [0.93387384, 0.06612616],\n",
       "       [0.71655153, 0.28344847],\n",
       "       [0.94139491, 0.05860509],\n",
       "       [0.96051284, 0.03948716],\n",
       "       [0.19538951, 0.80461049],\n",
       "       [0.97375051, 0.02624949],\n",
       "       [0.7002962 , 0.2997038 ],\n",
       "       [0.07188441, 0.92811559],\n",
       "       [0.99116814, 0.00883186],\n",
       "       [0.14035391, 0.85964609],\n",
       "       [0.00979045, 0.99020955],\n",
       "       [0.96217391, 0.03782609],\n",
       "       [0.88084143, 0.11915857],\n",
       "       [0.58394773, 0.41605227],\n",
       "       [0.01920237, 0.98079763],\n",
       "       [0.67887409, 0.32112591],\n",
       "       [0.91662981, 0.08337019],\n",
       "       [0.95734278, 0.04265722],\n",
       "       [0.51367858, 0.48632142],\n",
       "       [0.99652752, 0.00347248],\n",
       "       [0.97962483, 0.02037517],\n",
       "       [0.06548521, 0.93451479],\n",
       "       [0.99080034, 0.00919966],\n",
       "       [0.6089113 , 0.3910887 ],\n",
       "       [0.99814664, 0.00185336],\n",
       "       [0.01837828, 0.98162172],\n",
       "       [0.96204152, 0.03795848],\n",
       "       [0.97375051, 0.02624949],\n",
       "       [0.78482933, 0.21517067],\n",
       "       [0.53552032, 0.46447968],\n",
       "       [0.40700336, 0.59299664],\n",
       "       [0.79520675, 0.20479325],\n",
       "       [0.98880303, 0.01119697],\n",
       "       [0.74469446, 0.25530554],\n",
       "       [0.93185794, 0.06814206],\n",
       "       [0.99091371, 0.00908629],\n",
       "       [0.46321694, 0.53678306],\n",
       "       [0.70589942, 0.29410058],\n",
       "       [0.2766666 , 0.7233334 ],\n",
       "       [0.11134787, 0.88865213],\n",
       "       [0.00394886, 0.99605114],\n",
       "       [0.02248034, 0.97751966],\n",
       "       [0.98696583, 0.01303417],\n",
       "       [0.98964178, 0.01035822],\n",
       "       [0.10734812, 0.89265188],\n",
       "       [0.45522288, 0.54477712],\n",
       "       [0.5969289 , 0.4030711 ],\n",
       "       [0.0050397 , 0.9949603 ],\n",
       "       [0.56296967, 0.43703033],\n",
       "       [0.66147882, 0.33852118],\n",
       "       [0.58308908, 0.41691092],\n",
       "       [0.21824871, 0.78175129],\n",
       "       [0.99149077, 0.00850923],\n",
       "       [0.99207365, 0.00792635],\n",
       "       [0.96750141, 0.03249859],\n",
       "       [0.91608066, 0.08391934],\n",
       "       [0.98628144, 0.01371856],\n",
       "       [0.52483762, 0.47516238],\n",
       "       [0.14989075, 0.85010925],\n",
       "       [0.27844761, 0.72155239]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2112,
     "status": "ok",
     "timestamp": 1588265315506,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "p6VMTb2O4hwM",
    "outputId": "a4f03a97-2942-45cd-f735-f4063277a96c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0        0          0\n",
       "1        0          0\n",
       "2        0          0\n",
       "3        0          0\n",
