heart-disease-project.ipynb

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    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks into various python-based machine learning models and data science libraries ina n attempt to build a machine learning model capable of predicting whether someone has heart disease or not based on their medical attributes\n",
    "\n",
    "We'll use the following steps\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1.Problem definition\n",
    "\n",
    "In a statement,\n",
    ">Given clinical parameters about a patient, can we predict whether or not they have  heart disease\n",
    "\n",
    "## 2.Data\n",
    "\n",
    "The originl data came from the Cleavland data from the UCI machine learning repository\n",
    "https://archive.ics.uci.edu/dataset/45/heart+disease\n",
    "\n",
    "There is also a version of it available on kaggle\n",
    "https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data\n",
    "\n",
    "\n",
    "## 3.Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient as heart-disease during the proof of concept,we'll pursue the project.\n",
    "\n",
    "## 4.Features\n",
    "\n",
    "This is where you'll get different information about the features used in the project.\n",
    " \n",
    " **Create data dictionary**\n",
    "\n",
    "\n",
    "* age - age in years\n",
    "* sex - (1 = male; 0 = female)\n",
    "* 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",
    "* trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern\n",
    "* chol - serum cholestoral in mg/dl\n",
    "* serum = LDL + HDL + .2 * triglycerides\n",
    "above 200 is cause for concern\n",
    "* fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "* '>126' mg/dL signals diabetes\n",
    "* 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",
    "* thalach - maximum heart rate achieved\n",
    "* exang - exercise induced angina (1 = yes; 0 = no)\n",
    "* oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more\n",
    "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",
    "* 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",
    "* 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",
    "* target - have disease or not (1=yes, 0=no) (= the predicted attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97365449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28e2799",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas,matplotlib and numpy for data analysis and manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4c4e42a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adars\\Desktop\\sample_project_1\\env\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "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",
    "\n",
    "# Import models from Sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model evaluations\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.metrics import precision_score,recall_score,f1_score\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "def plot_roc_curve(fpr,tpr):\n",
    "    #Plot roc curve given fpr and tpr\n",
    "    plt.plot(fpr,tpr,color=\"orange\",label=\"ROC\")\n",
    "    #Plot a line with no predictive power(baseline)\n",
    "    plt.plot([0,1],[0,1],color=\"black\",linestyle='--',label=\"guessing\")\n",
    "    #Customize the plot\n",
    "    plt.xlabel(\"False positive rate(fpr)\")\n",
    "    plt.ylabel(\"True positive rate(tpr)\")\n",
    "    plt.title(\"Reciever operating characteristic (ROC)curve\")\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7d7745",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4ee9fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape #(rows,columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9843ad",
   "metadata": {},
   "source": [
    "## Data exploration(EDA or exploratory data analysis)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter expert on the dataset \n",
    "\n",
    "1. What questions 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 we 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?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e8c8993",
   "metadata": {},
   "outputs": [
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       "      <th>fbs</th>\n",
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       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
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       "      <th>slope</th>\n",
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       "      <td>0.6</td>\n",
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       "      <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  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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   "source": [
    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64f26fdc",
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   "outputs": [
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       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
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       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
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       "      <td>3.4</td>\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  "
      ]
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "df.tail() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68240819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9d52ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='target'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['target'].value_counts().plot.bar(color=['salmon','blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82abb359",
   "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,
   "id": "42a6802f",
   "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,
   "id": "32927a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6281cdda",
   "metadata": {},
   "source": [
    "## Heart disease frequency according to sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9b74505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    207\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72e56e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare target column and sex column\n",
    "pd.crosstab(df.target,df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4aa7087",
   "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',figsize=(10,6),color=('salmon','lightblue'))\n",
    "plt.title(\"Heart-disease frequency according to sex\")\n",
    "plt.xlabel(\"0=No-disease,1=Heart-disease\")\n",
    "plt.ylabel(\"amount\")\n",
    "plt.legend([\"Female\",\"Male\"]);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3c559b",
   "metadata": {},
   "source": [
    "### Age vs max.heart rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "554141a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16ef56202b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "#Scatter with positive examples\n",
    "plt.scatter(df.age[df[\"target\"]==1],df.thalach[df[\"target\"]==1],c=\"salmon\")\n",
    "\n",
    "#Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],df.thalach[df.target==0],color=\"darkblue\")\n",
    "\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,
   "id": "bf16203b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "#Check the distribution of the age column with a histogram\n",
    "df['age'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da1b3e0",
