{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abalone Age Classification Project Report\n",
    "\n",
    "This report is for the data analysis project for DSCI 522 (Data Science workflows); a course in the Master of Data Science program at the University of British Columbia. Content includes key exploratory data analysis, statistical summaries and figures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Abalones are endangered marine snails that are found in the cold coastal water around the world. The price of an abalone is positively associated with its age. However, determining how old an abalone is can be a very complex process. Having a machine learning model that classifies the age of abalones will efficiently accelerate this manual process, and benefit researchers on abalones and add value to the domain.\n",
    "\n",
    "In this project we are classifying abalone snails into \"young\" and \"old\" according to their number of rings based on input features such as abalone's gender, height with meat in shell, weight of the shell etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the data set\n",
    "\n",
    "The Abalone data set that was used in this project was sourced from the UC\n",
    "    Irvine Machine Learning Repository published in 1995. It can be\n",
    "    found <a href=\"https://archive-beta.ics.uci.edu/ml/datasets/abalone\" >here</a>. Each row in\n",
    "    the data set represents the attributes and physical measurements of\n",
    "    abalones including number of rings, sex, length, diameter, height, weight,\n",
    "    etc. The number of rings were\n",
    "    counted manually using a microscope by the researchers. The age of an abalone is represented by its number of\n",
    "    rings plus 1.5 as number of years lived. \n",
    "    \n",
    "This dataset was developed in 1995. Despite the age of this dataset, predictive models that can be made from this dataset are likely still relevant for the modern day. It takes thousands to millions of years in order for any meaningful changes to be made to the biological characteristics and features of animals. Darwin's theory of evolution and natural selection applies to all animals, including abalone. Thus, the biological features of the abalone within this dataset are likely still relevant today, and meaningful predictive models can still be created from this dataset.\n",
    "\n",
    "The sex variable in this dataset includes three categories: female, male and infant. This is a curious component of the dataset since abalone sex is actually binary (male or female). Therefore, infant is not really considered a sex of abalone but instead is in reference to its age. Thus, this could pose a potential limitation in the predictive model which we will discuss later.\n",
    "Abalone of different sex has different body composition with distinct economic values.\n",
    "The data set has already removed its missing values and the range of the continuous values have been scaled for use with an ANN (by dividing by 200).\n",
    "\n",
    "In the research paper \"A Quantitative Comparison of Dystal and Backpropagation\" that David Clark, Zoltan Schreter and Anthony Adams submitted to the Australian Conference on Neural Networks (ACNN'96), the original abalone data set was treated as a 3-category classification problem (grouping ring classes 1-8, 9 and 10, and 11 on). In our project, we will treat the data set as a 2-categorical classification problem (grouping ring classes less or equal to 11, and more than 11).\n",
    "\n",
    "Here, we aim to answer one research question with a Logistic Regression classification model: \n",
    "\n",
    "- **Given the input features including sex, size and weight, is an abalone young or old?**\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Findings and results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering that the price of an abalone is positively associated with its age, creating a predictive model that is able to automate the manual process of determining the age of an abalone would be valuable to those wishing to determine the age of an abalone, whether it is researchers or those interested in making a profit in the abalone market. Of note, the number of rings present on the abalone directly determines the age of the abalone. For this project, we are separating the abalone into two classes, young and old, based on a threshold on the rings. Moreover, we are using a threshold whereby abalone that contain more than 11 rings would be placed in the old class and otherwise the abalone would be placed in the young class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../results/eda/target_distribution.