Machine Learning with Scikit-Learn - 37 - Preprocessing - Scaling.ipynb

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    "## <center> Preprocessing Methods </center>\n",
    "\n",
    "- binarization\n",
    "- scaling\n",
    "- normalization\n",
    "- mean removal\n",
    "- etc.\n",
    "\n",
    "### 1. Binarization"
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    "from sklearn import preprocessing\n",
    "import numpy as np \n",
    "\n",
    "data = np.array([[2.2, 5.9, -1.8], [5.4, -3.2, -5.1], [-1.9, 4.2, 3.2]])"
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      "Binarized data:\n",
      "\n",
      " [[ 1.  1.  0.]\n",
      " [ 1.  0.  0.]\n",
      " [ 0.  1.  1.]]\n"
     ]
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   "source": [
    "bindata = preprocessing.Binarizer(threshold=1.5).transform(data)\n",
    "print('Binarized data:\\n\\n', bindata)"
   ]
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    "### 2. Mean Removal"
   ]
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     "text": [
      "Mean (before)=  [ 1.9         2.3        -1.23333333]\n",
      "Standard Deviation (before)=  [ 2.98775278  3.95052739  3.41207008]\n"
     ]
    }
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   "source": [
    "print('Mean (before)= ', data.mean(axis=0))\n",
    "print('Standard Deviation (before)= ', data.std(axis=0))"
   ]
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      "Mean (after)=  [  0.00000000e+00   0.00000000e+00   7.40148683e-17]\n",
      "Standard Deviation (after)=  [ 1.  1.  1.]\n"
     ]
    }
   ],
   "source": [
    "scaled_data = preprocessing.scale(data)\n",
    "\n",
    "print('Mean (after)= ', scaled_data.mean(axis=0))\n",
    "print('Standard Deviation (after)= ', scaled_data.std(axis=0))"
   ]
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    "### 3. Scaling\n",
    "\n",
    "- StandardScaler - => features with a mean=0 and variance=1\n",
    "- MinMaxScaler - => features in a 0 to 1 range\n",
    "- Normalizer - => feature vector to a Euclidean length=1"
   ]
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     "data": {
      "text/plain": [
       "array([[ 2.2,  5.9, -1.8],\n",
       "       [ 5.4, -3.2, -5.1],\n",
       "       [-1.9,  4.2,  3.2]])"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data"
   ]
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     "text": [
      "MinMaxScaler applied on the data:\n",
      " [[ 0.56164384  1.          0.39759036]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.81318681  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "minmax_scaler = preprocessing.MinMaxScaler(feature_range=(0,1))\n",
    "data_minmax = minmax_scaler.fit_transform(data)\n",
    "print('MinMaxScaler applied on the data:\\n', data_minmax)"
   ]
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