   "metadata": {},
   "source": [
    "### Heart disease frequency per chest pain type\n",
    "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,
   "id": "c280c7b3",
   "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,
   "id": "89644edf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16ef6737f10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make the data more visual\n",
    "pd.crosstab(df.cp,df.target).plot.bar(figsize=(10,6),color=(\"salmon\",\"darkblue\"))\n",
    "plt.title(\"Heart-disease frequency per chest pain type\")\n",
    "plt.xlabel(\"Chest pain type\")\n",
    "plt.ylabel(\"Heart-disease frequency\")\n",
    "plt.legend([\"No disease\",\"Disease\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "31c1dc5d",
   "metadata": {
    "scrolled": true
   },
   "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 matrix\n",
    "df.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36a162da",
   "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 prettier\n",
    "corr_matrix=df.corr()\n",
    "fig,ax=plt.subplots(figsize=(15,10))\n",
    "ax=sns.heatmap(corr_matrix,annot=True,linewidths=0.5,fmt=\".2f\",cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb88b142",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"target\",axis=1)\n",
    "y=df[\"target\"]\n",
    "#Split data into training and test sets\n",
    "np.random.seed(36)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e595f61",
   "metadata": {},
   "source": [
    "We're going to try three different machine learning models\n",
    "1. Logistic Regression\n",
    "2. K-neighbours classifier\n",
    "3. Random Forest Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ac295f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's consolidate our models inside a dictionary\n",
    "models={\"Logistic_Regression\":LogisticRegression(max_iter=10)\n",
    "        ,\"K-neighbours_classifier\":KNeighborsClassifier(),\n",
    "        \"Random_Forest\":RandomForestClassifier()}\n",
    "\n",
    "#Create a function to access our models\n",
    "def fit_evaluate(models,X_train,X_test,y_train,y_test):\n",
    "    \n",
    "    \"Fits and evaluates given machine learning models\"\n",
    "    #Set random seed\n",
    "    np.random.seed(42)\n",
    "    #Create a dictionary for stroing model scores\n",
    "    scores={}\n",
    "    #Loop through models\n",
    "    for name,model in models.items():\n",
    "        model.fit(X_train,y_train)\n",
    "        scores[name]=model.score(X_test,y_test)\n",
    "    return scores    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab552935",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adars\\Desktop\\sample_project_1\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: 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': 0.7049180327868853,\n",
       " 'K-neighbours_classifier': 0.7049180327868853,\n",
       " 'Random_Forest': 0.819672131147541}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results=fit_evaluate(models,X_train,X_test,y_train,y_test)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d71b68f4",
   "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>Logistic_Regression</th>\n",
       "      <th>K-neighbours_classifier</th>\n",
       "      <th>Random_Forest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.704918</td>\n",
       "      <td>0.704918</td>\n",
       "      <td>0.819672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Logistic_Regression  K-neighbours_classifier  Random_Forest\n",
       "accuracy             0.704918                 0.704918       0.819672"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_compare=pd.DataFrame(results,index=[\"accuracy\"])\n",
    "model_compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "630320d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "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": [
    "model_compare.T.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd35a70b",
   "metadata": {},
   "source": [
    "Let's look at the following\n",
    "* Hyperparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Cross-validation\n",
    "* Precision \n",
    "* Recall\n",
    "* F1-score\n",
    "* Classification report\n",
    "* ROC Curve\n",
    "* Area under the curve\n",
    "\n",
    "### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cafeced7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's tune KNN \n",
    "train_scores=[]\n",
    "test_scores=[]\n",
    "\n",
    "#Create a list of different values for n neighbours\n",
    "neighbours=range(1,21)\n",
    "\n",
    "#Setup KNN instance\n",
    "knn=KNeighborsClassifier()\n",
    "\n",
    "#Loop through different n_neighbours\n",
    "for i in neighbours:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "\n",
    "    #Fit the algorithm\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    #Update the training scores list\n",
    "    train_scores.append(knn.score(X_train,y_train))\n",
    "    \n",
    "    #Update the test scoresd list\n",
    "    test_scores.append(knn.score(X_test,y_test))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "725a70e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.7892561983471075,\n",
       " 0.78099173553719,\n",
       " 0.7479338842975206,\n",
       " 0.7603305785123967,\n",
       " 0.7355371900826446,\n",
       " 0.7107438016528925,\n",
       " 0.7231404958677686,\n",
       " 0.7272727272727273,\n",
       " 0.6859504132231405,\n",
       " 0.7024793388429752,\n",
       " 0.7024793388429752,\n",
       " 0.7231404958677686,\n",
       " 0.6942148760330579,\n",
       " 0.6818181818181818,\n",
       " 0.6818181818181818,\n",
       " 0.6859504132231405,\n",
       " 0.6776859504132231,\n",
       " 0.6776859504132231,\n",
       " 0.6859504132231405]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2fbb3a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5081967213114754,\n",
       " 0.5409836065573771,\n",
       " 0.6721311475409836,\n",
       " 0.6557377049180327,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.6557377049180327,\n",
       " 0.6557377049180327,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.6557377049180327,\n",
       " 0.7049180327868853,\n",
       " 0.6721311475409836,\n",
       " 0.7213114754098361,\n",
       " 0.6721311475409836]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8ac9fe66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data:72.13%\n"
     ]
    },
    {
     "data": {
