FeatureScaling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling\n",
    "\n",
    "In this notebook, we will use the [Pima Diabetes Dataset](https://www.kaggle.com/uciml/pima-indians-diabetes-database) from the UCI Machine Learning Database to learn how to scale feature data. Scaling means to change the range of values for the data so that features (columns) that have vastly different numerical ranges can be more easily compared. We will look at 2 types of scaling - z-score standardization and Min-max scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#preprocessing functions will be used to standardize/normalize data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = \"../AdvData_DATASETS/diabetes.csv\"\n",
    "\n",
    "pima_df = pd.read_csv(filepath)\n",
    "pima_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 768\n",
       "Glucose                     768\n",
       "BloodPressure               768\n",
       "SkinThickness               768\n",
       "Insulin                     768\n",
       "BMI                         768\n",
       "DiabetesPedigreeFunction    768\n",
       "Age                         768\n",
       "Outcome                     768\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if any missing values in the data\n",
    "pima_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics\n",
    "pima_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001AFB2EFD198>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001AFB2F25710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001AFB2F4AB00>,\n",
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       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the frequency count for each column\n",
    "pima_df.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the descriptive statistics and the frequency distribution plots that each feature (column) has varying ranges. Features such as Insulin have a high maximum value but other features like DiabetesPedigreeFunction have low maximum values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot frequency count data but using the same scale (minimum and maximum value out of all columns)\n",
    "pima_df.hist(figsize=(10,10), sharex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scaling will help to find any drastic effects that occur in the data when comparing multiple features. If we try to see any effects when putting the features on the same scale in the default data, as in the above charts, smaller values cannot be seen.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Z-score Standardization\n",
    "\n",
    "Z-score standardization is the process of converting the data to have a mean of 0 and a standard deviation of 1. The z-score is calculcated by subtracting the mean for a feature (column) by each data point value and dividing by the standard deviation.\n",
    "### \\begin{align}  z = \\frac{(value - mean)}{std dev} \\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first 5 rows of the 'Glucose' column\n",
    "pima_df['Glucose'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Method 1: manual calculation of z-scores for 'Glucose' column\n",
    "\n",
    "#mean for the column\n",
    "mean = pima_df['Glucose'].mean()\n",
    "\n",
    "#standard deviation of the column\n",
    "std = pima_df['Glucose'].std()\n",
    "\n",
    "#each value in column minus the mean and then divide by the standard deviation\n",
    "glucose_z_manual = (pima_df['Glucose'] - mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#values for mean and standard deviation of 'Glucose column'\n",
    "mean, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first 5 rows of z-score standardized 'Glucose' column\n",
    "glucose_z_manual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Method 2: use scikit-learn to calculate z-scores\n",
    "\n",
    "#set StandardScale function to a variable (easier to type)\n",
    "#scaler will use z-score formula on the column\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#fit_transform calculates the mean and std, and replaces any missing values w/ mean if needed\n",
    "#'Glucose' is in double set of square brackets in order to make it a dataframe\n",
    "glucose_zscore = scaler.fit_transform(pima_df[['Glucose']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean and standard deviation of z-score standardized 'Glucose' column\n",
    "glucose_zscore.mean(), glucose_zscore.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#because fit_transform made an array, we have to change it back into a Series type (pandas dataframe column)\n",
    "glu_z_col = pd.Series(glucose_zscore.reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first 5 rows of z-score standardized glucose column\n",
    "glu_z_col.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BEFORE: frequency count plot of 'Glucose' column\n",
    "pima_df['Glucose'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#AFTER: frequency count plot of 'Glucose' column (z-score standardization)\n",
    "#range is much smaller\n",
    "glu_z_col.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#show z-score standardization for all columns\n",
    "\n",
    "#list to hold column names\n",
    "colnames = list(pima_df.columns)\n",
    "\n",
    "#calculate z-scores\n",
    "zscore_df = pd.DataFrame(scaler.fit_transform(pima_df), columns = colnames)\n",
    "\n",
    "#plot frequency distribution with same scale range\n",
    "zscore_df.hist(figsize=(10,10), sharex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Min-max Scaling\n",
    "Max-min scaling transforms the data into a range from 0 to 1. The new minimum value of the column will always be 0 and the new maximum value of the column will always be 1. The values in between are calculated by using the original value and subtracting the column's minimum value, then dividing by the maximum value of the column minus the minimum value.\n",
    "\n",
    "### \\begin{align}  m = \\frac{(value - min)}{max - min} \\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MinMaxScaler function will calculate the formula\n",
    "minmax_sc = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use MinMaxScaler function and fit_transform function on the 'Glucose' column\n",
    "glucose_minmax = minmax_sc.fit_transform(pima_df[['Glucose']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean and standard deviation of Min-Max 'Glucose' column\n",
    "glucose_minmax.mean(), glucose_minmax.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert array into a dataframe column\n",
    "#look at first 5 rows of min-max values\n",
    "glu_mm_col = pd.Series(glucose_minmax.reshape(-1))\n",
    "glu_mm_col.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#verify the range is between 0 and 1\n",
    "glu_mm_col.min(), glu_mm_col.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "glu_mm_col.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#show min-max scaling for all columns\n",
    "\n",
    "#calculate z-scores\n",
    "minmax_df = pd.DataFrame(minmax_sc.fit_transform(pima_df), columns = colnames)\n",
    "\n",
    "#plot frequency distribution with same scale range\n",
    "minmax_df.hist(figsize=(10,10), sharex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tips for Predictive Models\n",
    "\n",
    "- Z-score standardization is most useful for regression models, such as linear regression and logistic regression\n",
    "- Min-Max scaling is more relevant for algorithms that calculate distances between data points, such as K-Nearest Neighbors and K-Means Clustering"
   ]
  }
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