end-to-end-heart-disease-prediction.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook is a binary classification problem that looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "Below is the approach we are going to follow:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "In a statement,\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease\n",
    "\n",
    "There is also a version of it available on Kaggle. https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-dataset\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where you'll get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset).\n",
    "\n",
    "**Create a data dictionary**\n",
    "\n",
    "1. age - age in years\n",
    "2. sex - (1 = male; 0 = female)\n",
    "3. cp - chest pain type\n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl\n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        * can range from mild symptoms to severe problems\n",
    "        * signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        * Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved\n",
    "9. exang - exercise induced angina (1 = yes; 0 = no)\n",
    "10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy\n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising\n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas, Matplotlib and NumPy for data analysis, data manipulation and data visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all the required tools\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "# Regular EDA (exploratory data analysis) and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# we want our plots to appear inside the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "# Models from Scikit-Learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import RocCurveDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter export on the dataset you're working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <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   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       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  "
      ]
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     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>0</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
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       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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733lds3fvXj366KMaOnSoBgwYoAcffFBvvvmm3G53g+tra2u1adMmjR8/XgMHDtTgwYP12GOPaf/+/S0dFwAAGK5FcVNaWqrp06erqqrK65rXX39djz76qA4ePKg+ffooPT1d//u//6t58+bp6aefrrfe7XZrzpw5mj9/vv75z38qIyNDd9xxh/bu3aupU6dq8+bNLRkZAAAYzt7cHY8ePaqZM2equLjY65pTp07p+eefV2RkpNatW6c+ffpIks6dO6epU6fq7bff1rBhwzRq1CjPPps3b1Zubq7uvPNOrVmzRjExMZKkffv2KTs7WwsWLFBGRoaSkpKaOzoAADBYk8/clJWVaenSpXrooYdUXFysrl27el27atUqOZ1OPfbYY56wkaTOnTtr/vz5njVftnLlSknSvHnzPGEjSUOHDtXUqVNVU1OjdevWNXVsAAAQJJocNzk5OVq1apXi4uL06quvauzYsV7X5uXlSZJ+8IMf1NuWkZGhqKgoHT58WBcvXpQknTx5UqdPn1aHDh00aNCgevuMHDlSkvT+++83dWwAABAkmhw3iYmJmjNnjrZv366srCyv60pLS3X58mWFhISoR48e9bbbbDbP48ePH5ckFRYWSpJSU1NlsVjq7VP3+OnTp7/2Oh8AABC8mnzNzcSJExu1rqSkRJLUsWNHWa0NN1R8fPxta+v+TEhIaHB9aGiooqOjVVZWptLSUiUnJzdpdgAAYL5mX1D8TSorKyVJDofD65rQ0NDb1l6/fr3R+9StbS67Pbg+4sdmC66fF7fwvAcHnufgxPPunc/ipu5sTUNvL31V3efd2Gy2Ru/TElarRbGxET79HkBbEB0d5u8RAPgIx7d3PoubiIhb8VBdXe11TU1NjSQpPDy8yfuEhTX/SXW53Covr2z2/oHIZrNyIASh8vIqOZ0uf48BH+P4Dk7BdnxHR4c1+myVz+Km7rqZ0tJSud3uBs/G1F1jU3ftTd0+ly5davBr1tTUqLy8XBaLRZ06dWrRfLW1wfOCQPByOl281gFDcXx757M37GJiYpSQkKAbN240+EF/TqdTn3/+uSQpLS3ttj9PnDjR4Nc8ceKE3G63kpOTPWd7AAAAvsynVyMNGzZMkvTee+/V27Znzx5VVFSod+/eSkxMlCR169ZNKSkpKikp0aefflpvn23btkmSMjMzfTYzAAAIbD6Nm8mTJ8tms2nlypW3xcq5c+f0/PPPS5Kys7Nv22fKlCmSpGeeeUalpaWex/ft26ecnByFhIToZz/7mS/HBgAAAcxn19xIUu/evfXEE0/oxRdf1MMPP6zBgwfL4XBo//79qqys1MSJEzV69Ojb9pk0aZJ27typDz74QCNHjlR6eroqKir08ccfy+12a/HixfxeKQAA4JVP40aSpk2bpp49e2rNmjXKz8+XxWJRz549NWnSJI0bN67eeqvVqldeeUV//vOf9fbbb2v37t2KjIxURkaGsrOzNXjwYF+PDAAAApjFXfchM0HE6XTpypWWfQhgoLHbrYqNjdDNlcukC2f9PQ58LbGLQrJn6erV69xNEQTqju9/FF3StZpaf48DH4sJtSsrpVPQHd9xcRGNvhWcjzcEAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGsfv6G2RlZens2bPfuG7w4MFat26dJGnTpk2aP3++17WpqanKzc391mYEAADm8Hnc3Hfffbpy5UqD29xut7Zt26ba2lr17dvX8/iRI0ckSenp6YqPj6+3X1JSkm+GBQAAAc/ncfP000973bZ8+XLV1tbqu9/9rp588knP43Vxs2DBAnXv3t3XIwIAAIP47Zqb/fv365VXXlF0dLRefPFF2e23OuvmzZsqLCxUVFSUUlJS/DUeAAAIUH6Jmxs3bmj+/PlyuVyaPXu2EhISPNtOnjypGzduqF+/frJYLP4YDwAABDCfvy3VkDVr1qioqEj9+/fXhAkTbttW95ZUQkKClixZory8PJ07d06xsbHKzMzU9OnTb4shAACAL2v1uPniiy/03//935KkGTNm1Ds7Uxc3W7ZsUWRkpO655x4lJSXpyJEj2rhxo3bs2KHVq1crLS2tRXPY7cF1F7zNFlw/L27heQ8OPM/Biefdu1aPm40bN6qiokJ9+/bVvffeW297QUGBJGnkyJFauHChIiMjJUkVFRWaO3eutm/frhkzZmjr1q2e63Saymq1KDY2ovk/BBAgoqPD/D0CAB/h+PauVePG6XQqJydHkjRt2rQG16xdu1ZnzpxRt27d1K5dO8/jUVFRWrRokQ4dOqSioiLt3LlTWVlZzZrD5XKrvLyyWfsGKpvNyoEQhMrLq+R0uvw9BnyM4zs4BdvxHR0d1uizVa0aNwcOHNDFixfVvn17r2HicDiUmpra4LaIiAgNGTJE7777rg4fPtzsuJGk2trgeUEgeDmdLl7rgKE4vr1r1Tfstm/fLunWW05fPivTFHUf4FdVVfWtzQUAAMzRqnHz4YcfSpJGjRrV4PaLFy/qqaee0owZM+R0Ohtcc/78eUlSYmKib4YEAAABrdXelrp06ZLOnTsnu92uAQMGNLgmKipKW7duVU1NjQ4cOKChQ4fetv3atWvKy8uTxWJp8GJkAACAVjtzk5+fL0m64447FB4e3uCa8PBwjR07VpL03HPP3fYLN8vKyjRz5kxVVFRozJgx6tGjh89nBgAAgafVztycOXNGkpScnPy162bPnq2jR48qPz9fo0eP1sCBA+VwOHTw4EFVVFRo0KBBevbZZ1tjZAAAEIBaLW6uXr0q6Zt/o3dkZKTWr1+vtWvXKjc3V5988olsNpu6d++uMWPGaPLkyQoJCWmNkQEAQACyuN1ut7+HaG1Op0tXrlz39xitym63KjY2QjdXLpMunP3mHRDYErsoJHuWrl69zq2iQaDu+P5H0SVdq6n19zjwsZhQu7JSOgXd8R0XF9Hoz7nhs5sBAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGsbfGNzl16pRGjRr1tWv27dunuLg4z9/37t2r1157TceOHVN1dbV69Oihn/zkJ5owYYIsFouvRwYAAAGqVeKmoKBAktSrVy/deeedDa4JDQ31/PPrr7+u5557TiEhIUpPT1dISIg++ugjzZs3T//zP/+jRYsWtcbYAAAgALVK3Hz22WeSpClTpujHP/7x1649deqUnn/+eUVGRmrdunXq06ePJOncuXOaOnWq3n77bQ0bNuwbzwQBAIDg1CrX3NSduenfv/83rl21apWcTqcee+wxT9hIUufOnTV//nzPGgAAgIa0WtyEhoYqNTX1G9fm5eVJkn7wgx/U25aRkaGoqCgdPnxYFy9e/NbnBAAAgc/ncXPmzBmVl5crJSVFr7/+usaPH68BAwYoPT1d06dPV35+vmdtaWmpLl++rJCQEPXo0aPe17LZbJ7Hjx8/7uvRAQBAAPL5NTd119scP35cS5Ys0cCBAzVkyBAdO3ZM77//vj788EMtWrRIY8aMUUlJiSSpY8eOslob7q74+HhJ8qxtLrs9uO6Ct9mC6+fFLTzvwYHnOTjxvHvn87j58p1Sf/zjH9WtWzdJksvl0muvvaZly5Zp7ty5uvvuu1VZWSlJcjgcXr9e3V1VdWubw2q1KDY2otn7A4EiOjrM3yMA8BGOb+98HjczZ87UxIkTFRkZedvn2FitVmVnZ+vQoUPKy8vT66+/7rnOpjGfY+N2u5s9k8vlVnl58+MoENlsVg6EIFReXiWn0+XvMeBjHN/BKdiO7+josEafrfJ53Njtdv3Lv/yL1+3Dhw9XXl6eDh8+rHHjxkmSqqurva6vqamRJIWHh7dortra4HlBIHg5nS5e64ChOL698/sbdklJSZKkqqoqJSQkSLp1YbG3MzN119rUXXsDAADwZT6PmxdeeEG//OUvdeLEiQa3nz9/XpKUmJiomJgYJSQk6MaNGyouLq631ul06vPPP5ckpaWl+W5oAAAQsHweN0eOHNGOHTv0t7/9rcHt77zzjiQpMzNTkjRs2DBJ0nvvvVdv7Z49e1RRUaHevXsrMTHRNwMDAICA5vO4mTx5sqRbnyr80UcfeR53Op36/e9/r4MHDyolJUU/+tGPPOttNptWrlypTz/91LP+3Llzev755yVJ2dnZvh4bAAAEKJ9fUPzAAw/owIED2rhxox555BHdfffdSkhI0GeffaazZ8+qU6dO+uMf/+i5xbt379564okn9OKLL+rhhx/W4MGD5XA4tH//flVWVmrixIkaPXq0r8cGAAABqlV+ceaCBQs0ZMgQbdiwQQUFBSooKFBSUpIeffRRTZs27bZbxCVp2rRp6tmzp9asWaP8/HxZLBb17NlTkyZN8txRBQAA0JBWiRtJuv/++3X//fc3ev3w4cM1fPhwH04EAABM5PdbwQEAAL5NxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACj2FvrG73zzjt66623dOzYMVVVValDhw4aMmSIpk2bpp49e962dtOmTZo/f77Xr5Wamqrc3FxfjwwAAAKQz+PG7XbrySefVG5urux2u/r376+4uDgdO3ZMW7Zs0bZt27R8+XJ9//vf9+xz5MgRSVJ6erri4+Prfc2kpCRfjw0AAAKUz+Pm3XffVW5urjp16qRVq1apd+/ekiSn06mXX35ZK1as0OzZs7Vjxw5FRERI+r+4WbBggbp37+7rEQEAgEF8fs3NW2+9JUn6zW9+4wkbSbLZbHriiSeUmpqqy5cva8+ePZKkmzdvqrCwUFFRUUpJSfH1eAAAwDA+j5vo6Gj17NlT99xzT71tFovFc2bm4sWLkqSTJ0/qxo0b6tevnywWi6/HAwAAhvH521LLly/3us3pdHregqq7jqbu7wkJCVqyZIny8vJ07tw5xcbGKjMzU9OnT1dCQoKvxwYAAAHKr7eCb9iwQWfPnlVMTIyGDh0q6f/iZsuWLXrjjTfUrVs3DRo0SFVVVdq4caPGjRun48eP+3NsAADQhrXareBftW/fPv3+97+XJD355JOei4kLCgokSSNHjtTChQsVGRkpSaqoqNDcuXO1fft2zZgxQ1u3bpXd3vzx7fbg+ogfmy24fl7cwvMeHHiegxPPu3d+iZu8vDw98cQTunHjhiZNmqSJEyd6tq1du1ZnzpxRt27d1K5dO8/jUVFRWrRokQ4dOqSioiLt3LlTWVlZzfr+VqtFsbERLf45gLYuOjrM3yMA8BGOb+9aPW7WrVunRYsWyel0avLkyXrmmWdu2+5wOJSamtrgvhERERoyZIjeffddHT58uNlx43K5VV5e2ax9A5XNZuVACELl5VVyOl3+HgM+xvEdnILt+I6ODmv02apWi5va2lr97ne/06ZNm2SxWPTrX/9a//Zv/9bkr1N34XFVVVUL5wmeFwSCl9Pp4rUOGIrj27tWiZvq6mr94he/0O7duxUWFqbFixdr1KhR9dZdvHhR//Vf/6Xr16/rpZdeks1mq7fm/PnzkqTExESfzw0AAAKPz+PG6XR6wqZDhw5asWKF7rrrrgbXRkVFaevWraqpqdGBAwc8d1DVuXbtmvLy8mSxWHTvvff6enQAABCAfH6p9auvvqrdu3crPDxca9eu9Ro2khQeHq6xY8dKkp577jmdPXvWs62srEwzZ85URUWFxowZox49evh6dAAAEIB8euamrKxMf/rTnyRJ8fHxWrlypde1P/zhDzVs2DDNnj1bR48eVX5+vkaPHq2BAwfK4XDo4MGDqqio0KBBg/Tss8/6cmwAABDAfBo3Bw4cUGXlrbuSioqKVFRU5HXtnXfeqWHDhikyMlLr16/X2rVrlZubq08++UQ2m03du3fXmDFjNHnyZIWEhPhybAAAEMB8GjcjRoxo1qcJt2vXTo8//rgef/xxH0wFAABMxscbAgAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAo9j9PYA3p06d0vLly/XJJ5/o8uXLSkxM1P3336/s7GyFh4f7ezwAANBGtckzN/n5+Ro/frz++te/qmPHjsrMzFRlZaVWrFihn/zkJ/riiy/8PSIAAGij2lzc1NbWatasWaqsrNQLL7ygN998Uy+//LL+/ve/KysrS8ePH9eyZcv8PSYAAGij2lzcbN26VWfOnNHQoUM1YcIEz+MOh0MLFy5UeHi43njjDZWVlflxSgAA0Fa1ubj5xz/+IUkaMWJEvW2xsbFKT0/XzZs3tWvXrtYeDQAABIA2FzeFhYWSpLS0tAa39+rVS5J07NixVpsJAAAEjjZ3t1RJSYkkKSEhocHt8fHxt61rDqvVori4iGbvH4gsllt/2v/1ccnp9O8w8D2bTZLUvn2Y3G4/zwKfqzu+M7rG8XwHgbrnO9iOb6vV0ui1bS5uKisrJd26xqYhdY/XrWsOi8Uim63x/5JMYomI8vcIaEVWa5s7OQsfctht/h4BrYjj27s292/G9v//H6fF8vXx4Q6mXAUAAI3W5uImIuLW20VVVVUNbq+urpYkhYWFtdpMAAAgcLS5uKm7pubSpUsNbq+71qZuHQAAwJe1ubipu0vq5MmTDW6ve9zb3VQAACC4tbm4GTZsmCRp+/bt9bZdvXpV+/fvV0hIiL73ve+19mgAACAAtLm4GTFihDp37qzdu3dr/fr1nserq6s1d+5cVVZWasKECerYsaMfpwQAAG2Vxd0Gbzvav3+/pk2bpurqavXt21ddu3bVoUOHVFJSoj59+mjdunWKjIz095gAAKANapNxI936pOJXXnlFBw4cUGVlpbp27aqRI0fqscceI2wAAIBXbTZuAAAAmqPNXXMDAADQEsQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxi9/cAAAB8nYqKCu3atUvHjh1TSUmJKisr5Xa7FRYWpvj4eKWlpel73/ue4uLi/D0q2gjiBgDQJl2/fl0vvfSS3njjDd28eVPePnPWYrEoJCREEydO1KxZsxQREdHKk6Kt4ROKAQBtTlVVlSZNmqTjx4/L4XAoPT1dqampio+Pl8PhkHTrFyqXlJToxIkT2r9/v6qrq9WnTx/l5OQQOEGOMzcAgDZnxYoVOnbsmLKysrRw4ULFxMR87fpr167p6aefVl5enlavXq1f/vKXrTMo2iTO3CDg/cd//EeL9p83b963NAmAb8vIkSNVVVWlv//972rXrl2j9qmpqdGIESMUHR2t3NxcH0+ItowzNwh4mzdvVnV1tefvTel1i8VC3ABt0IULF5SZmdnosJGk0NBQDRgwQB9++KEPJ0MgIG4Q8P7yl79o+vTp+vzzz5WcnKwf/ehH/h4JQAt16NBBFy5caPJ+Z86cUXh4uA8mQiAhbhDwUlJStG7dOk2aNElnzpxRv379lJmZ6e+xALTAwIEDtXXrVv3lL3/RuHHjGrXPhg0bVFBQoFGjRvl4OrR1XHMDYxw9elQ//vGP1bFjR23btq1Jp7MBtC1FRUUaN26cqqurlZGRofvuu0933HGHEhISFBYWJovFoqqqKl26dEknTpzQe++9p927d8vhcOjNN99Ur169/P0jwI+IGxhl6dKl+tOf/qTf/va3euSRR/w9DoAWyM/P16xZs/TPf/5TFovla9e63W517txZixcv1uDBg1tpQrRVxA2MUl5erpycHCUmJmrChAn+HgdACzmdTuXm5iovL0+FhYWeTyi2Wq2KiIhQQkKC0tLSNGzYMI0YMUKhoaH+HhltAHEDAACMwi/OBAAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGCU/wdenbcohwWw+gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "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>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\",\n",
    "                                    figsize=(10, 6),\n",
    "                                    color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0 = No Diesease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"]);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age vs. Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Scatter with postivie examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            c=\"salmon\")\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "            c=\"lightblue\")\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Disease\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the age column with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\n",
    "\n",
