project-heartdisease.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "## preparing the tools\n",
    "we're going to use pandas , matplotlib, numpy for data analysis and manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e385cdc4-b59e-42b0-87b7-4db84498009d",
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   "outputs": [],
   "source": [
    "# import all tools we need \n",
    "\n",
    "# regular EDA(exploratory data analysis) and plotting librariess\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#we want our plots to appear inside the notebook\n",
    "%matplotlib inline  \n",
    "# import from sklearn\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "## model evalutions\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70cdc7d0-e506-48ca-be0e-dfb6d9211550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df  = pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e686d088-98b3-4083-9241-b6ae30af6846",
   "metadata": {},
   "source": [
    "## data exploration (exploratory data analysis or EDA)\n",
    "\n",
    "the gole here is to find more about the data and become a subject matter export on the dataset you're working with\n",
    "\n",
    "1] what kind question are you trying to solve?\n",
    "2] what lind of sdata do we have and how do we trat diffrent types?\n",
    "3]what's misssing from data and how do you dael with it \n",
    "4]where are the outliers and why should you care about them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4438f349-5dee-479c-a6b9-ed271d364b66",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2910306a-86ee-4872-837a-b8788db9d810",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "083cb05f-1f6d-42d0-bfae-21f893384232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets find out how many of each class there\n",
    "\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8eb13c9-cb94-4d39-84b0-a4355c92989e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='target'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "df[\"target\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eab0e48a-c4cb-4021-bdab-c8d8a518480d",
   "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": 8,
   "id": "9ab25223-6c99-4a50-9af0-f0e62ec7c40f",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc63d4e9-82f1-4f62-88c1-1af729d33053",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "801912f9-9f16-4a0d-a748-7787a213df87",
   "metadata": {},
   "source": [
    "### heart disease frequency accordind to sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb512ee0-d470-4647-a573-e17166d67b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    207\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3e37515-153c-4a28-8546-ae9543e52f4d",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare trarget coloum with sex coloum\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6454790-8c2d-4331-b2a3-c4cba0a04d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, '0'), Text(1, 0, '1')])"
      ]
     },
     "execution_count": 12,
     "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": [
    "## create a plot of crosstab\n",
    "\n",
    "pd.crosstab(df.target, df.sex).plot(kind= \"bar\", color = [\"salmon\", \"lightblue\"])\n",
    "plt.title(\"heart disease frequency for sex\")\n",
    "plt.xlabel(\"0 = no diesease, 1 = diesease\")\n",
    "plt.ylabel(\"amount\")\n",
    "plt.legend([\"female\", \"male\"])\n",
    "plt.xticks(rotation = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cbb6d9e5-6481-4212-bc94-d970b6b88ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thalach\n",
       "162    11\n",
       "160     9\n",
       "163     9\n",
       "152     8\n",
       "173     8\n",
       "       ..\n",
       "202     1\n",
       "184     1\n",
       "121     1\n",
       "192     1\n",
       "90      1\n",
       "Name: count, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"thalach\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f91de74-a5d6-4909-9c73-5df7b48effaa",
   "metadata": {},
   "source": [
    "## age vs max heart rate for heart disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c9ae5f6-f08b-45b3-af7c-c08cdf03e25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1230f3c4d90>"
      ]
     },
     "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": [