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 1. There is an imbalance in the distribution of young and old abalone in the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking at the distributions of young and old abalone in the training, it's quite clear that there is a class imbalance in the age of the abalone (Figure 1). In fact, the number of young abalone is around triple the number of old abalone in the training data. In the model, we will test a bunch of metrics including accuracy, precision, recall, f1 score, ROC AUC, and average precision. We are going to focus more on f1 score and ROC AUC because we want to observe the overall performance of our model instead of putting more weight on one class over another.\n",
    "\n",
    "Next, we looked to elaborate upon the distribution of numerical features in the training data in relation to the target class (Figure 2). The distribution of the numerical features seemed to follow a similar shape for both the old class and the young class. The distribution of the length and diameter features was left-skewed, while the whole weight, viscera weight, shucked weight, and shell weight appeared to have a right-skewed distribution. The height feature did not have a clear skewness to the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../results/eda/histograms.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 2. The distributions of the numerical features are similar between young and old abalone, but may provide insight into slight differences in the features between young and old abalone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at these histograms, we can see the pronounced effect of the class imbalance. The majority of the values in each numerical feature histogram has a higher proportion of young abalone examples compared to old abalone examples. It's difficult to say for certain whether there are clear distinctions in these features between the young and old class. However, there are a few areas to be aware of that might help us understand how the model might make predictions. Observing the length feature, we can see that when the length of the abalone is below 0.38, almost all of the examples are from the young class, with very few examples from the old class. Similarly for the diameter feature, when the diameter of the abalone is below 0.25, the majority of examples are from the young class, with hardly any examples from the old class. This aligns with our intuitions about abalone, since we should expect younger abalone to be smaller (i.e smaller diameter and length).\n",
    "\n",
    "There are rare occasions when there are examples that are predominantly from the old class. For example, when shell weight is above 0.6, the majority of examples are of the old class. Additionally, when whole weight is above 2.2, the old class begins to be the more predominant class. Again, this aligns with our intuitions about abalone. We would expect older abalone to be larger, and thus, have a larger whole weight. In terms of shell weight, perhaps abalone of the old class require a larger shell for their larger bodies compared to young abalone, which could explain why there are more examples of old abalone which have a shell weight above 0.6.\n",
    "\n",
    "Observing the distribution of sexes in the training data, there appears to be a relatively even spread of Female (F), Male (M) within both the young and old target classes, whereas there are a greater number of Infant (I) examples in the young class compared to the old class (Figure 3). Specifically, there were 354 examples of abalone that were male and 340 examples of abalone that were Female in the old class and there were 882 examples of abalone that were male and 684 examples of abalone that were female in the young class. For the Infant class, there was a greater number of examples of Infant in the young class (1009) compared to the old class (72)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HgcxV23UQhBDIzc3F73//exQUFOiO+5WWlmLPnj2IiorCnTt3dNMNtSciaspsdoghOzsbOTk5Nab6WDoONQEymQyhoaGQyWS4fv06unXrBgBwcnLCW2+9hQULFuDGjRvw9/evsz0RUVNms0MMSqUSSqVSb1o2RxCoHqKiohAVFYWgoCBkZmbi8ePH8PHxwbRp0zB58mT0798fe/fuxdOnT9G5c2eD7YmIiOcg0EskNDQUH330Ec6dO4fevXtj27ZtaN68OYYOHYpPP/0Up06dQlBQELZu3QqFQmGwPRER8V4M1MjxXgxERJbBCyURERGRBAsEIiIikmCBQERERBI8SZGIiMgCTD2vbueff2dSu6oLLpk6vb44gkBEREQSRkcQSkpKar38rKHp9aFscw3z589v0GUSERGR+YyOIKxatQoqlUp3UwkhBJKSkmq9NjQRERHV3+nTpxEWFgYXFxeEhYXh1KlTunmXL1/G8OHD4ezsjMDAQItlMDqC4ODggIULF+LUqVNISkrCxx9/jG3btiE+Pr7Bw2Q/9LHq1RQ/HBeCiP5drNYfERGRKaKiojB9+nRMnDgR6enpiI6O1ps3duxY7NmzB6WlpXBzs8y1YIxeKOnZs2eYPXs2UlJS4ODgAK1Wi08++QRffvklFIqGPcfR2hdKYoFARESWUp+TFF1cXKBWq+Hk5ISSkhK0a9cOGo0GQgi4urri/v37cHJyAmDDkxQdHR2RnJyMcePGobKyEjNnzsSyZcsavDggIiKi5wICApCWlobS0lKkpaWhZ8+eunl+fn7YunUrSkpKcODAAYtlMFoglJeXY8aMGdi2bRscHBywdu1azJ07F+Xl5RYLRURE1JR9++23SE1NRdu2bZGamopvvvlGN++rr77C4sWL4e7ujo0bN1osg9ECYeXKlVi/fj3GjRuHq1evIjIyEomJiUhISLBYKFuLiYmBTCbT+0lJSQEAaDQaREVF6R4TERE1tN69e+PEiRPQaDQ4efIk+vbtqzuMoFQqcf78eWg0Gnz77bcWObwAmFAgVFRUIC4uDps3b4aXlxe2bNmCxMRE3bcajFGpVLpfsiqVqr55rSI5ORlCCN3P6NGj0bVrVwBAbGwsvvvuOxsnJCIisiyjJxLExsbiwYMHWLt2LQIDA6FUKhEcHIxWrVqZ1EH1oqCxFAjV5eTkoLi4GEOHDgXwvHggIiIyxtQrJNoroyMIT548QWBgIGbPno2srCwAQFZWFrKzsy0ezh4sXLgQixcvtnUMIiIiqzJaIGRkZMDR0RGhoaG6aVqtFoWFhRYNZg/27dsHZ2dnDB482NZRiIiIrMroIYbHjx/jvffe07uscn5+PiIiIurVcXZ2NnJycmpM9anXMhuaSqXC119/besYRETUCJUs7WhSO+fP7lg4yYsxWiD06tULy5cvh7+/PwDgzJkz2LNnDz799NN6daxUKqFUKvWmWfMqisZs3boVXl5eCAoKsnUUIiIiqzNaIERERCAsLAy7du3STRs7dqzkl7shKpUKixYt0nts77RaLZYsWYL09HTJvJiYGL2vOE6bNs2a0YiIiKzC6DkIMpkM27dvx44dO5CQkICMjAxs27bN5A5UKpXu64KNoTgAALlcjvz8fPTo0UMyr/pXIFkcEBFRQ6uoqICfnx/Onj0LADh37hy8vb2Rn58vuYGTTCbTe27VY5lMhpkzZ8Ld3R2enp7IyMgAAFy9elV3o6d+/frVeVVkgwWCWq3WfVNBLpdj4MCBcHR0xIMHD1BSUlK/tSciIqJaKRQKLFiwQHf1xNTUVMyaNQvTp09HTEwM1Go1oqOj9W7gVJvhw4fjypUrSE9PR0xMDAAgOjoao0aNQlFREQ4ePIjKykqDzzd4s6ZLly4hLS0NKpUKlZWVCA4ORkFBAQAgODgYJ0+ehIODwwutvCG8WRMREb0s6nOSYnl5OQYMGICDBw8iKCgI+fn58PT0rPUGTlqtVm/kQAihdwMnIQTkcrnZN3oyOIJw8OBB3f8PHz6MgoICuLu7Y9asWTh//jx2795t0ooTERGReZo1a4Zp06Zh6tSpGDNmDNq0aVPrDZzc3Nxw9OhRPHnyRHcYoabqhyG8vb2xfft2lJaWIjc3t84MBguEx48f48GDBwCAvXv3AgBmz56NNWvWIDo6GufPnzd7hYmIiMg077//Pk6cOIGPPvoIQO03cIqPj8dbb70FPz8/nDhxwugy161bh7i4OLRv3x7Lly+v8xwEg3N69eqFhQsXorKyEps2PR/6f/311wEArVu3NvleDERERGS+vLw8DBw4EF26PD8UXnUDp+r69u2rd8J8fHw8AEgOG1Q9Hjx4MP75z3+isrIS+/btw7lz5wz2b7BAGDlyJPr27Yt169YBAAYNGoT+/fvr5r/yyismraA5lG2uYf78+Q2+XCIiosaksrISs2fPxurVqxt82Z06dcLdu3fh5+dX58UADRYIcrkc+/fvx6ZNm1BZWYmJEyfqjmMolUp4eXk1eGgiIqKXRX2ukOjg4GD0HIEX9csvv5jUzuC3GGzB2t9ioMaF3zohIrIeoxdKIiIioqaHBQIRERFJsEAgIiIiCRYI1Cjt2bMHb775Jtzc3ODu7o6PPvoI5eXluvn379+Hj4+P3o21NBoNoqKi9KYREVHtGrxAUKlUkMlket/VPH/+PORyeaO5WRPZv7///e+YNGkSbt26hYKCAhw7dgyZmZkAnl+idNKkSXBzc9N7TmxsLL777jtbxCUianSM3u75RQQGBmLFihXYsmULAGDFihUIDAy0RFfURH3++ee6/9+9excymUx39805c+Zg/PjxyM/P13tOcnKyVTMSETVmFjnEMHbsWFy4cAFnz57FvXv3kJ+fjzFjxliiK2riYmJi0LVrV8yfPx/dunXDf//3f0MIobtzGRERvRiLjCDIZDLMnTsXCQkJ8PDwwJw5c3D16lVLdEVNXHJyMv7rv/4Lo0ePRp8+fXDs2DGkpKTorgBapfqlSImIyDiLFAgAMHnyZHzxxRdo2bIl8vLysHTpUr352dnZyMnJqfEsH0vFoZdYp06d0KdPH+Tm5iI5OVl3KCEmJgbBwcEsDoiIXoDFCoTmzZsjLi4Ocrkcjo6OkvlKpRJKpVJvWjavpEgmiouLQ58+fTBq1CicPn0aBw8e1DsvgYiI6sdiBQLw/FaVRJYwYMAArFq1Cu+99x68vLyQmJiIbt261fmcmJgYva84cmSBiMgw3ouBGg3ei4GIyHp4oSQiIiKSYIFAREREEiwQiIiISIIFAhEREUmwQCAiIiIJi37N0VzKNtcwf/58W8cgIiJq8uxqBGHQoEG2jkBERESws+sgEBERkX2wqxEEIiIisg8sEIiIiEiCBQIRERFJsEAgIiIiCRYIREREJMECgYiIiCRYIBAREZEECwQiIiKSYIFAREREEiwQiIiISIIFAhEREUmwQCAiIiIJFghEREQkwQKBiIiIJFggEBERkQQLBCIiIpJggUBEREQSLBCIiIhIggUCERERSbBAICIiIgkWCERERCTBAoHIiioqKuDn54ezZ88CAM6dOwdvb2/k5+cjLCwMLi4uCAsLw6lTpyCTyfSeW/VYJpNh5syZcHd3h6enJzIyMgAAV69exfDhw+Hs7Ix+/fpBoVBYd+WI6KXCAoHIihQKBRYsWIBvvvkGAJCamopZs2Zh+vTpiImJgVqtRnR0NKKjo+tczvDhw3HlyhWkp6cjJiYGABAdHY1Ro0ahqKgIBw8eRGVlpcXXh4heXjIhhLB1CKKmpLy8HAMGDMDBgwcRFBSE/Px8eHp6Qq1Ww8nJCSUlJWjXrh00Gg20Wq3eyIEQQvcvAAghIJfLIYSAq6sr7t+/DycnJ732REQvgiMIRFbWrFkzTJs2DVOnTsWYMWPQpk0bBAQEIC0tDaWlpUhLS0PPnj3h5uaGo0eP4smTJ7rDCDVVPwzh7e2N7du3o7S0FLm5udZaHSJ6SXEEgcgGnj17Bm9vb2RnZ6NLly44ffo0oqOjcfr0aQQEBCAlJQUnT57EZ599BoVCgalTp2LZsmWSEQTgXyMFR44cQXR0NO7cuYOIiAhs27YN5eXlNlxLImrMWCAQ2cDx48eRkJCALVu2NPiyKysrsW/fPnzyySc4c+ZMgy+fiJoGnuZMZGWVlZWYPXs2Vq9e3eDL7tSpE+7evQs/Pz98/fXXDb58Imo6OIJAREREEjxJkYiIiCRYIBAREZEECwQiIiKSYIFAREREEiwQiIiISIIFAhEREUmwQCAiIiIJFghEREQkwQKBiIiIJOyqQMjOzrZ1BCIiIoKdXWq5ZGlHW0cgoiZC3qE3Wnywz9YxiOyWXY0gEBERkX1ggUBEREQSLBCIiIhIggUCETV5eXl5kMlkej8hISEAACEEDh8+rHsMAGq1Gl988QV8fX3