    "3. cp - chest pain type\n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the crosstab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                   figsize=(10, 6),\n",
    "                                   color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "# Add some communication\n",
    "plt.title(\"Heart Disease Frequency Per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "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>3</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>2</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   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       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": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "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>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.5, -0.5)"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make our correlation matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 linewidths=0.5,\n",
    "                 fmt=\".2f\",\n",
    "                 cmap=\"YlGnBu\");\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "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>3</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>2</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   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       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": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into X and y\n",
    "X = df.drop(\"target\", axis=1)\n",
    "\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
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       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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",
       "    </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",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
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       "      <td>120</td>\n",
       "      <td>236</td>\n",
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       "      <td>354</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <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>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
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       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let us view X\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let us view y\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train & test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>196</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>250</td>\n",
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       "      <td>2</td>\n",
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       "      <th>176</th>\n",
       "      <td>60</td>\n",
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       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>230</td>\n",
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       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</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",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's view X_train\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132    1\n",
       " 202    0\n",
       " 196    0\n",
       " 75     1\n",
       " 176    0\n",
       "       ..\n",
       " 188    0\n",
       " 71     1\n",
       " 106    1\n",
       " 270    0\n",
       " 102    1\n",
       " Name: target, Length: 242, dtype: int64,\n",
       " 242)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's view y_train\n",
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test sets, it's time to build a machine learning model.\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\n",
