chest_pain_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import sklearn \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(context='talk', style='white', rc={'figure.facecolor':'white'}, font_scale=1)\n",
    "sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read in data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "based on kaggle data from: https://www.kaggle.com/ronitf/heart-disease-uci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart = pd.read_csv(\"heart.csv\")  # load kaggle heart data  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart[\"cp\"] = np.where(heart[\"cp\"] > 0, 1, 0)  # presence vs absence only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   1       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   1       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.head(n=5) # shows us the first few entries of a dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we just want to explore the data. are our cases and controls balanced? are there any apparent biases? what is the relationship between our input variables and our case status? are there any that look particularly useful or not useful? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ECRPclk+cOFF1dXU6ffp0d40JAOhEwMfmwoULCgsL04IFC5ScnKxRo0Zp/fr1crlcqqqqUlNTk+Lj491uExsbK+nWW3AAAP/rEW+juVwupaWlaenSpfr444+Vm5ur8vJyvfjii5KkyMhIt9tERERIklwul8f9OZ3OTh+vvr5eDofDR9MDAKQeEJvt27erd+/eGjZsmCRp1KhRio6O1ksvvaRTp05Jkmw2W4e3bTuJAADgXwEfm9GjR3sse+KJJ9x+vnMPpu3njvZQioqKOn28rvZ8AADeC+hf/Wtra1VQUKCqqiq35Q0NDZKk6Oho2e12VVZWuq1v+/nOYzkAAP8I6NjYbDatX79eb775ptvyP/7xj7Lb7frBD34gp9Opo0ePyrKs9vVHjhyRw+HQyJEju3tkAEAHAvpttKioKD3//PPat2+fIiMj5XQ6VVxcrF27dun5559XbGysli1bpoyMDK1evVozZszQmTNntGfPHmVlZXV4ujQAoPsFdGwkad26dYqJidHBgwe1e/duxcTEaNWqVVq4cKEkaezYscrNzVVOTo6WL1+umJgYrV27lq+rAYAAEvCxCQ0N1aJFi7Ro0aK7bpOamqrU1NRunAoA4I2APmYDAAgOxAYAYByxAQAYR2wAAMYRGwCAccQGAGAcsQEAGEdsAADGERsAgHHEBgBgHLEBABhHbAAAxhEbAIBxxAYAYByxAQAYR2wAAMYRGwCAccQGAGAcsQEAGEdsAADGERsAgHHEBgBgHLEBABhHbAAAxhEbAIBxxAYAYByxAQAYR2wAAMYRGwCAccQGAGAcsQEAGEdsAADGERsAgHHEBgBgHLEBABhHbAAAxhEbAIBxxAYAYByxAQAYR2wAAMYRGwCAccQGAGAcsQEAGEdsAADGERsAgHHEBgBgHLEBABhHbAAAxhEbAIBxxAYAYByxAQAYR2wAAMYFTWz+8Ic/aOrUqXr44Yc1efJkvfPOO/4eCQDw/4IiNocPH9aaNWs0btw47dixQ6NHj9a6dev03nvv+Xs0AICkb/h7AF/Ytm2bJk+erJ///OeSpB/+8If63//+p1/96leaNGmSn6cDAPT4PZuqqipVVlZqwoQJbssnTpyosrIyVVVV+WkyAECbHr9nU1ZWJkmKj493Wx4bGytJKi8v1+DBg9uXO53OTu+vvr7+S233ZVjW174LBBGbzd8TfIHXJm7ni9dmfX29bJ3cUY+PTVscIiMj3ZZHRERIklwul9f32dkfmHf345O76dHa/v84HA4/T4Lb8drktelrNptNISF3f7Osx8fG+v9f0e4MRNvyO598UVFR9wwGSV/sIfLnjkDDa7N79fhjNm2/ldy5B3Pt2jW39QAA/+nxsWk7VlNZWem2vKKiwm09AMB/enxsYmNj9eCDD3p8pubo0aOKi4vTwIED/TQZAKBNjz9mI0nLly/Xz372M/Xu3VtPPPGEjh8/rsOHD2v79u3+Hg0AoCCJzY9//GPdvHlTeXl5Kigo0ODBg7Vp0yZNmTLF36MBABQksZGkWbNmadasWf4eAwDQgR5/zAYAEPhslsVniQEAZrFnAwAwjtgAAIwjNgAA44gNjOHqqQh0paWlGjFihC5duuTvUYIesYERXD0Vga6srExLlixRc3Ozv0e5J3A2GoxITU3VyJEj3b7F4YUXXtCFCxd0+PBhP06Ge11zc7MOHDigrVu3KjQ0VP/973/15z//Wf379/f3aEGNPRv4HFdPRSArLi7Wli1blJmZqTVr1vh7nHsGsYHPfZmrpwL+kpCQoGPHjmnFihWy2+3+HueeETRfV4PAYeLqqYCv9O3b198j3JPYs4HPeXv1VADBj7/18DmungrgTsQGPsfVUwHcidjA57h6KoA7cYIAjODqqQBuR2xgBFdPBXA7vkEAAGAcx2wAAMYRGwCAccQGAGAcsQEAGEdsAADGERsAgHHEBgBgHLEBABhHbAAAxhEbAIBxfDcaEIDOnTun119/XWfPnlVISIiSkpKUlZWlYcOGafz48Ro7dqy+973vadeuXaqtrVViYqJeeOEFjRkzxt+jAx3iu9GAAFNUVKT58+erX79+mjlzpsLDw5Wfn69r167p4MGDmjt3rizLUk1NjdLT0/XAAw/orbfe0meffaa8vDyNHj3a308B8EBsgACTlpamixcv6t1331WfPn0kSeXl5ZoyZYoyMjL03nvvqbq6Wjt27NCPfvQjSdKVK1c0ceJEDR06VAcOHPDn+ECHOGYDBJDa2lqVlJRo+vTp7aGRbl3d9ODBg1q0aJEkaejQoe2hkaSoqCg9/fTTOnfunGpra7t9bqArxAYIINXV1bIsS7GxsR7rhg8f3h6gb3/72x7rY2NjZVmWqqurjc8JeIvYAAGktbVVkhQS0vlfzdDQUI9lLS0tkiS73e77wYCvibPRgAAyYMAASVJFRYXHus2bN6t3796SpMrKSo/1FRUVstvtevDBB80OCXwF7NkAASQmJkaJiYkqLCyUy+VqX15VVaX8/HzV1NRIkkpKSnT27Nn29TU1NTp06JDGjBnTHiQgkHA2GhBgPvzwQy1cuFCDBg1SWlqaQkJC9Oabb6qxsVHvvPOOnnvuOV2+fFnh4eGaN2+ewsPDtX//fl29elVvvfWWEhMT/f0UAA/EBghAxcXFysnJ0UcffaRevXpp1KhReumllzRkyBCNHz9egwYN0tSpU7Vz507V19fL6XQqKyuL0CBgERugh2mLzb59+/w9CvClccwGAGAcsQEAGEdsAADGccwGAGAcezYAAOOIDQDAOGIDADCO2AAAjCM2AADjiA0AwLj/A8xpOZZTBc/6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a countplot is useful for categorical data, for example looking at the number of cases we have, \n",
    "# or the number of sexes we have \n",
    "\n",