    "## creat another figure\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# scatter with positive examples\n",
    "plt.scatter(df.age[df.target==1], \n",
    "           df.thalach[df.target==1],\n",
    "           c=\"salmon\")\n",
    "\n",
    "# scater  with negetive example\n",
    "plt.scatter(df.age[df.target ==0],\n",
    "           df.thalach[df.target==0],\n",
    "           c=\"lightblue\")\n",
    "\n",
    "#3 add some helpful info\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": "bcd682b1-4b70-4ae5-9c39-daf33a7a4940",
   "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 coloumn with a histogram\n",
    "\n",
    "df.age.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c081b5a1-6a8c-4bd8-b81a-e86de8a8e82d",
   "metadata": {},
   "source": [
    "## heart disease frequency per chest pain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c70fe51-ed87-4097-83f3-1f82f693f8e0",
   "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": "f8a6cad6-0595-4431-84d1-c81cb845fb6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]),\n",
       " [Text(0, 0, '0'), Text(1, 0, '1'), Text(2, 0, '2'), Text(3, 0, '3')])"
      ]
     },
     "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 crossstab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                  figsize = (10, 6),\n",
    "                                  color = [\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"heart disease frequency per chest pain\")\n",
    "plt.xlabel(\"chest pain type\")\n",
    "plt.ylabel(\"amount\")\n",
    "plt.legend([\"no disease\", \"disease\"])\n",
    "plt.xticks(rotation = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b1e77f0b-898c-4483-9d31-9337d93f39f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7fd9376d-8099-469c-8092-2a4d8806ace9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.5, -0.5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets make our correlation matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig,ax = plt.subplots(figsize= (15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot=True,\n",
    "                linewidths=0.5,\n",
    "                fmt=\".2f\",\n",
    "                cmap=\"YlGnBu\")\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom+0.5, top-0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5f19aa7-3b67-4fa7-9dad-f1a987756e9a",
   "metadata": {},
   "source": [
    "## 5. modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e120476b-6efa-4689-ab94-9bbb1777756e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7d3149d-8dc3-482f-8b22-3c21150d5b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "## split data into x and y\n",
    "x = df.drop(\"target\",axis=1 )\n",
    "y = df[\"target\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b10d39f-c855-419b-86b3-8304fc8bf852",
   "metadata": {},
   "outputs": [
    {
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       "      <th>298</th>\n",
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       "      <th>299</th>\n",
       "      <td>45</td>\n",
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       "      <td>110</td>\n",
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "x"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "559c4815-9262-48ad-9974-2adcb35d3c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
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    "y"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f741bea-9f07-4722-8e5a-6da4d698c6bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into train and test sets\n",
    "np.random.seed(42)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4c01dbca-e30c-4b25-949c-07e0dbf6a9e6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>131</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>63</td>\n",
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       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1e3169ad-faab-4672-afd7-61a2a40f63d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132    1\n",
       " 202    0\n",
       " 196    0\n",
       " 75     1\n",
       " 176    0\n",
       "       ..\n",
       " 188    0\n",
       " 71     1\n",
       " 106    1\n",
       " 270    0\n",
       " 102    1\n",
       " Name: target, Length: 242, dtype: int64,\n",