       "4        0          0\n",
       "..     ...        ...\n",
       "95       1          0\n",
       "96       0          0\n",
       "97       1          0\n",
       "98       1          1\n",
       "99       1          1\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\n",
    "# or\n",
    "pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h4Hwj34ziWQW"
   },
   "source": [
    "## Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[65,  3],\n",
       "       [ 8, 24]], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(65+24)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.96      0.92        68\n",
      "           1       0.89      0.75      0.81        32\n",
      "\n",
      "    accuracy                           0.89       100\n",
      "   macro avg       0.89      0.85      0.87       100\n",
      "weighted avg       0.89      0.89      0.89       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[65,  3],\n",
       "       [ 8, 24]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision:\n",
    "\n",
    "accuracy of the positive predictions"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAATIAAABgCAYAAACe9/6NAAAVO0lEQVR4Ae2d72sbV7rH5w/QG7/0C4NBGPwiEIzxC5dwkV84pBjssiGINEHIIUEu3SC3Jc4P6qRs7JREy3YV7q3S7hU0Jr1Vlop24/authu1a6dE2bW6VyU2uUpi5dqbKI0d+QeykWPZ872ckWZ0NJqRJUf+MfETMBnNnDlzns/RfHV+PM85AugfESACRMDgBASDl5+KTwSIABEACRl9CYgAETA8ARIyw1chGUAEiAAJGX0HiAARMDwBEjLDVyEZQASIAAkZfQeIABEwPAESMsNXIRlABIgACRl9B4gAETA8ARIyw1chGUAEiAAJGX0HiAARMDwBEjLDVyEZQASIAAkZfQeIABEwPAESMsNXIRlQSQLi9B187o9gJi2Wke0yZiJ/wmfDT1DOXWU8gJKuQYCEbA1AdHknEVjFfPA0qgUBQtl/9XAMPuFgJRHx/hptjdX5eZktOGB7E22WvThgd+Lsx3/EUDSBNHcnHZZPgISsfGZ0xytLYB5hVytMLedwYzSOpNIqE7EU6kctE7cGNyIrHID0HMYHz6BJsMAVTnIXsofpOAI9TZKYmTo+xejiauaCmML02NfobamBIDTA7v0ZSWrOFfIr8QwJWYmgKNkOILB6D969begPJVRdxGVM+jolMapyBpBQo1i5C8+eo/BNLquvAEgi7LJAEKqwx3MXvAYCItIT12GvEiCY3oA7Mq9xP50qhQAJWSmUKM2OICDGBvCrTj+eFLSMEhjubdYRI4ZmEv4jHyA4n21t5dF6gkFHPQShET3B6bwrmQ+/IODcDUEwocEdUQmdRnI6pUmAhEwTC53ceQREpGJ3cCu2UGi61FJjY116YjSP6K27mC4QQADpMFxmNuZ2AN5oqjBvpcUmQLD5EddIQafWJkBCtjYjSrHTCcwH0VNdTIz0AYmTPljZ2FrNOQwvaCndHEJ9ezJjaI5BTOlnRVeKECAhKwKHLhEBRmBlzIM9RcVIj5OIheFzqGH3Wn2Y1NIxpbVXjb3ee9DqnOrlTudzBEjIcizoiAhoEFhBIvAeqpgYdQwgpiVGGndlTqUQ9R6QWlu1fSEsFaQTsRj+LZpZ3qbDGBgvTFFwC53QJEBCpomFThIBmcACIu59RcRITqf1vzyQb4bNP6lKICId/w5nZfeLgTFojaCpbqKPOgRIyHTA0GkikCEwCb/NDEGohdX3SOWWsQajlQjcDSZpkqDbfw8zKdZxXEVqJoYRvwv2pmoIplZ0++6SD9kaKNe6TEK2FiG6vrMJLIXQV8sG+nUcXovRifthY93GqjYcP3MMLWYmaiwvE8wtDvR5v0U4vlieOBZ73g6+RkK2gyufTF+bAPMt65DGsI5jcCrfnbX43Vw0wF4voqwxlk5iOv4Ms1LLrPjddLU8AiRk5fGi1DuKADfruMeDsXJ0DEuIDRySWmA1vcPQ8E7bUSQ32lgSso0mTPkbmEBu1lEzNKmoZdMI9jRCEGrQMfCAuo9FWb38RRKyl2dIObyyBORZx3X4eEnxl1UQhD3oC829soS2i2EkZNulJqgc24+AIkZ6oUlFijw1CIeJDex36gSTF7mXLpVNgISsbGR0w04hIMb9sEti1AHP2GIZZnNja2YXwrTYWBns1peUhKwEbuLU33ChvQFNXV8gmirLtTs/d/Ephi/sR12TE19ENdauyk9Nn7aSQHoaI+4DMEnuEg1wDv6r5MUPxVQU17vY+BhzvTiGaw8XaIxsg+uShGxNwGnE/Y6s/886fIn4/GW/IkGA2RUu+cXgs6DjDSawGIbnyJuFK7sy3y/Lfhz1RjRCjbJlYvd2tqFRasWxbqX8V43Gtk70BZ9ucOF3bvYkZCXUPbXISoBESYjAFhIgIdtC+PRoIkAEKkNgU4RMTM1hbuklxpYqYyvlQgSIwCtKYIOELI3kVAyR7/34zz4HWsxd8MfTgDiP2Pde9NpaYDZbYHVewNXvH2oHzKaTiEdDGLzyG/QFHgNYQjx0Fe8f2IMm60UEJtRrBawiNRnCdfcp2Cz1MFsOwtl3Fd/H5vUHWsVFxMPfwtvnhNVSByFbpoHQE278Ko1kPIrwTR/cPS4EnmpMQaWfIfLVZZzqOYMzNgua2mxwnr2K0BSfVkQ6GUc0/Ff43L1ZmzS+VekEokPX4T75Dk70HEWrpQPHelwYCEYxq2yGobovPY/J0VscqxUkYz/A22uHxVwFs8WOXu9txPXuV2VHH4mA0QhsjJCtxBD43WnYWHS/NOB5CJ6bN/D7zl/BeuI8LvW+jXZlm6watJz7AVNKg20Fs//4CG1sQwbpXrYEyjimhvrRogyiqjdyWEI8eBEd7e/jy8gvSPE71NQdg3d0TiVmbAmVH+HpsqLz0n/hZmgEI8N/wpWeDpjZM03ZuLqVB/AdYuupy2VxZASZr+Xl+/B1NqK++ytMSDF0bI9DL+x1u7jtwVIY9zlQp+Sjt6zLj/A4/g319k8QkoOJmbDdOIcWkwl19k8xMsWvWbWC2dsXYVG4mGH78i7Gv7kAa7sDvZc+RK9jb8YmoQYt/bcwo3DmjaBjImBsAhsjZFkmSsCt0ACb+ztEZ3O7zIipRwicfT07vf0aTgafcWIjIp0YQl8z84yuxf7z/XjnrU9xe/wRxm5+gp63fo9gXH6hVzA/8hFaq6zwjPIuDblF60ztn2BUcZvIrgPV+gbO3lRPqS9hauQqzv1uiBPWZSSGL2QWvxPUQraK+eEPUF/gvb2CRPB9NOUtpsfbVChkYjKMy+21EOpPIvCMb8kxmEmMeqwwCUzMrqlcQFi+QfTuYisrsFUVunFleAKKuVjCk8ETqJcE+gh8Ey/K+MauIhn9AX6/v8y/HxBN0lqnZYCmpC9JYEOFDIq7gVoAsqVejmLgYJ3U4jEd9GEir7UgrwMlQGg6j+EZnYjdxZ/gbq1BVdcgnuXdz3qj8hIs++COZMN2l8fg7ahFdXcACXV6PZi6dshbfe1C1+BjTogBvLiPwNej4KVV2m1HWttKLWSLiHrflIRqV99taLleiokAuqV148046IvlP4vt4qOZb9agpRG4dmfWxdLeyUfPcN71RG6VlvK/Tn3rPYbOE4GXJLC1QoY0ng06M8sIV59Wbaclv5zF4tzk+6vR6v4RT+JxxJW/x4j98zM4si9/ZoVOOf1uOAO/lI5OV8heYMJ3JNOqrDuKK6HHXEtIK3vZJpWQvfgfuF9jrc9i5ZKDkAUI8rIwyiN08i35upKQDoiAIQlssZABue6n+ld8rZeT8eZebqnr1Ii2wzbYbDbY7E70XnLB5boMry+AyDTrrslBwGU6tuoKGSDO3EK/tFwxa6lUo8nugj+sJ2jaNuUYFCtXbiUGoeo9BBJ8C1U739w3cq3ruZR0RASMSGDLhUzpfhZsl1XCy6cE9RYTAK5alKWHS0wv31pEyKTdouN/g9v+Wna8LytoXV7VwDzLTNumdNiVHZAvVi6+m1eu6Gs/VzZvq//PTaaU0m2lNBvJa6u/C+t9/pYLmdwaMdn9iOeNWZXw8inCVAe7f0I1bqSBpNz0chZFhSybSHLl8MOlCJoJdV1+TOS5PGjbpGw3VrRryQlZ2aKv/VzZPP3/N2ewfyNfTMq7POHX/y5s7ytbLGSrmA+eRrWgnrVk0Ep5+eTt6AVUaW51r4Yvdy1LTZ+9X0/I0tN48Gg2X0DTzxHxHsu6Wqh3l9axSdkA1oTdrhGdWL5c17J80dd5rhpPwWdOPBXXkVJeDHWLsSBjOkEEKkpga4VMnIC/cxfqOn0YX85rjpUoZNxgu2DBycAk58ia4yQuJJHZ5HkZT/xvZSYXhEZ0+WPa6ZMTuP9Udu8AlO6v2v1iYRi9bVcwpi778l14WpkPHTdbKhVHT1BmEeqzZPzVmn+L8KKaBbtZFuF6dBa0PvXylRmsdV1OR/8TAWMS2CQhs+LKWDK/5SLOYdTrQLP1IwwpPmE8xNJevrzBdtM+nPTdQWwmJT1LTD3DWPAPcFo/zLlvpH6Gh/lrSZMDe+D4+DuMTWfSIz2PiZEvcLLj7XwnWt0WWRgus4Ygig8w0FEDYdcFhPJESd8mceqvONXEZi7rcHAgipzHXYaJ+MSPzirTOkVf/7k88e1znMZ06Ev4I881f2h0y8law/7rGI6r6eneQRdeEQKbJGQCBHM7ut2f48/DIQz/+XO4uw/D3j+IaJKffeOoKi4JVWjuv415rUaKlHwFydFr6FKiCPiujwnm9g/xlwl+PSh+Y1Q+bebY1HQcA3mRACuYD13MOsTuRX8owQlytmtreh29N+5lQojEBUwEfoOWqtdxbugpl5ZFaN1Gv+Tkq2XTCpLjg+hlM6B8fmwfxPhtXLE3o6nrGkY1eImL/4BLct+oRqvnbqEIKs/Vvs5R3yaHqtnocrq1clSGZImIpYgXR9oauYkYVs91sBw4jMNtLWg50Ann2f+Ab6hICNhmU1l9CP87HRrLARV+XzNjgKqW/1IE3iPq5YQyyxDZDrfD0rIfducH+Ng3lOekvtlmVvJ5myRkFjh6e3Cs7TU0tf86s5/fxLzOr+1zjHjezcQ+Kl9gtp7TUfR4RzCrab2I9GwUQe85HJO+tNVobHfC5QthUnPrrWz6qxdwvJ19yVkls3jEYH7Fzv4dHudBWJT9CJkgsxjRP2BklgnwCpITd+D3fACnfT9aLAfxXk8PTrmvYzgvxlPLpjpYrO/CM/I8zyIx9Rjhrzw46zyIFhZn6XTCedKt86KxfLu5cK/sS2o7j8EYi0VNITZ4HjYWR6qwrINFuZ736O3zQXLgrUVL79cYjSe578kcQn17pAiGBncEuZ9AFsd6H4MnLRA0V2RdxvPAKdRIrfCDuDIqx9+uIjV9Fzd6WYQJi5r4TPOHYqvA5H74TNh1dqjgx1xMPcWI5zCq1EMeUoFFpJ//BT017DtRi44rP0PuHEg9FSnsTYCgGcK3VRav/7mbJGQ0+Lv+Ktp5d65GvdjbfBGh+ZxUSRTER/BZ2