heart-disease-classification.ipynb

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    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook 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",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modeling\n",
    "6. Experimentation"
   ]
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   "source": [
    "## 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 [Cleveland database from UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/45/heart+disease).\n",
    "\n",
    "Howevever, we've downloaded it in a formatted way from [Kaggle](https://www.kaggle.com/datasets/cherngs/heart-disease-cleveland-uci).\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "The evaluation metric is something you might define at the start of a project.\n",
    "\n",
    "\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",
    "However, due to the nature of experimentation, the evaluation metric may change over time.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "Features are different parts of the data. During this step, you'll want to start finding out what you can about the data.\n",
    "\n",
    "One of the most common ways to do this, is to create a **data dictionary**.\n",
    "\n",
    "### Heart Disease Data Dictionary\n",
    "\n",
    "A data dictionary describes the data you're dealing with. Not all datasets come with them so this is where you may have to do your research or ask a **subject matter expert** (someone who knows about the data) for more.\n",
    "\n",
    "The following are the features we'll use to predict our target variable (heart disease or no heart disease).\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)\n",
    "    * 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 \n",
    "    * looks at stress of heart during excercise\n",
    "    * 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)\n"
   ]
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   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas, Matplotlib and NumPy for data analysis and manipulation."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Import all the tools we need\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, RocCurveDisplay\n",
    "from sklearn.metrics import RocCurveDisplay"
   ]
  },
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   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/heart-disease/heart-disease.csv\")\n",
    "df.shape # (rows, columns)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "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 expert on the dataset you're working with.\n",
    "\n",
    "1. What question(s) are you trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do you deal with it?\n",
    "4. Where are the outliers and why should you care about them?\n",
    "5. How can you add, change, or remove features to get more out of your data?"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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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",
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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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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</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>"
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      "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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     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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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>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",
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       "    <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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       "      <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",
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       "  </tbody>\n",
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      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there are\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);\n",
    "# fyi this is a balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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",
       "      <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 Disease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age vs. Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 positive 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": 15,
   "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();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\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": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrixc\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into X and y\n",
    "X = df.drop(\"target\", axis=1)\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "    </tr>\n",
       "    <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",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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"
      ]
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>188</th>\n",
       "      <td>50</td>\n",
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       "      <td>2</td>\n",
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       "      <td>51</td>\n",
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       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>227</td>\n",
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      ],
      "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
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     "execution_count": 26,
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   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "attachments": {},
   "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",
    "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": "code",
   "execution_count": 27,
   "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",
    "\t\"\"\"\n",
    "\tFits and evaluates given machine learning models.\n",
    "\tmodels: a dict of different Scikit-Learn machine learning models\n",
    "\tX_train: training data (no labels)\n",
    "\tX_test: testing data (no labels)\n",
    "\ty_train: training labels\n",
    "\ty_test: test labels\n",
    "\t\"\"\"\n",
    "\t\n",
    "\t# Set random seed\n",
    "\tnp.random.seed(42)\n",
    "\t# Make a dictionary to keep model scores\n",
    "\tmodel_scores = {}\n",
    "\t# Loop through models\n",
    "\tfor name, model in models.items():\n",
    "\t\t# Fit the model to the data\n",
    "\t\tmodel.fit(X_train, y_train)\n",
    "\t\t# Evaluate the model and append its score to model_scores\n",
    "\t\tmodel_scores[name] = model.score(X_test, y_test)\n",
