heart_failure_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3b67e15f",
   "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 Lasso, LassoCV\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 mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239d54ef",
   "metadata": {},
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "236184bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset from https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a9630226",
   "metadata": {},
   "outputs": [],
   "source": [
    "heart = pd.read_csv(\"heart_failure_clinical_records_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2928c1ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since we want to do a linear regression, we will be using clinical record data to predict age\n",
    "# so I will be removing some data we no longer need \n",
    "\n",
    "heart = heart.drop([\"time\", \"DEATH_EVENT\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4103e0b0",
   "metadata": {},
   "source": [
    "# lasso regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ca7fec53",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = heart.loc[:, heart.columns != \"age\"] # use all columns that aren't the outcome for input\n",
    "Y = heart[\"age\"] # use the binary outcome column for Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ea80321e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f20ddc3",
   "metadata": {},
   "source": [
    "## run model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4f8ec42e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the lasso model \n",
    "model = Lasso(alpha=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "69b2b301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=None,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit/train the lasso model \n",
    "model.fit(x_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1743952a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00, -2.85886465e-04, -0.00000000e+00,  1.00521428e-01,\n",
       "        0.00000000e+00, -1.02616554e-05,  1.66205352e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the coefficients for the input variables (0 means not used for the task)\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "25b32fca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.0, 'anaemia'), (-0.00028588646515042713, 'creatinine_phosphokinase'), (-0.0, 'diabetes'), (0.10052142843033679, 'ejection_fraction'), (0.0, 'high_blood_pressure'), (-1.0261655427803669e-05, 'platelets'), (1.662053523093983, 'serum_creatinine'), (0.0, 'serum_sodium'), (0.0, 'sex'), (0.0, 'smoking')]\n"
     ]
    }
   ],
   "source": [
    "print(list(zip(model.coef_, list(x_train)))) # these are the coefficients with each of the input variable names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8cf7a2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the model \n",
    "predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3903923",
   "metadata": {},
   "source": [
    "## evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fd8ba643",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(zip(predictions, y_test), columns=[\"predicted\", \"true\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ac501ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"predicted\", y=\"true\", data=results)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7431fd63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0577506936984169"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5bcf9b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157.93507562083886"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " mean_squared_error(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7943d619",
   "metadata": {},
   "source": [
    "# optimizing lasso \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6e20577b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# try to do cross validation to improve fit \n",
    "# will run to find the best value of alpha for the problem \n",
    "model = LassoCV(cv=5, random_state=0, max_iter=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3159e115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=None, copy_X=True, cv=5, eps=0.001, fit_intercept=True,\n",
       "        max_iter=10000, n_alphas=100, n_jobs=None, normalize=False,\n",
       "        positive=False, precompute='auto', random_state=0, selection='cyclic',\n",
       "        tol=0.0001, verbose=False)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e18ad1ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1015.5155581158358"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.alpha_  # optimized alpha value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "020197aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1015.5155581158358, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=None,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_best = Lasso(alpha=model.alpha_) # rerun lasso with the best alpha value \n",
    "lasso_best.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4cdb266a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.0, 'anaemia'), (-0.0, 'creatinine_phosphokinase'), (-0.0, 'diabetes'), (0.0, 'ejection_fraction'), (0.0, 'high_blood_pressure'), (-9.547379367666523e-06, 'platelets'), (0.0, 'serum_creatinine'), (-0.0, 'serum_sodium'), (0.0, 'sex'), (0.0, 'smoking')]\n"
     ]
    }
   ],
   "source": [
    "print(list(zip(lasso_best.coef_, list(x_train)))) # these are the coefficients with each of the input variable names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ce6cddc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py37",
   "language": "python",
   "name": "py37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}