bCAtuOwNPtr9WFSo/WuuLhobeuWfgifvV7a8ajV/ZNmZMWW1II8RKFjLyuTOOmDtWAWO1vaNBv6YEKmtVR3ChM+hzRebGq9jIisclti6Ms/lITs5RlSDhUlwPaDdGhMaABgkytSC0PrxcxMyhzpCWK+oDwrmBo8LnUvq3WuJwLvZSaBGtyIqPSzILvNOrGWvawcTKz26ey5KW+AUhA1kzUgEYBTWpxB1TXdLPsq+BwSsgrCpKwqQWABsVt3EMtFvSuZSi011j3UezGT93ErMpU3Lpm5WY6J1Q93U1psmt00pQiberCmvaw0K/+H4f++p4rpzRRTsakgpC1rhtJiU4XMbaqVlXkYCVllOFIuG05A9jnUijVd4+FKl7QZvcMJjcQilkL9qJXG0LJLOGmkKnpKmtmupCC8hL1SQZcx6euUxkZ1dzpXFlWo55acKmrltr1IQrZtq4YKlk9gEWOejuIvZv4NuU9KF01vj8mcs3FhQH4um6JHFReytexlExwJzGpOZrGSJjDc2ywN9Ft9jzRaqYDS4hPUjttFLd2WFzdQyF7gWeB0Zh0sYTe6/I+42adtyYIKta0JyA7BNegYeKD5YuoVX3lha/sR0lpynbmvZNe+e2PgPta1klrFhUyOWtGzdw5h1ykMxDjHbR7A6j149zKnbL2dztn9+6QfBtMbAxhfl9H8A7f2eGOEbDqI8wW+O8zF4RBtibW19W3cpytxsnovpp5pK1AG8m1+xNXJ0v/CTWmBT61FK9WJi3yutJAVs5fF9d7+d1jrdISZFVMZyNfyGFhC/Ob5zIrLdQ4MvAJ7rG6MkBWpb7pEBNZFQBIK5kqg1z3Uy3UBEXem5VHT7cdDJeojgdiIHORfA0u3D2MazsZ6uRacr7CQSW4Viu8fs1vjT8fVhJVtJeJGA7un5l34HyYy6+SJKczE/g6/6wiaTAJMlhPwjamXgS+wzBAnSMgMUU07vZDcYLymw2sxPrnwrLbjJ+Bo4ZyDzXvh6PPiG93144rlq7pWUSHj7N3dh+BjfsHQOOLjQ7hsrSuyyXMu2L+q7W2cUfZtYGJYhxZHH7zf/IS47viayjYDfCQhM0AlURGZb9mhzHiOYxBT5QBRZubkAe00ktNP8XQ2G19bTl4srdIy1GghabWapHPlzmbm7K1yBlA4z8qE6p0iM41yBITsbsImBp4j/nR2jRWMy4WxfdKTkG2fuqCS6BKQZ+DUu2fp3qBckNe7E/S835WUJR6k4hgLhxFW/wVcaBNq0Ob6tvBa+CeMxbV2YtB75lr2snXiQgjHdTaSUSIC9NxN9J5r3PMkZMatu51TcmUGTjs0SR8E54vVMYCYRuiS/r1lXqlk11KxtxHlbRaTLbOyvt0h/VnNMs3b7slJyLZ7DVH5uBk4uXtYKpScL1Zt3tZ8pd5fRrpKCpky47g+IVJWHNZzNynDLKMkJSEzSk3t4HIqL6ZeaJIuG9kXqxZ6TqG6t5Z7oYJCpti7ru5wLq5UKDKrWa552z09Cdl2r6EdXz42sN2VWU9Ma+WKYnwUj/5im7oUy6CMaxUTMs7edXWH5fE1ocisZhl2GSQpCZlBKmpnFlNEeuYO3PKKvtXvYvCZzgC3GpC4gPHrx7N7J9TDfu1/N3bGriJCprJ31zkEE+WsdruK1Pgf0VXHNmMWUGX/HA9fIRcLdRXzn0nIeBp0vE0IiFgMf4Ijh9WrnGb8oCwHnPBG8vdwzxV8DmHPW2hrZOE5+S4SpsY2HO4LYjqXuHJH00H02Y6tO3JFjAdwdl32MhMyvDoLomkECCa21+slBKe3y9pElUPO50RCxtOgYyJABAxJgITMkNVGhSYCRIAnQELG06BjIkAEDEmAhMyQ1UaFJgJEgCdAQsbToGMiQAQMSYCEzJDVRoUmAkSAJ0BCxtOgYyJABAxJgITMkNVGhSYCRIAnQELG06BjIkAEDEmAhMyQ1UaFJgJEgCdAQsbToGMiQAQMSYCEzJDVRoUmAkSAJ0BCxtOgYyJABAxJgITMkNVGhSYCRIAnQELG06BjIkAEDEmAhMyQ1UaFJgJEgCdAQsbToGMiQAQMSYCEzJDVRoUmAkSAJ0BCxtOgYyJABAxJgITMkNVGhSYCRIAnQELG06BjIkAEDEmAhMyQ1UaFJgJEgCdAQsbToGMiQAQMSYCEzJDVRoUmAkSAJ0BCxtOgYyJABAxJgITMkNVGhSYCRIAnQELG06BjIkAEDEmAhMyQ1UaFJgJEgCdAQsbToGMiQAQMSYCEzJDVRoUmAkSAJ/D/FrB/JkNpCsYAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TP / (TP + TN )\n",
    "\n",
    "24 / (24 + 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Precision:** \n",
    "\n",
    "The **precision** will be \"how many are correctly classified among that class\"\n",
    "\n",
    "24 out of 27\n",
    "\n",
    "is correct 88% of the time in detection of the customers who Purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[65,  3],\n",
       "       [ 8, 24]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recall (or sensitivity or the true positive rate) :\n",
    "\n",
    "ratio of positive instances that are correctly detected by the classifier"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# == TP / (TP + FN )\n",
    "24 / (24 + 8 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recall:**\n",
    "The **recall** means \"how many of this class you find over the whole number of element of this class\"\n",
    "\n",
    "24 out of 32\n",
    "\n",
    "it detects 75% of the customers who Purchased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "The **f1-score** is the harmonic mean between precision & recall"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **support** is the number of occurence of the given class in your dataset (so you have 68 of class 0 and 32 of class 1, which is a really well balanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another Eg.:"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6OMC_P0diaoD"
   },
   "source": [
    "## Visualising the Training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 23189,
     "status": "ok",
     "timestamp": 1588265336596,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "_NOjKvZRid5l",
    "outputId": "6fa60701-9aa4-46f2-a6aa-0f9b0aad62b3"
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "X_set, y_set = sc.inverse_transform(X_train), y_train\n",
    "\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",
    "\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",
    "\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()\n",
    "\n",
    "## Visualising the Test set results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SZ-j28aPihZx"
   },
   "source": [
    "## Visualising the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 43807,
     "status": "ok",
     "timestamp": 1588265357223,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "qeTjz2vDilAC",
    "outputId": "00fb10bc-c726-46b8-8eaa-c5c6b584aa54"
   },
   "outputs": [],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.88725351, 0.11274649],\n",
       "       [0.83324206, 0.16675794],\n",
       "       [0.80549253, 0.19450747],\n",
       "       [0.91056209, 0.08943791],\n",
       "       [0.90253903, 0.09746097],\n",
       "       [0.99212979, 0.00787021],\n",
       "       [0.9848589 , 0.0151411 ],\n",
       "       [0.26779092, 0.73220908],\n",
       "       [0.99419108, 0.00580892],\n",
       "       [0.49579544, 0.50420456],\n",
       "       [0.96395406, 0.03604594],\n",
       "       [0.97081333, 0.02918667],\n",
       "       [0.83936154, 0.16063846],\n",
       "       [0.63040685, 0.36959315],\n",
       "       [0.98450737, 0.01549263],\n",
       "       [0.66506265, 0.33493735],\n",
       "       [0.72016534, 0.27983466],\n",
       "       [0.98774666, 0.01225334],\n",
       "       [0.01139953, 0.98860047],\n",
       "       [0.95799724, 0.04200276],\n",
       "       [0.91070984, 0.08929016],\n",
       "       [0.03668135, 0.96331865],\n",
       "       [0.72304389, 0.27695611],\n",
       "       [0.11787963, 0.88212037],\n",
       "       [0.99551213, 0.00448787],\n",
       "       [0.02704431, 0.97295569],\n",
       "       [0.92232099, 0.07767901],\n",
       "       [0.92371914, 0.07628086],\n",
       "       [0.80661256, 0.19338744],\n",
       "       [0.84341507, 0.15658493],\n",