    "\treturn model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\houwa\\Desktop\\Projects\\ML\\supervised-ml-classifier-regressor\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\tX_train=X_train,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\tX_test=X_test,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\ty_train=y_train,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\ty_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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();"
   ]
  },
  {
   "attachments": {},
   "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 look at the following:\n",
    "* Hyperparameter tuning\t\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",
    "### Hyperparameter tuning (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different 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",
    "\tknn.set_params(n_neighbors=i)\n",
    "\n",
    "\t# Fit the algorithm'\n",
    "\tknn.fit(X_train, y_train)\n",
    "\t\n",
    "\t# Update the training scores list\n",
    "\ttrain_scores.append(knn.score(X_train, y_train))\n",
    "\n",
    "\t# Update the test scores list\n",
    "\ttest_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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8awK2oiiZ5hYpigKQ4bi1dcbeMK1WS9euXZk2bVqmNl1dXXMUtz5JAmRAXo5WnA57wA2ZBySEEPqj0ehlKMpQGjRowKVLl6hSpcpTz7G0tKRbt25069aNd955hxo1anDmzBkaNGiAmZkZaWlpz7zO09qoU6cOWq2WPXv26IbA/svHx4e1a9dmSIQOHjyIjY0NFSpUyPZ1rV27Fi8vL0xMDJ9+yBCYAXk6pBdDlCEwIYQQqs8++4wVK1YwceJEzp07x4ULF1izZg3jx48HYNmyZSxZsoSzZ88SHBzMTz/9hKWlJZ6enoBaB2jv3r3cunXrqSussmvDy8uLAQMGMGjQINavX09ISAi7d+/m119/BeDtt98mLCyM9957j4sXL7JhwwYmTJjA6NGjdfN/svLOO+8QHR1Nv379OHr0KMHBwWzbto1BgwblKGHTN0mADOjfYojSAySEEELVsWNH/vrrLwIDA2nUqBFNmzZl5syZugTH3t6eRYsW4e/vT926ddmxYwd//vknjo6OAEyePJnr16/j7e1NuXLlsrzGs9qYP38+vXv35u2336ZGjRoMHTqUhw/V76oKFSqwefNmjh49iq+vL8OHD2fw4MG6BO1p3NzcOHDgAGlpaXTs2JHatWszcuRI7Ozssk2cCopGSR+4EzqxsbHY2dkRExODrW3Bzc05cSOal+YfooK9JQc+altg1xFCiJIqMTGRkJAQKlWqhIWFhaHDEYUgu995br6/pQfIgNJ7gG7HPCIptfC7/4QQQojSShIgA3K0NqOMuQmKAmHRjwwdjhBCCFFqSAJkQBqN5j9bYsg8ICGEEKKwSAJkYP9uiSErwYQQQojCIgmQgXlID5AQQuSbrOcpPfT1u5YEyMC8HKUWkBBC5FV65eOEBPk3tLRITk4GwNjYOF/tGL4UYykntYCEECLvjI2Nsbe3JzIyEgArK6tM2zSIkkOr1XL37l2srKzyXU1aEiADS58DdPP+I1LStJgaS6ecEELkRvqu5+lJkCjZjIyM8PDwyHeiKwmQgZW3McfC1IjEFC23HzzS9QgJIYTIGY1Gg6urK+XLlyclJcXQ4YgCZmZmppfK0ZIAGZiRkQZPB2su3Ynj+r0ESYCEECKPjI2N8z0vRJQeMt5SBEgtICGEEKJwSQJUBHg5Pa4FFCWrGIQQQojCYPAEaN68eboNzfz8/Ni3b1+253///ffUrFkTS0tLqlevzooVKzI8vmzZMjQaTaZbYmJiQb6MfPFwkB4gIYQQojAZdA7QmjVrGDVqFPPmzcPf358ffviBzp07c/78eTw8PDKdP3/+fMaNG8eiRYto1KgRR48eZejQoZQtW5auXbvqzrO1teXSpUsZnluUdwn+txq0JEBCCCFEYTBoAjRz5kwGDx7MkCFDAJg1axZbt25l/vz5TJkyJdP5P/30E2+++SZ9+/YFoHLlyhw+fJhp06ZlSIA0Go1uWWROJCUlkZSUpLsfGxub15eUJ+lzgMKiH5GmVTA2khoWQgghREEy2BBYcnIyJ06coEOHDhmOd+jQgYMHD2b5nKSkpEw9OZaWlhw9ejTD0sf4+Hg8PT2pWLEiL774IqdOnco2lilTpmBnZ6e7ubu75/FV5Y2bvSWmxhqS07SEx8iu8EIIIURBM1gCFBUVRVpaGs7OzhmOOzs7ExERkeVzOnbsyOLFizlx4gSKonD8+HGWLl1KSkoKUVFRANSoUYNly5axceNGVq1ahYWFBf7+/ly5cuWpsYwbN46YmBjdLSwsTH8vNAeMjTS4P54HFCpbYgghhBAFzuB1gJ6s5KgoylOrO3766adERETQtGlTFEXB2dmZgQMHMn36dF3th6ZNm9K0aVPdc/z9/WnQoAHfffcdc+bMybJdc3NzzM3N9fSK8sbL0Zrguw+5fi+B5lUMGooQQghR4hmsB8jJyQljY+NMvT2RkZGZeoXSWVpasnTpUhISErh+/TqhoaF4eXlhY2ODk5NTls8xMjKiUaNG2fYAFQVSC0gIIYQoPAZLgMzMzPDz8yMwMDDD8cDAQJo3b57tc01NTalYsSLGxsasXr2aF1988allsRVFISgoCFdXV73FXhA8HdJ3hZcESAghhChoBh0CGz16NAEBATRs2JBmzZqxcOFCQkNDGT58OKDOzbl165au1s/ly5c5evQoTZo04f79+8ycOZOzZ8+yfPlyXZuTJk2iadOmVK1aldjYWObMmUNQUBDff/+9QV5jTnk6pe8KL3OAhBBCiIJm0ASob9++3Lt3j8mTJxMeHk7t2rXZvHkznp6eAISHhxMaGqo7Py0tjRkzZnDp0iVMTU157rnnOHjwIF5eXrpzHjx4wLBhw4iIiMDOzo769euzd+9eGjduXNgvL1f+Wwsou3lQQgghhMg/jaIoiqGDKGpiY2Oxs7MjJiYGW1vbQrlmcqqWmp9tIU2rcOTjdjjbFt3CjUIIIURRlJvvb4NvhSFUZiZGVLC3BGQYTAghhChokgAVIekrwWQitBBCCFGwJAEqQtLnAclSeCGEEKJgSQJUhPzbAyRDYEIIIURBkgSoCPGUHiAhhBCiUEgCVIR4pVeDjkpAFucJIYQQBUcSoCLE3cEKjQbiklKJfphs6HCEEEKIEksSoCLEwtQY18f1f25EyzwgIYQQoqBIAlTEyDwgIYQQouBJAlTEeDk9XgkWJT1AQgghREGRBKiIkR4gIYQQouBJAlTEeDpILSAhhBCioEkCVMRID5AQQghR8CQBKmLSq0HfT0ghJiHFwNEIIYQQJZMkQEWMtbkJ5WzMAbgRLb1AQgghREGQBKgI0lWElnlAQgghRIGQBKgIknlAQgghRMGSBKgI8pJd4YUQQogCJQlQEeQhPUBCCCFEgZIEqAiSHiAhhBCiYEkCVAR5Oqg9QHfjkniYlGrgaIQQQoiSRxKgIsjOypSyVqaArAQTQgghCoIkQEVU+kqwUKkFJIQQQuidJEBFlMwDEkIIIQqOJEBFlNQCEkIIIQqOJEBFVPqeYNejpAdICCGE0DdJgIoo6QESQgghCo4kQEVU+hyg2zGJJKakGTgaIYQQomSRBKiIcrA2w8bcBICwaBkGE0IIIfRJEqAiSqPR4OkkK8GEEEKIgiAJUBEm84CEEEKIgiEJUBGWPg9IqkELIYQQ+iUJUBGW3gN0XXqAhBBCCL0yeAI0b948KlWqhIWFBX5+fuzbty/b87///ntq1qyJpaUl1atXZ8WKFZnOWbt2LT4+Ppibm+Pj48O6desKKvwC5ekgPUBCCCFEQTBoArRmzRpGjRrFJ598wqlTp2jZsiWdO3cmNDQ0y/Pnz5/PuHHjmDhxIufOnWPSpEm88847/Pnnn7pzDh06RN++fQkICOD06dMEBATQp08fjhw5UlgvS2+8nNQeoJv3E0hO1Ro4GiGEEKLk0CiKohjq4k2aNKFBgwbMnz9fd6xmzZr06NGDKVOmZDq/efPm+Pv78/XXX+uOjRo1iuPHj7N//34A+vbtS2xsLH///bfunE6dOlG2bFlWrVqVo7hiY2Oxs7MjJiYGW1vbvL68fFMUhZqfbSExRcuuD9tQ6XFCJIQQQojMcvP9bbAeoOTkZE6cOEGHDh0yHO/QoQMHDx7M8jlJSUlYWFhkOGZpacnRo0dJSUkB1B6gJ9vs2LHjU9tMbzc2NjbDrSjQaDR4yTwgIYQQQu8MlgBFRUWRlpaGs7NzhuPOzs5ERERk+ZyOHTuyePFiTpw4gaIoHD9+nKVLl5KSkkJUVBQAERERuWoTYMqUKdjZ2elu7u7u+Xx1+pO+J1iozAMSQggh9Mbgk6A1Gk2G+4qiZDqW7tNPP6Vz5840bdoUU1NTunfvzsCBAwEwNjbOU5sA48aNIyYmRncLCwvL46vRP+kBEkIIIfTPYAmQk5MTxsbGmXpmIiMjM/XgpLO0tGTp0qUkJCRw/fp1QkND8fLywsbGBicnJwBcXFxy1SaAubk5tra2GW5Fxb/FEKUHSAghhNAXgyVAZmZm+Pn5ERgYmOF4YGAgzZs3z/a5pqamVKxYEWNjY1avXs2LL76IkZH6Upo1a5apzW3btj2zzaIqfQhMeoCEEEII/TEx5MVHjx5NQEAADRs2pFmzZixcuJDQ0FCGDx8OqENTt27d0tX6uXz5MkePHqVJkybcv3+fmTNncvbsWZYvX65rc+TIkbRq1Ypp06bRvXt3NmzYwPbt23WrxIqb9AQoLDqBNK2CsdHTh/KEEEIIkTMGTYD69u3LvXv3mDx5MuHh4dSuXZvNmzfj6ekJQHh4eIaaQGlpacyYMYNLly5hamrKc889x8GDB/Hy8tKd07x5c1avXs348eP59NNP8fb2Zs2aNTRp0qSwX55euNpZYmZsRHKaltsPHuH+uDiiEEIIIfLOoHWAiqqiUgcoXbsZu7l29yE/D25Ci6pOhg5HCCGEKJKKRR0gkXPpK8FuRMs8ICGEEEIfJAEqBmQlmBBCCKFfkgAVA15Oj1eCRUkPkBBCCKEPkgAVAx6yK7wQQgihV5IAFQP/nQOk1cqcdSGEECK/JAEqBiqUtcTYSENiipbIuCRDhyOEEEIUe5IAFQOmxkZULGsJSEVoIYQQQh8kASom/l0JJgmQEEIIkV+SABUTXo4yEVoIIYTQF0mAigmpBSSEEELojyRAxYSX7AovhBBC6I0kQMWE53+GwGT7NiGEECJ/JAEqJiqWtUKjgfikVO49TDZ0OEIIIUSxJglQMWFhaoybnboUXlaCCSGEEPkjCVAxkj4Mdj1KJkILIYQQ+SEJUDGiWwkWLQmQEEIIkR+SABUj/9YCkiEwIYQQIj8kASpG0nuArkstICGEECJfJAEqRjylB0gIIYTQC0mAipH0BOhBQgoPEmQpvBBCCJFXkgAVI1ZmJpS3MQdkSwwhhBAiPyQBKma8dPOAZBhMCCGEyCtJgIqZ9GGwUOkBEkIIIfJMEqBixstJVoIJIYQQ+SUJUDEjK8GEEEKI/JMEqJjxklpAQgghRL5JAlTMeDzuAYqKTyI+KdXA0QghhBDFkyRAxYythSkO1maADIMJIYQQeSUJUDH07zwgGQYTQggh8kISoGJIagEJIYQQ+SMJUDEktYCEEEKI/JEEqBiSHiAhhBAif/KUAF27do3x48fTr18/IiMjAdiyZQvnzp3Ta3AiazIHSAghhMifXCdAe/bsoU6dOhw5coQ//viD+Ph4AP755x8mTJig9wBFZp6Pe4DCYxJJTEkzcDRCCCFE8ZPrBOijjz7iiy++IDAwEDMzM93x5557jkOHDuU6gHnz5lGpUiUsLCzw8/Nj37592Z6/cuVKfH19sbKywtXVlTfeeIN79+7pHl+2bBkajSbTLTExMdexFVVlrUyxsTABIDRaeoGEEEKI3Mp1AnTmzBl69uyZ6Xi5cuUyJCI5sWbNGkaNGsUnn3zCqVOnaNmyJZ07dyY0NDTL8/fv30///v0ZPHgw586d47fffuPYsWMMGTIkw3m2traEh4dnuFlYWOQqtqJMo9H8Ow8oSuYBCSGEELmV6wTI3t6e8PDwTMdPnTpFhQoVctXWzJkzGTx4MEOGDKFmzZrMmjULd3d35s+fn+X5hw8fxsvLixEjRlCpUiVatGjBm2++yfHjxzOcp9FocHFxyXAraWQekBBCCJF3uU6AXn31VcaOHUtERAQajQatVsuBAwf48