RsmVLjBo1Cjdu3LBVfCKLsGiBoFKpJG86lUplyS6JiMwWHBwMIYTuZ8eOHfD39wcA5OfnY9iwYXrtb9y4gYcPH+LQoUO4c+cO/P398cc//tEW0YksxuIjCHFxcXpvPBYIRGTPtFot4uLi8PnnnwN4Xjzk5ubqtQkKCsKqVavg7e0NV1dXREZGorCw0BZxiSyGhxiIiKrZtGkT+vTpgx49epjUXgiBDRs2ICkpycLJiKzL4gXCokWLeIiBiBqFiooKfPnll4iLizP5OZ9//jn+8z//E927d7dgMiLrU1i6g7i4uFqLguzsbOTk5OhNm23pMEREddiwYQMGDx6Mzp07G21bUVGBNWvWIDIyEr1797ZCOiLrsniBYIhSqYRSqdSbVrI00UZpiKipKysrw4oVK3Do0CGjbTUaDQ4ePIiYmBg4OztbIR2R9fEcBCIiAOvXr8fYsWPh4eGhNz0vLw8hISG6fwHg/PnzGDt2LFxcXCRfiyR6WfBeDETUJPFeDER14wgCERERSbBAICIiIgkWCERERCTBAoGIiIgkWCAQERGRhNHrIJSUlNT6PV9D0+tjjcMczJ8/v0GXSUREROYzOoKwatUqqFQqaLVaAM+vO56UlITVq1dbPBwRERHZhtERBAcHByxcuBCnTp1CUlISPv74Y2zbtg3x8fENHmZ2ZSKvpkhEREY5vrECiqDf2zrGS81ogTBv3jxcu3YNKSkp2LFjB7RaLf74xz9i3rx51shHRERENmD0EIOjoyOSk5Mxbtw4VFZWYubMmVi2bBkUCpvdxoGIiIgszGiBUF5ejhkzZmDbtm1wcHDA2rVrMXfuXJSXl1sjHxEREdmA0QJh5cqVWL9+PcaNG4erV68iMjISiYmJSEhIsEY+IiKiOgkhsGDBArzyyivw9fXFjh076pwOPL8jZ1RUFFJSUmwV2+4ZLRAqKioQFxeHzZs3w8vLC1u2bEFiYqLuWw3GqFQq3d3OVCpVffMSERHp2bJlC44fP46LFy/if/7nfzBjxgwUFRUZnA4AsbGx+O6772yc3L4ZPZEgNjYWDx48wNq1axEYGAilUong4GC0atXKpA6qFwUsEIiIqKEdP34c48aNw29+8xsMGDBAd3tuQ9PDw8ORnJxs69h2z+gIwpMnTxAYGIjZs2cjKysLAJCVlYXs7GyLhyMiIjKmc+fOyMzMxMOHD3H//n2UlZXh9u3bBqeTaYyOIGRkZMDR0RGhoaG6aVqtFoWFhRYNRkREZIpp06YhJycHHTt2RKdOnVBRUYFXXnkFEyZMqHU6mcZogfD48WO89957epdVzs/PR0RERL06zs7ORk5Ojt602fVaIhERNUWOjo7YuHEjgOcnJvbs2RO9evUyOJ1MY7RA6NWrF5YvXw5/f38AwJkzZ7Bnzx58+umn9epYqVRCqVTqTeNVFImI6EU9efIESUlJ6NKlC3x8fIxOp7oZLRAiIiIQFhaGXbt26aaNHTtW8svdEJVKhUWLFuk9JiIiakizZs3Chg0bMHHiRGzatMno9JiYGL2vOE6bNs2qeRsDmRBCGGuk1WqRmZmJCxcuwM/PD3XKNYwAAA29SURBVGPGjIFc3vB3ii5Z2rHBl0lERC8f3ovB8gz+ller1bpvKsjlcgwcOBCOjo548OABSkpKrBaQiIiIrM/gIYaHDx8iKysLSqUSlZWV+Pd//3cUFBQAANauXYuTJ0/CwcHBakGJiIjIegyOIBw8eFD3/8OHD6OgoADu7u6YNWsWzp8/j927d1slIBEREVmfwRGEx48f48GDBwCAvXv3AgBmz56NhQsXQqFQ4Pz58xgzZox1UhIREZFVGSwQevXqhYULF6KyslJ35ufrr78OAGjdurXJ92IgIiKixsdggTBy5Ej07dsX69atAwAMGjQI/fv31823xNWo1jjMwfz58xt8uURERGQegwWCXC7H/v37sWnTJlRWVmLixImQyWQAnl/kyMvLy2ohiYiIyLpMug6CtfA6CERE1tPigx8h79DH1jHITjX81Y6IiIio0WOBQERERBIsEIiIiEiCBQIREeHGjRuIiIiAs7MzOnfujNTUVABASEgIZDKZ3k9eXh6Sk5MxbNgwuLq6wtvbG4mJvBvvy6bBCwSVSgWZTIYTJ07opp0/fx5yuZx3ciQislOxsbEYNGgQioqKsHnzZsybNw937txBbm4uhBAQQqCsrAzdu3eHl5cX8vLyEBcXh8LCQmRmZmLJkiX45ZdfbL0a1IAsMoIQGBiIFStW6B6vWLECgYGBluiKiIgawNOnT+Hs7IzmzZujT58+aN26NRwdHfXapKSkYPTo0WjXrh1SUlIwdOhQKBQKlJSUwMPDA23btrVRerIEg9dBqI+xY8di69atOHv2LNq2bYv8/HyMHTvWEl0REVEDSE5OxsCBA7Fr1y4oFAqkpKToXRBPo9FgzZo1OHbsmN7z+vfvj0uXLuHIkSNwdna2dmyyIIuMIMhkMsydOxcJCQn4y1/+gjlz5uguskRERPZnx44dGDFiBNasWYOBAwfi22+/RfXL5PzlL3/BxIkTJaMEf//737F//3688847KCsrs3ZssiCLjCAAwOTJk/HFF1+gZcuWyMvLw9KlS/XmZ2dnIycnR2/abEuFISKiOqWnp+OLL75AQEAAAgIC4OfnhwsXLqBbt254+PAhvvvuO+Tm5kqeJ5PJEBoaCplMhuvXr6Nbt242SE+WYLECoXnz5oiLi4NcLpccxwKeX65ZqVTqTStZyrNgiYhswc/PDxs2bEBwcDAKCgrw+PFjvPrqqwCAhIQE/OEPf0Dr1q117aOiohAVFYWgoCBkZmbi8ePH8PHxsVF6sgSLFQgA8P7771ty8URE1ECWLl2KqKgoeHh44NVXX8UPP/wAFxcX/Prrr9i0aRMKCgr02oeGhuKjjz7CuXPn0Lt3b2zbtg3Nmze3UXqyBN6LgYioieK9GKguvFASERERSbBAICIiIgkWCERERCTBAoGIiIgkWCAQERGRhEW/5miuNQ5zMH/+fFvHICIiavLs6muO69atw+PHj20dg4iIGhkvLy9MmjTJ1jFeLsKOxMfH2zpCnZivfpivfpivfpivfpiv6eE5CERERCTBAoGIiIgkWCAQERGRhINKpVLZOkR1nTp1snWEOjFf/TBf/TBf/TBf/TBf02JX32IgIiIi+8BDDERERCRhd4cYzp49i40bN6JVq1Zwd3e3WY4zZ87gwoULuiGr2nLZIuvVq1exadMmHD58GMDz7/7aU74rV67gr3/9Kw4dOoRmzZrB09PTrvJVl5GRAbVajU6dOtlNvuzsbGzcuBE5OTnIyckBALvKBwAajQapqakoLy+3u9dfY9h+R48exf/+7//i2bNn6Ny5s8EstsqXm5uLTZs2oXXr1nVmqU++mp+v1Znal60/P5oCuxpBUKvVCA8Px4ULFxAeHo4HDx7YJMfevXsxfvx4ZGVlGcxlq6yXL1/GtWvXcPv2bfz2t7/F4cOH7SrflStXcP36ddy7dw/jx4/H3r177SpflR9//BETJkxAVlaWXeXLysrCzp07UVRUhKKiIpSVldlVPq1Wi5EjR+LYsWMoKSkBYF/vj7KyMt2227lzJ44cOWJX+Q4cOIAJEybg9u3bmDBhAn788Ue7yvePf/wDkZGRuHnzJkaOHAm1Wt3g+Wp+vlZnal+2/vxoKuxqBGHr1q1wcHBASkoKCgoKIJfL0bt3b6vnuHbtGn766Sf4+vpi6NChtea6ePGiTbL6+voiPDwcI0eOxN27d1FcXIx79+7ZZb5r166hefPmuH79ut3kA4CLFy/iww8/RGRkJJycnFBYWGg3+Q4fPgxfX18sWbIE4eHh8PX1tavX39GjR3Ho0CHs3LkTvr6+AGp/39r69TdkyBAkJiZi+fLlOHDggN3k2717N9q0aYOvvvoKN2/etLv3R3p6Onx9fZGQkIBr165Bo9Hg8uXLDZqv5udrdaa+lmz5+dGU2NW9GG7duqUbkvby8sLNmzdtkiM8PBzZ2dl15tJqtTbNWl5ejr/97W/45ptvcODAAUkWIYTN8h05cgTp6enQaDSYOnUqEhMT7SZfcXExpkyZgu+//x6bN28GYH/7NycnB8uXL8fgwYMxYMAA3L5922623+nTp9GuXTskJiaib9++GDZsmF3lq5Kamorw8HB06tTJrvbvpEmTMHDgQEyePBlCCEyaNAlff/213Wy/kJAQTJ8+HTKZDAcOHICHhwfkcnmD5qv5+VqdqfvK1p+/TYVdHWKQyWSQyWR6j+1BbblsnXXevHn47LPPEBwcbLB/W22/Z8+ewcnJCfv378fx48cNZrFFvq+++gqtWrVCZmam7jj1sWPHJFlq7l9rUSqVCA4Oxq1btzBmzBj