       " 242)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22280a8e-7e40-4ac8-986e-32c577ffa3f9",
   "metadata": {},
   "source": [
    "now we got our data split into taining and test sets, its time to build a machine learning model.\n",
    "\n",
    "we'll train it(find patterns) on the training set.\n",
    "\n",
    "and we'll test it(use the patterns )on the test set\n",
    "\n",
    "we're going to try 3 diffrent machine learning models:\n",
    "    1. Logistic regression\n",
    "    2. K-nearest Neighbours classifier\n",
    "    3. Random forest Classifier\n",
    "    4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "414b6d66-1fea-4328-b03f-d00829ac025e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"Random Forest\": RandomForestClassifier()\n",
    "         }\n",
    "def fit_and_score(models,x_train, x_test, y_train, y_test ):\n",
    "    \"\"\"\n",
    "    fits and evalutes given machine learning model.\n",
    "    models : a dict of diffrent sckit-learn machine learning model\n",
    "    x_train : training data(no labels)\n",
    "    x_test ; testing data(no lables)\n",
    "    y_train : training lables\n",
    "    y_test: test lables\n",
    "    \"\"\"\n",
    "    # set random seed\n",
    "    np.random.seed(42)\n",
    "    model_scores={}\n",
    "    # loop through model\n",
    "    for name, model in models.items():\n",
    "        # fit the model to the data\n",
    "        model.fit(x_train, y_train)\n",
    "        #evalute the model and append its score to model_score\n",
    "        model_scores[name] = model.score(x_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3d95015f-7823-40db-9b70-5ba7bfe2c6fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models = models,\n",
    "                            x_train = x_train,\n",
    "                            x_test = x_test,\n",
    "                            y_train = y_train, \n",
    "                            y_test =  y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85fb88bd-8915-401c-9d2c-28838fe4bcdb",
   "metadata": {},
   "source": [
    "## model comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a8cc6f0c-5c8b-4d87-a478-772a5521bfae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j3t7eatiwoZKSkhxjNptNSUlJio6OzveYZs2a6ZdffpHNZnOMHTx4UMHBwfkWEQAA4F4KfJomNjZWc+fO1fvvv699+/bp+eefV1ZWluPqmp49e2rkyJGO/Z9//nllZmZq6NChOnjwoFavXq2JEydq8ODBt++rAAAAxVaBL+3t1q2bMjIyFBcXp9TUVEVFRSkxMdGxqPX48ePy8Pi944SGhmrt2rUaPny46tWrp5CQEA0dOlSvvvrq7fsqAABAsVXgMiJJQ4YM0ZAhQ/LdtnHjxjxj0dHR+vbbb2/lrQAAgIvj2TQAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAo26pjMyaNUthYWHy9fVVkyZNtHXr1ps6btGiRbJYLOrUqdOtvC0AAHBBBS4jixcvVmxsrOLj47Vjxw5FRkaqXbt2Sk9Pv+FxR48e1csvv6wHHnjglsMCAADXU+AyMmXKFPXv3199+vRR3bp1NXv2bPn5+Wn+/PnXPcZqtepvf/ubxo0bp+rVq/+pwAAAwLUUqIzk5uZq+/btiomJ+f0TeHgoJiZGycnJ1z1u/PjxqlSpkvr27XtT75OTk6MLFy44vQAAgGsqUBk5c+aMrFarAgMDncYDAwOVmpqa7zFff/215s2bp7lz5970+yQkJMjf39/xCg0NLUhMAABQjNzRq2kuXryoHj16aO7cuapYseJNHzdy5EidP3/e8UpJSbmDKQEAgEklCrJzxYoV5enpqbS0NKfxtLQ0BQUF5dn/0KFDOnr0qB577DHHmM1mu/bGJUrowIEDuvvuu/Mc5+PjIx8fn4JEAwAAxVSBZka8vb3VsGFDJSUlOcZsNpuSkpIUHR2dZ//atWvrxx9/1K5duxyvDh066KGHHtKuXbs4/QIAAAo2MyJJsbGx6tWrlxo1aqTGjRtr2rRpysrKUp8+fSRJPXv2VEhIiBISEuTr66vw8HCn4wMCAiQpzzgAAHBPBS4j3bp1U0ZGhuLi4pSamqqoqCglJiY6FrUeP35cHh7c2BUAANycApcRSRoyZIiGDBmS77aNGzfe8Nj33nvvVt4SAAC4KKYwAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFG3VEZmzZqlsLAw+fr6qkmTJtq6det19507d64eeOABlStXTuXKlVNMTMwN9wcAAO6lwGVk8eLFio2NVXx8vHbs2KHIyEi1a9dO6enp+e6/ceNGde/eXV999ZWSk5MVGhqqtm3b6uTJk386PAAAKP4KXEamTJmi/v37q0+fPqpbt65mz54tPz8/zZ8/P9/9P/roIw0aNEhRUVGqXbu23nnnHdlsNiUlJf3p8AAAoPgrUBnJzc3V9u3bFRMT8/sn8PBQTEyMkpOTb+pzZGdn68qVKypfvvx198nJydGFCxecXgAAwDUVqIycOXNGVqtVgYGBTuOBgYFKTU29qc/x6quvqnLlyk6F5r8lJCTI39/f8QoNDS1ITAAAUIwU6tU0kyZN0qJFi7R8+XL5+vped7+RI0fq/PnzjldKSkohpgQAAIWpREF2rlixojw9PZWWluY0npaWpqCgoBse+7//+7+aNGmSNmzYoHr16t1wXx8fH/n4+BQkGgAAKKYKNDPi7e2thg0bOi0+/W0xanR09HWPmzx5siZMmKDExEQ1atTo1tMCAACXU6CZEUmKjY1Vr1691KhRIzVu3FjTpk1TVlaW+vTpI0nq2bOnQkJClJCQIEl64403FBcXp4ULFyosLMyxtqR06dIqXbr0bfxSAABAcVTgMtKtWzdlZGQoLi5OqampioqKUmJiomNR6/Hjx+Xh8fuEy9tvv63c3Fw98cQTTp8nPj5eY8eO/XPpAQBAsVfgMiJJQ4YM0ZAhQ/LdtnHjRqePjx49eitvAQAA3ATPpgEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYdUtlZNasWQoLC5Ovr6+aNGmirVu33nD/JUuWqHbt2vL19VVERITWrFlzS2EBAIDrKXAZWbx4sWJjYxUfH68dO3YoMjJS7dq1U3p6er77b9myRd27d1ffvn21c+dOderUSZ06ddKePXv+dHgAAFD8FbiMTJkyRf