       "       [0.97980137, 0.02019863],\n",
       "       [0.70700864, 0.29299136],\n",
       "       [0.06782035, 0.93217965],\n",
       "       [0.84785394, 0.15214606],\n",
       "       [0.98566324, 0.01433676],\n",
       "       [0.99652123, 0.00347877],\n",
       "       [0.98018715, 0.01981285],\n",
       "       [0.93442624, 0.06557376],\n",
       "       [0.97244034, 0.02755966],\n",
       "       [0.44947292, 0.55052708],\n",
       "       [0.93387384, 0.06612616],\n",
       "       [0.71655153, 0.28344847],\n",
       "       [0.94139491, 0.05860509],\n",
       "       [0.96051284, 0.03948716],\n",
       "       [0.19538951, 0.80461049],\n",
       "       [0.97375051, 0.02624949],\n",
       "       [0.7002962 , 0.2997038 ],\n",
       "       [0.07188441, 0.92811559],\n",
       "       [0.99116814, 0.00883186],\n",
       "       [0.14035391, 0.85964609],\n",
       "       [0.00979045, 0.99020955],\n",
       "       [0.96217391, 0.03782609],\n",
       "       [0.88084143, 0.11915857],\n",
       "       [0.58394773, 0.41605227],\n",
       "       [0.01920237, 0.98079763],\n",
       "       [0.67887409, 0.32112591],\n",
       "       [0.91662981, 0.08337019],\n",
       "       [0.95734278, 0.04265722],\n",
       "       [0.51367858, 0.48632142],\n",
       "       [0.99652752, 0.00347248],\n",
       "       [0.97962483, 0.02037517],\n",
       "       [0.06548521, 0.93451479],\n",
       "       [0.99080034, 0.00919966],\n",
       "       [0.6089113 , 0.3910887 ],\n",
       "       [0.99814664, 0.00185336],\n",
       "       [0.01837828, 0.98162172],\n",
       "       [0.96204152, 0.03795848],\n",
       "       [0.97375051, 0.02624949],\n",
       "       [0.78482933, 0.21517067],\n",
       "       [0.53552032, 0.46447968],\n",
       "       [0.40700336, 0.59299664],\n",
       "       [0.79520675, 0.20479325],\n",
       "       [0.98880303, 0.01119697],\n",
       "       [0.74469446, 0.25530554],\n",
       "       [0.93185794, 0.06814206],\n",
       "       [0.99091371, 0.00908629],\n",
       "       [0.46321694, 0.53678306],\n",
       "       [0.70589942, 0.29410058],\n",
       "       [0.2766666 , 0.7233334 ],\n",
       "       [0.11134787, 0.88865213],\n",
       "       [0.00394886, 0.99605114],\n",
       "       [0.02248034, 0.97751966],\n",
       "       [0.98696583, 0.01303417],\n",
       "       [0.98964178, 0.01035822],\n",
       "       [0.10734812, 0.89265188],\n",
       "       [0.45522288, 0.54477712],\n",
       "       [0.5969289 , 0.4030711 ],\n",
       "       [0.0050397 , 0.9949603 ],\n",
       "       [0.56296967, 0.43703033],\n",
       "       [0.66147882, 0.33852118],\n",
       "       [0.58308908, 0.41691092],\n",
       "       [0.21824871, 0.78175129],\n",
       "       [0.99149077, 0.00850923],\n",
       "       [0.99207365, 0.00792635],\n",
       "       [0.96750141, 0.03249859],\n",
       "       [0.91608066, 0.08391934],\n",
       "       [0.98628144, 0.01371856],\n",
       "       [0.52483762, 0.47516238],\n",