87W9/0+WpyRb5iouLcf78edy5cwczZszAoUOHDGax1euvrKwMiYmJWLBggS6Hvbx/L1++jJCQEEyePBn5+fn45z//abB/W2y/oUOHYt26dejYsSN69OiBVq1aGcxiiXym7itbf/42FXZVIHh4eOgqwVu3bsHDw8PGiZ6rLZetsmq1WsyaNQvdunXD+PHja83n6elZ6zRrCQ8PR2JiIiZPnozc3Fy7yhcaGoqgoCAUFRWhtLQUpaWlcHNzs5t84eHhiI+PR1JSEt577z27234eHh4YNmwYli1bhjFjxiAvL8+u8gHPi8Bx48ahQ4cOusz28v49cuQI2rdvj1GjRmH06NG6v9LtafsNGzYMc+bMgVqtRp8+fayS7+jRo/j5559N3lf2+rvipWOzu0DU4v79+6JDhw5ixowZokOHDuL+/fs2yXH06FERHh4uwsPDxdGjR2vNZausq1atEgEBASI+Pl7Ex8fbXb6jR4+K+Ph4ERsbK9zd3cWZM2fsKl91cXFxIi4uzq7yVW2/efPmCXd3d3H69Gm7yqdWq4Wnp6eYO3eu8PDwEPn5+XaV79GjR8LPz08UFhbqptlTvuPHj4s2bdqIuXPnig4dOoi8vDy7yifE89fglClTxIABA4RWq23wfDU/X4UQ4u233xYXLlwwuS97+PxoCuxqBKFt27bIyspCt27dkJWVhbZt29okR1lZGYKDgxEcHIyysrJac9kqa+/evTF69Gi9s9ztKZ+HhwdcXFzg6emJQ4cOoVevXnaVrzqlUgmlUmlX+Tp06AAnJyd4enriyJEjCAgIsKt8r7zyCvbv3w8vLy9kZGQgKCjIrvJdvHgRy5Ytg5ubm26aPeUbMGAA9u7di1dffRW7du1Cv3797CofAMjlcgQHB2P37t2QyWQNnq/m5yvw/Jwqf39/k/uyh8+PpoBXUiQiIpspKSnB0aNHMXLkSFtHoRpYIBAREZGEXR1iICIiIvvAAoGIiIgkWCAQERGRBAsEIiIikrCrSy0Tvew0Gg327t2L69evo7y8HK1atUJ4eDj8/f0bvK/s7Gzk5ORg0KBBUCqVDb58Inq5cQSByEqKi4vRrVs3TJgwAXfv3sWDBw9w/fp1XL9+3SL9ZWVl4dNPP631rnlERMZwBIHISvbv349bt26hX79+WL58uWR+eXk5MjIycP36dfj4+CAyMhIKhUIyElD9MQDd/ysqKpCfn4/+/fvrjRjk5ORg2bJlHEkgIrNwBIHISq5cuQIA6NOnj2ReeXk5Bg8ejKlTp+LGjRv4j//4DwwZMgQVFRWSkYDqj6v+P3fuXOzcuRMrVqzA4MGDceLECd2yS0tLdVfdJCIyFUcQiKyktLQUAHQ3EaouIyMDJ06cwAcffICkpCQUFxfj+++/x/bt201a9ujRo6FSqQAAq1at0t0FEnh+A6iqeUREpuIIApGVODk5AQDu3bsnmXf16lUA0LsDIfCvUQdTtWzZEsC/ihEiohfFAoHISnx8fAAABQUFknm+vr4AgLt37wL4VxHh6+urKyw0Go1Z/TVv3hwAUFFR8UJ5iahp4yEGIisZMWIE2rVrh4KCAvzpT3+CQqGAk5MTBg0ahMjISAwcOBB//etf4eLigo0bN+K1117Dm2++idzcXMhkMqSnp0Or1eodPqjLv/3bv0Emk+H777+HTCbDyJEjeZIiEZmMN2sisqKHDx9i3759uHHjBioqKuDm5oZhw4bB398fFRUVyMjIwLVr1+Dj44M333wTzZo1A/D8mgYnT55Ev379UFlZiaysLISHhwOA7v/h4eG6ExerHp84cQLHjh1DeXk5hg8fjtDQUFuuPhE1IiwQiIiISILnIBAREZEECwQiIiKSYIFAREREEiwQiIiISIIFAhEREUn8P2sTJwHSyk1UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../results/eda/sex_dist.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 3. There is an even distribution of male and female abalone within the young and old classes, but a major imbalance in the infant category between young and old abalone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, there is no roughly bias for one particular sex (Male or Female) depending on if the abalone is old or young. However, the greater number of Infant abalone in the young class does give pause. Our intuitions do indeed tell us that more Infant abalone would classified as young, but the bigger issue in this dataset could be that we are predicting whether an abalone is young or old, after being given information about whether an abalone is an Infant which creates redundancy in the predictive model. It is curious why the researchers decided to include the category Infant within the sex feature column. Perhaps when an abalone is an infant, it is difficult to classify the abalone as Male or Female. Without speaking to domain experts, it is difficult to determine the significance behind having an Infant category within the Sex feature.\n",
    "\n",
    "Since the target classes, old and young, are directly determined by counting the number of rings, we were able to determine the correlation of numerical features with the number of rings, as well as the correlation among other features (Figure 4). Based on the correlation values, many of the features are highly correlated with other features. As for correlation with rings, the shell weight seemed to have the greatest correlation value (0.69) with rings, while shucked weight appeared to have the lowest correlation with rings (0.54). Based on the correlation heat map, it appears that the numerical features are at least moderately correlated with rings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../results/eda/correlation_map.