MMP6d+/f47bSU5O5sSJE3To0CHD8Q4dOnDw4MEsn9O8eXNu3rzJ5s2bURSFO3fu8Pvvv/PCCy9kOC8+Ph5PT08qVqzIiy++yKlTp7KNJSkpidjY2Ay3oi69B+hGtPQACSGEELmV6wToyy+/xMPDgwoVKhAfH4+Pjw+tWrWiefPmjB8/PsftREVFkZaWhrOzc4bjzs7OREREZPmc5s2bs3LlSvr27YuZmRkuLi7Y29vz3Xff6c6pUaMGy5YtY+PGjaxatQoLCwv8/f25cuXKU2OZMmUKdnZ2upu7u3uOX4ehSA+QEEIIkXe5SoAUReH27dssWrSIK1eu8Ouvv/Lzzz9z8eJFfvrpJ4yNjXMdgEajyXSNJ4+lO3/+PCNGjOCzzz7jxIkTbNmyhZCQEIYPH647p2nTprz++uv4+vrSsmVLfv31V6pVq5YhSXrSuHHjiImJ0d3CwsJy/ToKm5eT1AISQggh8sokNycrikLVqlU5d+4cVatWpXLlynm+sJOTE8bGxpl6eyIjIzP1CqWbMmUK/v7+jBkzBoC6detibW1Ny5Yt+eKLL3B1dc30HCMjIxo1apRtD5C5uTnm5uZ5fi2G4Omg9gDduv+I5FQtZiZS01IIIYTIqVx9axoZGVG1atVcr/bKipmZGX5+fgQGBmY4HhgYSPPmzbN8TkJCAkZGGUNO73VSFCXL5yiKQlBQUJbJUXFWzsYcS1NjtArcvC/DYEIIIURu5LrbYPr06YwZM4azZ8/m++KjR49m8eLFLF26lAsXLvD+++8TGhqqG9IaN25chonVXbt25Y8//mD+/PkEBwdz4MABRowYQePGjXFzcwNg0qRJbN26leDgYIKCghg8eDBBQUEZhslKAo1GI/OAhBBCiDzK1RAYwOuvv05CQgK+vr6YmZlhaWmZ4fHo6Ogct9W3b1/u3bvH5MmTCQ8Pp3bt2mzevBlPT08AwsPDM9QEGjhwIHFxccydO5cPPvgAe3t72rZty7Rp03TnPHjwgGHDhhEREYGdnR3169dn7969NG7cOLcvtcjzcrTmYkSczAMSQgghckmjPG3s6CmWL1+e7eMDBgzIV0BFQWxsLHZ2dsTExGBra2vocJ5qyt8X+GFPMAObezGxWy1DhyOEEEIYVG6+v3PdA1QSEpySQlcLSHqAhBBCiFzJdQIEkJaWxvr167lw4QIajQYfHx+6deuWp2XwIu9kDpAQQgiRN7lOgK5evUqXLl24desW1atXR1EULl++jLu7O5s2bcLb27sg4hRZSO8BCrufQGqaFhNjWQovhBBC5ESuvzFHjBiBt7c3YWFhnDx5klOnThEaGkqlSpUYMWJEQcQonsLF1gIzEyNS0hTCY0rObvdCCCFEQct1D9CePXs4fPgwDg4OumOOjo5MnToVf39/vQYnsmdkpMHDwYqrkfFcv/cQ98fFEYUQQgiRvVz3AJmbmxMXF5fpeHx8PGZmZnoJSuSc1+N5QNdlHpAQQgiRY7lOgF588UWGDRvGkSNHUBQFRVE4fPgww4cPp1u3bgURo8iGZ/pKsChZCSaEEELkVK4ToDlz5uDt7U2zZs2wsLDQ7bZepUoVZs+eXRAximyk9wDdiJYeICGEECKncj0HyN7eng0bNnD16lUuXLiAoij4+PhQpUqVgohPPIOn1AISQgghci1PdYAAqlSpIklPEfBvMcQEtFoFIyONgSMSQgghir5cD4H17t2bqVOnZjr+9ddf8/LLL+slKJFzbvYWmBhpSErVcidOlsILIYQQOZHrBGjPnj288MILmY536tSJvXv36iUokXMmxkZULKtuSHs9SuYBCSGEEDmR6wToacvdTU1NiY2N1UtQIndkHpAQQgiRO7lOgGrXrs2aNWsyHV+9ejU+Pj56CUrkjtQCEkIIIXIn15OgP/30U1566SWuXbtG27ZtAdixYwerVq3it99+03uA4tnSe4BCo6UHSAghhMiJXCdA3bp1Y/369Xz11Vf8/vvvWFpaUrduXbZv307r1q0LIkbxDF5Oj3uAZA6QEEIIkSN5Wgb/wgsvZDkRWhjGf+cAKYqCRiNL4YUQQojs5HoOUFhYGDdv3tTdP3r0KKNGjWLhwoV6DUzkXMWylmg08DA5jaj4ZEOHI4QQQhR5uU6AXn31VXbt2gVAREQE7du35+jRo3z88cdMnjxZ7wGKZzM3McbNTl0KLyvBhBBCiGfLdQJ09uxZGjduDMCvv/5KnTp1OHjwIL/88gvLli3Td3wih3TzgGQlmBBCCPFMuU6AUlJSMDc3B2D79u26HeBr1KhBeHi4fqMTOSa1gIQQQoicy3UCVKtWLRYsWMC+ffsIDAykU6dOANy+fRtHR0e9ByhyRmoBCSGEEDmX6wRo2rRp/PDDD7Rp04Z+/frh6+sLwMaNG3VDY6Lw6WoBSQ+QEEII8Uy5Xgbfpk0boqKiiI2NpWzZsrrjw4YNw8rKSq/BiZxL3xVeeoCEEEKIZ8tTHSBjY+MMyQ+Al5eXPuIReeThoCafMY9SeJCQjL1V5v3ahBBCCKHK9RCYKJoszYxxtlUnp0svkBBCCJE9SYBKEFkJJoQQQuSMJEAlSPpKsJAoSYCEEEKI7EgCVILUcLEFYNXRUKIfypYYQgghxNPkaBL0nDlzctzgiBEj8hyMyJ9+jT1YeeQG1+4+ZOzaf1gY4CcbowohhBBZ0CiKojzrpEqVKuWsMY2G4ODgfAdlaLGxsdjZ2RETE4Otra2hw8mVs7di6DnvAClpCl/2rM1rTTwNHZIQQghRKHLz/Z2jHqCQkBC9BCYKXu0KdoztVIMvNl3g87/O06SSA1XK2xg6LCGEEKJIyfMcoOTkZC5dukRqaqo+4xF6MMi/Ei2rOpGYouW9VUEkpaYZOiQhhBCiSMl1ApSQkMDgwYOxsrKiVq1ahIaGAurcn6lTp+o9QJF7RkYaZrzsi4O1GRfCY/l6yyVDhySEEEIUKblOgMaNG8fp06fZvXs3FhYWuuPt27dnzZo1uQ5g3rx5VKpUCQsLC/z8/Ni3b1+2569cuRJfX1+srKxwdXXljTfe4N69exnOWbt2LT4+Ppibm+Pj48O6detyHVdxV97Wgukv1QVg8f4Q9l6+a+CIhBBCiKIj1wnQ+vXrmTt3Li1atMiwwsjHx4dr167lqq01a9YwatQoPvnkE06dOkXLli3p3LmzrlfpSfv376d///4MHjyYc+fO8dtvv3Hs2DGGDBmiO+fQoUP07duXgIAATp8+TUBAAH369OHIkSO5fanFXnsfZwKaqpOgP/jtNPfikwwckRBCCFE05GgV2H9ZWVlx9uxZKleujI2NDadPn6Zy5cqcPn2aVq1aERMTk+O2mjRpQoMGDZg/f77uWM2aNenRowdTpkzJdP4333zD/PnzMyRa3333HdOnTycsLAyAvn37Ehsby99//607p1OnTpQtW5ZVq1ZlGUdSUhJJSf8mB7Gxsbi7uxfLVWBPSkxJo+t3+7kSGU+7GuVZPKChLI0XQghRIuVmFViue4AaNWrEpk2bdPfTv0wXLVpEs2bNctxOcnIyJ06coEOHDhmOd+jQgYMHD2b5nObNm3Pz5k02b96MoijcuXOH33//nRdeeEF3zqFDhzK12bFjx6e2CTBlyhTs7Ox0N3d39xy/jqLOwtSYOf3qY2ZixI6Lkfx8+IahQxJCCCEMLtcJ0JQpU/jkk0946623SE1NZfbs2Tz//PMsW7aML7/8MsftREVFkZaWhrOzc4bjzs7OREREZPmc5s2bs3LlSvr27YuZmRkuLi7Y29vz3Xff6c6JiIjIVZugzmuKiYnR3dJ7k0qKmq62fNSpBgBfbLrA5TtxBo5ICCGEMKxcJ0DNmzfnwIEDJCQk4O3tzbZt23B2dubQoUP4+fnlOoAnh2MURXnqEM358+cZMWIEn332GSdOnGDLli2EhIQwfPjwPLcJYG5ujq2tbYZbSfOGvxetq5UjKVXLiFWnSEyRpfFCCCFKrxwVQnxSnTp1WL58eb4u7OTkhLGxcaaemcjIyEw9OOmmTJmCv78/Y8aMAaBu3bpYW1vTsmVLvvjiC1xdXXFxcclVm6WFRqPhm5d96Tx7Lxcj4pi25SITutYydFhCCCGEQeSoByg2NjbHt5wyMzPDz8+PwMDADMcDAwNp3rx5ls9JSEjAyChjyMbGxoDaywPQrFmzTG1u27btqW2WJuVszPm6ty8APx64zq5LkQaOSAghhDCMHPUA2dvb53jlUFpazodWRo8eTUBAAA0bNqRZs2YsXLiQ0NBQ3ZDWuHHjuHXrFitWrACga9euDB06lPnz59OxY0fCw8MZNWoUjRs3xs3NDYCRI0fSqlUrpk2bRvfu3dmwYQPbt29n//79OY6rJHuuRnkGNvdi2cHrjPntNH+PbEU5G3NDhyWEEEIUqhwlQLt27dL9fP36dT766CMGDhyoW/V16NAhli9fnuXS9ez07duXe/fuMXnyZMLDw6lduzabN2/G01OtXRMeHp6hJtDAgQOJi4tj7ty5fPDBB9jb29O2bVumTZumO6d58+asXr2a8ePH8+mnn+Lt7c2aNWto0qRJrmIryT7qXIND1+5x6U4cY34/zY8DG8nSeCGEEKVKrusAtWvXjiFDhtCvX78Mx3/55RcWLlzI7t279RmfQRTn3eBz6lJEHF3n7ic5VcuErj684V/J0CEJIYQQ+VKgdYAOHTpEw4YNMx1v2LAhR48ezW1zwkCqu9jwSZeaAEz5+yIXI3I+f0sIIYQo7nKdALm7u7NgwYJMx3/44YcSVUCwNOjfzJO2NcqTLEvjhRBClDK5Xgb/7bff8tJLL7F161aaNm0KwOHDh7l27Rpr167Ve4Ci4Gg0Gqb3rkunWfu4fCeerzZfYHL32oYOSwghhChwue4B6tKlC1euXKFbt25ER0dz7949unfvzuXLl+nSpUtBxCgKkFMZc2b0UZfGrzh0gx0X7hg4IiGEEKLg5XoSdGlQGiZBP+nzv86zZH8IDtZmbBnZkvK2FoYOSQghhMiV3Hx/56kS9IMHD1iyZAkXLlxAo9Hg4+PDoEGDsLOzy1PAwvD+16k6B6/d40J4LB/8dprlbzTGyEiWxgshhCiZcj0Edvz4cby9vfn222+Jjo4mKiqKmTNn4u3tzcmTJwsiRlEIzE2MmfNKPcxNjNh3JYqlB0IMHZIQQghRYHI9BNayZUuqVKnCokWLMDFRO5BSU1MZMmQIwcHB7N27t0ACLUylcQgs3c+HbzB+/VnMjI1Y905zarlJr54QQojioUDrAB0/fpyxY8fqkh8AExMT/ve//3H8+PHcRyuKlNeaePC8jzPJaerS+EfJsjReCCFEyZPrBMjW1jbD9hTpwsLCsLGx0UtQwnA0Gg3TXqpLeRtzrt19yBebzhs6JCGEEELvcp0A9e3bl8GDB7NmzRrCwsK4efMmq1evznJ7DFE8OVibMbNPPQBWHgll27kIwwYkhBBC6FmuV4F98803aDQa+vfvT2pqKgCmpqa89dZbTJ06Ve8BCsNoUdWJYa0qs3BvMGPX/oOvuz3OsjReCCFECZHnOkAJCQlcu3YNRVGoUqUKVlZW+o7NYErzJOj/Sk7V0nPeAc7djsW/iiM/DWoiS+OFEEIUWQU6CTqdlZUVderUoW7duiUq+RH/MjMxYk6/+liaGnPg6j0W7w82dEhCCCGEXuR4CGzQoEE5Om/p0qV5DkYUPd7lyvBZVx/G/XGGr7deorm3E7UryNJ4IYQQxVuOe4CWLVvGrl27ePDgAffv33/qTZQ8rzRyp2MtZ1LSFEasOsXDpFRDhySEEELkS457gIYPH87q1asJDg5m0KBBvP766zg4OBRkbKKI0Gg0TO1Vl9Nh+wiOesjwn0+weEBDzE2MDR2aEEIIkSc57gGaN28e4eHhjB07lj///BN3d3f69OnD1q1bkf1US76y1mYsCPDDysyYfVeiGP3radK08nsXQghRPOVqErS5uTn9+vUjMDCQ8+fPU6tWLd5++208PT2Jj48vqBhFEVHP3Z4Fr/thaqxh0z/hfLbhrCS/QgghiqU8rwLTaDRoNBoURUGr1eozJlGEtapWjm/71kOjUYskfht42dAhCSGEELmWqwQoKSmJVatW8fzzz1O9enXOnDnD3LlzCQ0NpUyZMgUVoyhiXqzrxufdawMwZ+dVlu6XneOFEEIULzmeBP3222+zevVqPDw8eOONN1i9ejWOjo4FGZsowl5v6sn9h8nMCLzM5L/OU9balJ71Kxo6LCGEECJHclwJ2sjICA8PD+rXr49G8/RqwH/88YfegjMUqQSdM4qiMPmv8/x44DomRhoW9vejbQ1nQ4clhBCilMrN93eOe4D69++fbeIjSh+NRsOnL/jwICGFdadu8dbPJ/l5SBMaeUl5BCGEEEVbnvcCK8mkByh3UtK0vPnTCXZejMTGwoRf32xGTVd534QQQhSuQtkLTIh0psZGfP9qAxp6liUuMZX+S48Sei/B0GEJIYQQTyUJkNALSzNjlgxsRA0XG+7GJfH6kiNExiUaOiyd2MQUVhy6zh8nb/IgIdnQ4QghhDAwGQLLggyB5V1kbCK9FxwiNDqBGi42rHmzGXaWpgaLR1EUNp+JYNKf54iMSwLA2EhDYy8HOtRy5nkfZyqWtTJYfEIIIfQnN9/fkgBlQRKg/Llx7yEvzT9EVHwSjbzKsmJQEyzNCn/fsNB7CXy28Sy7L90FwMvRCgtTYy5GxGU4z8fVlud91GSolputTPYXQohiShKgfJIEKP/O346l78JDxCWm0q5GeRYE+GFqXDgjrsmpWhbtC2bOjiskpWoxMzbirTbevNXGGwtTY0LvJbDtfASB5+9w7Ho0/93SrIK9Jc/7ONPBx5lGlRwKLWYhhBD5JwlQPkkCpB/Hrkfz+uIjJKVq6VW/At+87IuRUcH2rhy7Hs0n685w+Y66N11zb0c+71Eb73JZVyqPfpjMzouRbDsXwd4rd0lM+XdbF1sLE9rWKE+HWi60qlaOMuY5rhohhBDCACQByidJgPRn58U7DF1xgjStwiD/Snz6Ys0CGWK6/zCZqX9fZM3xMAAcrc345IWa9KxfIcfXe5Scxv6rUQSej2D7hUiiH/47WdrM2Aj/Ko487+NCe5/ylLex0PtrEEIIkT+SAOWTJED69cfJm4z+9TQAYzpW553nquitbUVR+OPkLb7cfEGXsPRr7M7YTjWwtzLLc7tpWoWTofcJPH+HbeciuP7Esv76HvaPh8pcqFJe9sErsRQF1r8NN4/Bc+OgVi+QOWJCFFnFKgGaN28eX3/9NeHh4dSqVYtZs2bRsmXLLM8dOHAgy5cvz3Tcx8eHc+fOAbBs2TLeeOONTOc8evQIC4uc/dUuCZD+Ld0fwuS/zgPwZc/avNbEM99tXrsbz/h1ZzkUfA+A6s42fNmzNg31XIlaURSuRsaz7fwdtp2/w+mwBxker+xkzfO11HlD9d3LFvgwnyhEVwJhZe9/73u3hS7fgKO34WISQjxVsUmA1qxZQ0BAAPPmzcPf358ffviBxYsXc/78eTw8PDKdHxMTw6NHj3T3U1NT8fX15b333mPixImAmgCNHDmSS5cuZXiui4tLjuOSBKhgfLP1EnN3XUWjgbn9GvBCXdc8tZOYksa8XVdZsCeY5DQtFqZGjGxXjSEtKxXKpOWImES2X1CToUPXokhJ+/cj5FTGnPY1y/O8jzP+VZywMC381W9CT7RpsKAlRJ6Dio0g/B9ISwJjc2j5AbQYBSbmho5SCPEfxSYBatKkCQ0aNGD+/Pm6YzVr1qRHjx5MmTLlmc9fv349vXr1IiQkBE9PtUdh2bJljBo1igcPHuQ5LkmACoaiKHyy/iy/HAnF1FjD0oGNaFm1XK7a2H8livHrz+iGpJ6rXo7J3Wvj7mCYWj5xiSnsvnSXwPN32HUxkrikVN1jVmbGtKpajg61nGlbo3y+huSEAQT9AuvfAgs7GBEEj+7Dpg8geJf6uGMVeGEGVG5jyCiFEP9RLBKg5ORkrKys+O233+jZs6fu+MiRIwkKCmLPnj3PbKNr164kJSWxbds23bFly5YxZMgQKlSoQFpaGvXq1ePzzz+nfv36T20nKSmJpKQk3f3Y2Fjc3d0lASoAaVqFEatOselMOFZmxvwytCn13O2f+by7cUl8sek8G4JuA+Bsa86ErrXoXNulyNTtSU7VciTkHtvO3SHw/B0iYv+thJ1efDG93pChEjaRQymJ8J0fxN6E9pPU3h5Q5wSd+wO2jIP4O+qxOi9Dhy/Bxtlg4QohVMUiAbp9+zYVKlTgwIEDNG/eXHf8q6++Yvny5ZmGsJ4UHh6Ou7s7v/zyC3369NEdP3z4MFevXqVOnTrExsYye/ZsNm/ezOnTp6latWqWbU2cOJFJkyZlOi4JUMFISk1jyPLj7LsShb2VKb8Pb0aV8jZZnqvVKqw6Fsq0vy8Sm5iKkQb6N/Pigw7VsLEwXIXpZ1EUhTO3Ygg8ryZDTxZfrOlqSwcpvlh0HZgDgZ+CbQV47wSYWmZ8PDEGdn4JxxaBogVzO2j3KTQcBEYy7CmEoRSrBOjgwYM0a9ZMd/zLL7/kp59+4uLFi9k+f8qUKcyYMYPbt29jZvb0oQWtVkuDBg1o1aoVc+bMyfIc6QEqfA+TUnl18RFOhz3A1c6C399qTgX7jF8yF8Jj+WTdGU6GPgCgdgVbvupZh7oV7Qs/4HxKL7647fwdjkvxxaLt0X2YXQ8SH0D376H+608/99ZJ+Ot9CA9S77s1gBe/Bbd6BR+nECKT3CRABqvs5uTkhLGxMRERERmOR0ZG4uycfVeyoigsXbqUgICAbJMfACMjIxo1asSVK1eeeo65uTnm5jKZsTBZm5vw48BG9PnhEFcj4wlYcoTf3myGYxlzEpJTmb39Cov3h5CmVShjbsIHHarRv5kXxsV0hZWHoxVDWlZmSMvKRD9MZscFtWdo75W73HrwiGUHr7Ps4HVsLUxoV1PtGZLiiway/1s1+SnvA779sj+3QgMYuhOOL4Udk+H2SVj0HDQeBs99AhbyB5QQRZXBJ0H7+fkxb9483TEfHx+6d++e7STo3bt389xzz3HmzBlq166d7TUURaFx48bUqVOHpUuX5igumQRdeMJjHtF7/iFuPXhEnQp2DG/tzVebL3Drgbrar3NtFyZ0rYWLXcksPJht8UUTI/y9HelQy4V2NaX4YqGIuQlzGqirvV79Fap1zPlz4yJg68dwdq16v4wLdJoCtXpK7SAhCkmxGAKDf5fBL1iwgGbNmrFw4UIWLVrEuXPn8PT0ZNy4cdy6dYsVK1ZkeF5AQABXrlzh8OHDmdqcNGkSTZs2pWrVqsTGxjJnzhx++uknDhw4QOPGjXMUlyRAheva3XheXnAow5d/BXtLPu9Ri7Y1Ss/E0vTii9vOqUNlN/5TfFGjgcZeDnzVq85Tt/UQerD+HQj6GTz9YeCmvCUu13aqq8Wig9X73u3ghW/AobJ+YxVCZFIshsAA+vbty71795g8eTLh4eHUrl2bzZs365a0h4eHExoamuE5MTExrF27ltmzZ2fZ5oMHDxg2bBgRERHY2dlRv3599u7dm+PkRxQ+73JlWPZGI15ddITElDQGt6zEyHZVsTIrXcM/xkYaGnk50MjLgY+71ORKZLxaifpx8cUjIdF0n3uAb16uS6faeauhJLJx5zyc/kX9+fnJee+18W4Lbx2CA7Ng3wy4tgO+bwqtPgT/kVI7SIgiwuCVoIsi6QEyjMjYRNIUBVc7y2efXMrcvJ/A6F9PczQkGoBhrSrzv47VMZEJ0/qzsg9c2Qo+3aHPimefnxP3rmVRO2gmVG6tn/aFEBnk5vtb/vUURUZ5WwtJfp6iYlkrVg5pwrBW6jDKwr3BvLr4CJFxic94psiR6/vV5EdjDG0/01+7jt4QsA5eWgJlnOHeVVjRDdYOhfhI/V1HCJFrkgAJUUyYGhvxcZeazH+tAWXMTTgaEs2Lc/Zz7Hq0oUMr3hQFAieoP/sNBCf9bdYLqENpdXrDu8fU1WFo4Myv8F1DOLZY3XJDCFHoZAgsCzIEJoq6a3fjGf7TCa5ExmNspGFc5xoMblFJCirmxfkN8Gt/MLWGEacKvqLzk7WDKvhBgwHFt4CihR1UfwGM5O9pYXjFZhVYUSUJkCgOHialMu6PM2w8rW4P8kJdV6a9VFdqB+VGWgp83wSir0HrsfDcx4VzXW0aHFsCOz+HpNjCuWZB6jQVmr5l6CiEkAQovyQBEsWFoiisOHSDz/86T6pWwbucNT8E+D11axHxhGOL1UnKVk4wMgjMC/l9i4uAvd/Ag9Bnn1sUJcZA2GGwdFDfPws7Q0ckSjlJgPJJEiBR3Jy4cZ93Vp4kIjYRKzNjpveuy4t13QwdVtGWFA9z6sPDSOj8NTQZZuiIip+0VJjfDKIuQ4vR0H6CoSMSpZysAhOilPHzLMtfI1rQrLIjCclpvPvLKSb9eY6UNK2hQyu6Dn2vJj9lK6mTn0XuGZtAu8dJz+H5EHvbsPEIkQuSAAlRQjiVMeenwY15q403AD8euE6/hYe5EytL5TOJvwsHH2+O3O5TMMl+T0GRjRovgHsTSH0Eu5++hZEQRY0kQEKUICbGRoztVIOFAX7YmJtw/MZ9Xpizj0PX7hk6tKJl73RIjge3+uDT09DRFG8ajVo5G+DUz3D3kmHjESKHJAESogTqUMuFP99rQQ0XG6Lik3l9yRF+2HMNmfKHWp35+OONkZ+fLMu39cGjqboUXtHC9kmGjkaIHJFPvhAllJeTNeve9qdX/QqkaRWm/H2R4T+fIDYxxdChGdbOL0CbClXaQ6VWho6m5Gg/ATRGcGkThGbeqFqIokYSICFKMEszY2b08eXzHrUxNdaw9dwdus89wKWIOEOHZhi3TsC5PwANtM9ZT4WiKKTKZPJnK1cd6r+u/hz4mVphW4giTBIgIUo4jUZDQFNPfhveHDc7C0KiHtLj+wOsP3XL0KEVrv9ueeH7CrjUfuZTElPSeG3xEZpP3cnZWzEFHGAJ0OZjMLGEsCNwcZOhoxEiW5IACVFK1HO3568RLWlZ1YlHKWmMWhPEZxvOkpxaSno3ru6A6/vA2CxHFZ8VRWHcH2c4eO0ekXFJDFh6lOC78YUQaDFm6wrN3lZ/3jFJrRMkRBElCZAQpYiDtRnL3mjMe23VDT9XHLpBnx8OcfvBIwNHVsC0abD9ce9P42Fg7/HMpyw9cJ11p25hbKShspM19x4mE7DkKBExUlYgW/4j1crQUZch6GdDRyPEU0kCJEQpY2yk4YMO1Vk6sCG2FiYEhT3gxe/289vxMC5GxJKYUgJ3J//nV7hzFsztoOUHzzz9wNUovtp8AYBPutTk1+HNqORkza0HjwhYcoQHCckFHXHxZWEHrcaoP++aAskPDRuPEE8hW2FkQbbCEKVF6L0Ehv98gvPh/27IqdGAe1krKpezxrtcGbzLldH97FTGrPjtOJ+SCHMbQkwYtJ8ILd7P9vSw6AS6zt3Pg4QUXmpQkW9erotGoyEsOoHeCw5yJzaJ+h72rBzSBCsz2Xg2S6lJ6nv+IBTafgqtPjR0RKKUkL3A8kkSIJFrYcdg/7dQtw/4dFeziGIiMSWNOTuucCj4Htci44lNfPq8DVsLE7zLl6GyUxm8y6cnSNZ4OFhjZlJEO5QPfgfbxoONG4w4CaaWTz01ITmVXvMOcjEiDt+Kdqx5sxkWpsa6xy/fiePlBYeIeZRCq2rlWNy/YdF93Yb2z6/wx1Awt4URQWDtaOiIMnp0X/3/omJj8Btg6GiEnkgClE+SAIlcuX8dFj4Hj6LV+1XaQ5evwaGyQcPKC0VRuPcwmWuR8Vy7+5Brd+O5djee4LsPCbuf8NSVzcZGGjwdrKj8OCHyLqcmSJWdylDW2oDbTDx6ALN9IfEBdJsLDQKeeqqiKLy76hSb/gnHqYwZf77XAle7zMnSydD7vLboCI9S0nixriuzX6mPsVHxSXgLjVYLC1tBxBlo8hZ0nmroiP6lTYOVL8O1Her93kuh9kuGjUnohSRA+SQJkMixpHhY2lGdX2LvCXHhkJYMJhbQ8kPwHwEm5oaOUi8SU9K4fu8h1yIfEvw4MUpPkhKSnz5vyMHajCrlyjCoRSU61XYpxIhRl70fmAXlasJbB8DI+Kmnztt9lelbLmFqrOGXoU1p5OXw1HP3XL7LkOXHSElTCGjqyeTutYrf0GBhuLoDfu4FRqbw3nEo62XoiFTbPv13LzhQl+4P3gaudQ0Xk9ALSYDySRIgkSOKAr/2hwsbwbo8DNsNKY9g02gI2aOe41gVXpxZoisOK4rCndgkXW/Rtch4gqMeci0yntv/WTGl0cDk7rUJaOpZOIHF3ITv/CA1EfqtgeqdnnrqrkuRDFp2DEWBL3vW5rUmz45x4+nbjFx9CkWBke2q8v7z1fQZfcmxojsE74Y6L8NLiw0dzb9DcwAvLYHTq+DqdrBzVz/D1k4GDU/kjyRA+SQJkMiRPV/Dri/Uv24HbgKPJupxRYEzv8PWcfDwrnqsbl/o8AWUKW+4eA3gYVIqIVEPWXkklFVHQwH4sEM13nmuSsH3mGx4R92c06M5vLH5qfOyQqIe0m3ufuISU+nX2IMpverk+BI/HbrOpxvOATCxqw8D/SvpJfQS5XYQLGyt/vzmXnD1NVwst07Cj53VpLjlB9DuM3Uu0KJ2EH0NPP2h/wYwNjVcjCJfcvP9LbP3hMiLi5vU5AfUHp705AfUL9q6L8O7x6HREEAD/6xRV8UcX6rOjSglrM1NqF3Bjq961tbVHvpm22W+2nyhYDdmjbwAQb+oPz8/+anJT3xSKkNXHCcuMRU/z7JM7OaTq8sENPPi/fZqz8/EP8+zIaiUVdfOCbd6ULu3+nN6JW5DiLsDa15Xk59qneC58epxy7LQbxWY2cCNA7DlI8PFKAqVJEBC5FbkBfhjmPpz42HQoH/W51nawwszYMgOcKkLiTHw1/uw5HkI/6fQwi0KNBq19tD4F2oCsGhfCGPX/lNwe2xtn6juTF6zK7g3yvIUrVbh/TVBXI2Mx9nWnPmvNcDc5OlzhJ5mRLsqDGzuBcAHv55m16XIfAReQrX7VO0pDd4F13YW/vVTk+DXAIi9BU7VoNdCMPrP11+56vDSIkADxxbDiWWFH6ModJIACZEbCdGwqh8kx4NXS+j41bOfU9EPhu6CTtPUvzJvHVeHBLaMg6TStSnpkJaVmf5SXYw08Ovxm7z7yymSUvVcePH6Abi8BTTG0O7pPQ5zdl4h8PwdzIyNWPC6H+VtLfJ0OY1Gw2cv+tC9nhupWoW3fj7BiRvReY2+ZCrr9bg3FLUXqDB7QRUFNn+o7k9mbgevrFKLNT6pemdo+4n686YPZUf7UkASICFyKi0Vfh8E90PAzgNeXp7zuQLGJtB0OLx7DGr1VHsnDs+DuY3h3PpStXN2n0buzHutAWbGRmw5F8GQ5cd5mKSnPaMU5d8tL/wGgFPVLE/bdi6CWduvAPBFz9rU9yibr8saGWn45mVf2lQvR2KKljd+PMbFiNhnP7E0afWh+gdAxD9wdm3hXffYYji5AtCoy92dqjz93JYfgk8P0KbAmgB1Ir0osSQBEiKntk9Qu/BNraDfL3kr7GbrCi8vg9fXQtlKEHcbfhug1iSJDtF7yEVVp9quLB3YCCszY/ZdieJ1fW0vceFPuHlM/R21HpvlKVfuxPH+miAABjb3ok9D9/xfFzA1NmL+a374eZYlNjGV/kuOEhadoJe2SwRrJ2gxUv1552R1WKqgXd//75ye9hOhavvsz9dooMc8cK4NDyNh9Wvqyk5RIkkCJEROnF4Nh+aqP/eYBy45XymUpSrt4e1D0Op/6u7kVwNhXlPY+3XhfDEUAS2qOrFySBPsLE05FfqAvj8cJjI2HxuNpqWoO5ADNHsXbDLXHIp5lMKwn07wMDmNJpUc+OTxnCR9sTQzZumARlR3tiEyLonXlxzhblzp+H3mSNO3oYyLukXG8aUFe60HoWqZCm2qOgnbf2TOnmdmDa/8om7oGh4EG0eUqh7a0kQSICGe5dZJ9R9BUDd5rNVTP+2aWqpzDt46qNYJSk2EnV/AghYQsk8/1yji6nuU5dc3m1HexpxLd+LoveAQoffy2GtycgXcuwpWjtD8vUwPp2kVRq4+RUjUQyrYWzLvtQaYGuv/n0A7K1NWDG5MxbKW3LiXQP+lR4l5lKL36xRLZtbQ5nGPzJ7p6sKAgpD8EFa/Cgn31GX33b7L3fY0ZT2hzwp1HtmZX9XtVESJIwmQENmJu6N2g6clQbXO0OZj/V/DqSr03wi9FoN1OYi6DMtfhD/ehPi7+r9eEVPdxYbfhzfHw8GK0Mcbjl6KyOXk8KR42P14q4XWY8Eic/2PGdsusfvSXSxMjfghwA/HMgVXodvZ1oKfBzfBqYwZF8JjGbr8OIkpep7sXVzVD1ALhD6KhgNznn1+bimKWgMq4oz6eeq7Esysct9OpZbQeZr68/YJcGW7fuMUBicJkBBPk750Nu42OFXPvHRWn3S1g45Bw8GotYNWw1y/UlE7yMPRit+HN9MNHfX54RCnQu/nvIHD89Q5G2W9wO+NTA//9c9t5u2+BsC0l+pSu0IWq4D0zMvJmuWDGmNjbsLR69G8+8vJglv2X5wYm0D7xxPVD30PseH6bX//TDi3Tl123+cnsM/HHK9GQ9QyF4pWXQARdVV/cQqDkwRIiKwoCmz6QF06a2GnFkrLoldB7yzLqoUVn6wdtLRDia8dVN7WgjVvNqW+hz0xj1J4bfER9l+JevYT4+/Cgdnqz20/BZOMm6+evx3LmN/U925Yq8p0r1dB36E/VS03OxYPaIi5iRHbL0Qydu0ZtFqZT0KNF8G9CaQ+gj163CT10hbY8bn6c5evwbNZ/trTaKDLN2qsSTGwuh8klpLVfalJ6lBiCSYJkBBZObYYTv0EGiN16ayjd+FeX1c7aKq6dPjmsce1gz4u0bWD7K3M+HlwE1pWdSIhOY1By46x5Ww2PQTh/8AvfdS6TK71oFavDA/ff5jMsJ+O8ygljZZVnfhfx+oF+wKy0KSyI9+/2gBjIw1rT94s+CrYxYFGA+0fT1g/+RPcvZz/Nu9egrVDAEXtRW2YuScwT0zM1Z4kGzd1ePqPoSW7R1abBkd+gOne6sKMgpqnVQRIAiTEk0L2wd+Pl1C3n6Su2DIEYxNo+ha8e1StTaJo4fD3au2g8xtK7MoUa3MTFg9oSOfaLiSnaXl75Ul+PR6W8aSkODUZXNgabp9Uk8Qu32QYokxN0/LuqpPcvP8IDwcrvutXH5MCmPScE+19nJn+krrT+OL9Iczfc80gcRQpns2gehdQ0v5dvZdXjx48LlAap+7n1UmPvUoANs7wykowsVCLbO76Ur/tFxW3TsKitvD3/9T38kEo7J9l6KgKjMEToHnz5lGpUiUsLCzw8/Nj376nr34ZOHAgGo0m061WrVoZzlu7di0+Pj6Ym5vj4+PDunXrCvpliJLi/g116aySBnX6ZLmaqNDZukGf5fDaWnWOS9xtNcZf+sD964aOrkCYmxjzXb/69GlYEa0C//v9HxbvC1aTvvMb1CTw8PdqUlirpzp36oktL6b8fZEDV+9hZWbMwv5+2FuZPeVqheMlv4q6rUCmb7mk2xy2VGs3Qe1lvfgXhB7JWxvaNFg7WN3M1M5dLVBqUgC/6woNoOvjSdv7voGzf+j/GoaSGKNWv17UVl36b24HDQaojx2eD7G3DRpeQTFoArRmzRpGjRrFJ598wqlTp2jZsiWdO3cmNDTrfxhmz55NeHi47hYWFoaDgwMvv/yy7pxDhw7Rt29fAgICOH36NAEBAfTp04cjR/L44RKlR/rS2UfR6nBKtzm5Wzpb0Kq2h7cPq7WDjEzhyjb4vgns/QZS9VBEsIgxMTZi2kt1GdpS3WF9+ebdBM/uoiZ/cbfVQpKvr1ULS9q6ZnjuHydvsmS/Wlhyxsu+1HAphPlbOTCkZWXebqMOp36y7gybz+h5AnBxU74G1H9d/Tnws7z1au6YBFe3g4ml2ktTppx+Y/wv375qjSlQV5oV93l5igJnfoe5jeDYIkBR//B79xh0nf3vPK3dUwwdaYHQKAYcjG7SpAkNGjRg/vz5umM1a9akR48eTJny7Dd8/fr19OrVi5CQEDw9PQHo27cvsbGx/P3337rzOnXqRNmyZVm1alWW7SQlJZGU9G+xstjYWNzd3YmJicHWtmj8wykKmKKoFZnPbwDr8jBsN9gV3mTZXIu6AptGQ8he9b5TdXXj1UotDRtXAVBSEjmychL1QhZhoUkhVWOCcYv30bT6QK2l9IR/bj6g94JDJKdqefe5KnxogHk/2VEUhY/XnWHV0TDMjI348Y1G+FdxMnRYhhN7G+Y0UL9oX/kFaryQ8+f+8xv88XiPsd5LofZLBRPjf6Wlwi8vq5u62nnAsF1qlevi5t419d+Q4N3qfccq6r8hldv8e07oEXUBhsYI3jqkJqxFXGxsLHZ2djn6/jZYD1BycjInTpygQ4cOGY536NCBgwcP5qiNJUuW0L59e13yA2oP0JNtduzYMds2p0yZgp2dne7m7q6f0viiGNn3jZr8GJlC35+KdvID/6kdtOhx7aBLau2gdcNLVu2gkH1ofmhJ0+vzsNCkcEBbiw6JUxl19wVSjDLX8bkbl8SbP50gOVVLuxrlGf18NQMEnT2NRsMXPero5jgNW3GcI8H3Su/EaFs3da4bwPZJaoKRE7dPwcbHvTEtRhdO8gPq3LzeS8GhMsSEwq8D1CrkxUVKIuyaok5wDt4Nxubw3OOCrP9NfgA8mqgr9hQt7JhsiGgLlMESoKioKNLS0nB2ds5w3NnZmYiIiGc+Pzw8nL///pshQ4ZkOB4REZHrNseNG0dMTIzuFhYW9tRzRQl0cbNagRnghW/Ao6lh48kpjQbq9slYO+j0KpjbEI7/WLxXqsTfVQtBLn9RXXljXQ56LSKq56+EaiqwIeg2b/50IkNxweRULe+sPEl4TCKVy1nz7Sv1MDIqQkOY/2FspGHWK/Xwr+LIw+Q0+i48zHPf7OarzRc4dj2atNK2VN5/pFoCIuoSBK189vnxj/fpSk2Eqh2h7fiCj/G/LMuqu8qblYEb+2FrARRILQhXd8D8ZmrpgbRk8G6nbsnT+n/qarestPtM7QG6tAlCDxduvAXM4JOgNU/MsVAUJdOxrCxbtgx7e3t69OiR7zbNzc2xtbXNcBOlRORF+GOY+nOjoeA30KDh5ImudtB2dY+yxAfw1yi16zrijKGjyx2tVk3e5jZUC0GiUZO7d49B3T50r1+Rhf39MDcxYufFSPovPUpsovrX9+d/nefo9WjKmJuwMKAhthamhn0tz2BuYswPAQ15sa4rZsZGXL+XwMK9wby84BCNv9zO/34/zfbzd0pHBWlLe3WbGVDnmyRnsx1KarK6U3vsLbWi9EuLwMi4UMLMoHwNtQcW4OhCOLG88GPIqbgItZDjz70gOljdjy19U+ZnlfgoV12t3g15n6dVRBksAXJycsLY2DhTz0xkZGSmHpwnKYrC0qVLCQgIwMws42x/FxeXPLUpSqFH99XCZslx4NkCOhXziX4VG8LQ3Y9rB5VRawf9UIxqB0WcUZO2v0apSZxLXbUg5Isz1STvsbY1nPlpcBO1wnJINK8uOsyCPdf46fANNBqY1bceVcqXMdjLyI0y5ibMfbUBJz97nnmvNaBHPTdsLUy49zCZX4/fZMiK49SfHMiwFcf5/cRN7j8seZPddRoNUefUxIXDkflZn6MosPlDCDusrlTqt0otVGooNbrAc497nzZ9kPeVbAVFmwZHFqqTnM+uVXtymryl/kFRq2fOF3m0GadOMg87Ahc3FWzMhcjgk6D9/PyYN2+e7piPjw/du3fPdhL07t27ee655zhz5gy1a9fO8Fjfvn2Ji4tj8+bNumOdO3fG3t7+qZOgn5SbSVSimCopExmfJvY2bBkH59er923coPNUqNmtaK1sAzU52zUFjixQyw+Y2aibxDYaqs63eIqzt2IYsPQo9/6TFHzwfDXea1e1MKIuMClpWo6GRBN4/g7bzkVwOyZR95iRBhp5OfC8jzMdfFzwcMzDHlf5FJ+USsjdh1y7G8+1u/HEPkqhmbcTrao5YWX29N9XjpxeA+uGgbktjAgCa8eMjx9brCYaaOC136Dq8/m7nj4U1QUUt06qVeTDg9T7bg3gxW/BrV7e2tsxGfbNAKdq6oTobD6bhpSb72+DJkBr1qwhICCABQsW0KxZMxYuXMiiRYs4d+4cnp6ejBs3jlu3brFixYoMzwsICODKlSscPpx5PPLgwYO0atWKL7/8ku7du7NhwwbGjx/P/v37adKkSY7ikgSoFNg2Xt3h2dQKBm0F17qGjqhgXNkOmz/4t15Q1Q7qFgFlvQwZlUpR4MJG+PsjdVk7qAUfO01RJ8bmQPDdeF5ffITbMYl0quXCvNcaFNl5P3mhKArnbsey7fwdAs/f4UJ4xm0YarjY6JKh2hVsczR9ICe0WoWI2EQ1yYmMJzjqccIT+ZCI2MQsn2NuYkTLqk487+NMu5rOOOVls1mtFha2UnsDm74Dnb7697HrB2BFN9CmqgVKW4zK24srCEnxsLQj3DkLbvXhjb+zXKFYKBJj1O1Aji0GFLWnrP1n6h55+RkqTIyB2fXUMiFdZxfZ6QLFJgECtRDi9OnTCQ8Pp3bt2nz77be0atUKUAsfXr9+nd27d+vOj4mJwdXVldmzZzN06NAs2/z9998ZP348wcHBeHt78+WXX9KrV68sz82KJEAlXPpfmaCOg9fqadBwClzKI/Uvt/2zQJuiVrNtNQaajyiYgnE5cf86bB6j1jICNSHrMkOtdZRLd+OSOHgtio61XLAwNcBckEIUFp1A4ONk6OgTk6Vd7Sx43seZ532caVLJETOTZ89weJScRsjj5Cb4P706wXcf8iibuUdOZcyo7FQG7/LWmBkbsfNSJGHRj3SPazTg51FWTc5quVDJyTrnL/LqDnWuirEZvHscynqqFYkXPgcJUVC7N7y0uOj1ZN6/rsb4KBrq9oWePxRujIqiDnNt/Rji76jH6vSBDl+olaz14dA82DpOnUM04iSY5eL3WkiKVQJUFEkCVILdOglLO0FaErT8ENp9auiICs/dy2rdj+uPq607VVfn13i1KLwYUpPh0HewZ7q6gsfIVP1LvmXWNX3E0z1ISGbnxUgCz99hz+W7JCT/m7DYWJjwXPXyPO/jTOvq5UhMTuNqhiTnIdci47kd8+ipc1pNjDR4OFrhXa7M45s1lR//98mq2oqicDEiTh22Ox/B2VsZe6qqlC9Dh8fJmW9F++x76RQFVnSHkD1qIvHirH8n9Lv6whtbwKzwh/5yJGQvrOihDuV2+KLwKsnnpKaPPqQmqQsUHoSqGw+3+lC/7euBJED5JAlQCRV3Bxa2UYdbqnVWi64ZGXwhZOFSFDjzm/pX4sPH9YJ8+8HznxdsBV2A6/vhr9HqUmcAr5bwwkwoV/Rq9RQ3iSlpHLwWxbZzd9h+4Q5R8TmfLG1naYp3OWs1ySlfhspO1niXL4OHgxWmedw77faDR2y/cIdt5+5wOPgeqf/pqSpvY057H2c6+DjTzNsRc5Mseu1un1I/q2jUvb1u7FdLIQzdBfZFvE7bkYXw9xh1wvFrvxXsXoIpiXBgFuybqf5RZ2yuJiX+I5++rD2/0otPPm2eloFJApRPkgCVQI8eqHtnhR1Rez6GbAeLUvy7fXRfndR4/EdAAQt7taaQUQEtHY8JU+f7gPpF1vErqPNy0RvGKAG0WoVTYQ/Ydj6CwHN3CI56iJEG3B2snujJUX92sDbT29yhrMQ8SmH3pUi2nb/Dnkt3iU/6t9BhGXMTWlcrR4dazrSpXh47y//8//f7IHVIB8DIBAb8CZ7NCyxOvVEU2PgenPpJXaFW7zWgIN5fRd2YNTpYvevdTp3f96xl7fml1aqbEEf8o64o66znjWfzSRKgfJIEqAR5ssfD3A6G7gSnKoaOrGi4eVxddl4o9YI00PANtbDaf5a1i4IVGZeInaVp1j0thSwpNY1D1+7p5jFFxv27BZGJkYamlR1185jctBHq8m1tirp6qeEgA0aeS6lJsOxFuHm04K9VxkVNQnx6FN4fFNd2wk891T+Y3jteNBZVPCYJUD5JAlRCPLlflmNV6LlArZcj/pWWCv+sgXtXCu4aGmO1ZkoFv4K7hihWtFqFf27FsO1cBIHn73AlMj7D47Ur2DLI9QbNXcHF/3UDRZl3qfHRRO1dSHmTRwW3MtHKUd213RC92St6QPAutSf3pcWFf/2nkAQonyQBKuZSHqlj4gdmqeXeTSzUcfHmIwpuXFwIkS8hUQ8JPK8mQ8dv3M8wOfu56uUY3tqbxpUcCnS4Th8eJaex5lgoi/aFcOvBIxp42PP9aw1wtSthk/xvB6lDYQDD9uS9vpCeSQKUT5IAFWNXt8OmD+F+iHq/yvPquLhDJcPGJYTIsaj4JHZeiGTLuQh2X4okfQ51fQ97hrf25vmazkWu3tP9h8ksP3Sd5Qevcz8h4+aojtZmfNevPs2rlKBiqwBrh6hTDCo/B/3XGzoaQBKgfJMEqBiKDVfrU5xbp963cYXO04pm5WMhRI5dj3rIwn3B/H7iJsmp6ga/3uWsebO1Nz3qVchRvaOCdOvBIxbvC2b10TBd7SQPByuGtqpMYy8HRq0J4kJ4LEYaGNOxBsNbVy7yvVg5dv86fNdQnacVsA682xo6IkmA8ksSoGIkLVWteLrzC3VPL40RNBkOz30M5jaGjk4IoSeRcYn8eOA6Px+6QdzjlWQuthYMaVmJVxp7UMa8cLdmuBQRxw97rrHx9G3dMv9abrYMb+1N59oumDwuIfAoOY3x68+y9uRNAJ73cWZGH98ivVlvSpqWNcfCSEnT0tuvIjbZxfr3R+rebS511aEwA5cWkQQonyQBKiZunXi8181p9X6FhmphP1dfw8YlhCgwcYkp/HIklCX7Q3SryGwtTOjfzIuB/l5524IjF45dj2bB7mvsuBipO9bc25Hhrb1pWdUpy94dRVFYdTSMiRvPkZymxcvRivmv+1HTteh9v5y4cZ9P1p3hYoS6gbKNhQmvN/XkDX8vyttYZH7Cw3sw21f9A7TXYqj7ciFHnJEkQPkkCVAR9+gB7Pwcji1BrWFjB+0nQoOBBv/rQwhROJJS01h38hYL9wYTHPUQUPcje7lhRYa19NbrRrFarcKOi5Es2HONEzfuA+rIeufaLrzZyhtfd/sctXM67AFvrzzJrQePsDA1YkqvOvSsX1FvceZHTEIKU7dcZNXRUADKWplS1tqM4Lvqe2tmYkRvv4oMa1kZrye3Ntn7tdoLb++hbl9iwMUmkgDlkyRARZSiwJnfH9f0efzXV92+asn5MuUNG5sQwiDStAqB5yOYvyeY02EPADDSQJc6rgxv7U3tCnZ5bjs5VcvG07f5Yc813TJ9M2MjXvKrwNCWlalcrkyu27z/MJmRa4LYe1mtxP56Uw8+fdHHYHWaFEVhQ9Btvth0XldB/GW/iozrUhN7S1MCL9xhwZ5rnAp9AKjvbec6rrz13/c2+SHMaQDxEdBpKjR9yyCvBSQByjdJgIqgqKuPa/rsUe87Vn28101rw8YlhCgSFEXhcHA0C/ZcY8/j5AKgZVUn3mrtTTNvxxxPPn6YlMqqo+owW3hMIgA25ia81tSTQf5elLfNYigoF9K0CrN3XGHODrX2lq+7PfNea0AF+8JdKh8S9ZBP159l/9UoQN2z7csetWlSOeP2FoqicDREfW93Xfr3vW1RxYnhrb3xr+KI5uRy+HMkWDrAyCC1Z94AJAHKJ0mAipCURNg/E/Z/KzV9hBA5cu52DD/sCeavf27rltDXrWjH8NbedKzlgvFTltDfi09i2cHrrDh0g5hH6lL2cjbmDG5RiVebeOh94vKui5GMWhNEzKMUylqZMqdffVpWLeA9+VCHDxfsDub73VdJTtVibmLEe22rMKyV9zNX1V2MiOWHPcFsPH2btMdvbp0Kdgxv6UGX/S+hibps0I2mJQHKJ0mAioirO2Dzh//udVOl/eOaPpUNG5cQolgIi05g0b5g1hwLI+nxEvpKTtYMa1WZnvUrYGFqnO15b7aqTM8GFQp0eCosOoG3Vp7g7K1YNBr44PlqvN2mSoHVOTp4LYrx68/q5va0rOrEFz1q4+lo/YxnZhQWncCS/SGsPhZKYor6nr1u9w9fJE1FMbFEM+IU2LrqPf5nkQQonyQBMrDYcHWez7k/1Ps2ruq4sk93qekjhMi1e/FJLD94neVP9OwMaObJ5TvxbDoTruvN8H3cU9Qhm54ifUtMSWPixnOsPhYGQLsa5ZnZpx52VvrrcboXn8SXmy/wx8lbADiVMeezrj50reuar7pE0Q+TH7+313mQkMzvZpNoaHSZc649qdh/UcYNbguBJED5JAmQgWjT1Jo+Oz7PWNOnzbjSvXO7EEIvsprbky4vc4X07ddjYYzfcJbkVC3uDpbMf80vX5O4QV3B9tuJMKb8fZEHCSloNPB6E08+7Fhdr8lJQnIqa46FcWTPZhYkf0yaoqEHM2nWpBmDW1TCOZ/zpnJKEqB8kgTIAG6dfFzTJ0i9X8FP3QFaavoIIfQsfXXX7yfCHhdTrJzvRENfzt6K4a2VJwiLfoS5iRGf96hNn4bueWrr8p04Pll3hmPX1aX7NV1t+apnbep7lNVnyBmkpGm5u+gl3CJ2sjWtIW+mjMbM2Iie9SswrHVlvPOwci43JAHKJ0mACtGjB2r9iGOLAQXM7aD9BPAbCEaGWRYqhBCGFJOQwvu/BrHzcbHFfo3dmdC1lm7O0rM8Sk5jzs4rLNobTKpWwcrMmNHPV2Ngcy9dheoCFXkRZX4zNIqWTx1m8NNtdS6QRgMdfJwZ3tq7wJIwSYDySRKgQqAocHYtbBknNX2EEOIJWq3C97uuMnP7ZRRFXWk177UGuDtkX+Bx16VIPttwlrDoR4C69cbEbrUKfYk9G9+DkyvAvSkn2q1iwd5gAs/f0T3ctLIDw1t707paOb0OOUoClE+SABWwTDV9qsALM6WmjxBCPGHv5buMXH2K+wkp2FmaMuuVejxXPfMfiXdiE5n853k2nQkHwM3OgondatGhlkthh6yKva0WR0x9BK/8AjVe4MqdOH7YG8yGoFukpClULGvJ7g/b6LVXShKgfJIEqICkJKr1fPbPVGv6GJtDqzHgLzV9hBDiaW49eMTbP5/g9M0YNBoY0bYqI9tVxchIQ5pW4efDN/hm6yXiklIxNtIwyN+LUe2rYV3IG8Rmsn2S+u+9U3V46yAYq/GExzxiyb4QqjqXoW8jD71eUhKgfJIEqABITR8hhMizpNQ0Jv95npVH1L26Wlcrx5utKjN1y0X+uRkDqBWlv+pZm1puRWNCN4kx6kapj+5D1zngN6DALykJUD5JAqRHcRFqTZ+za9X7ZVyg81Tw6SE1fYQQIpfWnrjJx+vO6Ao2grpj+/861eDVxh6FVrsoxw59r34H2LjCeyfBTH+b1GYlN9/fsnW2KBjaNDiyEOY2UpMfjRE0eQvePQa1ekryI4QQefCSX0XWve2P5+Pd7rv6urHjg9YENPUseskPQKMh6i7xceFwZL6ho8lAeoCyID1A+fRkTR+3BmpNH7d6hoxKCCFKjMSUNCJiEvFyyt0WFgbxz6/wx1Awt4URQWDt+Myn5JX0AAnDSIyBTR/CorZq8mNup+7YPmS7JD9CCKFHFqbGxSP5AajdG1zqQFIs7Jth6Gh0JAES+acocOZ3dbjr2CJAgTp91OGuRkOkoKEQQpRmRkbQfpL687FFcP+GYeN5TBIgkT/3rsFPPWDtYIi/o9b06b8BXloENs6Gjk4IIURRUKUdVG6jlkDZ+YWhowEkARJ5lZIIu6bAvKYQvFut6fPcJ2qth8ptDB2dEEKIoqb9RPW/Z36F8NMGDQUkARJ5cXUHzG8Ge6aq2bx3O3j7ELT+nxQ0FEIIkTW3+up8IIDtEw0aCkgCJHIjLgJ+HwQ/91ILGpZxgZeXwetrwdHb0NEJIYQo6tqOByNTuLYTru0yaCiSAIlnk5o+Qggh9MGhEjQarP68fQJotdmfX4AMvFGIKPKkpo8QQgh9ajUGLvwJPt1BScNQfTEG7wGaN28elSpVwsLCAj8/P/bt25ft+UlJSXzyySd4enpibm6Ot7c3S5cu1T2+bNkyNBpNpltiYmJBv5SSJauaPl2+kZo+Qggh8sfaCUaehpYfgLGpwcIwaA/QmjVrGDVqFPPmzcPf358ffviBzp07c/78eTw8st4htk+fPty5c4clS5ZQpUoVIiMjSU1NzXCOra0tly5dynDMwsKiwF5HiaIo6jDX1o/VZe0AdV6GDl/KsnYhhBD6YcDEJ51BE6CZM2cyePBghgwZAsCsWbPYunUr8+fPZ8qUKZnO37JlC3v27CE4OBgHBwcAvLy8Mp2n0WhwcXEp0NhLpHvXYNNodVk7qDV9Xpghy9qFEEKUOAYbAktOTubEiRN06NAhw/EOHTpw8ODBLJ+zceNGGjZsyPTp06lQoQLVqlXjww8/5NGjRxnOi4+Px9PTk4oVK/Liiy9y6tSpbGNJSkoiNjY2w61USUmE3VNhXjOp6SOEEKJUMFgPUFRUFGlpaTg7ZxxWcXZ2JiIiIsvnBAcHs3//fiwsLFi3bh1RUVG8/fbbREdH6+YB1ahRg2XLllGnTh1iY2OZPXs2/v7+nD59mqpVq2bZ7pQpU5g0aZJ+X2BxcW0nbPpAXdYO4N1Wnesjy9qFEEKUYAZfBaZ5Ygm1oiiZjqXTarVoNBpWrlyJnZ0doA6j9e7dm++//x5LS0uaNm1K06ZNdc/x9/enQYMGfPfdd8yZMyfLdseNG8fo0aN192NjY3F3d8/vSyva4iLUeT5n16r3y7hApymyrF0IIUSpYLAEyMnJCWNj40y9PZGRkZl6hdK5urpSoUIFXfIDULNmTRRF4ebNm1n28BgZGdGoUSOuXLny1FjMzc0xNy8lFYy1aXBsCez8XN2ZV2MEjYepQ14WtoaOTgghhCgUBpsDZGZmhp+fH4GBgRmOBwYG0rx58yyf4+/vz+3bt4mPj9cdu3z5MkZGRlSsWDHL5yiKQlBQEK6urvoLvri6dVJd1v73GDX5cWsAQ3dB52mS/AghhChVDFoHaPTo0SxevJilS5dy4cIF3n//fUJDQxk+fDigDk31799fd/6rr76Ko6Mjb7zxBufPn2fv3r2MGTOGQYMGYWlpCcCkSZPYunUrwcHBBAUFMXjwYIKCgnRtlkqJMbB5jNT0EUIIIR4z6Bygvn37cu/ePSZPnkx4eDi1a9dm8+bNeHp6AhAeHk5oaKju/DJlyhAYGMh7771Hw4YNcXR0pE+fPnzxxRe6cx48eMCwYcOIiIjAzs6O+vXrs3fvXho3blzor8/gpKaPEEIIkSWNoiiKoYMoamJjY7GzsyMmJgZb22I6NCQ1fYQQQpQyufn+NvgqMKFnKYlwYBbsmwlpSWpNn1Yfgv9IMCklE72FEEKIZ5AEqCS5tutxTZ9r6n2p6SOEEEJkSRKgkiDuzuOaPr+r96WmjxBCCJEtSYCKM20aHF8KOyY/UdPnY7Cwe/bzhRBCiFJKEqDi6vYp+Ot99b8AbvXhxVmyrF0IIYTIAUmAipvEGNj5JRxbBIoWzG2h3WfQcBAYGRs6OiGEEKJYkASouFAUOPcHbPkY4h9vHyI1fYQQQog8kQSoOLh3DTZ/qO7cDuDgrdb08X7OsHEJIYQQxZQkQEVZahLsnwX7Zvxb06flB2pNH1MLQ0cnhBBCFFuSABVVUtNHCCGEKDCSABU1cXdg2ydw5jf1fhkX6PQV1OolNX2EEEIIPZEEqKjQ1fT5HJJi1Jo+jYZC20+kpo8QQgihZ5IAFQW3gx7X9Dmp3nerDy9+q/5XCCGEEHonCZAhJcbCri/h6EKp6SOEEEIUIkmADEFR4Nw62DLu35o+tXtDxy/BxsWwsQkhhBClgCRAhS06GDZ9CNd2qPelpo8QQghR6CQBKkznN8LaIf+p6TMa/EdJTR8hhBCikEkCVJgqNgRjM/Bsrvb6SE0fIYQQwiAkASpMtm7w5h5wqCw1fYQQQggDkgSosEmvjxBCCGFwRoYOQAghhBCisEkCJIQQQohSRxIgIYQQQpQ6kgAJIYQQotSRBEgIIYQQpY4kQEIIIYQodSQBEkIIIUSpIwmQEEIIIUodSYCEEEIIUepIAiSEEEKIUkcSICGEEEKUOpIACSGEEKLUkQRICCGEEKWO7AafBUVRAIiNjTVwJEIIIYTIqfTv7fTv8exIApSFuLg4ANzd3Q0ciRBCCCFyKy4uDjs7u2zP0Sg5SZNKGa1Wy+3bt7GxsUGj0ei17djYWNzd3QkLC8PW1lavbRd0+8U59oJuvzjHXtzbL86xF/f2i3Psxb394hx7QbavKApxcXG4ublhZJT9LB/pAcqCkZERFStWLNBr2NraFsj/VIXRfnGOvaDbL86xF/f2i3Psxb394hx7cW+/OMdeUO0/q+cnnUyCFkIIIUSpIwmQEEIIIUodSYAKmbm5ORMmTMDc3LzYtV+cYy/o9otz7MW9/eIce3FvvzjHXtzbL86xF0b7OSGToIUQQghR6kgPkBBCCCFKHUmAhBBCCFHqSAIkhBBCiFJHEiAhhBBClDqSABWSvXv30rVrV9zc3NBoNKxfv15vbU+ZMoVGjRphY2ND+fLl6dGjB5cuXdJb+/Pnz6du3bq6glXNmjXj77//1lv7/zVlyhQ0Gg2jRo3SS3sTJ05Eo9FkuLm4uOil7XS3bt3i9ddfx9HRESsrK+rVq8eJEyf00raXl1em+DUaDe+8845e2k9NTWX8+PFUqlQJS0tLKleuzOTJk9FqtXppPy4ujlGjRuHp6YmlpSXNmzfn2LFjeWrrWZ8hRVGYOHEibm5uWFpa0qZNG86dO6e39v/44w86duyIk5MTGo2GoKAgvcWfkpLC2LFjqVOnDtbW1ri5udG/f39u376tl9gnTpxIjRo1sLa2pmzZsrRv354jR47oJfYnvfnmm2g0GmbNmqW39gcOHJjpM9C0aVO9xX7hwgW6deuGnZ0dNjY2NG3alNDQUL20n9XnV6PR8PXXX+ul/fj4eN59910qVqyIpaUlNWvWZP78+TlqOyft37lzh4EDB+Lm5oaVlRWdOnXiypUrOWo7J99N+f3c5ockQIXk4cOH+Pr6MnfuXL23vWfPHt555x0OHz5MYGAgqampdOjQgYcPH+ql/YoVKzJ16lSOHz/O8ePHadu2Ld27d9f7/6THjh1j4cKF1K1bV6/t1qpVi/DwcN3tzJkzemv7/v37+Pv7Y2pqyt9//8358+eZMWMG9vb2emn/2LFjGWIPDAwE4OWXX9ZL+9OmTWPBggXMnTuXCxcuMH36dL7++mu+++47vbQ/ZMgQAgMD+emnnzhz5gwdOnSgffv23Lp1K9dtPeszNH36dGbOnMncuXM5duwYLi4uPP/887q9/fLb/sOHD/H392fq1Km5jv1Z7SckJHDy5Ek+/fRTTp48yR9//MHly5fp1q2bXmKvVq0ac+fO5cyZM+zfvx8vLy86dOjA3bt39dJ+uvXr13PkyBHc3Nxy1G5u2u/UqVOGz8LmzZv10va1a9do0aIFNWrUYPfu3Zw+fZpPP/0UCwsLvbT/35jDw8NZunQpGo2Gl156SS/tv//++2zZsoWff/6ZCxcu8P777/Pee++xYcOGfLevKAo9evQgODiYDRs2cOrUKTw9PWnfvn2Ovl9y8t2U389tviii0AHKunXrCqz9yMhIBVD27NlTYNcoW7assnjxYr21FxcXp1StWlUJDAxUWrdurYwcOVIv7U6YMEHx9fXVS1tZGTt2rNKiRYsCa/9JI0eOVLy9vRWtVquX9l544QVl0KBBGY716tVLef311/PddkJCgmJsbKz89ddfGY77+voqn3zySb7afvIzpNVqFRcXF2Xq1Km6Y4mJiYqdnZ2yYMGCfLf/XyEhIQqgnDp1Ktft5qT9dEePHlUA5caNG3pvOyYmRgGU7du356rt7Nq/efOmUqFCBeXs2bOKp6en8u233+a67ae1P2DAAKV79+55au9Zbfft21cv/78/rf0nde/eXWnbtq3e2q9Vq5YyefLkDMcaNGigjB8/Pt/tX7p0SQGUs2fP6o6lpqYqDg4OyqJFi3Ld/pPfTfr+3OaW9ACVQDExMQA4ODjove20tDRWr17Nw4cPadasmd7afeedd3jhhRdo37693tpMd+XKFdzc3KhUqRKvvPIKwcHBemt748aNNGzYkJdffpny5ctTv359Fi1apLf2/ys5OZmff/6ZQYMG6W2T3hYtWrBjxw4uX74MwOnTp9m/fz9dunTJd9upqamkpaVl+kva0tKS/fv357v9/woJCSEiIoIOHTrojpmbm9O6dWsOHjyo12sVlpiYGDQajd56E9MlJyezcOFC7Ozs8PX11UubWq2WgIAAxowZQ61atfTS5pN2795N+fLlqVatGkOHDiUyMjLfbWq1WjZt2kS1atXo2LEj5cuXp0mTJnqdovBfd+7cYdOmTQwePFhvbbZo0YKNGzdy69YtFEVh165dXL58mY4dO+a77aSkJIAMn2FjY2PMzMzy9Bl+8rvJ0J9bSYBKGEVRGD16NC1atKB27dp6a/fMmTOUKVMGc3Nzhg8fzrp16/Dx8dFL26tXr+bkyZNMmTJFL+39V5MmTVixYgVbt25l0aJFRERE0Lx5c+7du6eX9oODg5k/fz5Vq1Zl69atDB8+nBEjRrBixQq9tP9f69ev58GDBwwcOFBvbY4dO5Z+/fpRo0YNTE1NqV+/PqNGjaJfv375btvGxoZmzZrx+eefc/v2bdLS0vj55585cuQI4eHheoj+XxEREQA4OztnOO7s7Kx7rDhJTEzko48+4tVXX9XbRpF//fUXZcqUwcLCgm+//ZbAwECcnJz00va0adMwMTFhxIgRemnvSZ07d2blypXs3LmTGTNmcOzYMdq2bav7gs6ryMhI4uPjmTp1Kp06dWLbtm307NmTXr16sWfPHj1F/6/ly5djY2NDr1699NbmnDlz8PHxoWLFipiZmdGpUyfmzZtHixYt8t12jRo18PT0ZNy4cdy/f5/k5GSmTp1KRERErj/DWX03GfpzK7vBlzDvvvsu//zzj97/wq5evTpBQUE8ePCAtWvXMmDAAPbs2ZPvJCgsLIyRI0eybdu2HI+550bnzp11P9epU4dmzZrh7e3N8uXLGT16dL7b12q1NGzYkK+++gqA+vXrc+7cOebPn0///v3z3f5/LVmyhM6dO+d6fkV21qxZw88//8wvv/xCrVq1CAoKYtSoUbi5uTFgwIB8t//TTz8xaNAgKlSogLGxMQ0aNODVV1/l5MmTeog+syd7xhRF0VtvWWFJSUnhlVdeQavVMm/ePL21+9xzzxEUFERUVBSLFi2iT58+HDlyhPLly+er3RMnTjB79mxOnjxZYO913759dT/Xrl2bhg0b4unpyaZNm/KVTKRP9u/evTvvv/8+APXq1ePgwYMsWLCA1q1b5y/wJyxdupTXXntNr//WzZkzh8OHD7Nx40Y8PT3Zu3cvb7/9Nq6urvnuUTc1NWXt2rUMHjwYBwcHjI2Nad++fYZ/V3Mqu+8mQ31upQeoBHnvvffYuHEju3btomLFinpt28zMjCpVqtCwYUOmTJmCr68vs2fPzne7J06cIDIyEj8/P0xMTDAxMWHPnj3MmTMHExMT0tLS9BD9v6ytralTp06OVzE8i6ura6YksGbNmjleQZJTN27cYPv27QwZMkSv7Y4ZM4aPPvqIV155hTp16hAQEMD777+vt944b29v9uzZQ3x8PGFhYRw9epSUlBQqVaqkl/bTpa/se/KvxsjIyEx/XRZlKSkp9OnTh5CQEAIDA/XW+wPq//tVqlShadOmLFmyBBMTE5YsWZLvdvft20dkZCQeHh66z/CNGzf44IMP8PLyyn/gWXB1dcXT0zPfn2MnJydMTEwK5TO8b98+Ll26pNfP8KNHj/j444+ZOXMmXbt2pW7durz77rv07duXb775Ri/X8PPz0/3xGx4ezpYtW7h3716uPsNP+24y9OdWEqASQFEU3n33Xf744w927typ9y+Xp10zv93PAO3atePMmTMEBQXpbg0bNuS1114jKCgIY2NjPUT7r6SkJC5cuICrq6te2vP398+0rPPy5ct4enrqpf10P/74I+XLl+eFF17Qa7sJCQkYGWX8Z8DY2Fhvy+DTWVtb4+rqyv3799m6dSvdu3fXa/uVKlXCxcVFt0oO1Lkue/bsoXnz5nq9VkFJT36uXLnC9u3bcXR0LNDr6eszHBAQwD///JPhM+zm5saYMWPYunWrHiLN7N69e4SFheX7c2xmZkajRo0K5TO8ZMkS/Pz89DbvCtT/Z1JSUgrlM2xnZ0e5cuW4cuUKx48fz9Fn+FnfTYb+3MoQWCGJj4/n6tWruvshISEEBQXh4OCAh4dHvtp+5513+OWXX9iwYQM2Nja6bNrOzg5LS8t8tQ3w8ccf07lzZ9zd3YmLi2P16tXs3r2bLVu25LttGxubTHOVrK2tcXR01Mscpg8//JCuXbvi4eFBZGQkX3zxBbGxsXoZ3gF1CWrz5s356quv6NOnD0ePHmXhwoUsXLhQL+2D2k3/448/MmDAAExM9PuR7dq1K19++SUeHh7UqlWLU6dOMXPmTAYNGqSX9rdu3YqiKFSvXp2rV68yZswYqlevzhtvvJHrtp71GRo1ahRfffUVVatWpWrVqnz11VdYWVnx6quv6qX96OhoQkNDdbV50r80XVxcclRbKrv23dzc6N27NydPnuSvv/4iLS1N9zl2cHDAzMwsz207Ojry5Zdf0q1bN1xdXbl37x7z5s3j5s2bOS6n8Kz35slkzdTUFBcXF6pXr57v9h0cHJg4cSIvvfQSrq6uXL9+nY8//hgnJyd69uyZ79jHjBlD3759adWqFc899xxbtmzhzz//ZPfu3fmOPf3f9tjYWH777TdmzJiRozZz037r1q0ZM2YMlpaWeHp6smfPHlasWMHMmTP10v5vv/1GuXLl8PDw4MyZM4wcOZIePXpkmLj8NM/6bkqv+Zafz22+FPg6M6EoiqLs2rVLATLdBgwYkO+2s2oXUH788cd8t60oijJo0CDF09NTMTMzU8qVK6e0a9dO2bZtm17azoo+l8H37dtXcXV1VUxNTRU3NzelV69eyrlz5/TSdro///xTqV27tmJubq7UqFFDWbhwoV7b37p1qwIoly5d0mu7iqIosbGxysiRIxUPDw/FwsJCqVy5svLJJ58oSUlJeml/zZo1SuXKlRUzMzPFxcVFeeedd5QHDx7kqa1nfYa0Wq0yYcIExcXFRTE3N1datWqlnDlzRm/t//jjj1k+PmHChHy3n760Pqvbrl278tX2o0ePlJ49eypubm6KmZmZ4urqqnTr1k05evSo3t6bJ+V2GXx27SckJCgdOnRQypUrp5iamioeHh7KgAEDlNDQUL3FvmTJEqVKlSqKhYWF4uvrq6xfv14vsaf74YcfFEtLyzz9v/+s9sPDw5WBAwcqbm5uioWFhVK9enVlxowZOS6V8az2Z8+erVSsWFH33o8fPz7H/z7k5Lspv5/b/NA8DlIIIYQQotSQOUBCCCGEKHUkARJCCCFEqSMJkBBCCCFKHUmAhBBCCFHqSAIkhBBCiFJHEiAhhBBClDqSAAkhhBCi1JEESAghhBCljiRAQgi9u379OhqNhqCgIEOHonPx4kWaNm2KhYUF9erVK7DrTJw4Mdftt2nThlGjRmV7jkajYf369XmOSwiRkSRAQpRAAwcORKPRMHXq1AzH169fj0ajMVBUhjVhwgSsra25dOkSO3bsKLDrfPjhhwXavhBCPyQBEqKEsrCwYNq0ady/f9/QoehNcnJynp977do1WrRogaenZ4HutF6mTJkC38ldX/LzfgpR3EkCJEQJ1b59e1xcXJgyZcpTz8lquGbWrFl4eXnp7g8cOJAePXrw1Vdf4ezsjL29PZMmTSI1NZUxY8bg4OBAxYoVWbp0aab2L168SPPmzbGwsKBWrVqZdtg+f/48Xbp0oUyZMjg7OxMQEEBUVJTu8TZt2vDuu+8yevRonJyceP7557N8HVqtlsmTJ1OxYkXMzc2pV68eW7Zs0T2u0Wg4ceIEkydPRqPRMHHixCzbadOmDSNGjOB///sfDg4OuLi4ZDo3JiaGYcOGUb58eWxtbWnbti2nT59+6nuamprKiBEjsLe3x9HRkbFjxzJgwAB69OiR6TVkd12A8PBwOnfujKWlJZUqVeK3337L8PiZM2do27YtlpaWODo6MmzYMOLj43WPp/8up0yZgpubG9WqVQNg3rx5VK1aFQsLC5ydnendu3eW748QJYkkQEKUUMbGxnz11Vd899133Lx5M19t7dy5k9u3b7N3715mzpzJxIkTefHFFylbtixHjhxh+PDhDB8+nLCwsAzPGzNmDB988AGnTp2iefPmdOvWjXv37gHql3nr1q2pV68ex48fZ8uWLdy5c4c+ffpkaGP58uWYmJhw4MABfvjhhyzjmz17NjNmzOCbb77hn3/+oWPHjnTr1o0rV67orlWrVi0++OADwsPD+fDDD5/6WpcvX461tTVHjhxh+vTpTJ48mcDAQAAUReGFF14gIiKCzZs3c+LECRo0aEC7du2Ijo7Osr1p06axcuVKfvzxRw4cOEBsbGyWc3myu266Tz/9lJdeeonTp0/z+uuv069fPy5cuABAQkICnTp1omzZshw7dozffvuN7du38+6772ZoY8eOHVy4cIHAwED++usvjh8/zogRI5g8eTKXLl1iy5YttGrV6qnvjxAlRqHsOS+EKFQDBgxQunfvriiKojRt2lQZNGiQoiiKsm7dOuW/H/sJEyYovr6+GZ777bffKp6enhna8vT0VNLS0nTHqlevrrRs2VJ3PzU1VbG2tlZWrVqlKIqihISEKIAydepU3TkpKSlKxYoVlWnTpimKoiiffvqp0qFDhwzXDgsLUwDl0qVLiqIoSuvWrZV69eo98/W6ubkpX375ZYZjjRo1Ut5++23dfV9fX2XChAnZttO6dWulRYsWmdoZO3asoiiKsmPHDsXW1lZJTEzMcI63t7fyww8/KIqS+T11dnZWvv76a9391NRUxcPDQ/f7ycl1FUVRAGX48OEZzmnSpIny1ltvKYqiKAsXLlTKli2rxMfH6x7ftGmTYmRkpERERCiKov4unZ2dlaSkJN05a9euVWxtbZXY2Nhs3xshShrpARKihJs2bRrLly/n/PnzeW6jVq1aGBn9+8+Fs7MzderU0d03NjbG0dGRyMjIDM9r1qyZ7mcTExMaNmyo67E4ceIEu3btokyZMrpbjRo1AHW+TrqGDRtmG1tsbCy3b9/G398/w3F/f3/dtXKjbt26Ge67urrqXteJEyeIj4/H0dExQ9whISEZYk4XExPDnTt3aNy4se6YsbExfn5+ubpuuv++n+n301/jhQsX8PX1xdraWve4v78/Wq2WS5cu6Y7VqVMHMzMz3f3nn38eT09PKleuTEBAACtXriQhISHrN0eIEsTE0AEIIQpWq1at6NixIx9//DEDBw7M8JiRkRGKomQ4lpKSkqkNU1PTDPc1Gk2Wx7Ra7TPjSV+FptVq6dq1K9OmTct0jqurq+7n/36h56TddIqi5GnFW3avS6vV4urqmmkuE4C9vX2uYsvNdbOT3nZ2r/e/x598P21sbDh58iS7d+9m27ZtfPbZZ0ycOJFjx45l+5qEKO6kB0iIUmDq1Kn8+eefHDx4MMPxcuXKERERkeELWZ+1ew4fPqz7OTU1lRMnTuh6eRo0aMC5c+fw8vKiSpUqGW45TXoAbG1tcXNzY//+/RmOHzx4kJo1a+rnhTzWoEEDIiIiMDExyRSzk5NTpvPt7Oxwdnbm6NGjumNpaWmcOnUqT9f/7/uZfj/9/fTx8SEoKIiHDx/qHj9w4ABGRka6yc5PY2JiQvv27Zk+fTr//PMP169fZ+fOnXmKUYjiQhIgIUqBOnXq8Nprr/Hdd99lON6mTRvu3r3L9OnTuXbtGt9//z1///233q77/fffs27dOi5evMg777zD/fv3GTRoEADvvPMO0dHR9OvXj6NHjxIcHMy2bdsYNGgQaWlpubrOmDFjmDZtGmvWrOHSpUt89NFHBAUFMXLkSL29FlBX1jVr1owePXqwdetWrl+/zsGDBxk/fjzHjx/P8jnvvfceU6ZMYcOGDVy6dImRI0dy//79PPVO/fbbbyxdupTLly8zYcIEjh49qpvk/Nprr2FhYcGAAQM4e/Ysu3bt4r333iMgIABnZ+entvnXX38xZ84cgoKCuHHjBitWrECr1VK9evVcxydEcSIJkBClxOeff55p6KVmzZrMmzeP77//Hl9fX44ePZrtCqncmjp1KtOmTcPX15d9+/axYcMGXU+Jm5sbBw4cIC0tjY4dO1K7dm1GjhyJnZ1dhvlGOTFixAg++OADPvjgA+rUqcOWLVvYuHEjVatW1dtrAXUoafPmzbRq1YpBgwZRrVo1XnnlFa5fv/7UJGPs2LH069eP/v3706xZM8qUKUPHjh2xsLDI9fUnTZrE6tWrqVu3LsuXL2flypX4+PgAYGVlxdatW4mOjqZRo0b07t2bdu3aMXfu3GzbtLe3548//qBt27bUrFmTBQsWsGrVKmrVqpXr+IQoTjRKVoPRQgghCoRWq6VmzZr06dOHzz//3NDhCFFqySRoIYQoQDdu3GDbtm20bt2apKQk5s6dS0hICK+++qqhQxOiVJMhMCGEKEBGRkYsW7aMRo0a4e/vz5kzZ9i+fbveJ2gLIXJHhsCEEEIIUepID5AQQgghSh1JgIQQQghR6kgCJIQQQohSRxIgIYQQQpQ6kgAJIYQQotSRBEgIIYQQpY4kQEIIIYQodSQBEkIIIUSp83/Ii5KCV2bo4wAAAABJRU5ErkJggg==",