       "       [0.14989075, 0.85010925],\n",
       "       [0.27844761, 0.72155239]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# proba of positive class\n",
    "ypred_prob = classifier.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import  roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9540441176470588"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, ypred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, ypred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ff27ccdd88>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(fpr, tpr, \"b:\", label=\"LogisticReg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--') # Dashed diagonal\n",
    "# [...] # Add axis labels and grid\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_test, y_test, cv=3,method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_test,y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr, \"b:\", label=\"Logistic Regression\")\n",
    "\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(y_trues, y_preds, labels, x_max=1.0):\n",
    "    fig, ax = plt.subplots()\n",
    "    for i, y_pred in enumerate(y_preds):\n",
    "        y_true = y_trues[i]\n",
    "        fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "        auc = roc_auc_score(y_true, y_pred)\n",
    "        ax.plot(fpr, tpr, label='%s; AUC=%.3f' % (labels[i], auc), marker='o', markersize=1)\n",
    "\n",
    "    ax.legend()\n",
    "    ax.grid()\n",
    "    ax.plot(np.linspace(0, 1, 20), np.linspace(0, 1, 20), linestyle='--')\n",
    "    ax.set_title('ROC curve')\n",
    "    ax.set_xlabel('False Positive Rate')\n",
    "    ax.set_xlim([-0.01, x_max])\n",
    "    _ = ax.set_ylabel('True Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\n",
    "    [y_test]*2,\n",
    "    [ypred_prob,y_scores_forest],\n",
    "    ['Logistic Regression','Random Forest']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k fold cross validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "kfold = model_selection.KFold(n_splits=5)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForest with Cross Validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "modelRf = RandomForestClassifier(n_estimators=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsRf = model_selection.cross_val_score(modelRf,X_train,y_train,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.86666667, 0.85      , 0.85      , 0.9       , 0.98333333])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsRf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsRf.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_Rf=model_selection.cross_val_predict(modelRf,X_test,y_test,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[63,  5],\n",
       "       [ 6, 26]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predicted_Rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predicted_Rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOsvB/iqEjYj3VN6C/JbvkE",
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "logistic_regression.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}