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 4. Features are highly correlated with each other and moderately correlated with rings, which is a proxy for the age of the abalone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These correlation values give us some insight into how the predictive model might make its decision. For example, since shell weight and Rings have a moderately high correlation, the shell weight might be an important feature for predicting the age of the abalone. In the context of abalone, this could mean that older abalone require a heavier shell, whereas younger abalone may only need a lighter shell. With these correlation values, our model might be able to pick up on these types of associations. One trend to note is that many of the explanatory features are correlated with each other. For example, the diameter of an abalone is highly correlated with the length of the abalone. This is quite understandable, considering that as an abalone gets larger in diameter, one might expect the length of the abalone to also get larger. However, this does pose some implications for our model. It begs the question, how essential is it to include every single explanatory feature in this model? If diameter is encapsulating the information provided by length, would it be necessary to include both of these features? Discussing with domain experts can help us to determine which features may be more essential, or in the event that we lack access to domain experts, we could conduct automated feature selection in the future to address the redundancy in explanatory features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model results\n",
    "\n",
    "An important note about the training data is that there exists a class imbalance between old and young target classes. With more examples of the target class, young, this could have an effect on the accuracy of the model. Because of this fact, we are considering the f1 score and ROC AUC to account for this class imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../results/model/cv_result.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 5. As the hyperparameter C of logistic regression increases, the validation score increases before leveling off at higher values of C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit a logistic regression on the training data as we are dealing with a binary classification problem. The model set the target class old as 0 and young as 1. We first built a preprocessor which transformed the Sex category by using One-Hot-Encoding and we applied standard scaler on other numeric features. We then used a Grid Search cross validation to determine the best hyperparameter for the logistic regression. As we can see, as the value of C increases, the model's performance on validation sets increase and plateau at around $C = 100$. Note that the hyperparameter, C, of logistic regression is associated with the regularization strength (complexity penalty) of the model. Based on the tuning results, the best logistic regression model occurs when $C = 100$ (Figure 5 and Table 1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>param_logisticregression__C</th>\n",
       "      <th>mean_fit_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.826403</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.034382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.826403</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.028127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.826104</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.048729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.822811</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.820115</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.037579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.798865</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.029433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.775518</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.025489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('../results/model/train_result_table.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 1. A closer look to each parameter and validation score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>Test Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f1</td>\n",
       "      <td>0.902511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>recall</td>\n",
       "      <td>0.953198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>precision</td>\n",
       "      <td>0.856942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>roc_auc</td>\n",
       "      <td>0.856794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>average_precision</td>\n",