      "image/png": 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f17x586hcuTJubs5PLTQs5UA1ymnGlIiI/OvVV19l1qxZjB07lt27d7N3717mzJnDyy+/DMCXX37JjBkz2LVrF0eOHOHrr7/G29ubsLAwwFznZvXq1URGRl53JlJWbVSuXJmBAwcyePBgFi5cSHh4OCtXruSHH34AYNiwYRw/fpwnn3ySffv28dNPPzFmzBhGjRplq7e5lscff5zY2Fjuv/9+Nm7cyJEjR/j9998ZPHhwtpIxe1Ny40DahkFERK7UtWtXfvnlF5YtW0aLFi1o3bo177//vi15CQgI4LPPPqNdu3Y0bNiQP/74g59//pnSpUsD8Prrr3P06FGqVatG2bJlr3mOG7UxdepU7rnnHoYNG0bt2rUZMmQIly5dAqBChQosWbKEjRs30qhRI4YOHcrDDz9sS76uJyQkhLVr15Kenk7Xrl2pX78+I0aMwN/fP8ukyFEsRsZgWjERHx+Pv78/cXFx+Pn5OfRc322I4MUFO+lYqyxf/q+lQ88lIlLUJCUlER4eTpUqVfDy8nJ2OJIPsvqd5+T6rZ4bB6oRpD2mRERE8puSGweqXtZMbiLPJ3IpOWdT+URERCR3lNw4UClfD8qU8AS03o2IiEh+UXLjYP+uVKzkRkREJD8ouXGwmkGaMSUikhfFbN5LsWav37WSGwfL2B38kIqKRURyJGNF3oQEJ22UKfkuJSUFAFdX1zy14/xlBIs4DUuJiOSOq6srAQEBxMTEAODj43PV1gBSdFitVk6fPo2Pj0+eVzlWcuNgGcnN8XMJJKak4+2Rt2xURKQ4ydj9OiPBkaLNxcWF0NDQPCexSm4crHQJTwJ9PYi9lMLh0xepX8Hf2SGJiBQaFouF4OBgypUrR2pqqrPDEQfz8PCwy4rGSm7yQY1yJdgQHsvBmAtKbkREcsHV1TXPdRhSfKigOB9opWIREZH8o+QmH9h2B1dRsYiIiMMpuckHGUXFWqVYRETE8ZTc5IPql4eljp29RFJqupOjERERKdqU3OSDsiU8CfBxx2rAkdOXnB2OiIhIkabkJh9YLJYrFvPTNgwiIiKOpOQmn1S/XFSsuhsRERHHUnKTT2w9N5oOLiIi4lBKbvJJxlo3BzQsJSIi4lBKbvJJzcu7gx87m0BymmZMiYiIOIqSm3xSrqQnJb3cSLcaHD2T4OxwREREiiwlN/lEM6ZERETyh5KbfGTbhkFFxSIiIg6j5CYf2TbQVM+NiIiIwyi5yUc1gtRzIyIi4mhKbvJRRs1N+JlLpKZbnRyNiIhI0aTkJh8F+3vh6+FKmtXg2FntMSUiIuIISm7ykcViobqGpkRERBxKyU0+yxiaOqDkRkRExCGU3OSzmpoxJSIi4lBKbvJZDe0OLiIi4lBKbvJZ9cvDUkdOXyJNM6ZERETsTslNPqsQ4I23uysp6VYiYrXHlIiIiL0puclnLi4WW++NiopFRETsT8mNE2Rsw3BIRcUiIiJ2p+TGCWwbaKqoWERExO6cntxMmTKFKlWq4OXlRbNmzVizZk2Wj//kk0+oU6cO3t7e1KpVi1mzZuVTpPaTsdaNFvITERGxP6cmN3PmzGHkyJG89NJLbNu2jZtvvpnu3bsTERFxzcdPnTqVF154gbFjx7J7925ee+01Hn/8cX7++ed8jjxvMoalDp++SLrVcHI0IiIiRYvFMAynXV1btWpF06ZNmTp1qu1YnTp16N27N+PGjbvq8W3btqVdu3a88847tmMjR45k8+bN/PXXX9c8R3JyMsnJybb78fHxVKpUibi4OPz8/Oz4arIv3WpQ99WlJKdZWflMRyqX8XVKHCIiIoVFfHw8/v7+2bp+O63nJiUlhS1bttClS5dMx7t06cK6deuu+Zzk5GS8vLwyHfP29mbjxo2kpqZe8znjxo3D39/f9lWpUiX7vIA8cL1ixpTqbkREROzLacnNmTNnSE9PJygoKNPxoKAgoqOjr/mcrl278vnnn7NlyxYMw2Dz5s3MnDmT1NRUzpw5c83nvPDCC8TFxdm+jh8/bvfXkhu2uhvNmBIREbErN2cHYLFYMt03DOOqYxleeeUVoqOjad26NYZhEBQUxKBBg5g4cSKurq7XfI6npyeenp52jzuvalzeHfyQiopFRETsymk9N2XKlMHV1fWqXpqYmJirenMyeHt7M3PmTBISEjh69CgRERFUrlyZkiVLUqZMmfwI2240LCUiIuIYTktuPDw8aNasGcuWLct0fNmyZbRt2zbL57q7u1OxYkVcXV2ZPXs2d9xxBy4uTp/VniMZw1KHYi5i1YwpERERu3HqsNSoUaPo378/zZs3p02bNkyfPp2IiAiGDh0KmPUykZGRtrVsDhw4wMaNG2nVqhXnzp3j/fffZ9euXXz11VfOfBm5Ehrog4erC4mp6USeT6RSoI+zQxIRESkSnJrc9OvXj7Nnz/L6668TFRVF/fr1WbJkCWFhYQBERUVlWvMmPT2d9957j/379+Pu7s4tt9zCunXrqFy5spNeQe65ubpQtawv+6IvcDDmgpIbERERO3HqOjfOkJN58o725Pfb+HnHSV7oXpv/61DNqbGIiIgUZIVinRu5cjq4iopFRETsRcmNE/27x5TWuhEREbEXJTdOlLHH1MGYixSz0UERERGHUXLjRGGlfXF3tZCQks7JuCRnhyMiIlIkKLlxIndXF6pc3jRTQ1MiIiL2oeTGyWqUu7wNg4qKRURE7ELJjZNlbMNwQD03IiIidqHkxsmuLCoWERGRvFNy42Q1r9gdXDOmRERE8k7JjZNVLu2Lq4uFC8lpnIpPdnY4IiIihZ6SGyfzcHOhcmlzX6mDMaq7ERERySslNwVAxoypA6dUdyMiIpJXSm4KgIyi4kPquREREckzJTcFQI3LRcUH1XMjIiKSZ0puCoArdwfXjCkREZG8UXJTAFQp44uLBeISUzl9UTOmRERE8kLJTQHg5e5KWOmMPaY0NCUiIpIXSm4KiIxtGLSBpoiISN4ouSkgamobBhEREbtQclNAZKx1o+RGREQkb5TcFBAZw1KHlNyIiIjkiZKbAqJa2RJYLBB7KYUzmjElIiKSa0puCghvD1cqlbq8x5RmTImIiOSakpsCpKa2YRAREckzJTcFSHUVFYuIiOSZkpsCxLYNg4alREREck3JTQFSQ2vdiIiI5JmSmwKkWlkzuTlzMZlzl1KcHI2IiEjhpOSmAPH1dKNiKW9AvTciIiK5peSmgLHV3WjGlIiISK4ouSlgagRdnjGlomIREZFcUXJTwGgbBhERkbxRclPAZAxLHTilYSkREZHcUHJTwGQMS8VcSCYuIdXJ0YiIiBQ+Sm4KmBKeboT4ewFw6LR6b0RERHJKyU0BVF1FxSIiIrmm5KYA+nc6uJIbERGRnFJyUwCpqFhERCT3lNwUQBlFxZoOLiIiknNKbgqgjLVuouKSuJCkGVMiIiI5oeSmAPL3difIzxNQ742IiEhOKbkpoGqUuzxjSsmNiIhIjii5KaAyhqYOqqhYREQkR5TcFFA1g9RzIyIikhtKbgqoGkEZPTdKbkRERHJCyU0BVb2smdxEnk/kUnKak6MREREpPJTcFFClfD0oU8KcMXX4tHpvREREskvJTQH270rFSm5ERESyS8lNAVYzo+4mRjOmREREskvJTQGWsTv4IfXciIiIZJuSmwJMu4OLiIjknJKbAiwjuTl+LoHElHQnRyMiIlI45Cq5OXz4MC+//DL3338/MTExACxdupTdu3fbNbjirnQJTwJ9PTAMzZgSERHJrhwnN6tWraJBgwZs2LCB+fPnc/GiedH9559/GDNmjN0DLO7+HZpSUbGIiEh25Di5ef7553nzzTdZtmwZHh4etuO33HIL69evt2twopWKRUREcirHyc3OnTu56667rjpetmxZzp49a5eg5F/aHVxERCRncpzcBAQEEBUVddXxbdu2UaFCBbsEJf/KGJY6pORGREQkW3Kc3DzwwAM899xzREdHY7FYsFqtrF27lmeeeYYBAwbkOIApU6ZQpUoVvLy8aNasGWvWrMny8d9++y2NGjXCx8eH4OBg/ve//xXpHqPql4eljp29RFKqZkyJiIjcSI6Tm7feeovQ0FAqVKjAxYsXqVu3Lu3bt6dt27a8/PLLOWprzpw5jBw5kpdeeolt27Zx88030717dyIiIq75+L/++osBAwbw8MMPs3v3bubOncumTZt45JFHcvoyCo2yJTwJ8HHHasCR05ecHY6IiEiBZzEMw8jugw3DICIigrJlyxIdHc3WrVuxWq00adKEGjVq5PjkrVq1omnTpkydOtV2rE6dOvTu3Ztx48Zd9fh3332XqVOncvjwYduxjz/+mIkTJ3L8+PFsnTM+Ph5/f3/i4uLw8/PLcczOcO+0dWw6eo4P72tMr8Ya+hMRkeInJ9fvHPXcGIZBjRo1iIyMpGrVqtxzzz307ds3V4lNSkoKW7ZsoUuXLpmOd+nShXXr1l3zOW3btuXEiRMsWbIEwzA4deoUP/74I7fffvt1z5OcnEx8fHymr8Km+uWiYtXdiIiI3FiOkhsXFxdq1KhhlxqXM2fOkJ6eTlBQUKbjQUFBREdHX/M5bdu25dtvv6Vfv354eHhQvnx5AgIC+Pjjj697nnHjxuHv72/7qlSpUp5jz2+2tW40HVxEROSGclxzM3HiREaPHs2uXbvsEoDFYsl03zCMq45l2LNnD8OHD+fVV19ly5YtLF26lPDwcIYOHXrd9l944QXi4uJsX9kdvipIMta6OaCF/ERERG7ILadPeOihh0hISKBRo0Z4eHjg7e2d6fuxsbHZaqdMmTK4urpe1UsTExNzVW9OhnHjxtGuXTtGjx4NQMOGDfH19eXmm2/mzTffJDg4+KrneHp64unpma2YCqqal3cHP3Y2geS0dDzdXJ0ckYiISMGV4+Rm0qRJdjmxh4cHzZo1Y9myZZkWBVy2bBm9evW65nMSEhJwc8scsqureaHPQV10oVOupCclvdy4kJTG0TMJ1Cpf0tkhiYiIFFg5Tm4GDhxot5OPGjWK/v3707x5c9q0acP06dOJiIiwDTO98MILREZGMmvWLADuvPNOhgwZwtSpU+natStRUVGMHDmSli1bEhISYre4ChqLxUKNciXYGnGegzEXlNyIiIhkIcfJDUB6ejoLFy5k7969WCwW6tatS8+ePW29KNnVr18/zp49y+uvv05UVBT169dnyZIlhIWFARAVFZVpzZtBgwZx4cIFJk+ezNNPP01AQACdOnViwoQJuXkZhUqNciXN5EZFxSIiIlnK0To3AIcOHaJHjx5ERkZSq1YtDMPgwIEDVKpUicWLF1OtWjVHxWoXhXGdG4DP1xzhzcV78XJ3oWloKVpUDqRllUCahAbg45GrHFVERKTQyMn1O8fJTY8ePTAMg2+//ZbAwEAAzp49y0MPPYSLiwuLFy/OfeT5oLAmN+FnLnHf9PWcik/OdNzNxUK9Cv60rGwmPM0rBxLo63GdVkRERAonhyY3vr6+/P333zRo0CDT8R07dtCuXTsuXizYwyaFNbkBsFoNDp2+yMbwWDYdjWVTeCwn45Kuelz1ciUu9+yYCU/FUj5OiFZERMR+cnL9zvF4hqenJxcuXL3eysWLF/HwUI+BI7m4WKgZVJKaQSV5qLVZlxR5PpFN4bFsvJzsHIy5yKHLX99vNOuVQvy9aFEl0DaUVb1sCVxcrr2WkIiISGGX456bAQMGsHXrVmbMmEHLli0B2LBhA0OGDKFZs2Z8+eWXjojTbgpzz012xF5KYfNRs2dn49Fz7I6MI82a+Vcc4ONO87BAWlQuRYsqgTSo4I+7a47XcxQREck3Dh2WOn/+PAMHDuTnn3/G3d0dgLS0NHr27MmXX36Jv79/7iPPB0U9ufmvhJQ0tkWcN4exjsay9dh5ElPTMz3Gy92FJpVK0bZaaQa1q0xJL3cnRSsiInJtDk1uMhw6dIi9e/diGAZ169alevXquQo2vxW35Oa/UtOt7D4ZbxvK2nw0lnMJqbbvN6jgz1eDW6ooWURECpR8SW4Kq+Ke3PyX1Wpw+PRFNoTH8sGyA5y9lELNoBJ883Aryvl5OTs8ERERIGfX7xwXWtxzzz2MHz/+quPvvPMO9957b06bEydzcbFQ43KB8pz/a0OQnycHTl2k76frOXEuwdnhiYiI5FiOk5tVq1Zx++23X3W8W7durF692i5BiXNUL1eCuf/XlkqB3hw9m0DfaesJP3PJ2WGJiIjkSI6Tm+tN+XZ3dyc+Pt4uQYnzhJb24Yf/a0PVsr6cjEvi3mnr2R999dR/ERGRgirHyU39+vWZM2fOVcdnz55N3bp17RKUOFewvzc//F8b6gT7ceZiMv2mr+efE+edHZaIiEi25HgRv1deeYW7776bw4cP06lTJwD++OMPvv/+e+bOnWv3AMU5ypTwZPaQ1gz8YiPbj5/ngc828MX/WtCicqCzQxMREclSjntuevbsycKFCzl06BDDhg3j6aef5sSJEyxfvpzevXs7IERxFn8fd755pBWtqwZyMTmN/jM2sObgaWeHJSIikiVNBZcbSkpNZ+g3W1i5/zQeri5MfqAJXeqVd3ZYIiJSjDh0Kvjx48c5ceKE7f7GjRsZOXIk06dPz3mkUih4ubsyvX9zutcvT0q6lce+3cpP2yOdHZaIiMg15Ti5eeCBB1ixYgUA0dHRdO7cmY0bN/Liiy/y+uuv2z1AKRg83Fz4+P4m9GlagXSrwcg525l9eWNOERGRgiTHyc2uXbtsG2b+8MMPNGjQgHXr1vHdd98V+E0zJW/cXF14955GPNQ6FMOA5+fvZMZf4c4OS0REJJMcJzepqal4enoCsHz5cnr27AlA7dq1iYqKsm90UuC4uFh4o1d9/q99VQDe+GUPk/88SDEr3RIRkQIsx8lNvXr1mDZtGmvWrGHZsmV069YNgJMnT1K6dGm7BygFj8Vi4fnutRl1W00A3v39ABOW7ndagpOSZmXl/hgOxWixQRERycU6NxMmTOCuu+7inXfeYeDAgTRq1AiARYsW2YarpOizWCwMv7UGPh6uvLl4L9NWHSYhJY2xd9bDxcXi8PNbrQabj51jwbZIluyMIi4xFXdXC890qcWQm6vmSwwiIlIw5WoqeHp6OvHx8ZQqVcp27OjRo/j4+FCuXDm7Bmhvmgpuf99tiOClhTsxDLinWUXG92mAm2uOOwWz5cCpCyzYFsmi7SeJPJ9oO17S040LyWkAtK1Wmvf7Nqa8v3Y1FxEpKnJy/dY6N2IXC7dF8vTcHaRbDW5vEMwH/Rrj4WafBCcqLpFF20+ycPtJ9kb9u39ZSU83utUvT+8mFWhdtTRzNx/ntZ/3kJiaToCPO+P7NKRbfa3HIyJSFCi5yYKSG8dZuiua4d9vIyXdSqfa5ZjyYFO83F1z1VZcYipLd0WxcNtJ/g4/S8b/UndXCx1rlaN34wrcWqfcVe0fOX2REbO3szMyDoD7WlTi1Tvr4uOR4xFYEREpQJTcZEHJjWOtOnCa//t6M0mpVtpULc3nA5vj65m9xCI5LZ2V+0+zcFskf+yLISXNavtey8qB9GoSwu0NggnwuXpX+iulpFl5f9kBPl19GMOAqmV8+fC+JjSo6J+n1yYiIs6j5CYLSm4cb8ORszz81WYuJqfRNDSAL/7XEn9v92s+1mo12Hg0lp+2R7L4nyjik9Js36sZVIJejSvQq3EIFUv55DiOdYfPMGrODqLjk3B3tfB0l1o8qmJjEZFCSclNFpTc5I8dx88zYOZG4hJTqRvsx9cPt6R0CU/b9/dHZxQGR3IyLsl2vLyfFz0bh9C7cQXqBJfEYslbInI+IYUX5u/k113RALSpWpr3+zUi2N87T+2KiEj+snty89FHH2X75MOHD8/2Y51ByU3+2RsVT/8ZGzhzMYXq5Urwft9GrD98lgXbItkX/e+aNCU93ejewCwMblWlNK527lkxDIMfNh9n7CKz2Njf250JdzegW/1gu55HREQcx+7JTZUqVbJ1YovFwpEjR7IXpZMouclfR05f5MHPNxB1Re8MmIXBt9QqR+8mFehU++rCYEfFMnLOdv45YRYb92tuFhtntyZIREScR8NSWVByk/+OxybQf8YGjp5NoGWVQHo3rkCPBuVvWBjsCClpVj5YfoBpq8xi4yplfPnwvsY0rBiQ77GIiEj25Utyk5KSQnh4ONWqVcPNrfB88lVy4xxJqelcSk7LVHfjTOsPn+WpOduJjk/CzcXCqC41+b/21ew+JCYiIvaRk+t3jldZS0hI4OGHH8bHx4d69eoREREBmLU248ePz13EUuR5ubsWmMQGoE210iwdeTM9GpQnzWowcel+Hvz8b6LiEm/8ZBERKdBynNy88MIL7Nixg5UrV+Ll9e/y9p07d2bOnDl2DU7EkQJ8PPjkgaZMvKchPh6u/H0klm6T1vDrTu1uLyJSmOU4uVm4cCGTJ0/mpptuyjRNt27duhw+fNiuwYk4msVioW/zSiwefjONKvoTl5jKY99u5dkfd3ApOe3GDYiISIGT4+Tm9OnT19wc89KlS3lek0TEWaqU8eXHx9oyrGM1LBb4YfMJbv9oDTuOn3d2aCIikkM5Tm5atGjB4sWLbfczEprPPvuMNm3a2C8ykXzm7urCs91q8/2Q1gT7e3H0bAJ3T13HJysOkW4tVpMKRUQKtRxPcxo3bhzdunVjz549pKWl8eGHH7J7927Wr1/PqlWrHBGjSL5qXbU0S0e058UFO1m8M4p3ftvP6gOn+aBfY0ICtLKxiEhBl+Oem7Zt27J27VoSEhKoVq0av//+O0FBQaxfv55mzZo5IkaRfOfv487kB5rYio03hMfSbdJqFv+jYmMRkYJOi/iJ3MDRM5cYMXsbOy6vbHxvs4qM7VlPKxuLiOQjuy/iFx8fn+2TF/SEQcmN5EZqupUPlx/kk5WHMAyoXNqHSfc1oXGlAGeHJiJSLNg9uXFxccn2TKj09PTsRekkSm4kLzYcMVc2Phlnrmz81G01GdpBKxuLiDhaTq7f2epXX7Fihe320aNHef755xk0aJBtdtT69ev56quvGDduXB7CFin4WlUtza8j2vPiwp0s/kfFxiIiBVGOa25uvfVWHnnkEe6///5Mx7/77jumT5/OypUr7Rmf3annRuzBMAx+3HKCMYt2k5CSjp+XG+P6NOT2hsHODs1hLianEXkukZPnEzlx3vw38lwi0fFJ3FYniCHtqzo7RBEpwhy6caaPjw87duygRo0amY4fOHCAxo0bk5CQkPOI85GSG7Gno2cuMWLOdttif/c2q8iYnvUoUciKja1Wg9MXk4m8nLCcPJ9I5OUE5sTl+/FJWa/Y/M49Dbm3eaV8ilhEihu7D0tdqVKlSkybNo333nsv0/FPP/2USpX0h02Kl8plfPlxaBtbsfHcLSfYeDSWDwtYsXFSajonzydy8nwSkecTiDyflCmJiYpLJDX9xp9z/L3dCQnwpkKANxUCvKhQypujZxP4bkMELy7YSWigD62qls6HVyQicn057rlZsmQJd999N9WqVaN169YA/P333xw+fJh58+bRo0cPhwRqL+q5EUe5stjY1cXCU51r8FjH6k4rNj4em8CiHSf5aXskB05dvOHjXSxQ3s9MWDISmJAAbyqU+vf2tXqkrFaDJ7/fxuKdUQT4uPPT4+0IK+3riJckl6VbDb7bGMG0lYe5qXoZXutVDy93V2eHJeJQDh2WAjhx4gRTpkxh3759GIZB3bp1GTp0aKHouVFyI44Ul5BqKzYGaFklkA/6NaZCVsXGERtgx/dw01NQKixP5z93KYXFO6P4aXskm46ey/Q9Hw/XqxKWK+8HlfTEzTXH63oCkJiSTr/p6/nnRBzVyvoyf1g7/L3d8/Ra5Nq2HIvllYW7OR0VwVNuP7LWWp/ICt2YPqAZ5Up6OTs8KQw2fQ5nD0Pn18DNw9nRZJvDk5vCTMmNOJphGMzbGsmYn3Zx6XKx8dt9GnBHw5CrH5ySAB81gYvRUDIEBvwEZWvm6HxJqeks33uKhdtOsupAjG14yWKBNlVL07txBW6tU45AXw+Hbm4bE59Er0/WEhWXxM01yvDFoBa5TpbkajEXkpjw637mbT1BBU7zndc4wogG4I3Uh/i1RB+mD2hO/Qr+To5UCrSDy+Dbe8zbN42CzmOcG08OODy5OX/+PDNmzGDv3r1YLBbq1q3L4MGD8fcv+G8qJTeSX/5bbHx304q81us/xcZ/TYLlV/xx8SkD/edDcKMs2063Gqw/fJaF2yNZuiuai8n/FvvWDfajd5MQejaqQHn//P0kvysyjnunrScxNZ0BbcJ4vVf9fD1/UZSabmXW+mNMWnaAC8lpVLWcZH6JiQSkxoCnPySbK2dPSuvDp5a+fNCvMd3qF91Ze5IHl87AlDZwKebyAQsMWgyV2zk1rOxyaHKzefNmunbtire3Ny1btsQwDDZv3kxiYiK///47TZs2zVPwjqbkRvLTf1c2Divtw6R+jWkSWgoSz8GHjSApDrq+Df/8AFHbzQvWgz9AaOtMbRmGwe6T8SzcFsmiHSeJuZBs+16FAG96NQ6hd5MK1Awqmc+vMrPfdkcz9JstGAa81rMeA9tWdmo8hdn6w2cZu2g3+09dAKBn+bO8lzQW96SzUKam2dO3/Tv48w0APk/rzptpD/FMl1o8fkt1h/bUSSFjGDD7Adi/BMrWNj9A/TMH/CvB0L/AO8DZEd6QQ5Obm2++merVq/PZZ5/h5mZ+Ak1LS+ORRx7hyJEjrF69OveR5wMlN+IMG46cZdQPO4g8n2grNh6W/h0ua9+HsnXgsbWQchG+uw8i1oG7D9z3LVTrxPHYBH7aHsnC7Sc5FPNvYbC/tzu3Nwymd+MKNA8rhUsBWiV56srDTFi6DxcLfPG/lnSoWdbZIRUq0XFJvLVkLz/vOAlAKR93JrRK4rZtT2BJioPyDaH/AvAtYz5hw6fw67MAfJ92Cy+lPcydjSsy4e6GKjQW0+Yv4JeR4OoBQ/6EUpVh2s1wLhwa3At3f+7sCG/IocmNt7c327Zto3bt2pmO79mzh+bNm2udG5HriEtM5aUFO/nlnyjKco413qPwMpLhvu+h9uVZhikJ8EN/OLScdIs77/o9z9RTdWxteLi5cFudIHo1DqFjrXJ4uBXMmhbDMHhm7j/M23qCkp5uzB/WlhpO7lEqDFLSrMxcG85HfxwkISUdFws82CqM52pFU2L+AEi9BJVamz17Xv8pA9j+Hfz0OBhWfklvw1Opj1G3Uhk+69+Mcn4qNC7WzhyCT2+G1ATo8ia0fdI8fnwTzOwKRjr0+Rwa3uvcOG8gJ9fvHP9l9PPzIyIi4qrjx48fp2RJ/fESuR5/b3c+vr8J793biFGeP+FlJLOdmvycZNbXJKWm88u+8/xf2tMstbbE1Ujl6fNvcZfrX7SrXpqJ9zRk88ud+eTBpnSpV77AJjYAFouFt/vUp2XlQC4kp/HwV5uJvZTi7LAKtNUHTtNt0mrG/7qPhJR0moYGsOiJm3ijdgQlfnzATGyqdTJrsv6b2AA0fgDu/RJc3LnDdT0zvCax73gMvT5Zy67IuHx/PVJApKfC/EfMxKZKB2j9+L/fq9QCOpg9fiweBeevvrYXVjnuuRk+fDgLFizg3XffpW3btlgsFv766y9Gjx7N3XffzaRJkxwUqn2o50acLvYIxuQWWKxp9Et+hQ1GHVpWDmRPVLytMNiVdKb5fcVtKcvN59z+HrR4xIlB507spRR6f7KWiNgEWlYO5OtHWuLppmGSK504l8Cbv+xl6W5z5lOZEp680L02dzWpgMuuH2HB/5mfrGvfAffMBDfPrBs8uBzmPARpiWx3rc+Dl57C6l6C9/s2onsDFRoXO3+8AWveBa8AeGwd+FfI/P30NPiiG5zYBGHtYODP4FIw36MOHZZKSUlh9OjRTJs2jbQ08w+xu7s7jz32GOPHj8fT8wZvPCdTciNON28I7PwBa7Vb+SBoHJ+sOIT18ruwQoA3vZuE0LtxBWqU9YWlz8PGT81vdh5rroVTyBw8dYE+U9ZxITmNu5tW5N17G6rQFbOnbvrqI0xZeYikVCuuLhYGtqnMyNtq4OflfrlG4inAgIb3Qa9PwDWbi8ofWwff9oWUCxz2qE2f+FHEUYKnb6vJE51UaFxsHFsPX/YAw2r26tW769qPiz1i1t+kXIRbx8DNo/I1zOzKl3VuEhISOHz4MIZhUL16dXx8fHIVbH5TciNOFb0Lpt0EGPB/qyG4EVuOxfLH3hhuqV2OZqH/KQw2DFjxFqx+x7x/0yi49VVzEZtCZNWB0/zvi41YDXiuW20e61jN2SE51R97T/Haz3uIiDVrFFtVCeT1XvWpVf7y0P7aD2HZq+btFkOg+0RwyeEwZORW+OZuSIzllFdV7jj/DKcJoGejECbeo0LjIi8pDqbeBHER0OgBuGtq1o/f9o1Zs+XiBo8sh5Am+RNnDmgRvywouRGn+q4fHFgK9frAvV9k/3lXroeT24udk3217ihjFu3GYoFpDzWja73yzg4p3x07e4nXft7Dn/vMdUbK+3nx0u11uKNhsNmbYu9kNmYfzOoFF6O54BPK7eefIcJahkYV/flsQHMVGhdl8x81p3oHhJlTvb1ucL0zDPhhAOxdBKVrmB++PApWp4VDkpvBgwdn6+QzZ87M1uOcRclNEWQYsHMu+FUo2ItRHVtvjm1bXOGJTVA6h70Xm2bA4qcBAxrdDz0nZ3+YooB4ZeEuvv77GN7urswd2qbYrKabmJLOlJWH+HTVEVLSrbi7Wnj4pqo82ak6vhmLOlqt8NsLsGGaed9ewwOx4TCrJ5yPINknmL6Jz7EjsRzl/bz4fKBWNM6V1CT4ZzZUvjnn7+P8sPNHmPcwWFzgf0shtFX2npcQC1PbwoUoaD4Y7vjAsXHmkENmS3355ZesWLGC8+fPc+7cuet+5dSUKVOoUqUKXl5eNGvWjDVr1lz3sYMGDcJisVz1Va9evRyfV4qQbd/A/CHw9V0Qs9fZ0VybYcAfr5m3m/bP3R/EFg9Dn+lmcrTje/hxEKQl3/BpBcmYO+tyc40yJKamM2TWZmLik5wdkkMZhsHSXVF0fn8VH/95iJR0KzfXKMPSke15vnvtKxKbdFj05L+JTY937Vf3EFgFBv8GZWrimRDFfK83uC0whuj4JO6Zto4lO6Psc57i5Ndn4ecR8NktcHyjs6PJ7Pxx+OXy/532o7Of2AD4BELvy8NXm2fC/qX2jy+fZLvnZtiwYcyePZvQ0FAGDx7MQw89RGBgYJ5OPmfOHPr378+UKVNo164dn376KZ9//jl79uwhNDT0qsfHxcWRmJhou5+WlkajRo148sknGTt2bLbOqZ6bIubsYbMQLvWSeT+ovrlA1Y1mlOS3A7/Dd/eCmxcM3wZ+19hnKrv2LYa5gyA9xZwa3O8b8Cg8u3DHJabSZ8paDp++RKOK/sx+tA3eHvlf/3H0zCU+XX2YDeGxOGpwPiXNSuR5829WhQBvXrmjLl3rBWUu6E1LMafq7vnJTFx7T4FG99k/mEtnzA8A0f9gePrzRsDrzDxmLq74VOeaDL9VhcbZsvcXmPPgv/fdfeH+76BqR6eFZGNNh696wrG/oEJzGLwUXHOxge1vL8H6yeZ2MMPWQ4ly9o81FxxWc5OcnMz8+fOZOXMm69at4/bbb+fhhx+mS5cuuXpTtGrViqZNmzJ16r+FTnXq1KF3796MGzfuhs9fuHAhffr0ITw8nLCw7O2mrOSmCElPhZndIHIzVGplJjoJZ6DNE9D1LWdH9y+r1VxA69QuaDscuryR9zYPrzCXUk9NuP6ibgXYsbOX6P3JWs4lpHJ7g2A+vr9Jvq2wvDcqnikrD7P4n5O2WWqO5OHmwtAO1XisQ7Wrk7grFm3E1cOc6l3nTscFk3jerPs6/jeGuy/fVB7HKzvNVY7vbBTCOyo0ztqFaHNvpsRYaPkonD0Eh/80f3f3fvXvYpzOklGb5+4LQ9fkfsgsNQk+6wQxu6FGF3jghwIxiSFfCoqPHTvGl19+yaxZs0hNTWXPnj2UKFEi289PSUnBx8eHuXPnctdd/05PGzFiBNu3b2fVqlU3bOPOO+8kOTmZ33///bqPSU5OJjn53677+Ph4KlWqpOSmKFjxNqyaYF7UH1sH0Tvh+8ufeAf8VDA+SQH8M9f8ZO7pDyO2m12/9hCxAb6919w4MbgRPDT/3+X4C4ENR87y0IwNpKYbDO9UnVFdajn0fFuOxfLJisO2Yl6AW2qVpX+bMHPqtYOElfalbMlr9CQmxZuJRsQ6cPM2t9uofqvD4rBJuWSug3P4T3D1ZHXjdxi8vixpVoNGFf2ZPqA5QSo0vprVau6mffgPKN8AHvnDPP7jYNj3i9nrdtenzlvl9+R2+LwzWFOh58fQdEDe2ju1B6Z3hPRkc5i05RB7RJknOUlucl2NmFHvYhgGVqs1x88/c+YM6enpBAUFZToeFBREdHT0DZ8fFRXFr7/+ynfffZfl48aNG8drr72W4/ikgIvY8O+Mkjs+AP+K5lez/8GWL2DBY+Z+TfZKJHIrLQVWvGnebjfcvvGEtoJBv5hDDVE74IseMGBh3oa88lGrqqV5664GPPvjP3z05yGqlStBr8YVbvzEHDAMgzUHz/DJikNsCI8FzA+gtzcI5rGO1agX4qTeroRY+KYPnNwGnn7w4NyrNkp1GA9fuH+27aLcfutT/NrpXe5dV5EdJ+LoNXktnw1oToOKhacnMF9snG4mNm5e5lYFGUPf935lTqH+Z7ZZ+5dyEZr/L39jS0kwz21NNRd7bNI/720G1YXbXjPX2vr9ZajSHso69gOIPeVoLmlycjLff/89t912G7Vq1WLnzp1MnjyZiIiIHPXaXOm/w1mGYWRriOvLL78kICCA3r17Z/m4F154gbi4ONvX8ePHcxWnFCBJ8eYb2bCai5vVv/vf73V9C0pXhwsnzU3inL3SwbZZcO4o+JaD1o/Zv/3ghua4ul8FOLPfHKaLDbf/eRykb/NK/F/7qgCM/vEfthzL+aSEa7FazULenpPXMmDmRjaEx+LuaqFf80r8MaoDkx9o6rzEJj4KvuhuJjY+pc0ENb8SmwxunuZFueF9YKRT469R/NEhnOrlShAdn8S9n65j8T8qNLY5teffdYduewPKXbG3oqubWYTbYghgmH931n6Yv/EtewXOHIAS5c1eG3sNIbX8P7OuLy0J5j1iflgrJLKd3AwbNozg4GAmTJjAHXfcwYkTJ5g7dy49evTAJRfrbZQpUwZXV9eremliYmKu6s35L8MwmDlzJv3798fDwyPLx3p6euLn55fpSwq5pc/D+WPgHwo9Jmb+nocv9PnMXIhqz0/mrCJnSbkEqy7H1+FZxxX9lqlhJjilqpg/l5ndzPVNColnu9XmtrpBpKRZ+b+vN3PiXO43301NtzJvywm6TFrN0G+2sjMyDi93Fwa3q8LqZ29hwj0NqVo2dx/E7OLcUXM5gNP7oGQI/O9Xc0jRGWwX5UcAg9IrnuWXZlvpULMsSalWHv9uKx8uP0h6fhQmFWRpyeaHqfRkqH7btYdnXFygxzvmukRgJkJ/vpk/H64O/A6bLu/ofddU+/YOu7iY/0e8AyH6H3MNpkIi2zU3Li4uhIaG0qRJkyx7VubPn5/tk7dq1YpmzZoxZcoU27G6devSq1evLAuKV65cyS233MLOnTupX79+ts8HKigu9HYvhLkDzfUbBi2BsDbXftzqd+HPN8CjhLmAVWCVfA0TgDXvm9O/A8Lgic3glnUinmcXoi9Ph99j/jHqP79ArjJ6LZeS07hn2nr2RsVTK6gk84a1pYRn9kfNk1LT+WHzcT5ddcQ2O6mklxuD2lZmUNvKlC5RAGbPnT5gLqh34SSUqmzWhZWq7Oyo/l2m4C9zTRPrTc/wZkIfZq47CkCQnyc9G4XQq3EF6oX4Fb8ZVbaZQ6XhsfVQMusP37b3PUCrodB1nOMW3Lx4Gqa2gUunofUw6HbjiTi5svdns04Li7n3VJWbHXOeG3BIQXHGGjM38sUX2V91NWMq+LRp02jTpg3Tp0/ns88+Y/fu3YSFhfHCCy8QGRnJrFmzMj2vf//+HDx4kL///jvb58qg5KYQi4s0F5hKOg83PwO3vnL9x1rT4cs7zGLNii3NT8j5ueBd4jn4sJG5BPpd06FRv/w5b0KsueT+ya3gUdKcRRXWNn/OnUcnzyfS65O1nL6QTKfa5fhsQHNcbzCD6kJSKt/8HcGMv45w5qLZZV6mhCcP31SFh1qHUtKBhcI5ErXDTDwTzkLZ2tB/IfgVsE0s/3NRnlP6Md7+9QBxiam2h1QvV4K7mlSgZ6MQKgUWrNVrHeLISjMhBbjv++zPhtr4GSx5xrzd+CHo+ZH9N6M0DHMCxYGlUK4uDFkB7g4sBP/pCdj2NfhVhMf+Au9SjjvXdRSq7RemTJnCxIkTiYqKon79+nzwwQe0b98eMBOqo0ePsnLlStvj4+LiCA4O5sMPP2TIkJxXbyu5KaSsVvi6N4SvgpCm8PDvN16/4XwETG0HyfHQ8UXo+Fy+hArA8rHmJ+Fy9cwpmfm5y25SPHx/v7nWhZu3uQ5Ojc75d/482H78PP0+XU9ympVHbqrCy3fUvebjzl5M5st1R/ly3VEuJJkb+FYI8GZoh6rc27xSwZrOHPG3uYllchwEN748q620s6O6tv9clJN7fMDKg7H8tD2S5XtjSEn7d/JI87BS9G5SgdsbBFPK18G9ks6QEGv+/bhwEpoNgjtzWEezYzYsHGbu6F631+UiZDv+nDbNgMWjzGnoQ1ZA+ZyNYuRY8kVzSYvYI2ad490z8n16eKFKbvKbkptCat3HZsW+uw/83xooUz17z/vnB3O83OJqrtJaqYVj4wSzYPSjJpCWaM5KqdXd8ef8r9REc5+Yg7+Di7u5to6ffWci2VgsENbObmP9P+84yZPfbwNgfJ8G3Nfy3wU9T55P5LM1R/h+YwRJqeaFtlpZX4Z1rE7PxiG4uxaw/bYO/wmzHzTXIwptCw/MufEeP862/Xv4aZhZsF/nTmjQF4CE1HS2HTvHxqOx7D91wVZO4uZioV6IHy2rBNKwYgAeOfkdlApzXs1RVgzDHP7e85M5QeH/VueuZm7vz+astPQUqN4Z+n5tn/2aTh+AT9ubf2O6vg1tHs97m9lxYjPM6GImbPnZI32ZkpssKLkphKJ3mgtKpafAHZNyPs3yx4dh149mwe3Qv8DTwQWlvzxlLl1eqZWZUDmrRiEtxUzs9ix0/Ll8y5pDLXb69Dhp+QEmLT+Im4uFWQ+3JNjfm2krDzN/2wlS080/WQ0q+PP4LdXpUjco3xYAzBFHXdjyw55FZuzW1Bs/Nq86vQI3P10gFomz2fatmeC5uMHDy6BC09y3degPM8FNS7RPgpuWAjM6m0OdVTvCQwvydxPdVRPNwmJPP/PvaansLaBrD0pusqDkppBJTYTpt8DpvVDrdnOhs5z+EUw8b3Yvx58w13/oNdkhoQLmKsmftARrmlnn4+x6F2u6udDhkRsviplrcSfMn62XvznkUrF5nps0DIMRs7ezaMdJvN1dSU5Lt60m3LpqII/fUp2bqpcpuMWtO+bAwsfMT7h1eppd+I4uKLe3I6tg7SRzDZUsJKamc/ZSMmcvppCS/u+wlburC6V9PQj09cDHw42rflPpKWZtGEC7EdD5tYKR4MSGw7SbzPVqOr0C7Z/BMAzOJaRy8nwiJ84lcvJ8IjEXkgkr7UOLyoFUK+ub9f/FiL8vL7gZbxb5PzQ/9z2dy1+Dv943a14eW5//tVvpafBlDzi+wUzWBv1iG3a/mJzGtohzbAqPxQCetvPCnEpusqDkppD59TlzM8ESQeYqxLldgffoX2aBMYZZg+KoJe4zeomq3wYP/eiYcxQ0iefNP9wnNprLvj8w21zwK4+SUtO5b/rfbD9+HoBba5dj2C3VaBbm5IUZbyRT3cqDcOdHhW739tywWg22RJxjwbZIFv8TlakQuVpZX+5qUoFejStkLkTOGG4GcxfqHu/lby/EZanpVqLjkoiMvUCNxX0pfW474T4NGVt6IifOJ3PyfBKJqenXfX5pXw+aVy5Fi8qBtKwSSN1gP9z+Ozxnj6Lyo2vhy9sBw+wJrNszx6/VLs4dhak3QcoFDtQbyWyvfmw6GsueqHjb0gH+3u5se+U2u/aqKrnJgpKbQuTQcnPmD8CD8/JeFLtsjPlJ1FGfeKL+MQvuwKwLCm5o3/YLsuSL5maCR1aCqyf0/coutUbnE1L4ccsJ2lUvQ53gQvB+zc9pwAVYSpqVVQdOs3BbJMv3niL5ikLkZlcUIgf6esCWL+HnkYBh1vf0npK7zR6zcCEplZPnk4g8n0Dk+SQiL/e+RJ43/z0Vn4TVgOGu8xnl/iPxhjc9UsZzwiibqZ0yJTypEOBFhVLelPb1ZP+pC2w/fj5ToTWAr4crTcPMZKdF5UAaVwow9xU7vR9m9c7dcgCJ580epbjj5gys3p/Y40eTbYZhcOJcIhvDY9l0NJaAgz/yfNKHpBqu9El5jZ2GuRhnhQBvWlYxX/c9zSri4Wa///9KbrKg5KaQuHTGnPZ98ZS5SuZ/F+vLjUxj1beYXcP2vPB8e69ZwFv/Hrhnhv3aLSxSk8w6jf2LzVqFuz6FBvc4O6r8YRjwx+vmcAFA+9Fwy0sFY5jFyeKTUlm6K5qftkey7vDZTIXIjSsF4OHmQpuElTx2biJupLPZqw0fB75IqiVvw3iGAecSUjh5PpH4yzPqstLC7TDfu43BDSvfVXiZU5V7USHAmwqlvAkJ8CbY3+uas/CS09LZFRnHxvBzbDoay+ajsVedz93VQv0K/rSsHMjNZS7RZt3DuJ4/ai7kOOAnKFvzxi9o3iOwc+7l2sE14Fkyuz+KXLFaDQ7EXGBTeCwbj5pDTdHxSVc8wmCy+0fc4bqB056V2HDbAppWr0hIgLfDYlJykwUlN4WAYZgFePsXm923j64Edzu9YTLNMhgHbYbZp91j68wl9V3c4PGNud+Nt7BLTzWnv+78AbDAnZPMabRFmdUKvz4Lmz4z73d+DW4a6dSQCqrouCR+3nGShdsj2X0yPtP3OrlsZar7h3haUvkrvR6Ppj5NAvZbt8Xf250KAWaiUrGUNyEBXlQI8CEkwIuKPumU+bYzlnPheZ7mbLUa7D91gU1HY229HKfikzM9phznmOsznjDrcZI9ShF/7w+UrdHy+o1mbL7rwFmfKWlWdkaezzJJc3Ox0KCimaS1qBxIiyDw/7Jj7qfL55CSmywouSkEtnwJP48wpzAP+dP+wzubPofFT5vDJ4+ugKB6eWvPMMwtD47/bdYN3PGBfeIsrKxWWPK0OWMMoMtb0PYJ58bkKOlpsOiJy9t8WOD296DFw86OqlA4FHPhqgSnzOkNtNrwOG5pCcQGNmZ962mkeeT+77Sfl7ut5yXLFa8zLVC3FrwDcn3O/zIMg+OxiWy8nDBsPBrLkdOXKEU8X3lMoKFLOPGGD895vox3tXY0rxxIyyqlqFa2hFmknGm9rheg4/N2ietichpbj52zJWHbj5/PNHwI4OPhStPQy8NrVUrRpFIpc3jtSkdWXV7o0MjZQoe5oOQmC0puCrizh81x5dQEc4O6dsPtfw7DgO/6wcHfzEX2hvyZt5U99y+F7/uZC+YN31bwVp51BsOA5WP+3UCww3PmH+aiNEyTlgzzHjanfFtczWG4hvc6O6rC78Rms9Yu6TwENYD+C6BE2Rs+LdecsLXAmYvJbD4ay/ZDJ7hj1wjqp+0mwfDk0dRR/GVtAEAJTze8XQ2mWV+jGXvYQU2GuLxBusU+C1SeT0y9at+wQF8PWlT+t1aoXsg1CqOv5feXzeLw7G5RkUtKbrKg5KYAS081F4g6udWcbdP/JwfuyRIDU9pAwhlo/Th0ezt37VitZjIWsxvajYTbXrNrmIXemvfMWhQw977p+nbRSHBSLpkXxMN/mivE3vsl1L7d2VEVHdG7zJlFl2KgdA0YsBD8K9r/PPFR5t5MiefM6ei3vW7/c9xISgJpsx/C7cgfpFnc+TDgeaafrkdympXHXBfxnPtsLhpe9EgZR4Rh36ShYilvc4jpcgHwDae0X09aMnx2K5zaaa7p9OCPDnmfK7nJgpKbAuzPN2H1O+Z6KY+tB38HraibIaPHBcxPh9U65byNjBWQvfxhxA6n7LdS4G2YDr+ONm836W+Oy+fndhT2lhRnbqdw/G9zxez7voNqtzg7qqLnzCFzuCP+BPiHmgmOPWvZrFb4pg8cWQHlG8IjfzhvLaK0FLOmZs9PYHElrecnRLmHUnF+TyzWNKJveY/42vZdDdjf250gPzvuRRWzF6Z3hLQk6P4OtHrUfm1fpuQmC0pursMwzIXn7DwFM9uOrTcXhjKscM8XUL9P/pz3l1GweQaUDDbX0cnJwlppKTC5OZw/Bre+aq6yKte2/Tv46XHz91vvLnPp9sK2qB2Ys/i+6WPOuPP0hwfnQmgrZ0dVdJ0/biY4sYfNta76L4Sga+83lmPrp8BvL4Cbl7m9Qln7LjiXY+lp8PNw2P6ted+3nNlzVacn9J1VOHo8N3xqFte7ecGjq6Bcbbs2n5Prd/FbgEGubcM0eKMMfN3HXPAuP3PepDhY8Kh54Wt0f/4lNgBd3oQyNeFClPmHJSeve+tXZmJTIshc00Sur/ED5tCNizvsXmCuiZOa6Oyocib+JHzRw0xsfMqYK7MqsXGsgEoweCkE1TeXhfiyB0RuyXu7p3abm9uC+TfA2YkNmAs99pz879+SSzHmh647PywciQ1Ay0fNYam0JLMnKi35xs9xECU3Ytr6tfnv4T/MFTBndIH9v5pdt4625FlzRkBAGHS3w3o2OeHhA30+M6dw7/35309NN5JyydxjBaDDs7nbVK+4qdvL3EjUzdtcD+ibe8wdzAuD2HBzRtyZ/eYGpIOXFq9FGp2pRDkzkazQ3KyN+aqn+QEst1KTYN4QSE+GGl2hxSP2izWvXFyg23jo9LJZa3T3DLttSJsvLBboNcUsLPYuZS7u6SRKbsQsro3Zbd5u0t+cIn1iI3x/H0xrZ+6Tk37jRbByZdc8+Gc2WFygz3Tn7Jgc0thccA3M7R5ij9z4OX9PNT9ZlaoMTQY4MrqipUZn6D/f3HTv2F/mkENCrLOjylrMPjOxOX/MXEBt8FIoU8PZURUv3qXMmpsq7c09n765Gw78nru2/njd/HvnU8bcZ66g9YpYLOYikE9uhsrtnB1NzpUMMjcb7f8T+JZ2WhhKbgTCV5v/lm9ovtlH7jRn/niUhJg95pDRx03MPXPsOZQQd8LcQRvg5mcgtLX92s6pdiMgrJ35h3P+o1kncwmxsPYj8/YtLxfO2hFnCmsLAxeBd6A5M+7L2+FCtLOjuraT28zFGS9GQ7m6ZmITEOrsqIonz5LwwFyo2d0c9ph9P+yan7M2Dv8Jf1/etqDXJ2avkNhf6WpO33ZEyY2YswUAqnY0/y0ZZE5pfmqXuSuuTxlz2GjJMzCpgbl/TlJc3s5ptcKCoWY7FZqZQzvO5HJ5nRJPfzixyZy1dT1rJ0FynFkHUP/ufAuxSAlpYu6aXqK8mUB/0d38P1aQHFtnDoEkxkJIUxi0GEqWd3ZUxZu7F/T72tzixJpmrjOUMaR+Iwmx5urZYC62Waub4+IUp1NyU9wZBhxead7OSG4yeAdA+2fMnpwe75rTMS+dNjcG/KC+WZB3MSZ3513/MRxdY+4i3ecz583SulJAJbjj8t5AqyfC8Y1XPyb+pDkjAMwZUsVwU0S7KVf7ck9ImDkUOLMbnDno7KhMh5abxfXJ8RB2k9nTVJhqH4oyV3dzCLvpQHMSwqInzJlPWTEMc8LAhSizlqXLW/kTqziN/jIXd7FHzHUkXD0gtM21H+PhAy2HwPCtZu9G2drmH/2/PjB7chY/DeeOZf+cUTvgjzfM293GFax9mBrcY+5MbFjN9WuSL2T+/qqJZpd4aBuo0cU5MRYlgRk1LLUgPtJMcKJ3OjemPYvgu/vM/cdqdIGHfnT4JoWSQy6u5iyitk+a9397AVZOuP5sx23fmBMGXNzg7s/Mv2lSpCm5Ke4yhqQqtbrxG97VHRrdZy6wd9935uyFtCRzr6aPmpi1Kqf2ZN1GSoI5U8GaCrXvgKYFsBj39nfBvxKcOwq/XrGPy9nDsHWWefvWMQWvELGw8guB/y0xa74Szpg1ONfqNcsP27+DuQPN/591e0O/b+23aavYl8VibtFyy8vm/ZVvm9sA/DfBiT1iThQAc+JASJP8jVOcQslNcXdklflv1Q7Zf46Li7nU/CPLYeAv5sq+Rjr8M8dcyvy7+65/cVo+xpxOWyII7vyoYCYIXv5mDxUW2P6NuWoomCsoG+nm9NGw6/RySe74Xl43plJrsw5rVm84sjJ/Y9gwHRY+ZvbaNXkI7pmpYvGCzmKBDqPN6dMA6yebm+5a08376Wnmh67US+aEgXYjnBer5CslN8WZNf3fmVJVc7F8vMVibjLXfwE8utJcxwQLHPgVZtwGX9xu1i5kfJI6uAw2Tjdv957i1GmCN1S5Hdx0eSbXouHmVg275wMWs9ZG7M/L35wmXq2TeTH69l7YtyR/zr3mvX+3iGg9DO78uHBvEVHctH7MXADP4mIurjl/iLlX3ep3zAkCnv5w1zT9TosRbb9QnEVuhc9uMd/4zx4xV8jMqzOHzNlEO2abXftgDje0GmoWIF+KMW93n5D3czlaWoqZpEVtByyAAQ3uhbs/d3JgRVx+7rZtGOb/y7WTzPsdnoeOzxfMHkW5sd0L/h32Dm1j9iAb6dDnc+3YXgRob6ksKLm5wpr3zZlPtW6H+7+zb9txkbD+E9jypfkpPEPZOvDoisJTx3DmIHzaHlITzGLEJzZBYFVnR1X0paeZs2B2fA9YHDdTyZoOSefN213e/LdAVQqvg8vMHdvTksz7+kBSZOTk+m2Hj+pSaGXUNPx3Crg9+FeAbm+bU8k3Tjf3rkpPM2cqFJbEBsyVaLtPNC+0rR9TYpNfXN3MZdw9/WDjp5Bw1nHncnEzlzpo/j/HnUPyT43b4KF5MPsBs7avx7vOjkicQD03xVVqIowPM/dXeXwTlK3p2POlJZufpLz8HXseR7l0xlxRV+va5L+4E47do8a3bMGu/5LcSU00a3DcPJ0didiJem7kxo5vMBObkiH5s0+Om2fh/iPjW8bZERRf/hWdHYEURoWph1jsTh9Di6srh6RUPCkiIkWIkpviypH1NiIiIk6k5KY4SoiFk9vN21XaOzUUERERe1NyUxwdXQMY5h5RfsHOjkZERMSulNwUR7YtFzo6NQwRERFHUHJTHKneRkREijAlN8XN+QiIPWwuax/WztnRiIiI2J2Sm+ImY0iqQjPwKsaLGIqISJGl5Ka40ZCUiIgUcUpuihPDgHAVE4uISNGm5KY4idkDl06Duw9UbOHsaERERBxCyU1xkjEkFdYO3DycGoqIiIijKLkpTmz1Nh2cGoaIiIgjKbkpLtJS4Oha87bqbUREpAhTclNcRG6B1EvgUwbK1XN2NCIiIg6j5Ka4uHJIykW/dhERKbp0lSsutL6NiIgUE0puioOkeDixybxdRcXEIiJStCm5KQ6OrQMjHUpVgVJhzo5GRETEoZTcFAdalVhERIoRJTfFgeptRESkGFFyU9RdOGVuu4AFqrR3djQiIiIOp+SmqMsYkgpuCD6Bzo1FREQkHyi5Keo0JCUiIsWMkpuizDDgiIqJRUSkeFFyU5SdPQzxJ8DVE0LbODsaERGRfKHkpig7ssL8N7QVuHs7NxYREZF8ouSmKMuot9GqxCIiUowouSmqrOkQvsa8XfUW58YiIiKSj5TcFFUnt0NyHHj6Q0hjZ0cjIiKSb5TcFFXhK81/q9wMLq5ODUVERCQ/KbkpqrS+jYiIFFNOT26mTJlClSpV8PLyolmzZqxZsybLxycnJ/PSSy8RFhaGp6cn1apVY+bMmfkUbSGRkgARf5u3ldyIiEgx4+bMk8+ZM4eRI0cyZcoU2rVrx6effkr37t3Zs2cPoaGh13xO3759OXXqFDNmzKB69erExMSQlpaWz5EXcMf/hvQU8KsApas7OxoREZF85dTk5v333+fhhx/mkUceAWDSpEn89ttvTJ06lXHjxl31+KVLl7Jq1SqOHDlCYKC5T1LlypWzPEdycjLJycm2+/Hx8fZ7AQXVlUNSFoszIxEREcl3ThuWSklJYcuWLXTp0iXT8S5durBu3bprPmfRokU0b96ciRMnUqFCBWrWrMkzzzxDYmLidc8zbtw4/P39bV+VKlWy6+sokLTlgoiIFGNO67k5c+YM6enpBAUFZToeFBREdHT0NZ9z5MgR/vrrL7y8vFiwYAFnzpxh2LBhxMbGXrfu5oUXXmDUqFG2+/Hx8UU7wUmIhagd5m0t3iciIsWQU4elACz/GTYxDOOqYxmsVisWi4Vvv/0Wf39/wBzauueee/jkk0/w9r56iwFPT088PT3tH3hBFb4aMKBsHSgZdMOHi4iIFDVOG5YqU6YMrq6uV/XSxMTEXNWbkyE4OJgKFSrYEhuAOnXqYBgGJ06ccGi8hYamgIuISDHntOTGw8ODZs2asWzZskzHly1bRtu2ba/5nHbt2nHy5EkuXrxoO3bgwAFcXFyoWLGiQ+MtNJTciIhIMefUdW5GjRrF559/zsyZM9m7dy9PPfUUERERDB06FDDrZQYMGGB7/AMPPEDp0qX53//+x549e1i9ejWjR49m8ODB1xySKnbOHYNz4WBxhcrtnB2NiIiIUzi15qZfv36cPXuW119/naioKOrXr8+SJUsICwsDICoqioiICNvjS5QowbJly3jyySdp3rw5pUuXpm/fvrz55pvOegkFS/jlWVIVW4BnSefGIiIi4iQWwzAMZweRn+Lj4/H39ycuLg4/Pz9nh2NfPw6GXfOgw3Nwy4vOjkZERMRucnL9dvr2C2InVqvWtxEREUHJTdERsxsSzoC7L1Ro7uxoREREnEbJTVGR0WtTuR24eTg3FhERESdSclNUaAq4iIgIoOSmaEhLgWNrzdvackFERIo5JTdFwYlNkJoAvmWhXF1nRyMiIuJUSm6KgowhqSodwEW/UhERKd50JSwKwjUFXEREJIOSm8IuKR5ObDZvK7kRERFRclPoHVsLRjoEVoWASs6ORkRExOmU3BR2mgIuIiKSiZKbwk7JjYiISCZKbgqzC9Fweh9ggco3OzsaERGRAkHJTWGWseVCSGPwCXRqKCIiIgWFkpvC7Mr1bURERARQclN4GYbqbURERK5ByU1hdeYgXDgJrp4Q2trZ0YiIiBQYSm4Kq4xem9DW4O7t1FBEREQKEiU3hZW2XBAREbkmJTeFUXoahK8xb1dVMbGIiMiVlNwURlHbITkOvPwhuLGzoxERESlQlNwURkdWmP9WaQ8urs6NRUREpIBRclMYHVG9jYiIyPUouSlsUhLg+AbzdtVbnBuLiIhIAaTkprCJWA/pKeBXEQKrOjsaERGRAkfJTWFz5arEFoszIxERESmQlNwUNtpyQUREJEtKbgqTS2ch+h/ztta3ERERuSYlN4XJ0dXmv+XqQYlyzo1FRESkgFJyU5jYhqTUayMiInI9Sm4KE9XbiIiI3JCSm8Li3FHzy8UNwto6OxoREZECS8lNYZAUBwuGmrcrtgTPks6NR0REpABzc3YAcgOXzsI3d0HUDvD0hy5vOjsiERGRAk3JTUEWfxK+vgtO7wOfMtB/PgQ3cnZUIiIiBZqSm4IqNhxm9YLzx8CvAvRfCGVrOjsqERGRAk/JTUEUsw++7g0XoqBUFRjwE5QKc3ZUIiIihYKSm4Lm5Hb4pg8knIWydWDAQihZ3tlRiYiIFBpKbgqSY+vhu76QHA8hTeCh+eAT6OyoREREChUlNwXFoeUw+yFIS4SwdnD/bPDyc3ZUIiIihY6Sm4JgzyL4cTBYU6H6bdB3Fnj4ODsqERGRQkmL+Dnb9u9h7kAzsanbG+77TomNiIhIHii5caaNn8HCoWBYoclDcM9McPNwdlQiIiKFmpIbZ1nzHix5xrzd6jG482NwcXVuTCIiIkWAam7ym2HA8rGwdpJ5v8Nz0PEFsFicGZWIiEiRoeQmP1mtZm/N5hnm/dvegHbDnRuTiIhIEaPkJr+kp8FPj8M/swEL3PEBNP+fs6MSEREpcpTc5Ie0ZHOq975fwOIKfaZDg3ucHZWIiEiRpOTG0VIuwewH4cgKcPWEvl9Bre7OjkpERKTIUnLjSInnze0Ujm8Ad1+4/3uo2sHZUYmIiBRpSm4c5dIZc2fv6J3g5Q8PzoNKLZwdlYiISJGn5MYR4iLNxObMAfAtC/0XQPkGzo5KRESkWFByY2+xR+CrXhAXAX4VYcBPUKa6s6MSEREpNpTc2FPMXpjVGy5GQ2BVGLAIAio5OyoREZFiRcmNvUT9A7N6QuI5KFfPHIoqGeTsqERERIodJTf2UrI8eAeaPTYP/gg+gc6OSEREpFhScmMvJcrBwJ/Byw88Szo7GhERkWLL6buCT5kyhSpVquDl5UWzZs1Ys2bNdR+7cuVKLBbLVV/79u3Lx4iz4F9BiY2IiIiTOTW5mTNnDiNHjuSll15i27Zt3HzzzXTv3p2IiIgsn7d//36ioqJsXzVq1MiniEVERKSgc2py8/777/Pwww/zyCOPUKdOHSZNmkSlSpWYOnVqls8rV64c5cuXt325urrmU8QiIiJS0DktuUlJSWHLli106dIl0/EuXbqwbt26LJ/bpEkTgoODufXWW1mxYkWWj01OTiY+Pj7Tl4iIiBRdTktuzpw5Q3p6OkFBmadLBwUFER0dfc3nBAcHM336dObNm8f8+fOpVasWt956K6tXr77uecaNG4e/v7/tq1IlrTsjIiJSlDl9tpTFYsl03zCMq45lqFWrFrVq1bLdb9OmDcePH+fdd9+lffv213zOCy+8wKhRo2z34+PjleCIiIgUYU7ruSlTpgyurq5X9dLExMRc1ZuTldatW3Pw4MHrft/T0xM/P79MXyIiIlJ0OS258fDwoFmzZixbtizT8WXLltG2bdtst7Nt2zaCg4PtHZ6IiIgUUk4dlho1ahT9+/enefPmtGnThunTpxMREcHQoUMBc0gpMjKSWbNmATBp0iQqV65MvXr1SElJ4ZtvvmHevHnMmzfPmS9DREREChCnJjf9+vXj7NmzvP7660RFRVG/fn2WLFlCWFgYAFFRUZnWvElJSeGZZ54hMjISb29v6tWrx+LFi+nRo4ezXoKIiIgUMBbDMAxnB5Gf4uPj8ff3Jy4uTvU3IiIihUROrt9O335BRERExJ6U3IiIiEiRouRGREREihSnL+KX3zJKjLQNg4iISOGRcd3OTqlwsUtuLly4AKBVikVERAqhCxcu4O/vn+Vjit1sKavVysmTJylZsuR1t3nIrYytHY4fP+6QmViObL8wx17Y2y/MsRf29gtz7I5uvzDHXtjbL8yxO7J9wzC4cOECISEhuLhkXVVT7HpuXFxcqFixokPP4ehtHhzZfmGOvbC3X5hjL+ztF+bYHd1+YY69sLdfmGN3VPs36rHJoIJiERERKVKU3IiIiEiRouTGjjw9PRkzZgyenp6Frv3CHHthb78wx17Y2y/MsTu6/cIce2FvvzDHnh/tZ0exKygWERGRok09NyIiIlKkKLkRERGRIkXJjYiIiBQpSm5ERESkSFFyYwerV6/mzjvvJCQkBIvFwsKFC+3W9rhx42jRogUlS5akXLly9O7dm/3799ut/alTp9KwYUPbYktt2rTh119/tVv7Vxo3bhwWi4WRI0farc2xY8disVgyfZUvX95u7UdGRvLQQw9RunRpfHx8aNy4MVu2bLFL25UrV74qdovFwuOPP26X9tPS0nj55ZepUqUK3t7eVK1alddffx2r1WqX9i9cuMDIkSMJCwvD29ubtm3bsmnTply1daP3kGEYjB07lpCQELy9venYsSO7d++2W/vz58+na9eulClTBovFwvbt2+0Wf2pqKs899xwNGjTA19eXkJAQBgwYwMmTJ+0S+9ixY6lduza+vr6UKlWKzp07s2HDBrvE/l//93//h8ViYdKkSXZrf9CgQVe9B1q3bm3X+Pfu3UvPnj3x9/enZMmStG7dmoiIiDy3fa33r8Vi4Z133rFL7BcvXuSJJ56gYsWKeHt7U6dOHaZOnZqttrPT/qlTpxg0aBAhISH4+PjQrVs3Dh48mK22s3Ntyuv7Ni+U3NjBpUuXaNSoEZMnT7Z726tWreLxxx/n77//ZtmyZaSlpdGlSxcuXbpkl/YrVqzI+PHj2bx5M5s3b6ZTp0706tXL7v8BN23axPTp02nYsKFd2wWoV68eUVFRtq+dO3fapd1z587Rrl073N3d+fXXX9mzZw/vvfceAQEBdml/06ZNmeJetmwZAPfee69d2p8wYQLTpk1j8uTJ7N27l4kTJ/LOO+/w8ccf26X9Rx55hGXLlvH111+zc+dOunTpQufOnYmMjMxxWzd6D02cOJH333+fyZMns2nTJsqXL89tt91m2ysur+1funSJdu3aMX78+BzHfqP2ExIS2Lp1K6+88gpbt25l/vz5HDhwgJ49e9ol9po1azJ58mR27tzJX3/9ReXKlenSpQunT5+2S/sZFi5cyIYNGwgJCclWuzlpv1u3bpneC0uWLLFb+4cPH+amm26idu3arFy5kh07dvDKK6/g5eWV57avjDkqKoqZM2disVi4++677RL7U089xdKlS/nmm2/Yu3cvTz31FE8++SQ//fRTnts3DIPevXtz5MgRfvrpJ7Zt20ZYWBidO3fO1vUlO9emvL5v88QQuwKMBQsWOKz9mJgYAzBWrVrlsHOUKlXK+Pzzz+3W3oULF4waNWoYy5YtMzp06GCMGDHCbm2PGTPGaNSokd3au9Jzzz1n3HTTTQ5p+1pGjBhhVKtWzbBarXZp7/bbbzcGDx6c6VifPn2Mhx56KM9tJyQkGK6ursYvv/yS6XijRo2Ml156KU9t//c9ZLVajfLlyxvjx4+3HUtKSjL8/f2NadOm5bn9K4WHhxuAsW3bthy3m532M2zcuNEAjGPHjtm97bi4OAMwli9fnqO2s2r/xIkTRoUKFYxdu3YZYWFhxgcffJDjtq/X/sCBA41evXrlqr3stN+vXz+7/J/Pzs++V69eRqdOnezWfr169YzXX38907GmTZsaL7/8cp7b379/vwEYu3btsh1LS0szAgMDjc8++yzH7f/32mTv921OqeemkImLiwMgMDDQ7m2np6cze/ZsLl26RJs2bezW7uOPP87tt99O586d7dbmlQ4ePEhISAhVqlThvvvu48iRI3Zpd9GiRTRv3px7772XcuXK0aRJEz777DO7tP1fKSkpfPPNNwwePNhuG7redNNN/PHHHxw4cACAHTt28Ndff9GjR488t52WlkZ6evpVn369vb3566+/8tz+lcLDw4mOjqZLly62Y56ennTo0IF169bZ9Vz5JS4uDovFYrdewAwpKSlMnz4df39/GjVqZJc2rVYr/fv3Z/To0dSrV88ubf7XypUrKVeuHDVr1mTIkCHExMTYpV2r1crixYupWbMmXbt2pVy5crRq1cqupQMZTp06xeLFi3n44Yft1uZNN93EokWLiIyMxDAMVqxYwYEDB+jatWue205OTgbI9B52dXXFw8MjV+/h/16bnP2+VXJTiBiGwahRo7jpppuoX7++3drduXMnJUqUwNPTk6FDh7JgwQLq1q1rl7Znz57N1q1bGTdunF3a+69WrVoxa9YsfvvtNz777DOio6Np27YtZ8+ezXPbR44cYerUqdSoUYPffvuNoUOHMnz4cGbNmmWHyDNbuHAh58+fZ9CgQXZr87nnnuP++++ndu3auLu706RJE0aOHMn999+f57ZLlixJmzZteOONNzh58iTp6el88803bNiwgaioKDtE/6/o6GgAgoKCMh0PCgqyfa8wSUpK4vnnn+eBBx6w26aCv/zyCyVKlMDLy4sPPviAZcuWUaZMGbu0PWHCBNzc3Bg+fLhd2vuv7t278+233/Lnn3/y3nvvsWnTJjp16mS7+OZFTEwMFy9eZPz48XTr1o3ff/+du+66iz59+rBq1So7RP+vr776ipIlS9KnTx+7tfnRRx9Rt25dKlasiIeHB926dWPKlCncdNNNeW67du3ahIWF8cILL3Du3DlSUlIYP3480dHROX4PX+va5Oz3bbHbFbwwe+KJJ/jnn3/s/sm4Vq1abN++nfPnzzNv3jwGDhzIqlWr8pzgHD9+nBEjRvD7779na3w7N7p372673aBBA9q0aUO1atX46quvGDVqVJ7atlqtNG/enLfffhuAJk2asHv3bqZOncqAAQPy1PZ/zZgxg+7du+e4niErc+bM4ZtvvuG7776jXr16bN++nZEjRxISEsLAgQPz3P7XX3/N4MGDqVChAq6urjRt2pQHHniArVu32iH6q/23R8swDLv1cuWX1NRU7rvvPqxWK1OmTLFbu7fccgvbt2/nzJkzfPbZZ/Tt25cNGzZQrly5PLW7ZcsWPvzwQ7Zu3eqwn3W/fv1st+vXr0/z5s0JCwtj8eLFeU4UMorne/XqxVNPPQVA48aNWbduHdOmTaNDhw55av9KM2fO5MEHH7Tr37qPPvqIv//+m0WLFhEWFsbq1asZNmwYwcHBee4Jd3d3Z968eTz88MMEBgbi6upK586dM/1Nza6srk3Oet+q56aQePLJJ1m0aBErVqygYsWKdm3bw8OD6tWr07x5c8aNG0ejRo348MMP89zuli1biImJoVmzZri5ueHm5saqVav46KOPcHNzIz093Q7RZ+br60uDBg2yXfGfleDg4KsSvDp16mRrlkVOHDt2