Min Max Normalization Playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "   0    1   2   3    4     5      6   7  8\n",
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dataset/diabetes.csv\",header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "0     0.000\n",
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       "3     0.000\n",
       "4     0.000\n",
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       "dtype: float64"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_min = np.min(df, axis=0)\n",
    "old_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     17.00\n",
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       "2    122.00\n",
       "3     99.00\n",
       "4    846.00\n",
       "5     67.10\n",
       "6      2.42\n",
       "7     81.00\n",
       "8      1.00\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "old_max = np.max(df, axis=0)\n",
    "old_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     17.000\n",
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       "2    122.000\n",
       "3     99.000\n",
       "4    846.000\n",
       "5     67.100\n",
       "6      2.342\n",
       "7     60.000\n",
       "8      1.000\n",
       "dtype: float64"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "denominator = abs(old_max - old_min)\n",
    "denominator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "[768 rows x 9 columns]"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "scaler = np.divide((df - old_min), denominator)\n",
    "scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "<p>768 rows × 9 columns</p>\n",
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       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "763  0.588235  0.507538  0.622951  0.484848  0.212766  0.490313  0.039710   \n",
       "764  0.117647  0.613065  0.573770  0.272727  0.000000  0.548435  0.111870   \n",
       "765  0.294118  0.608040  0.590164  0.232323  0.132388  0.390462  0.071307   \n",
       "766  0.058824  0.633166  0.491803  0.000000  0.000000  0.448584  0.115713   \n",
       "767  0.058824  0.467337  0.573770  0.313131  0.000000  0.453055  0.101196   \n",
       "\n",
       "            7    8  \n",
       "0    0.483333  1.0  \n",
       "1    0.166667  0.0  \n",
       "2    0.183333  1.0  \n",
       "3    0.000000  0.0  \n",
       "4    0.200000  1.0  \n",
       "..        ...  ...  \n",
       "763  0.700000  0.0  \n",
       "764  0.100000  0.0  \n",
       "765  0.150000  0.0  \n",
       "766  0.433333  1.0  \n",
       "767  0.033333  0.0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_max = 1\n",
    "new_min = 0\n",
    "new_range = scaler * (new_max - new_min) + new_min\n",
    "new_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MinMaxNormalization(data, new_min=0, new_max=1):\n",
    "    old_min = np.min(data, axis=0)\n",
    "    old_max = np.max(data, axis=0)\n",
    "    denominator = abs(old_max - old_min)\n",
    "    \n",
    "    return np.divide((data - old_min), denominator) * ( new_max - new_min ) + new_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "            0         1         2         3         4         5         6  \\\n",
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       "767  0.033333  0.0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MinMaxNormalization(df)"
   ]
  }
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