Random Forest Classification (1).ipynb

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   "source": [
    "## Importing libraries and the dataset"
   ]
  },
  {
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    "colab": {},
    "colab_type": "code",
    "id": "YvGPUQaHhXfL"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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    }
   ],
   "source": [
    "ds = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = ds.iloc[:, :-1].values\n",
    "y = ds.iloc[:, -1].values\n",
    "print(X,y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YvxIPVyMhmKp"
   },
   "source": [
    "## Splitting the dataset into the Training and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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)"
   ]
  },
  {
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     "base_uri": "https://localhost:8080/",
     "height": 1000
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1300,
     "status": "ok",
     "timestamp": 1588269343329,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "P3nS3-6r1i2B",
    "outputId": "e4a38929-7ac1-4895-a070-4f241ad247c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " [    34  43000]\n",
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      " [    48  30000]\n",
      " [    29  43000]\n",
      " [    36  52000]\n",
      " [    27  54000]\n",
      " [    26 118000]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1294,
     "status": "ok",
     "timestamp": 1588269343330,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "8dpDLojm1mVG",
    "outputId": "2a9b0425-9e6d-480f-b32a-ebae6f413dbe"
   },
   "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",
      " 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0\n",
      " 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0\n",
      " 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1\n",
      " 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1613,
     "status": "ok",
     "timestamp": 1588269343657,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "qbb7i0DH1qui",
    "outputId": "b10e7737-ae02-4c0c-b49f-8d961e2921b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    30  87000]\n",
      " [    38  50000]\n",
      " [    35  75000]\n",
      " [    30  79000]\n",
      " [    35  50000]\n",
      " [    27  20000]\n",
      " [    31  15000]\n",
      " [    36 144000]\n",
      " [    18  68000]\n",
      " [    47  43000]\n",
      " [    30  49000]\n",
      " [    28  55000]\n",
      " [    37  55000]\n",
      " [    39  77000]\n",
      " [    20  86000]\n",
      " [    32 117000]\n",
      " [    37  77000]\n",
      " [    19  85000]\n",
      " [    55 130000]\n",
      " [    35  22000]\n",
      " [    35  47000]\n",
      " [    47 144000]\n",
      " [    41  51000]\n",
      " [    47 105000]\n",
      " [    23  28000]\n",
      " [    49 141000]\n",
      " [    28  87000]\n",
      " [    29  80000]\n",
      " [    37  62000]\n",
      " [    32  86000]\n",
      " [    21  88000]\n",
      " [    37  79000]\n",
      " [    57  60000]\n",
      " [    37  53000]\n",
      " [    24  58000]\n",
      " [    18  52000]\n",
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      " [    34  43000]\n",
      " [    31  34000]\n",
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      " [    27  88000]\n",
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      " [    27  84000]\n",
      " [    35  20000]\n",
      " [    43 112000]\n",
      " [    27  58000]\n",
      " [    37  80000]\n",
      " [    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",
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      " [    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": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1608,
     "status": "ok",
     "timestamp": 1588269343658,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "kj1hnFAR1s5w",
    "outputId": "1f3a92ea-9844-4d4c-ca5f-075fa4ba98e0"
   },
   "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": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9fQlDPKCh8sc"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1600,
     "status": "ok",
     "timestamp": 1588269343659,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "syrnD1Op2BSR",
    "outputId": "b1fa2925-b7de-4530-b015-01bb51e742b4"
   },
   "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": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1595,
     "status": "ok",
     "timestamp": 1588269343659,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "JUd6iBRp2C3L",
    "outputId": "48320ca4-33e0-4bfe-92ba-91c06bcf714e"
   },
   "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bb6jCOCQiAmP"
   },
   "source": [
    "## Random Forest Classification model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 154
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1589,
     "status": "ok",
     "timestamp": 1588269343659,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "e0pFVAmciHQs",
    "outputId": "79719013-2ffa-49f6-b49c-886d9ba19525"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yyxW5b395mR2"
   },
   "source": [
    "## Predicting results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1584,
     "status": "ok",
     "timestamp": 1588269343660,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "f8YOXsQy58rP",
    "outputId": "81727e50-9f85-49ad-a41e-5891aa34e6bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(scaler.transform([[30,87000]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1578,
     "status": "ok",
     "timestamp": 1588269343660,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "p6VMTb2O4hwM",
    "outputId": "f160d9d3-e4cd-4484-db9d-99028dfed42d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
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      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h4Hwj34ziWQW"
   },
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1898,
     "status": "ok",
     "timestamp": 1588269343985,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "D6bpZwUiiXic",
    "outputId": "b4ab126b-4118-461e-f02a-cfe538ae6a71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63  5]\n",
      " [ 4 28]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.91"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "confusionMatrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusionMatrix)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6OMC_P0diaoD"
   },
   "source": [
    "## Visualising the Training and Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 87793,
     "status": "ok",
     "timestamp": 1588269429885,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "_NOjKvZRid5l",
    "outputId": "7efb744e-3ecb-4303-8543-8fabf49f64bc"
   },
   "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 2D 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 2D 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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",
      "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 = scaler.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(scaler.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('Random Forest Classification (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 172477,
     "status": "ok",
     "timestamp": 1588269514574,
     "user": {
      "displayName": "Hadelin de Ponteves",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
      "userId": "15047218817161520419"
     },
     "user_tz": -240
    },
    "id": "qeTjz2vDilAC",
    "outputId": "f1a33b1a-e8b6-4b3e-e98a-a0c7c66fed56"
   },
   "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 2D 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 2D 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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",
      "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 = scaler.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(scaler.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('Random Forest Classification (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPA7K2PAkEFgaKFIvslUMEc",
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "random_forest_classification.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "7172489404153e401551447a6add1098f5ea319aeba3a174a7674b5487a0d64a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}