jOXLl/PII4/Ytd3Ro0fz/PPPc99999GgQQP69+/PU089ZbdetGrVqrFq1SouXrzI8ePH2bhxI6mpqVSpUsUu7WfImP323097MTExV30qLMhSU1Pp27cv4eHhLFu2zG69NmD+v69evTqtW7dmxowZuLm5MWPGjDy3u2bNGmJiYggNDbW9h48dO8bTTz9N5cqV8x74NQQHBxMWFmaX93CZMmVwc3Nz+Pt4zZo17N+/367v4cTERF588UXef/997rzzTho2bMgTTzxBv379ePfdd+1yjmbNmtk+2EZFRbF06VLOnj2bo/fw9a5Nzn7fKrkp4AzD4IknnmD+/Pn8+eefdr9wXO+c9ugSvvXWW9m5cyfbt2+3fTVv3pwHH3yQ7du34+rqaodoM0tOTmbv3r0EBwfnua127dpdNbXxwIEDhIWF5bntK33xxReUK1eO22+/3a7tJiQk4OKS+S3u6upqt6ngGXx9fQkODubcuXP89ttv9OrVy67tV6lShfLly9tmk4FZW7Jq1Sratm1r13M5SkZic/DgQZYvX07p0qUdej57vYf79+/PP//8k+k9HBISwujRo/ntt9/sEOnVzp49y/Hjx+3yHvbw8KBFixYOfx/PmDGDZs2a2a3OCcz/M6mpqfnyHvb396ds2bIcPHiQzZs3Z+s9fKNrk7PftxqWsoOLFy9y6NAh2/3w8HC2b99OYGAgoaGheWr78ccf57vvvuOnn36iZMmStizY398fb2/vPLUN8OKLL9K9e3cqVarEhQsXmD17NitXrmTp0qV5brtkyZJX1Qb5+vpSunRpu9UMPfPMM9x5552EhoYSExPDm2++SXx8vF2GXZ566inatm3L22+/Td++fdm4cSPTp09n+vTpdojcZLVa+eKLLxg4cCBubvZ9O95555289dZbhIaGUq9ePbZt28b777/P4MGD7dL+b7/9hmEY1KpVi0OHDjF69Ghq1arF//73vxy3daP30MiRI3n77bepUaMGNWrU4O2338bHx4cHHnjALu3HxsYSERFhW3sm42JYvnz5bK2blFX7ISEh3HPPPWzdupVffvmF9PR02/s4MDAQDw+PXLddunRp3nrrLXr27ElwcDBnz55lypQpnDhxIttLCtzoZ/PfRMzd3Z3y5ctTq1atPLcfGBjI2LFjufvuuwkODubo0aO8+OKLlClThrvuussu8Y8ePZp+/frRvn17brnlFpYuXcrPP//MypUr89w2QHx8PHPnzuW9997LVrw5ab9Dhw6MHj0ab29vwsLCWLVqFbNmzeL999+3S/tz586lbNmyhIaGsnPnTkaMGEHv3r0zFQFfz42uTRlrmuXlfZsnDp+PVQysWLHCAK76GjhwYJ7bvla7gPHFF1/kuW3DMIzBgwcbYWFhhoeHh1G2bFnj1ltvNX7//Xe7tH0t9p4K3q9fPyM4ONhwd3c3QkJCjD59+hi7d++2W/s///yzUb9+fcPT09OoXbu2MX36dLu1bRiG8dtvvxmAsX//fru2axiGER8fb4wYMcIIDQ01vLy8jKpVqxovvfSSkZycbJf258yZY1StWtXw8PAwypcvbzz++OPG+fPnc9XWjd5DVqvVGDNmjFG+fHnD09PTaN++vbFz5067tf/FF19c8/tjxozJc/sZ08uv9bVixYo8tZ2YmGjcddddRkhIiOHh4WEEBwcbPXv2NDZu3Gi3n81/5XQqeFbtJyQkGF26dDHKli1ruLu7G6GhocbAgQONiIgIu8Y/Y8YMo3r16oaXl5fRqFEjY+HChXZr+9NPPzW8vb1z9X//Ru1HRUUZgwYNMkJCQgwvLy+jVq1axnvvvZft5SJu1P6HH35oVKxY0fazf/nll7P99yE716a8vm/zwnI5SBEREZEiQTU3IiIiUqQouREREZEiRcmNiIiIFClKbkRERKRIUXIjIiIiRYqSGxERESlSlNyIiIhIkaLkRkRERIoUJTcikqWjR49isVjYvn27s0Ox2bdvH61bt8bLy4vGjRs77Dxjx47NcfsdO3Zk5MiRWT7GYrGwcOHCXMclIllTciNSwA0aNAiLxcL48eMzHV+4cCEWi8VJUTnXmDFj8PX1Zf/+/fzxxx8OO88zzzzj0PZFxDGU3IgUAl5eXkyYMIFz5845OxS7SUlJyfVzDx8+zE033URYWJhDd9guUaKEw3fwzk95+ZmLFCZKbkQKgc6dO1O+fHnGjRt33cdcawhl0qRJVK5c2XZ/0KBB9O7dm7fffpugoCACAgJ47bXXSEtLY/To0QQGBlKxYkVmzpx5Vfv79u2jbdu2eHl5Ua9evat2Vd6zZw89evSgRIkSBAUF0b9/f86cOWP7fseOHXniiScYNWoUZcqU4bbbbrvm67Barbz++utUrFgRT09PGjdunGmXeovFwpYtW3j99dexWCyMHTv2mu107NiR4cOH8+yzzxIYGEj58uWvemxcXByPPvoo5cqVw8/Pj06dOrFjx47r/kzT0tIYPnw4AQEBlC5dmueee46BAwfSu3fvq15DVucFiIqKonv37nh7e1OlShXmzp2b6fs7d+6kU6dOeHt7U7p0aR599FEuXryY6fX9d/ird+/eDBo0yHa/cuXKvPnmmwwaNAh/f3+GDBlCSkoKTzzxBMHBwXh5eVG5cuUs/1+JFEZKbkQKAVdXV95++20+/vhjTpw4kae2/vzzT06ePMnq1at5//33GTt2LHfccQelSpViw4YNDB06lKFDh3L8+PFMzxs9ejRPP/0027Zto23btvTs2ZOzZ88C5oW6Q4cONG7cmM2bN7N06VJOnTpF3759M7Xx1Vdf4ebmxtq1a/n000+vGd+HH37Ie++9x7vvvss///xD165d6dmzJwcPHrSdq169ejz99NNERUXxzDPPXPe1fvXVV/j6+rJhwwYmTpzI66+/zrJlywAwDIPbb7+d6OholixZwpYtW2jatCm33norsbGx12xvwoQJfPvtt3zxxResXbuW+Pj4a9bOZHXeDK+88gp33303O3bs4KGHHuL+++9n7969ACQkJNCtWzdKlSrFpk2bmDt3LsuXL+eJJ5647mu9nnfeeYf69euzZcsWXnnlFT766CMWLVrEDz/8wP79+/nmm28yJcAiRUK+7D0uIrk2cOBAo1evXoZhGEbr1q2NwYMHG4ZhGAsWLDCufAuPGTPGaNSoUabnfvDBB0ZYWFimtsLCwoz09HTbsVq1ahk333yz7X5aWprh6+trfP/994ZhGEZ4eLgBGOPHj7c9JjU11ahYsaIxYcIEwzAM45VXXjG6dOmS6dzHjx83AGP//v2GYRhGhw4djMaNG9/w9YaEhBhvvfVWpmMtWrQwhg0bZrvfqFEjY8yYMVm206FDB+Omm266qp3nnnvOMAzD+OOPPww/Pz8jKSkp02OqVatmfPrpp4ZhXP0zDQoKMt555x3b/bS0NCM0NNT2+8nOeQ3DMABj6NChmR7TqlUr47HHHjMMwzCmT59ulCpVyrh48aLt+4sXLzZcXFyM6Oho23lGjBiRqY1evXoZAwcOtN0PCwszevfunekxTz75pNGpUyfDarUaIkWVem5ECpEJEybw1VdfsWfPnly3Ua9ePVxc/n3rBwUF0aBBA9t9V1dXSpcuTUxMTKbntWnTxnbbzc2N5s2b23oatmzZwooVKyhRooTtq3bt2oBZH5OhefPmWcYWHx/PyZMnadeuXabj7dq1s50rJxo2bJjpfnBwsO11bdmyhYsXL1K6dOlMcYeHh2eKOUNcXBynTp2iZcuWtmOurq40a9YsR+fNcOXPM+N+xmvcu3cvjRo1wtfX1/b9du3aYbVa2b9/f3Zeus1/f+aDBg1i+/bt1KpVi+HDh/P777/nqD2RwsDN2QGISPa1b9+erl278uKLL2aqrQBwcXHBMIxMx1JTU69qw93dPdN9i8VyzWNWq/WG8WTM1rJardx5551MmDDhqscEBwfbbl95sc5OuxkMw8jVzLCsXpfVaiU4OPiq2iGAgICAHMWWk/NmJaPtrF5vxvHs/r7/+zNv2rQp4eHh/Prrryxfvpy+ffvSuXNnfvzxxxvGJ1JYqOdGpJAZP348P//8M+vWrct0vGzZskRHR2e64NlzbZq///7bdjstLY0tW7bYemeaNm3K7t27qVy5MtWrV8/0ld2EBsDPz4+QkBD++uuvTMfXrVtHnTp17PNCLmvatCnR0dG4ubldFXOZMmWuery/vz9BQUFs3LjRdiw9PZ1t27bl6vxX/jwz7mf8POvWrcv27du5dOmS7ftr167FxcWFmjVrAubvOyoqKlMsu3btyta5/fz86NevH5999hlz5sxh3rx5160zEimMlNyIFDINGjTgwQcf5OOPP850vGPHjpw+fZqJEydy+PBhPvnkE3799Ve7nfeTTz5hwYIF7Nu3j8cff5xz584xePBgAB5//HFiY2O5//772bhxI0eOHOH3339n8ODBpKen5+g8o0ePZsKECcyZM4f9+/fz/PPPs337dkaMGGG31wLmDLQ2bdrQu3dvfvvtN44ePcq6det4+eWX2bx58zWf8+STTzJu3Dh++ukn9u/fz4gRIzh37lyuepXmzp3LzJkzOXDgAGPGjGHjxo22guEHH3wQLy8vBg4cyK5du1ixYgVPPvkk/fv3JygoCIBOnTqxePFiFi9ezL59+xg2bBjnz5+/4Xk/+OADZs+ezb59+zhw4ABz586lfPnyWfZWiRQ2Sm5ECqE33njjqiGJOnXqMGXKFD755BMaNWrExo0bs5xJlFPjx49nwoQJNGrUiDVr1vDTTz/ZejhCQkJYu3Yt6enpdO3alfr16zNixAj8/f0z1fdkx/Dhw3n66ad5+umnadCgAUuXLmXRokXUqFHDbq8FzOGdJUuW0L59ewYPHkzNmjW57777OHr0qC2B+K/nnnuO+++/nwEDBtCmTRtKlChB165d8fLyyvH5X3vtNWbPnk3Dhg356quv+Pbbb6lbty4APj4+/Pbbb8TGxtKiRQvuuecebr31ViZPnmx7/uDBgxk4cCADBgygQ4cOVKlShVtuueWG5y1RogQTJkygefPmtGjRgqNHj7JkyZIc/55ECjKLca0BYxERuSGr1UqdOnXo27cvb7zxhrPDEZHLVFAsIpJNx44d4/fff6dDhw4kJyczefJkwsPDeeCBB5wdmohcQf2QIiLZ5OLiwpdffkmLFi1o164dO3fuZPny5XYvdhaRvNGwlIiIiBQp6rkRERGRIkXJjYiIiBQpSm5ERESkSFFyIyIiIkWKkhsREREpUpTciIiISJGi5EZERESKFCU3IiIiUqT8PyKM6pQzJIRLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbours,train_scores,label='Train score')\n",
    "plt.plot(neighbours,test_scores,label='Test score')\n",
    "plt.xlabel(\"Number of neighbours\")\n",
    "plt.xticks(np.arange(1,21))\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "print(f\"Maximum KNN score on the test data:{max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94c75fb",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune LogisticRegression and RandomForestClassifier using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc3826a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a hyperparameter grid for logisticregression\n",
    "log_reg_grid={\"C\":np.logspace(-4,4,20),\"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)\n",
    "         }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7b5381e5",
   "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;}#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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tune LogisticRegression\n",
    "np.random.seed(36)\n",
    "\n",
    "#Setup random hyperparamter for logistic regression\n",
    "rs_log_reg=RandomizedSearchCV(LogisticRegression(),param_distributions=log_reg_grid,cv=5,n_iter=20,verbose=True)\n",
    "\n",
    "#Fit random hyperparamter search model for logisticRegression\n",
    "rs_log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5974cfc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b947e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8360655737704918"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498bb401",
   "metadata": {},
   "source": [
    "We've tuned LogisticRegression,let's do the same for RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2200df04",
   "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;}#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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Setup random seed\n",
    "np.random.seed(36)\n",
    "\n",
    "#Setup random hyperparamter search for RandomForestClassifier\n",
    "rs_rf=RandomizedSearchCV(RandomForestClassifier(),\n",
    "                         param_distributions=rf_grid,\n",