       "      <td>0.945716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"../results/model/test_result_table.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 2. Test results on different metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting the model, we used a test set to assess how the model would perform on novel examples. The evaluation metrics on the test data set is shown (Table 2). Based on the model's performance on the test set, the f1 score is 0.9, where the f1 score is the harmonic mean of the model's recall score and precision score.\n",
    "\n",
    "To understand how the features in the dataset are influencing the model's predictions, we calculated the coefficients to demonstrate the importance of each feature in the model (Figure 6 and Table 3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shucked weight</th>\n",
       "      <td>4.142553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_I</th>\n",
       "      <td>0.983024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viscera weight</th>\n",
       "      <td>0.818804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length</th>\n",
       "      <td>0.537147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_F</th>\n",
       "      <td>0.129540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_M</th>\n",
       "      <td>0.121077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>-0.248307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diameter</th>\n",
       "      <td>-0.538840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shell weight</th>\n",
       "      <td>-1.070738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whole weight</th>\n",
       "      <td>-4.306860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"../results/model/coeff_sorted.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 3. Feature importance in our logistic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"../results/model/coeff_bar.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 6. Shucked weight appears to be an important feature in the predictive model for predicting the young class, while Whole weight is an important feature in the predictive model for predicting the old class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the coefficients, shucked weight influences the model the most towards predicting that an abalone is young, whereas whole_weight influences the model the most towards predicting that an abalone is old.\n",
    "\n",
    "It is interesting to observe that the whole weight of an abalone and the shucked weight of an abalone are influence the predictions in opposite directions. By observing the distribution of shucked weight (Figure 2), the shapes of the distributions are quite similar between old and young abalone, and at no point in the distribution are there more examples of old abalone compared to young abalone. In contrast, above a certain threshold, there are more examples of old abalone in the whole weight and shucked weight distributions. Understanding the distributions of these weight features helps us to understand why different types of weight are influencing the prediction in opposite directions. It would be useful to consult a domain expert to see if they would have insight in the differences between old and young abalone in regard to the different types of weight features. However, it's imperative that one takes these feature importances and model performance with a grain of salt, considering that the dataset is imbalanced and that these statistical models don't necessarily explain how the real world works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Findings\n",
    "\n",
    "Based on the model results, we can see that the logistic regression model is performing well on new examples of abalone, as described by an f1 score of 0.90 and ROC AUC score of 0.86. We focus on these two metrics because they evaluate overall performance of model instead of weighing one class over another. Moreover, given a certain set of biological features of abalone, we're able to predict whether an abalone is old or young fairly accurately while minimizing false negatives and false positives. We were able to obtain these results by testing different values for the model's hyperparameter, C, on various validation sets of the abalone training data in order to obtain an optimal logistic regression model (where $C = 100$). We also obtained the coefficients of the various biological features that helped us understand