3791efPn1Ut25dzZ49W35+fpo/f36++0+fPl0PP/yw/ud//kd16tTRhAkT1KBBA82cOfNPhwcAAMVfiYLsnJubq+3bt2vkyJGOMQ8PD8XExCg5OTnfY5KTkxUbG+s01q5dO61YseK675OTk6OcnBzHx+fPn5ckXbhwoSBxC50tJ9t0BJdR1L/XxQU/k7cPP5O3Dz+Xt0dx+Jn8LaPdbr/hfgUqI2fOnJHValVgYKDTeGBgoPbv35/vMampqfnun5qaet33SUhI0Lhx4/KMh4aGFiQuijH/aaYTAM74mURRU5x+Ji9evCh/f//rbi9QGSksI0eOdJpNsdlsyszMVIUKFWSxWAwmK94uXLig0NBQpaSkqGzZsqbjAJL4uUTRw8/k7WO323Xx4kVVrlz5hvsVqIxUrFhRnp6eSktLcxpPS0tTUFBQvscEBQUVaH9J8vHxkY+Pj9NYQEBAQaLiBsqWLcsfMBQ5/FyiqOFn8va40YzIbwq0gNXb21sNGzZUUlKSY8xmsykpKUnR0dH5HhMdHe20vyStX7/+uvsDAAD3UuDTNLGxserVq5caNWqkxo0ba9q0acrKylKfPn0kST179lRISIgSEhIkSUOHDtWDDz6oN998U48++qgWLVqkbdu2ac6cObf3KwEAAMVSgctIt27dlJGRobi4OKWmpioqKkqJiYmORarHjx+Xh8fvEy5NmzbVwoULNXr0aL322muqWbOmVqxYofDw8Nv3VeCm+Pj4KD4+Ps8pMMAkfi5R1PAzWfgs9j+63gYAAOAO4tk0AADAKMoIAAAwijICAACMoowAAACjKCMAALe2efNmXb16Nc/41atXtXnzZgOJ3A9X07iwrKwsTZo0SUlJSUpPT5fNZnPafvjwYUPJAKDo8PT01OnTp1WpUiWn8bNnz6pSpUqyWq2GkrmPIvlsGtwe/fr106ZNm9SjRw8FBwfzXB8AyIfdbs/378ezZ8+qVKlSBhK5H8qIC/viiy+0evVqNWvWzHQUQJK0YMGCm9qvZ8+edzgJIHXp0kWSZLFY1Lt3b6ebnFmtVv3www9q2rSpqXhuhTLiwsqVK6fy5cubjgE4DB069LrbLBaLsrKydPXqVcoICsVvD3Cz2+0qU6aMSpYs6djm7e2t+++/X/379zcVz62wZsSFffjhh1q5cqXef/99+fn5mY4DXNfp06c1btw4zZ8/X61atVJiYqLpSHAj48aN08svv8wpGYMoIy6sfv36OnTokOx2u8LCwuTl5eW0fceOHYaSAddcvHhRb7zxhqZPn657771XCQkJeuihh0zHgpv59ddfZbfbHf9oO3bsmJYvX666deuqbdu2htO5B07TuLBOnTqZjgDk68qVK3rrrbc0ceJEVahQQe+++66eeOIJ07Hgpjp27KguXbpo4MCBOnfunBo3bixvb2+dOXNGU6ZM0fPPP286ostjZgRAobHb7VqwYIHi4uJ09epVxcfHq2/fvvL09DQdDW6sYsWK2rRpk+6991698847euutt7Rz5059+umniouL0759+0xHdHnMjLiB7du3O/4w3Xvvvapfv77hRHBX9erV0+HDh/XCCy9o2LBh8vPzU1ZWVp79ypYtayAd3FV2drbKlCkjSVq3bp26dOkiDw8P3X///Tp27JjhdO6BmREXlp6erqeeekobN25UQECAJOncuXN66KGHtGjRIt11111mA8LteHj8ftPn/O7r8Nv9HrjJFApTvXr11K9fP3Xu3Fnh4eFKTExUdHS0tm/frkcffVSpqammI7o8ZkZc2AsvvKCLFy9q7969qlOnjiTpp59+Uq9evfTiiy/q448/NpwQ7uarr74yHQHIIy4uTk8//bSGDx+uVq1aKTo6WtK1WRJmkgsHMyMuzN/fXxs2bNB9993nNL5161a1bdtW586dMxMMAIqY1NRUnT59WpGRkY4ZvK1bt6ps2bKqXbu24XSujwfluTCbzZbncl5J8vLyyvOcGgBwZ0FBQSpTpozWr1+vX3/9VZJ03333UUQKCWXEhbVq1UpDhw7VqVOnHGMnT57U8OHD1bp1a4PJ4K48PDzk6el5w1eJEpw9RuE6e/asWrdurXvuuUft27fX6dOnJUl9+/bVSy+9ZDide+BPvQubOXOmOnTooLCwMIWGhkqSUlJSFB4erg8//NBwOrij5cuXX3dbcnKyZsyYwawdCt3w4cPl5eWl48ePO9bXSVK3bt0UGxurN99802A698CaERdnt9u1YcMG7d+/X5JUp04dxcTEGE4F/O7AgQMaMWKEVq1apb/97W8aP368qlatajoW3EhQUJDWrl2ryMhIlSlTRrt371b16tV1+PBh1atXT5cuXTId0eUxM+LiLBaL2rRpozZt2piOAjg5deqU4uPj9f7776tdu3batWuXwsPDTceCG8rKysr3+V2ZmZlOT/LFnUMZcTEzZszQgAED5OvrqxkzZtxw3xdffLGQUgG/O3/+vCZOnKi33npLUVFRSkpK0gMPPGA6FtzYAw88oAULFmjChAmSrv0jzmazafLkyTwrqZBwmsbFVKtWTdu2bVOFChVUrVq16+5nsVh0+PDhQkwGSJMnT9Ybb7yhoKAgTZw4UR07djQdCdCePXvUunVrNWjQQF9++aU6dOigvXv3KjMzU998843uvvtu0xFdHmUEQKHx8PBQyZIlFRMTc8Pn0SxbtqwQUwHXZuxmzpyp3bt369KlS2rQoIEGDx6s4OBg09HcAqdp3IjVatWPP/6oqlWrqly5cqbjwA316tXrD/e5ePFiISQBrrly5YoefvhhzZ49W6NGjTIdx21RRlzYsGHDFBERob59+8pqtapFixZKTk6Wn5+fPv/8c7Vs2dJ0RLiZevXqafjw4dfdfvHiRT388MOFmAjuzsvLSz/88IPpGG6Pm565sKVLlyoyMlKStGrVKh09elT79+/X8OHD+RcAjHjttde0YMGCfLdlZWXpkUce0dmzZws5FdzdM888o3nz5pmO4daYGXFhZ86cUVBQkCRpzZo1evLJJ3XPPffo2Wef1fTp0w2ngzv64IMP1KNHDwUEBKhDhw6O8UuXLunhhx9Wenq6Nm7caC4g3NLVq1c1f/58bdiwQQ0bNlSpUqWctk+ZMsVQMvdBGXFhgYGB+umnnxQcHKzExES9/fbbkqTs7OwbLh4E7pQnnnhC586dU/fu3bV69Wq1bNnSMSOSlpamTZs2qXLlyqZjws3s2bNHDRo0kCQdPHjQaZvFYjERye1QRlxYnz591LVrVwUHB8tisTjuvPrdd9/x8CcY069fP2VmZqpjx45auXKl4uLidOrUKYoIjPnqq69MR3B7lBEXNnbsWIWHhyslJUVPPvmk406Cnp6eGjFihOF0cGevvPKKMjMz1bp1a4WFhWnjxo2qUqWK6ViATpw4IUn8PBYy7jPiZs6dO6eAgADTMeCmunTp4vTxmjVrFBkZqZCQEKdx7jOCwmSz2fT3v/9db775puM5NGXKlNFLL72kUaNGycODaz3uNGZGXNgbb7yhsLAwdevWTZLUtWtXffrppwoODtaaNWtUr149wwnhbvz9/Z0+7t69u6EkwO9GjRqlefPmadKkSWrWrJkk6euvv9bYsWN1+fJlvf7664YTuj5mRlxYtWrV9NFHH6lp06Zav369unbtqsWLF+uTTz7R8ePHtW7dOtMRAcC4ypUra/bs2U5XeEnSypUrNWjQIJ08edJQMvfBzIgLS01NVWhoqCTp888/V9euXdW2bVuFhYWpSZMmhtMBQNGQmZmZ76L+2rVrKzMz00Ai98OJMBdWrlw5paSkSJISExMdV9PY7XZZrVaT0QCgyIiMjNTMmTPzjM+cOdNx40jcWcyMuLAuXbro6aefVs2aNXX27Fk98sgjkqSdO3eqRo0ahtMBQNEwefJkPfroo9qwYYOio6MlScnJyUpJSdGaNWsMp3MPrBlxYVeuXNH06dOVkpKi3r17q379+pKkqVOnqkyZMurXr5/hhABQNJw6dUqzZs3S/v37JUl16tTRoEGDuPdNIaGMAADc0uHDh1WtWjXusloEsGbExX3wwQdq3ry5KleurGPHjkmSpk2bppUrVxpOBgBm1axZUxkZGY6Pu3XrprS0NIOJ3BdlxIW9/fbbio2N1SOPPKJz5845Fq0GBARo2rRpZsMBgGH/fWJgzZo1ysrKMpTGvVFGXNhbb72luXPnatSoUU4PxmvUqJF+/PFHg8kAAPgdZcSFHTlyxLFo9T/5+PjQ/gG4PYvFkme9COtHzODSXhdWrVo17dq1S1WrVnUaT0xMVJ06dQylAoCiwW63q3fv3o6HiF6+fFkDBw5UqVKlnPbjWUl3HmXEhcXGxmrw4MG6fPmy7Ha7tm7dqo8//lgJCQl65513TMcDAKN69erl9PEzzzxjKAm4tNfFffTRRxo7dqwOHTok6dozGMaNG6e+ffsaTgYAwDWUERd19epVLVy4UO3atVNgYKCys7N16dIlVapUyXQ0AACcUEZcmJ+fn/bt25dnzQgAAEUJV9O4sMaNG2vnzp2mYwAAcEMsYHVhgwYN0ksvvaQTJ06oYcOGeVaI16tXz1AyAAB+x2kaF+bhkXfiy2KxyG63y2KxOO7ICgCAScyMuLAjR46YjgAAxcKpU6f09ddfKz09XTabzWnbiy++aCiV+2BmBADg1t577z0999xz8vb2VoUKFZzuwmqxWHT48GGD6dwDZcSFffbZZ/mOWywW+fr6qkaNGqpWrVohpwKAoiU0NFQDBw7UyJEj8z29jTuPMuLCPDw8HGtE/tN/rhtp3ry5VqxYoXLlyhlKCQBmVahQQVu3btXdd99tOorbogK6sPXr1+u+++7T+vXrdf78eZ0/f17r169XkyZN9Pnnn2vz5s06e/asXn75ZdNRAcCYvn37asmSJaZjuDVmRlxYeHi45syZo6ZNmzqNf/PNNxowYID27t2rDRs26Nlnn9Xx48cNpQQAs6xWq/7617/q119/VUREhLy8vJy2T5kyxVAy98HVNC7s0KFDKlu2bJ7xsmXLOhZk1axZU2fOnCnsaABQZCQkJGjt2rWqVauWJOVZwIo7j5kRF9a8eXOVKVNGCxYs0F133SVJysjIUM+ePZWVlaXNmzdrw4YNGjx4sA4cOGA4LQCYUa5cOU2dOlW9e/c2HcVtMTPiwubNm6eOHTuqSpUqCg0NlSSlpKSoevXqWrlypSTp0qVLGj16tMmYAGCUj4+PmjVrZjqGW2NmxMXZbDatW7dOBw8elCTVqlVLbdq04fI1APj/EhISdPr0ac2YMcN0FLdFGXETly9flo+PD+c/AeC/dO7cWV9++aUqVKige++9N88C1mXLlhlK5j7457ELs9lsmjBhgkJCQlS6dGnH7eHHjBmjefPmGU4HAEVDQECAunTpogcffFAVK1aUv7+/0wt3HjMjLmz8+PF6//33NX78ePXv31979uxR9erVtXjxYk2bNk3JycmmIwIAQBlxZTVq1NA///lPtW7dWmXKlNHu3btVvXp17d+/X9HR0fr3v/9tOiIAFBkZGRmOKwtr1arluAoRdx6naVzYyZMnVaNGjTzjNptNV65cMZAIAIqerKwsPfvsswoODlaLFi3UokULVa5cWX379lV2drbpeG6BMuLC6tatq3/96195xpcuXar69esbSAQARU9sbKw2bdqkVatW6dy5czp37pxWrlypTZs26aWXXjIdzy1wnxEXFhcXp169eunkyZOy2WxatmyZDhw4oAULFujzzz83HQ8AioRPP/1US5cuVcuWLR1j7du3V8mSJdW1a1e9/fbb5sK5CWZGXFjHjh21atUqbdiwQaVKlVJcXJz27dunVatWqU2bNqbjAUCRkJ2drcDAwDzjlSpV4jRNIWEBq5vatm2bGjVqZDoGABjXunVrVahQQQsWLJCvr68k6ddff1WvXr2UmZmpDRs2GE7o+igjLuzSpUvy9PRUyZIlHWO7du3SmDFjtGbNGlmtVoPpAKBo2LNnj9q1a6ecnBxFRkZKknbv3i1fX1+tXbtW9957r+GEro/TNC4oJSVF0dHRjhv2xMbGKjs7Wz179lSTJk1UqlQpbdmyxXRMACgSwsPD9fPPPyshIUFRUVGKiorSpEmT9PPPP1NECgkzIy7oqaee0oEDB9S3b18tW7ZMmzZtUoMGDdSkSRONGDFCVapUMR0RAAAHyogLqly5spYtW6b7779f6enpCgoK0pQpUzRs2DDT0QCgSPjss89uet8OHTrcwSSQKCMuydPTU6dOnXKsDi9durS2b9+uWrVqGU4GAEXDfz+53GKx6L9/Hf72YFHW1915rBlxUf/5B83Dw0Pe3t4G0wBA0WKz2RyvdevWKSoqSl988YXjpmdffPGFGjRooMTERNNR3QIzIy7Iw8ND/v7+jlZ/7tw5lS1bNs+/BDIzM03EA4AiJTw8XLNnz1bz5s2dxv/1r39pwIAB2rdvn6Fk7oM7sLqgd99913QEACg2Dh06pICAgDzj/v7+Onr0aKHncUfMjAAA3FqLFi3k6+urDz74wLHWLi0tTT179tTly5e1adMmwwldH2UEAODWfvnlF3Xu3FkHDx5UaGiopGv3a6pZs6ZWrFiR79PPcXtRRgAAbs9ut2v9+vXav3+/JKlOnTqKiYlxrL3DnUUZAQAARrGAFQDg9pKSkpSUlKT09HTZbDanbfPnzzeUyn1QRgAAbm3cuHEaP368GjVqpODgYE7NGMBpGhf2+OOPq3Hjxnr11VedxidPnqzvv/9eS5YsMZQMAIqO4OBgTZ48WT169DAdxW1xB1YXtnnzZrVv3z7P+COPPKLNmzcbSAQARU9ubq6aNm1qOoZbo4y4sEuXLuV7G3gvLy9duHDBQCIAKHr69eunhQsXmo7h1lgz4sIiIiK0ePFixcXFOY0vWrRIdevWNZQKAIqWy5cva86cOdqwYYPq1asnLy8vp+1TpkwxlMx9UEZc2JgxY9SlSxcdOnRIrVq1knRtxfjHH3/MehEA+P9++OEHRUVFSZL27NnjtI3FrIWDBawubvXq1Zo4caJ27dqlkiVLql69eoqPj9eDDz5oOhoAAJIoIwAAwDBO0wAA3N62bdv0ySef6Pjx48rNzXXatmzZMkOp3AdX07iY8uXL68yZM5KkcuXKqXz58td9AQCuLepv2rSp9u3bp+XLl+vKlSvau3evvvzyS/n7+5uO5xaYGXExU6dOVZkyZRz/zeIrALixiRMnaurUqRo8eLDKlCmj6dOnq1q1anruuecUHBxsOp5bYM0IAMCtlSpVSnv37lVYWJgqVKigjRs3KiIiQvv27VOrVq10+vRp0xFdHqdpXJinp6fS09PzjJ89e1aenp4GEgFA0VOuXDldvHhRkhQSEuK4vPfcuXPKzs42Gc1tcJrGhV1v0isnJyffO7MCgDtq0aKF1q9fr4iICD355JMaOnSovvzyS61fv16tW7c2Hc8tUEZc0IwZMyRdu1nPO++8o9KlSzu2Wa1Wbd68WbVr1zYVDwCKlJkzZ+ry5cuSpFGjRsnLy0tbtmzR448/rtGjRxtO5x5YM+KCqlWrJkk6duyYqlSp4nRKxtvbW2FhYRo/fryaNGliKiIAFAu//vqrSpYsaTqGy6OMuLCHHnpIy5YtU7ly5UxHAYBiJScnR7NmzdLkyZOVmppqOo7LYwGrC/vqq6+ciojVatWuXbv073//22AqACgacnJyNHLkSDVq1EhNmzbVihUrJEnvvvuuqlWrpqlTp2r48OFmQ7oJZkZc2LBhwxQREaG+ffvKarWqRYsWSk5Olp+fnz7//HO1bNnSdEQAMObVV1/VP//5T8XExGjLli3KyMhQnz599O233+q1117Tk08+yZWHhYSZERe2ZMkSRUZGSpJWrVqlo0ePav/+/Ro+fLhGjRplOB0AmLVkyRItWLBAS5cu1bp162S1WnX16lXt3r1bTz31FEWkEDEz4sJ8fX31yy+/qEqVKhowYID8/Pw0bdo0HTlyRJGRkbpw4YLpiABgjLe3t44cOaKQkBBJUsmSJbV161ZFREQYTuZ+mBlxYYGBgfrpp59ktVqVmJioNm3aSJKys7Np/ADcntVqdbrnUokSJZxuhYDCw31GXFifPn3UtWtXBQcHy2KxKCYmRpL03XffcZ8RAG7Pbrerd+/e8vHxkSRdvnxZAwcOVKlSpZz246m9dx5lxIWNHTtW4eHhSklJ0ZNPPun4A+fp6akRI0YYTgcAZvXq1cvp42eeecZQErBmBAAAGMXMiIuZMWOGBgwYIF9fX8dt4a/nxRdfLKRUAABcHzMjLqZatWratm2bKlSo4LgtfH4sFosOHz5ciMkAAMgfZQQAABjFpb0AAMAo1oy4sNjY2HzHLRaLfH19VaNGDXXs2FHly5cv5GQAAPyO0zQu7KGHHtKOHTtktVpVq1YtSdLBgwfl6emp2rVr68CBA7JYLPr6669Vt25dw2kBAO6K0zQurGPHjoqJidGpU6e0fft2bd++XSdOnFCbNm3UvXt3nTx5Ui1atOCplAAAo5gZcWEhISFav359nlmPvXv3qm3btjp58qR27Nihtm3b6syZM4ZSAgDcHTMjLuz8+fNKT0/PM56RkeF4SF5AQIByc3MLOxoAAA6UERfWsWNHPfvss1q+fLlOnDihEydOaPny5erbt686deokSdq6davuueces0EBAG6N0zQu7NKlSxo+fLgWLFigq1evSrr2VMpevXpp6tSpKlWqlHbt2iVJioqKMhcUAODWKCNu4NKlS467rVavXp1HZAMAihTuM+IGSpcu7biXCEUEAFDUsGbEhdlsNo0fP17+/v6qWrWqqlatqoCAAE2YMEE2m810PAAAJDEz4tJGjRqlefPmadKkSWrWrJkk6euvv9bYsWN1+fJlvf7664YTAgDAmhGXVrlyZc2ePVsdOnRwGl+5cqUGDRqkkydPGkoGAMDvOE3jwjIzM1W7du0847Vr11ZmZqaBRAAA5EUZcWGRkZGaOXNmnvGZM2cqMjLSQCIAAPLiNI0L27Rpkx599FH95S9/UXR0tCQpOTlZKSkpWrNmjR544AHDCQEAoIy4vFOnTmnWrFnav3+/JKlOnToaNGiQKleubDgZAADXUEbc0IkTJzR+/HjNmTPHdBQAACgj7mj37t1q0KCBrFar6SgAALCAFQAAmEUZAQAARlFGAACAUdwO3gV16dLlhtvPnTtXOEEAALgJlBEX5O/v/4fbe/bsWUhpAAC4Ma6mAQAARrFmBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABj1/wBxVMGqkjg+CQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_caompare  = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_caompare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa125d86-edfb-4641-9ba9-4481b6b18aa5",
   "metadata": {},
   "source": [
    "now we got baseline model..and we know a model's first predictions are't always what we should  based our next steps off.\n",
    "what should do?\n",
    "\n",
    "lets look at the following:\n",
    "\n",
    "* hypyterparameter tuning\n",
    "* feature importance\n",
    "*  confusion matrics\n",
    "* cross-validaYION\n",
    "* precision\n",
    "* recall\n",
    "* F1 score\n",
    "* classification report\n",
    "* roc cure\n",
    "* area under the curve(AUC)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8563be-e316-407a-9cbf-0ebe483b7e92",
   "metadata": {},
   "source": [
    "## hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c93cbfbd-b8fe-43b9-adce-82c67eadaf8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets tune KNN\n",
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "# create a list of diffrent values for n_neighbors\n",
    "neighbors = range(1,21)\n",
    "\n",
    "# setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "#loop through diffrent n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    # fit the algorithm\n",
    "    knn.fit(x_train, y_train)\n",
    "    # update the training scores list\n",
    "    train_score.append(knn.score(x_train, y_train))\n",
    "    # update the test score list\n",
    "    test_score.append(knn.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0407be37-defc-4329-952f-79aa0c9dbbb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "49d54f23-d56a-42bb-81dc-62322f4d0296",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum knn score on the test data:75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_score, label=\"train score\")\n",
    "plt.plot(neighbors, test_score, label = \"test score\")\n",
    "plt.xticks(np.arange(1,21,1))\n",
    "plt.xlabel(\"number of neighbors\")\n",
    "plt.ylabel(\"model score\")\n",
    "plt.legend()\n",
    "print(f\"maximum knn score on the test data:{max(test_score)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b50dc5-6747-4a31-b635-3c6b4059f925",
   "metadata": {},
   "source": [
    "## hyperparameter tuning with randomizedSearchCv\n",
    "we're going  to tune :\n",
    "* LogiticRegression()\n",
    "* RandomForestClassifier()\n",
    ".... using Randomizedsearchcv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb44b8b5-f90d-4cce-b0c1-b2fa6315817d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## create a hyperparameter grid for logisticRegression\n",
    "log_reg_grid = {\"C\":np.logspace(-4,4,20),\n",
    "               \"solver\": [\"liblinear\"]}\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0bf82ef-0429-4c1c-aea2-d88a4b12788e",
   "metadata": {},
   "source": [
    "noe we've got hyperparameter grid setup for each of our models, lets tune them using randomizedsearchcv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e655a80d-9b5b-420d-9574-cbb954fae41c",
   "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 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tune LogisticRegression\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperpameter search for logisticregression\n",
    "\n",
    "rs_log_res = RandomizedSearchCV(LogisticRegression(),\n",
    "                               param_distributions=log_reg_grid,\n",
    "                               cv=5,\n",
    "                               n_iter =20,\n",
    "                               verbose=True)\n",
    "\n",
    "#fit random hyperparameter search model for LogisticRegression \n",