    "                         cv=5,\n",
    "                         n_iter=20,\n",
    "                         verbose=True)\n",
    "#Fit random hyperparamter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "62ff194c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 310,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 11,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4cd191b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate the RandomizedSearch model \n",
    "rs_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a86a542e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic_Regression': 0.7049180327868853,\n",
       " 'K-neighbours_classifier': 0.7049180327868853,\n",
       " 'Random_Forest': 0.819672131147541}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b73747f",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning using GridSearchCV\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "57a904c2",
   "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;}#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": 37,
     "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 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 hyperparamter gridsearch model\n",
    "gs_log_reg.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c3e3b41f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.38566204211634725, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "de87a9f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate the GridSearch logisticregression model\n",
    "gs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "66b99b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic_Regression': 0.7049180327868853,\n",
       " 'K-neighbours_classifier': 0.7049180327868853,\n",
       " 'Random_Forest': 0.819672131147541}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646876df",
   "metadata": {},
   "source": [
    "## Evaluating our tuned machine learning classifier,beyond accuracy\n",
    "\n",
    "* ROC Curve and AUC curve\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 wherever possible\n",
    "\n",
    "To make comparisons and evaluate our trained models,we first need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39b20dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with tuned model\n",
    "y_preds=gs_log_reg.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2e811d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4c319b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12     1\n",
       "268    0\n",
       "132    1\n",
       "28     1\n",
       "49     1\n",
       "      ..\n",
       "163    1\n",
       "102    1\n",
       "301    0\n",
       "65     1\n",
       "73     1\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb06ee1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(confusion_matrix(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3018b206",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "def plot_conf_mat(y_test,y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap\n",
    "    \n",
    "    \"\"\"\n",
    "    fig,ax=plt.subplots(figsize=(3,3))\n",
    "    ax=sns.heatmap(confusion_matrix(y_test,y_preds),\n",
    "                   annot=True,\n",
    "                   cbar=False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "plot_conf_mat(y_test,y_preds)   \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98006e6",
   "metadata": {},
   "source": [
    "Let's now get a classification report and as well as a cross-validated precision recall,f1-score and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbe1bfd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab6b87d",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate precision,recall and f1-score of our model using cross-validation and to do so we'll be using cross_val_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3969a08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check best hyperparamters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8051cf77",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a new classifier with best parameters\n",
    "clf=LogisticRegression(C=0.38566204211634725, solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bc37201",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross-validated accuracy\n",
    "cv_acc=cross_val_score(clf,X,y,cv=5,scoring=\"accuracy\")\n",
    "cv_acc=np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c016bb84",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross-validated precision\n",
    "cv_precision=cross_val_score(clf,X,y,cv=5,scoring=\"precision\")\n",
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a4466fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross-validated recall\n",
    "cv_recall=cross_val_score(clf,X,y,cv=5,scoring=\"recall\")\n",
    "cv_recall=np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99c1ccfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cross-validated f1 score\n",
    "cv_f1=cross_val_score(clf,X,y,cv=5,scoring=\"f1\")\n",
    "cv_f1=np.mean(cv_f1)\n",
    "cv_f1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1014aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualize cross_validated metrics\n",
    "cv_metrics=pd.DataFrame({\"Accuracy\":cv_acc,\n",
    "                        \"Precision\":cv_precision,\n",
    "                        \"Recall\":cv_recall,\n",
    "                        \"F1\":cv_f1},\n",
    "                       index=[0])\n",
    "cv_metrics.T.plot.bar(legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e542b55d",
   "metadata": {},
   "source": [
    "### Feature importance\n",
    "\n",
    "Studying about which features contributed the most to our model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5f78e5",
   "metadata": {},
   "source": [
    "Let's find the feature importance for our logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "144431e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit an instance of logistic regression\n",
    "clf=LogisticRegression(C=0.38566204211634725,solver='liblinear')\n",
    "clf.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3a7967e",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdfd69dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Match coef's of features to columns\n",
    "feature_dict=dict(zip(df.columns,list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b9a2f36",
   "metadata": {},
   "outputs": [],
   "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": null,
   "id": "20f0927d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.crosstab(df['sex'],df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b2203a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.crosstab(df['slope'],df['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334cac58",
   "metadata": {},
   "source": [
    "## 6.Experimentation\n",
    "\n",
    "If evaluation metrics hasn't been reached yet, the following questions arise\n",
    "\n",
    "* Could you collect more data\n",
    "* Could you try a better model(Like catboost or XGboost)\n",
    "* Could you improve our current models(beyond what we've done so far)\n",
    "\n",
    "* If your model is good enough(you have hit your evauation metric),how can you export it and share it with others?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7850f75",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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