Program for Manual Computation (Standardizing Data) Function.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Program for Manual Computation (Standardizing Data) Function\n",
    "\n",
    "https://www.mathsisfun.com/data/standard-deviation-formulas.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization is utilized in the realms of Machine Learning when features of data being inpuuted into machine learning algorithims have large differences within their ranges. To simplify this when we think of units of measurements and how they are measured and their output (eg. euros, pounds, meters, miles etc.)\n",
    "\n",
    "When there are differences within ranges such as above certain machine learning models/algorithims will develop issue's depending on algorithim and specifized domain problem. To further simplify this, an example regarding algorithims that are based on distance computation (K - Means CLustering Algorithim), when one features has large ranging values, the distance will be governed by this particular feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mathematics within Standardization (Z - Score)\n",
    "Z-SCORE\n",
    "\n",
    "Z-Score is one of the most popular methods to standardize data, and can be done by subtracting the mean and dividing by the standard deviation for each value of each feature.\n",
    "\n",
    "Z-Score standard deviation\n",
    "Once the standardization is done, all the features will have a mean of zero, a standard deviation of one, and thus, the same scale.\n",
    "\n",
    "Important Notes\n",
    "- There exist other standardization methods.\n",
    "- Remember standardizing the data does change the distribution (shape) of the data but instead the magnitude. This will be clearly evident in our second function which has been designed to visually observe and compare the difference between the data in its original form and the data standardized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import square root calculation from math package\n",
    "from math import sqrt\n",
    "#import visualization packages\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function 1\n",
    "###### Perform data stadardization on an arbitrary number of elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ZScore(*args):\n",
    "    #Create empty list\n",
    "    list1 = []\n",
    "    #Initialize sum all *args\n",
    "    sum_all = 0\n",
    "    #Accumulate the sum\n",
    "    for x in args:\n",
    "        sum_all += x\n",
    "    #Add all elements \n",
    "    for i in args:\n",
    "        list1.append(i)\n",
    "    #Find the list length (total count)\n",
    "    length = len(list1)\n",
    "    #Calculate the average\n",
    "    average = sum_all / length\n",
    "    print('Average',average)\n",
    "    \n",
    "    #find the VARIANCE\n",
    "    list2 = []\n",
    "    list3 = []\n",
    "    #calculate the difference between each data point and the average\n",
    "    for x in args:\n",
    "        list2.append(x - average)\n",
    "    #square each value returned from the above calculation\n",
    "    for i in list2:\n",
    "        list3.append(i ** 2)\n",
    "    #Initialize sum all \n",
    "    sum_all1 = 0\n",
    "    #Accumulate the sum of the above (squared) calculation and divdie by number of elements (variance)\n",
    "    for x in list3:\n",
    "        sum_all1 += x\n",
    "    variance = sum_all1 / length\n",
    "    print('Variance', variance)\n",
    "    \n",
    "    #find the standard deviation \n",
    "    std = sqrt(variance)\n",
    "    print('Standard Deviation', std)\n",
    "    \n",
    "    #Z - Score Standardization\n",
    "    standardized1 = []\n",
    "    standardized2 = []\n",
    "    for i in args:\n",
    "        standardized1.append(i - average)\n",
    "    for x in standardized1:\n",
    "        standardized2.append(x / std)\n",
    "    print(standardized2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average 48.75\n",
      "Variance 942.1875\n",
      "Standard Deviation 30.695072894521687\n",
      "[-0.7737398142565998, -0.7737398142565998, -0.12216944435630522, 1.6696490728695048]\n"
     ]
    }
   ],
   "source": [
    "#Call the function \n",
    "ZScore(25,25,45,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Function One Evaluation\n",
    "\n",
    "All above calculations have been externally checked for error. Can confirm the above fucntion performs all calculation correctly and accurately.\n",
    "\n",
    "The next step is to create a function which we can visually compare the affect of standardizing data in comparison to the orginal date. This will be achieved through a kernel density plot to visualize the distribution of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function 2\n",
    "###### Standardize an arbitrary number of elements and visually compare the dissimilarities via kernel denstiy plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ZScoreVisual(*args):\n",
    "    #Create empty list\n",
    "    list1 = []\n",
    "    #Initialize sum all *args\n",
    "    sum_all = 0\n",
    "    #Accumulate the sum\n",
    "    for x in args:\n",
    "        sum_all += x\n",
    "    #Add all elements \n",
    "    for i in args:\n",
    "        list1.append(i)\n",
    "    print('Original Data', list1)\n",
    "    #Find the list length (total count)\n",
    "    length = len(list1)\n",
    "    #Calculate the average\n",
    "    average = sum_all / length\n",
    "    print('Average',average)\n",
    "    \n",
    "    #find the VARIANCE\n",
    "    list2 = []\n",
    "    list3 = []\n",
    "    #calculate the difference between each data point and the average\n",
    "    for x in args:\n",
    "        list2.append(x - average)\n",
    "    #square each value returned from the above calculation\n",
    "    for i in list2:\n",
    "        list3.append(i ** 2)\n",
    "    #Initialize sum all \n",
    "    sum_all1 = 0\n",
    "    #Accumulate the sum of the above (squared) calculation and divdie by number of elements (variance)\n",
    "    for x in list3:\n",
    "        sum_all1 += x\n",
    "    variance = sum_all1 / length\n",
    "    print('Variance :', variance)\n",
    "    \n",
    "    #find the standard deviation \n",
    "    std = sqrt(variance)\n",
    "    print('Standard Deviation', std)\n",
    "    \n",
    "    #Z - Score Standardization\n",
    "    standardized1 = []\n",
    "    standardized2 = []\n",
    "    for i in args:\n",
    "        standardized1.append(i - average)\n",
    "    for x in standardized1:\n",
    "        standardized2.append(x / std)\n",
    "    print('Data Standardized', standardized2)\n",
    "    \n",
    "    #Comparative Visualizations\n",
    "    #subplots\n",
    "    fig,(ax1,ax2) = plt.subplots(1,2)\n",
    "    #plot size\n",
    "    fig.set_size_inches(20, 8)\n",
    "    sns.set_style('darkgrid')\n",
    "    sns.distplot(args, kde = True, hist = True, color = 'b', ax=ax1)\n",
    "    sns.distplot(standardized2, kde = True, hist = True, color = 'r', ax=ax2)\n",
    "    #Adding title\n",
    "    fig.suptitle('Orignal Data | Z - Score Standardized Data')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Data [25, 25, 45, 100]\n",
      "Average 48.75\n",
      "Variance : 942.1875\n",
      "Standard Deviation 30.695072894521687\n",
      "Data Standardized [-0.7737398142565998, -0.7737398142565998, -0.12216944435630522, 1.6696490728695048]\n"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Call Z-Score Visual Function\n",
    "ZScoreVisual(25,25,45,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Function Two Evaluation\n",
    "\n",
    "We can clearly observe from the above example visuals on the second function call that the distribution has not changed after the data has been (Z -Score) standardized. This is an important feature to understand regarding this type of standardization. \n",
    "\n",
    "Furthermore, we can evidently see that the magnitude of the data has been changed when the original data has been standarized. This is also another important feature to understand when standaridizng data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}