      "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}%\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "... using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparamter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 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)}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids seup for each of our modles, let's tune them using RandomizedSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" 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-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(), let's do the same for RandomForestClassifier()..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" 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": 38,
     "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",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tparam_distributions=rf_grid,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tcv=5,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tn_iter=20,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tverbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter 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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" 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": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating 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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1d680760cd0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 AUC metric\n",
    "RocCurveDisplay.from_estimator(gs_log_reg, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "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",
    "\t\"\"\"\n",
    "\tPlots a nice looking confusion matrix using Seaborn's heatmap().\n",
    "\t\"\"\"\n",
    "\tfig, ax = plt.subplots(figsize=(3, 3))\n",
    "\tax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                  annot=True,\n",
    "                  cbar=False)\n",
    "\tplt.xlabel(\"true label\")\n",
    "\tplt.ylabel(\"predicted label\")\n",
    "\n",
    "\tbottom, top = ax.get_ylim()\n",
    "\tax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "\n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "attachments": {},
   "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": 51,
   "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))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calcaulte 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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tsolver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tX,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\ty,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tcv=5,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tscoring=\"accuracy\")\n",
    "\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "cv_precision = cross_val_score(clf,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tX,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\ty,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tcv=5,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tscoring=\"precision\")\n",
    "\n",
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(clf,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tX,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\ty,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tcv=5,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tscoring=\"recall\")\n",
    "\n",
    "cv_recall=np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\n",
    "cv_f1 = cross_val_score(clf,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tX,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\ty,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tcv=5,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tscoring=\"f1\")\n",
    "\n",
    "cv_f1=np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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-valiated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\"Precision\": cv_precision,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\"Recall\": cv_recall,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\"F1\": cv_f1},\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\tindex=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "\t\t\t\t\t\t\t\t\t\t\tlegend=False);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another way of asking, \"which features contributing most to the outcomes of the model and how did they contribute?\"\n",
    "\n",
    "Finding feature importance is different for each machine learning model. One way to find feature importance is to search for \"(MODEL NAME) feature importance\".\n",
    "\n",
    "Let's find the feature importance for our LogisticRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\tsolver=\"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316727, -0.86044582,  0.66067073, -0.01156993, -0.00166374,\n",
       "         0.04386131,  0.31275787,  0.02459361, -0.60413038, -0.56862852,\n",
       "         0.45051617, -0.63609863, -0.67663375]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_: how each independent variable contributes to the target labels\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672721856887734,\n",
       " 'sex': -0.860445816920919,\n",
       " 'cp': 0.6606707303492849,\n",
       " 'trestbps': -0.011569930902919925,\n",
       " 'chol': -0.001663741604035976,\n",
       " 'fbs': 0.04386130751482091,\n",
       " 'restecg': 0.3127578715206996,\n",
       " 'thalach': 0.02459360818122666,\n",
       " 'exang': -0.6041303799858143,\n",
       " 'oldpeak': -0.5686285194546157,\n",
       " 'slope': 0.4505161679452401,\n",
       " 'ca': -0.6360986316921434,\n",
       " 'thal': -0.6766337521354281}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Math 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": 63,
   "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": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "attachments": {},
   "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"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Experimentation\n",
    "\n",
    "If you haven't hit your evaluation metric yet... ask yourself...\n",
    "\n",
    "* Could you collect more data?\n",
    "* Could you try a better model? Like CatBoost or XGBoost?\n",
    "* Could you improve the current models? (beyond what we've done so far)\n",
    "* If your model is good enough (you have hit your evaluation metric), how would you export it and share it with others?"
   ]
  }
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