    "And we'll test it (use the patterns) on the test set.\n",
    "\n",
    "For this problem, we're going to try 3 different machine learning models:\n",
    "1. Logistic Regression \n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's develop our machine learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of differetn Scikit-Learn machine learning models\n",
    "    X_train : training data (no labels)\n",
    "    X_test : testing data (no labels)\n",
    "    y_train : training labels\n",
    "    y_test : test labels\n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models,\n",
    "                             X_train=X_train,\n",
    "                             X_test=X_test,\n",
    "                             y_train=y_train,\n",
    "                             y_test=y_test)\n",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a baseline model... and we know a model's first predictions aren't always what we should based our next steps off. What should we do?\n",
    "\n",
    "Let's explore the following:\n",
    "* Hypyterparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Cross-validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* Classification report\n",
    "* ROC curve\n",
    "* Area under the curve (AUC)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning\n",
    "\n",
    "We are going to do this in the following ways:\n",
    "\n",
    "* By Hand (Manually)\n",
    "\n",
    "* Using RandomizedSearch CV\n",
    "\n",
    "* Using  GridSearch CV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of differnt values for n_neighbors\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After doing a hyperparameter tuning by hand, we can see an improvement from an accuracy score of 69% to 75% but it still the worst performing model when  compared to the other models so we will drop it and continue with our hyperparameter tuning using the RandomizedSearch CV and GridSearchCV methods on the other models "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "\n",
    "... using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-5, 5, 20),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for each of our models, let's tune them using RandomizedSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-23 {color: black;background-color: white;}#sk-container-id-23 pre{padding: 0;}#sk-container-id-23 div.sk-toggleable {background-color: white;}#sk-container-id-23 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-23 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-23 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-23 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-23 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-23 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-23 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-23 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-23 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-23 div.sk-item {position: relative;z-index: 1;}#sk-container-id-23 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-23 div.sk-item::before, #sk-container-id-23 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-23 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-23 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-23 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-23 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-23 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-23 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-23 div.sk-label-container {text-align: center;}#sk-container-id-23 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-23 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-05, 3.35981829e-05, 1.12883789e-04, 3.79269019e-04,\n",