    "sns.countplot(x=\"cp\", data=heart, color=\"lightblue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 428.525x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a histogram (in python, a displot) can show us how quantitative variables change \n",
    "# the hue parameter lets us color our histogram (or other type of plot) by a categorical data variable\n",
    "# note seaborn comes with some preselected color palettes that are worth exploring to make nice plots\n",
    "\n",
    "sns.displot(x=\"age\", data=heart, hue=\"cp\", palette=sns.color_palette(\"mako\", 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn can also automatically calculate mean and average bars for quantitative data \n",
    "# in formats like a barplot \n",
    "\n",
    "sns.barplot(y=\"chol\", x=\"cp\", data=heart, palette=sns.color_palette(\"Blues\", 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplots, however, can show more about the distribution of the underlying data as \n",
    "# function of a categorical data variable \n",
    "\n",
    "sns.boxplot(y=\"chol\", x=\"cp\", data=heart, palette=sns.color_palette(\"Blues\", 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you should pay special attention to interactions of variables that might be obvious sources of \n",
    "# bias in your data. sometimes these are unavoidable, but they're always good to know about\n",
    "# and to discuss in any writeups you do \n",
    "\n",
    "sns.countplot(x=\"sex\", data=heart, palette=sns.color_palette(\"Blues\", 2), hue=\"cp\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you might also want to look for extreme outliers, that might affect your model's performance \n",
    "\n",
    "sns.displot(x=\"chol\", data=heart)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# statistics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it might also be useful to quantify your data by descriptive statistics, calculating them based on different quantitative or categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cp\n",
       "0    57\n",
       "1    54\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.groupby(\"cp\")[\"age\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cp  sex\n",
       "0   1      104\n",
       "    0       39\n",
       "1   1      103\n",
       "    0       57\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.groupby([\"cp\"])[\"sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart[\"cp\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# logistic regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a logistic regression is a simple model that predicts binary outcome (so chest pain, or no chest pain) based on input variables. It's roughly described as this (where ~ can be thought of as meaning, \"function of\"): \n",
    "\n",
    "y (outcome) ~ logit( X (input variables) )\n",
    "\n",
    "a logistic regression is divided into two steps, training and testing. the first is teaching the model the relationship between the input variables and outcome. the second step tests to see whether this relationship applies to data outside the training set.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we are separating our data into outcome and input variables \n",
    "\n",
    "Y = heart[[\"cp\"]]\n",
    "X = heart.loc[:, heart.columns != \"cp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we must split our data into test/train set. \n",
    "# Python does this randomly for us, but the random state variable ensures that the same \"random\"\n",
    "# set is chosen each time. \n",
    "# you can try exploring different test/train percentages. usually people pick this on some set of \n",
    "# what's common for their field and the amount of data they have available. 70/30 or 80/20 is common. \n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, Y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=[\"x_train\", \"x_test\", \"y_train\", \"y_test\"], \n",
    "            y=[len(x_train), len(x_test), len(y_train), len(y_test)],\n",
    "           color=\"lightblue\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test/train analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it's good practice to check that your test/train data is largely balanced. you might have problems with your model if the training data doesn't have enough cases, for example "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.549451\n",