    "rs_log_res.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "81622029-6ae1-4894-8a75-9b33f739f8f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_res.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b0e7fb60-2487-4fa9-9774-35226967b54f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_res.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19c56f3-ed9a-433a-b932-c30a1714617d",
   "metadata": {},
   "source": [
    "now we have tuned LogisticRegresion(), lets do the same for RandomforestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e5040dae-addb-4fd9-a470-9579ffebd512",
   "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 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">best_estimator_: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=3, min_samples_leaf=19, min_samples_split=4,\n",
       "                       n_estimators=210)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=3, min_samples_leaf=19, min_samples_split=4,\n",
       "                       n_estimators=210)</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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## setup rttandom seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperparameter search for RandomForestclassiier\n",
    "\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                          param_distributions=rf_grid,cv=5,\n",
    "                          n_iter=20,\n",
    "                          verbose=True)\n",
    "\n",
    "\n",
    "#fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9a60733b-721b-41dc-a595-e9efca3d8f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## find the best hyperparameter \n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "608aec52-6ef0-4514-885a-dfe4719d6db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evalute the randomized search RandomForestClassifier model\n",
    "rs_rf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "368658c0-7006-4f7a-9970-05640e43e1d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8a471f-2dba-4b24-a8ca-8349ec9f357a",
   "metadata": {},
   "source": [
    "1. by hand\n",
    "2. randomizedseachcv\n",
    "3. gridsearchcv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36ddcd4-048b-4384-8362-7474ff1ff3d5",
   "metadata": {},
   "source": [
    "## hyperparameter tuning with gridsearchcv\n",
    "\n",
    "since our logisticRegression model provides the best scores so far , we,ll try and  imporve them again using Gridsearchcv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b63ee943-3458-49c7-bb8a-c6006fa838e8",
   "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 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.20433597178569418, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.20433597178569418, solver=&#x27;liblinear&#x27;)</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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# diffrent hyperparameter for our logisticRegression model\n",
    "log_reg_grid = {\"C\":np.logspace(-4,4,30),\n",
    "               \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# setup grid hyperpameter search for Logisticregression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                         param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                         verbose=True)\n",
    "\n",
    "# fit grid hyperparameter search model\n",
    "gs_log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b501e985-deca-4a3b-a490-e0837a80d807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the best hyperparameters\n",
    "\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a3313c41-ab10-4796-9f68-f6d5ab3afee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e66c82d-28ae-4cb3-a1af-60a5dcf6ab88",
   "metadata": {},
   "source": [
    "## Evaluting our tuned machine learning classifier, beyond accuracy\n",
    "1.ROC curve and AUC score\n",
    "2.Confusion matrix\n",
    "3.Classification report\n",
    "4.Precision\n",
    "5.Recall\n",
    "6.F1-score\n",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "13ad0f14-b44e-4f2a-a25c-97f86da2a8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e0ae54a-cb30-48df-ab16-4250f58c6893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7b98d521-eff4-4902-bdb0-3e81c892750d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "95b0f3da-80ad-40f1-bc04-9495248ae33e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1230f8f2cd0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate and calculate AUC metric\n",