       "       1.27427499e-03, 4.28133240e-03, 1.43844989e-02, 4.83293024e-02,\n",
       "       1.62377674e-01, 5.45559478e-01, 1.83298071e+00, 6.15848211e+00,\n",
       "       2.06913808e+01, 6.95192796e+01, 2.33572147e+02, 7.84759970e+02,\n",
       "       2.63665090e+03, 8.85866790e+03, 2.97635144e+04, 1.00000000e+05]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-05, 3.35981829e-05, 1.12883789e-04, 3.79269019e-04,\n",
       "       1.27427499e-03, 4.28133240e-03, 1.43844989e-02, 4.83293024e-02,\n",
       "       1.62377674e-01, 5.45559478e-01, 1.83298071e+00, 6.15848211e+00,\n",
       "       2.06913808e+01, 6.95192796e+01, 2.33572147e+02, 7.84759970e+02,\n",
       "       2.63665090e+03, 8.85866790e+03, 2.97635144e+04, 1.00000000e+05]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-05, 3.35981829e-05, 1.12883789e-04, 3.79269019e-04,\n",
       "       1.27427499e-03, 4.28133240e-03, 1.43844989e-02, 4.83293024e-02,\n",
       "       1.62377674e-01, 5.45559478e-01, 1.83298071e+00, 6.15848211e+00,\n",
       "       2.06913808e+01, 6.95192796e+01, 2.33572147e+02, 7.84759970e+02,\n",
       "       2.63665090e+03, 8.85866790e+03, 2.97635144e+04, 1.00000000e+05]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.1623776739188721}"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's view our best parameters\n",
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's evaluate  the tuned Logistic Regression model \n",
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(), let's do the same for RandomForestClassifier()..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-24 {color: black;background-color: white;}#sk-container-id-24 pre{padding: 0;}#sk-container-id-24 div.sk-toggleable {background-color: white;}#sk-container-id-24 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-24 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-24 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-24 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-24 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-24 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-24 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-24 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-24 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-24 div.sk-item {position: relative;z-index: 1;}#sk-container-id-24 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-24 div.sk-item::before, #sk-container-id-24 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-24 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-24 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-24 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-24 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-24 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-24 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-24 div.sk-label-container {text-align: center;}#sk-container-id-24 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-24 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(), \n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=20,\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparamter Tuning with GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provides the best scores so far, we'll try and improve them again using GridSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid_1 = {\"C\": np.logspace(-5, 5, 30),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid_1,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1373823795883264, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparmaters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's evaluate our tuned model further "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluting our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate and calculate AUC metric\n",
