Multi Class Logistic Regression.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi Class Logistic Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = iris.data,iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Always make sure to scale the data before running Logistic Regression as it involves Gradient Descent and it can be computantionally expensive specially if data size is huge and its not scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
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       "       [5.1, 3.8, 1.5, 0.3],\n",
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       "       [4.6, 3.6, 1. , 0.2],\n",
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       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
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       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
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       "       [4.6, 3.2, 1.4, 0.2],\n",
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       "       [6.9, 3.1, 4.9, 1.5],\n",
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       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
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       "       [7.6, 3. , 6.6, 2.1],\n",
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       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
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       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
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       "         1.32509732e-01],\n",
       "       [-7.79513300e-01, -8.22569778e-01,  8.07091462e-02,\n",
       "         2.64141916e-01],\n",
       "       [-1.02184904e+00, -2.43394714e+00, -1.46640561e-01,\n",
       "        -2.62386821e-01],\n",
       "       [ 6.86617933e-02, -1.31979479e-01,  2.51221427e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 1.89829664e-01, -1.97355361e+00,  1.37546573e-01,\n",
       "        -2.62386821e-01],\n",
       "       [ 3.10997534e-01, -3.62176246e-01,  5.35408562e-01,\n",
       "         2.64141916e-01],\n",
       "       [-2.94841818e-01, -3.62176246e-01, -8.98031345e-02,\n",
       "         1.32509732e-01],\n",
       "       [ 1.03800476e+00,  9.82172869e-02,  3.64896281e-01,\n",
       "         2.64141916e-01],\n",
       "       [-2.94841818e-01, -1.31979479e-01,  4.21733708e-01,\n",
       "         3.95774101e-01],\n",
       "       [-5.25060772e-02, -8.22569778e-01,  1.94384000e-01,\n",
       "        -2.62386821e-01],\n",
       "       [ 4.32165405e-01, -1.97355361e+00,  4.21733708e-01,\n",
       "         3.95774101e-01],\n",
       "       [-2.94841818e-01, -1.28296331e+00,  8.07091462e-02,\n",
       "        -1.30754636e-01],\n",
       "       [ 6.86617933e-02,  3.28414053e-01,  5.92245988e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 3.10997534e-01, -5.92373012e-01,  1.37546573e-01,\n",
       "         1.32509732e-01],\n",
       "       [ 5.53333275e-01, -1.28296331e+00,  6.49083415e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 3.10997534e-01, -5.92373012e-01,  5.35408562e-01,\n",
       "         8.77547895e-04],\n",
       "       [ 6.74501145e-01, -3.62176246e-01,  3.08058854e-01,\n",
       "         1.32509732e-01],\n",
       "       [ 9.16836886e-01, -1.31979479e-01,  3.64896281e-01,\n",
       "         2.64141916e-01],\n",
       "       [ 1.15917263e+00, -5.92373012e-01,  5.92245988e-01,\n",
       "         2.64141916e-01],\n",
       "       [ 1.03800476e+00, -1.31979479e-01,  7.05920842e-01,\n",
       "         6.59038469e-01],\n",
       "       [ 1.89829664e-01, -3.62176246e-01,  4.21733708e-01,\n",
       "         3.95774101e-01],\n",
       "       [-1.73673948e-01, -1.05276654e+00, -1.46640561e-01,\n",
       "        -2.62386821e-01],\n",
       "       [-4.16009689e-01, -1.51316008e+00,  2.38717193e-02,\n",
       "        -1.30754636e-01],\n",
       "       [-4.16009689e-01, -1.51316008e+00, -3.29657076e-02,\n",
       "        -2.62386821e-01],\n",
       "       [-5.25060772e-02, -8.22569778e-01,  8.07091462e-02,\n",
       "         8.77547895e-04],\n",
