Normalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1d91a1b",
   "metadata": {},
   "source": [
    "#Normalisation - it is the technique to remove the units and keep the magnitude only on a common scale - most problems are usually solved by scaling however minmax when you already know the numerical quantity is going to bounded between two set values, use robust scaling when too many outliers, use maxabscaling when sparse dataset with too many zeros\n",
    "\n",
    "use everything as beginner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1125098",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d01b68a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('wine_data.csv',header=None,usecols=[0,1,2])\n",
    "df.columns=['Class label', 'Alcohol', 'Malic acid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb7172d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class label</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class label  Alcohol  Malic acid\n",
       "0            1    14.23        1.71\n",
       "1            1    13.20        1.78\n",
       "2            1    13.16        2.36\n",
       "3            1    14.37        1.95\n",
       "4            1    13.24        2.59"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a34dde54",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Alcohol', ylabel='Density'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#class label is dependent and alcohol and malic acid is independent\n",
    "sns.kdeplot(df['Alcohol'])\n",
    "sns.kdeplot(df['Malic acid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "502dacb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\fairy\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Alcohol', ylabel='Malic acid'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot\n",
    "sns.scatterplot(data =df,x = 'Alcohol', y = 'Malic acid', hue = 'Class label', palette = ['Red','Blue', 'Green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "312437c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((124, 2), (54, 2))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#minmax normalisation - (xi-xmin)/(xmax-xmean) - 0 to 1\n",
    "#first split train test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('Class label', axis=1),\n",
    "                                                    df['Class label'],\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=42)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce014549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#min max \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "mnscaler = MinMaxScaler()\n",
    "mnscaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1e9b256",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = mnscaler.transform(X_train)\n",
    "X_test_scaled = mnscaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e21ba41",
   "metadata": {},
   "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>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>124.000000</td>\n",
       "      <td>124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.508765</td>\n",
       "      <td>0.308143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.221444</td>\n",
       "      <td>0.224504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.331579</td>\n",
       "      <td>0.152749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.503947</td>\n",
       "      <td>0.205703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.701316</td>\n",
       "      <td>0.464358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Alcohol  Malic acid\n",
       "count  124.000000  124.000000\n",
       "mean     0.508765    0.308143\n",
       "std      0.221444    0.224504\n",
       "min      0.000000    0.000000\n",
       "25%      0.331579    0.152749\n",
       "50%      0.503947    0.205703\n",
       "75%      0.701316    0.464358\n",
       "max      1.000000    1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_train_scaled.describe()\n",
    "#min and max are now from 0 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb0e3262",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e0442c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4660c8dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After Scaling')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "sns.kdeplot(X_train, ax=axes[0])\n",
    "axes[0].set_title('Before Scaling')\n",
    "\n",
    "sns.kdeplot(X_train_scaled, ax=axes[1])\n",
    "axes[1].set_title('After Scaling')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7642bdc1",
   "metadata": {},
   "source": [
    "mean and sd can change . only we squish the dat between 0 to 1. outliers can too change a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e20f97ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean normalization\n",
    "#(xi-xmean)/(xmax- xmean) range from -1 to 1 - not used much people use standardscaler instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "067ca7fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MaxAbsScaler- data - sparse distribution/ if too many zeroes \n",
    "#xi/xmax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d52f12ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#robust scaling\n",
    "#xi-xmedian/IQR - used during outliers- "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}