    "RocCurveDisplay.from_estimator(gs_log_reg, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b5931e5b-46ca-4f4f-a1fa-ee710558b867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fee65032-4982-46e3-b887-cf2b235ad749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True,\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "    \n",
    "    bottom, top = ax.get_ylim()\n",
    "    ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ab30cf-4d0b-4445-af55-d25af4e432d9",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d985be9e-9478-42c3-9e61-4b6273639086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99aab1d0-0c87-4483-8afe-0796e40c9250",
   "metadata": {},
   "source": [
    "## Calculate evaluation metrics using cross-validation\n",
    "We're going to calculate accuracy, precision, recall and f1-score of our model using cross-validation and to do so we'll be using `cross_val_score().`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "312fb147-98eb-427a-ad63-d59bd79dcfbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8e523c42-879c-4766-aa05-6b0bfa4be2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "76c8947f-fc5a-43a6-8e3f-2b25f126f063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2a93d39a-4928-4a17-92f7-9f0e72fb3033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3b38d058-ba2c-404c-abf0-51e15e137644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215873015873015"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "cv_precision = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"precision\")\n",
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9b701adb-171d-4dcd-9397-9fe04339d8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9272727272727274"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"recall\")\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6d1700d6-8a49-481a-93f5-9e649120e15a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705403543192143"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\n",
    "cv_f1 = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"f1\")\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "82d77bba-6306-45b2-bcda-09ac386b23f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                           \"Precision\": cv_precision,\n",
    "                           \"Recall\": cv_recall,\n",
    "                           \"F1\": cv_f1},\n",
    "                          index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b2060a-5030-457c-a3f2-126bb756ed22",
   "metadata": {},
   "source": [
    "# Let's find the feature importance for our LogisticRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6b41565e-cf8c-4b72-9587-ca56857b8e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "clf.fit(x_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "091dc347-227c-461f-b21e-5c33fd7c0568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00320769, -0.86062047,  0.66001431, -0.01155971, -0.00166496,\n",
       "         0.04017239,  0.31603402,  0.02458922, -0.6047017 , -0.56795457,\n",
       "         0.45085391, -0.63733326, -0.6755509 ]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cee52a61-8678-4cde-94b7-0ef9de0bd217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cf001f23-34dd-495f-9c7d-ba7f4c2bf258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0032076873709286024,\n",
       " 'sex': -0.8606204735539111,\n",
       " 'cp': 0.6600143086174385,\n",
       " 'trestbps': -0.01155970641957489,\n",
       " 'chol': -0.0016649609500147373,\n",
       " 'fbs': 0.04017238940156104,\n",
       " 'restecg': 0.3160340177157746,\n",
       " 'thalach': 0.02458922261936637,\n",
       " 'exang': -0.6047017032281077,\n",
       " 'oldpeak': -0.567954572983317,\n",
       " 'slope': 0.4508539117301764,\n",
       " 'ca': -0.6373332602422034,\n",
       " 'thal': -0.6755508982355707}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "df4d8c6a-5a14-4dc1-9ed8-b702eb1b441a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "201395b9-f056-41e1-91a0-dc5a379b90ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9700cdd-d1fe-4716-851a-4423d7788db2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}