       "       [ 1.89829664e-01, -8.22569778e-01,  7.62758269e-01,\n",
       "         5.27406285e-01],\n",
       "       [-5.37177559e-01, -1.31979479e-01,  4.21733708e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 1.89829664e-01,  7.88807586e-01,  4.21733708e-01,\n",
       "         5.27406285e-01],\n",
       "       [ 1.03800476e+00,  9.82172869e-02,  5.35408562e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 5.53333275e-01, -1.74335684e+00,  3.64896281e-01,\n",
       "         1.32509732e-01],\n",
       "       [-2.94841818e-01, -1.31979479e-01,  1.94384000e-01,\n",
       "         1.32509732e-01],\n",
       "       [-4.16009689e-01, -1.28296331e+00,  1.37546573e-01,\n",
       "         1.32509732e-01],\n",
       "       [-4.16009689e-01, -1.05276654e+00,  3.64896281e-01,\n",
       "         8.77547895e-04],\n",
       "       [ 3.10997534e-01, -1.31979479e-01,  4.78571135e-01,\n",
       "         2.64141916e-01],\n",
       "       [-5.25060772e-02, -1.05276654e+00,  1.37546573e-01,\n",
       "         8.77547895e-04],\n",
       "       [-1.02184904e+00, -1.74335684e+00, -2.60315415e-01,\n",
       "        -2.62386821e-01],\n",
       "       [-2.94841818e-01, -8.22569778e-01,  2.51221427e-01,\n",
       "         1.32509732e-01],\n",
       "       [-1.73673948e-01, -1.31979479e-01,  2.51221427e-01,\n",
       "         8.77547895e-04],\n",
       "       [-1.73673948e-01, -3.62176246e-01,  2.51221427e-01,\n",
       "         1.32509732e-01],\n",
       "       [ 4.32165405e-01, -3.62176246e-01,  3.08058854e-01,\n",
       "         1.32509732e-01],\n",
       "       [-9.00681170e-01, -1.28296331e+00, -4.30827696e-01,\n",
       "        -1.30754636e-01],\n",
       "       [-1.73673948e-01, -5.92373012e-01,  1.94384000e-01,\n",
       "         1.32509732e-01],\n",
       "       [ 5.53333275e-01,  5.58610819e-01,  1.27429511e+00,\n",
       "         1.71209594e+00],\n",
       "       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 1.52267624e+00, -1.31979479e-01,  1.21745768e+00,\n",
       "         1.18556721e+00],\n",
       "       [ 5.53333275e-01, -3.62176246e-01,  1.04694540e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 7.95669016e-01, -1.31979479e-01,  1.16062026e+00,\n",
       "         1.31719939e+00],\n",
       "       [ 2.12851559e+00, -1.31979479e-01,  1.61531967e+00,\n",
       "         1.18556721e+00],\n",
       "       [-1.14301691e+00, -1.28296331e+00,  4.21733708e-01,\n",
       "         6.59038469e-01],\n",
       "       [ 1.76501198e+00, -3.62176246e-01,  1.44480739e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 1.03800476e+00, -1.28296331e+00,  1.16062026e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 1.64384411e+00,  1.24920112e+00,  1.33113254e+00,\n",
       "         1.71209594e+00],\n",
       "       [ 7.95669016e-01,  3.28414053e-01,  7.62758269e-01,\n",
       "         1.05393502e+00],\n",
       "       [ 6.74501145e-01, -8.22569778e-01,  8.76433123e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 1.15917263e+00, -1.31979479e-01,  9.90107977e-01,\n",
       "         1.18556721e+00],\n",
       "       [-1.73673948e-01, -1.28296331e+00,  7.05920842e-01,\n",
       "         1.05393502e+00],\n",
       "       [-5.25060772e-02, -5.92373012e-01,  7.62758269e-01,\n",
       "         1.58046376e+00],\n",
       "       [ 6.74501145e-01,  3.28414053e-01,  8.76433123e-01,\n",
       "         1.44883158e+00],\n",
       "       [ 7.95669016e-01, -1.31979479e-01,  9.90107977e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 2.24968346e+00,  1.70959465e+00,  1.67215710e+00,\n",
       "         1.31719939e+00],\n",
       "       [ 2.24968346e+00, -1.05276654e+00,  1.78583195e+00,\n",
       "         1.44883158e+00],\n",
       "       [ 1.89829664e-01, -1.97355361e+00,  7.05920842e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 1.28034050e+00,  3.28414053e-01,  1.10378283e+00,\n",
       "         1.44883158e+00],\n",
       "       [-2.94841818e-01, -5.92373012e-01,  6.49083415e-01,\n",
       "         1.05393502e+00],\n",
       "       [ 2.24968346e+00, -5.92373012e-01,  1.67215710e+00,\n",
       "         1.05393502e+00],\n",