    "RocCurveDisplay.from_estimator(gs_log_reg, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True,\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "    \n",
    "    bottom, top = ax.get_ylim()\n",
    "    ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate accuracy, precision, recall and f1-score of our model using cross-validation and to do so we'll be using `cross_val_score()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1373823795883264, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.1373823795883264,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"accuracy\")\n",
    "cv_acc\n",
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8218912117692605"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "cv_precision = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"precision\")\n",
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9077205882352942"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=10,\n",
    "                         scoring=\"recall\")\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8592661237088481"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\n",
    "cv_f1 = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=10,\n",
    "                         scoring=\"f1\")\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                           \"Precision\": cv_precision,\n",
    "                           \"Recall\": cv_recall,\n",
    "                           \"F1\": cv_f1},\n",
    "                          index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another way of asking, \"which features contributed most to the outcomes of the model and how did they contribute?\"\n",
    "\n",
    "Finding feature importance is different for each machine learning model\n",
    "\n",
    "Let's find the feature importance for our LogisticRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "clf = LogisticRegression(C=0.1373823795883264,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00134287, -0.72656137,  0.61500822, -0.01153453, -0.00155289,\n",
       "         0.03188443,  0.25406986,  0.02419149, -0.52054714, -0.54358459,\n",
       "         0.38943001, -0.5885884 , -0.61035413]])"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "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>3</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>2</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   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       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": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0013428692912390095,\n",
       " 'sex': -0.7265613692357671,\n",
       " 'cp': 0.6150082229577699,\n",
       " 'trestbps': -0.011534533296299512,\n",
       " 'chol': -0.001552889290192593,\n",
       " 'fbs': 0.03188442591192939,\n",
       " 'restecg': 0.25406985808983806,\n",
       " 'thalach': 0.02419148759768749,\n",
       " 'exang': -0.5205471382680149,\n",
       " 'oldpeak': -0.5435845938587073,\n",
       " 'slope': 0.38943000898837477,\n",
       " 'ca': -0.5885883982165802,\n",
       " 'thal': -0.6103541323527003}"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "slope - the slope of the peak exercise ST segment\n",
    "* 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "* 1: Flatsloping: minimal change (typical healthy heart)\n",
    "* 2: Downslopins: signs of unhealthy heart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see our Logistic Regression is the best performing model with an accuracy of 89% and a cross-validated accuracy of 85%. Unfortunately, we were unable to get an accuracy of 95% during our proof concept so we will not be continuing with our project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}