how the features were influencing the prediction. The weight features (shucked weight, whole weight, and shell weight) specifically had a large influence on the model's predictions. Contrasting the distributions of these weight features between the old and young abalone helped us to investigate why shucked weight was having an opposite predictive effect in comparison with whole weight and shell weight, although consulting with domain experts may help us further understand this opposing effect. Overall, the model's ability to predict whether an abalone is young or old based on specific biological characteristics is good but should be taken with a grain of salt given the imbalance of young and old abalone within the dataset, as well as some of the limitations of the included biological characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations and assumptions\n",
    "One limitation is that we found some of the input features are highly correlated. For example, the correlation between whole weight and length of abalone is 0.97, indicating that these two features are highly positively correlated. This will potentially raise the multicollinearity concern. As a result, it can become difficult for the model to estimate the relationship between each independent variable and the dependent variable independently. One method to address correlated features is to use recursive feature elimination to exclude features with little importance so we can fit a more interpretable model. Additionally, the high correlation between many of the features may insinuate that many of the features are redundant and the inclusion of all of them may be unnecessary. For example, including both diameter and length conveys very similar messages about the biology of the abalone, and may indicate that it is unnecessary to include both of these biological features. Since our primary goal is to make classification on the abalone age (old or young), and we don’t need to understand the role of each independent variable such as weight and height, we did not take additional actions to reduce the multicollinearity problem in this project.\n",
    "\n",
    "We fit a logistic regression and tuned it by using grid search. Other classification models like decision tree or KNN can be used in this project. We chose logistic regression for its good interpretability and its performance. However, with better feature engineering or better model selection, the performance can be improved.\n",
    "\n",
    "In regard of the sex feature, the infant category for sex of a abalone is included in this project which may be unnecessary and may harm the validity of the model. It is interesting that the researchers that collected this data included an Infant category within the sex feature, and makes us ponder the significance of its inclusion. Perhaps with consultation with domain experts, the significance of this collection method can be elucidated. In future additional analysis and after consultation with domain experts, we might consider removing the Infant category or the sex feature altogether, since being an infant inherently indicates that the abalone is young, and therefore makes the predictive model redundant.\n",
    "\n",
    "The lack of the domain knowledge to feature engineer the model inputs was a pronounced limitation throughout the project. Because of this limitation, we included all features in the data set in our classification model for predicting age. However, once greater knowledge is achieved through domain expert consultation, we may be able to conduct additional feature engineering and feature selection that would potentially improve the model's performance and reliability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future directions \n",
    "Future analyses can be performed to improve this classification model. For example, we are interested in adding additional features such as: the geographical location where the abalones are collected, abalone species, color, number of predators and living environment etc. Consultation with domain experts must also be considered for appropriate and accurate analysis directions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## References\n",
    "\n",
    "```{bibliography} references.bib\n",
    ":all:\n",
    "```"
   ]
  }
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