       "       [ 5.53333275e-01, -8.22569778e-01,  6.49083415e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,\n",
       "         1.18556721e+00],\n",
       "       [ 1.64384411e+00,  3.28414053e-01,  1.27429511e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 4.32165405e-01, -5.92373012e-01,  5.92245988e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 3.10997534e-01, -1.31979479e-01,  6.49083415e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,\n",
       "         1.18556721e+00],\n",
       "       [ 1.64384411e+00, -1.31979479e-01,  1.16062026e+00,\n",
       "         5.27406285e-01],\n",
       "       [ 1.88617985e+00, -5.92373012e-01,  1.33113254e+00,\n",
       "         9.22302838e-01],\n",
       "       [ 2.49201920e+00,  1.70959465e+00,  1.50164482e+00,\n",
       "         1.05393502e+00],\n",
       "       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,\n",
       "         1.31719939e+00],\n",
       "       [ 5.53333275e-01, -5.92373012e-01,  7.62758269e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 3.10997534e-01, -1.05276654e+00,  1.04694540e+00,\n",
       "         2.64141916e-01],\n",
       "       [ 2.24968346e+00, -1.31979479e-01,  1.33113254e+00,\n",
       "         1.44883158e+00],\n",
       "       [ 5.53333275e-01,  7.88807586e-01,  1.04694540e+00,\n",
       "         1.58046376e+00],\n",
       "       [ 6.74501145e-01,  9.82172869e-02,  9.90107977e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 1.89829664e-01, -1.31979479e-01,  5.92245988e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 1.28034050e+00,  9.82172869e-02,  9.33270550e-01,\n",
       "         1.18556721e+00],\n",
       "       [ 1.03800476e+00,  9.82172869e-02,  1.04694540e+00,\n",
       "         1.58046376e+00],\n",
       "       [ 1.28034050e+00,  9.82172869e-02,  7.62758269e-01,\n",
       "         1.44883158e+00],\n",
       "       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 1.15917263e+00,  3.28414053e-01,  1.21745768e+00,\n",
       "         1.44883158e+00],\n",
       "       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,\n",
       "         1.71209594e+00],\n",
       "       [ 1.03800476e+00, -1.31979479e-01,  8.19595696e-01,\n",
       "         1.44883158e+00],\n",
       "       [ 5.53333275e-01, -1.28296331e+00,  7.05920842e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,\n",
       "         1.05393502e+00],\n",
       "       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,\n",
       "         1.44883158e+00],\n",
       "       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,\n",
       "         7.90670654e-01]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the Data in Train and Test Set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X_scaled,y,test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_model = LogisticRegression(random_state=0, multi_class='ovr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(multi_class='ovr', random_state=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = log_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 2, 2, 1, 1, 2, 0, 2,\n",
       "       0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 0,\n",
       "       0, 1, 1, 2, 1, 2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Accuracy of the Model on Test Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0,  0],\n",
       "       [ 0, 11,  4],\n",
       "       [ 0,  1, 15]], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       0.92      0.73      0.81        15\n",
      "           2       0.79      0.94      0.86        16\n",
      "\n",
      "    accuracy                           0.90        50\n",
      "   macro avg       0.90      0.89      0.89        50\n",
      "weighted avg       0.91      0.90      0.90        50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict on Completely New Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model.predict(scaler.transform([[5.9, 3. , 5.1, 1.8]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model.predict(scaler.transform([[2., 3.1 , 5.1, 4.8]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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