       "0    0.450549\n",
       "Name: cp, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"cp\"].value_counts(normalize=True)  # normalize just transforms the counts into the percentage of cases/controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.518868\n",
       "0    0.481132\n",
       "Name: cp, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[\"cp\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## run model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### define model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit = LogisticRegression(solver=\"lbfgs\", max_iter=1000) # first we define the model, with some default parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 62.,   1., 128., ...,   0.,   2.,   1.],\n",
       "       [ 69.,   1., 160., ...,   1.,   2.,   1.],\n",
       "       [ 61.,   1., 140., ...,   1.,   3.,   0.],\n",
       "       ...,\n",
       "       [ 56.,   1., 120., ...,   0.,   3.,   1.],\n",
       "       [ 47.,   1., 138., ...,   0.,   2.,   1.],\n",
       "       [ 58.,   1., 120., ...,   0.,   2.,   0.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.values  # we must transform the pandas data to numpy arrays for input into the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212, 13)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>62</td>\n",
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       "      <th>106</th>\n",
       "      <td>69</td>\n",
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       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>321</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>284</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "137   62    1       128   208    1        0      140      0      0.0      2   \n",
       "106   69    1       160   234    1        0      131      0      0.1      1   \n",
       "284   61    1       140   207    0        0      138      1      1.9      2   \n",
       "44    39    1       140   321    0        0      182      0      0.0      2   \n",
       "139   64    1       128   263    0        1      105      1      0.2      1   \n",
       "..   ...  ...       ...   ...  ...      ...      ...    ...      ...    ...   \n",
       "251   43    1       132   247    1        0      143      1      0.1      1   \n",
       "192   54    1       120   188    0        1      113      0      1.4      1   \n",
       "117   56    1       120   193    0        0      162      0      1.9      1   \n",
       "47    47    1       138   257    0        0      156      0      0.0      2   \n",
       "172   58    1       120   284    0        0      160      0      1.8      1   \n",
       "\n",
       "     ca  thal  target  \n",
       "137   0     2       1  \n",
       "106   1     2       1  \n",
       "284   1     3       0  \n",
       "44    0     2       1  \n",
       "139   1     3       1  \n",
       "..   ..   ...     ...  \n",
       "251   4     3       0  \n",
       "192   1     3       0  \n",
       "117   0     3       1  \n",
       "47    0     2       1  \n",
       "172   0     2       0  \n",
       "\n",
       "[212 rows x 13 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.values.reshape(-1,).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit.fit(x_train.values, y_train.values.reshape(-1,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit.predict(x_test) # we then test it on our held out data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "evaluating a model is really important step that asks how well our model works on data outside the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a confusion matrix is a helpful way of understanding the number our model \n",
    "# is correctly or incorreclty classifying, and whether there is one category the model is favoring\n",
    "# This can help diagnose problems with the model. For example, it might just classify \n",
    "# everything as a control if there isn't enough cases to learn from in the training set \n",
    "\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30, 11],\n",
       "       [12, 38]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\")\n",
    "plt.xlabel(\"model prediction\")\n",
    "plt.ylabel(\"truth\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7472527472527473"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred) # the percentage of correctly classified samples "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25274725274725274"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE is also useful in knowing how well your model is describing your data\n",
    "\n",
    "mean_squared_error(y_test, y_pred) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "see for a RSME example  https://www.google.com/url?sa=i&url=https%3A%2F%2Fmedium.com%2F%40mygreatlearning%2Frmse-what-does-it-mean-2d446c0b1d0e&psig=AOvVaw0wjeuMNkOeslJ9SAbaj5yn&ust=1631148487269000&source=images&cd=vfe&ved=0CAsQjRxqFwoTCJi9xeOT7vICFQAAAAAdAAAAABAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.73      0.72        41\n",
      "           1       0.78      0.76      0.77        50\n",
      "\n",
      "    accuracy                           0.75        91\n",
      "   macro avg       0.74      0.75      0.75        91\n",
      "weighted avg       0.75      0.75      0.75        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# lastly, we might want to learn about the precision and recall \n",
    "# precision (tp/(tp+fp)) -- we are accurately naming cases\n",
    "# recall (tp/(tp+fn)) -- we aren't missing any cases \n",
    "# we want both to be close to 1-- higher the better. in biomedical applications, you want to weigh\n",
    "# the two against each other, you want to catch a lot of cases, but you don't want to classify\n",
    "# a person as having a disease if they don't have it \n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check for biases in results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is there some group that the model is consistently misclassifying? could the results be affected by an outlier? it's always good to see if there are similarities between incorrectly classified groups "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test[\"true\"] = y_test[\"cp\"]\n",
    "x_test[\"predicted\"] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test[\"correct\"] = np.where(x_test[\"true\"] == x_test[\"predicted\"], True, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.countplot(x=\"correct\", data=x_test, hue=\"sex\", palette=sns.color_palette(\"Blues\", 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x=\"age\", hue=\"correct\", data=x_test, palette=sns.color_palette(\"mako\", 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cross validated logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the cross validation splits \n",
    "cv = KFold(n_splits=20, random_state=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep the same logistic regression model \n",
    "logit = LogisticRegression(solver=\"lbfgs\", max_iter=1000) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = heart[[\"cp\"]].values\n",
    "X = heart.loc[:, heart.columns != \"cp\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = Y.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((303, 13), (303,))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the model over the entire set of data, for the splits we defined above\n",
    "scores = cross_val_score(logit, X, Y, scoring='accuracy', cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.625     , 0.8125    , 0.625     , 0.6       , 0.86666667,\n",
       "       0.73333333, 0.86666667, 0.66666667, 0.73333333, 0.93333333,\n",
       "       0.86666667, 0.86666667, 0.73333333, 0.73333333, 0.8       ,\n",
       "       0.73333333, 0.8       , 0.6       , 0.8       , 0.73333333])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7564583333333333"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data preparation \n",
    "is the data suited for a neural network? see obesity prediction exercise "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test/train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### defining the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### training the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### evaluating training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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