DSA_SoSe_24/Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "37d611da-6f56-46d8-905a-62026750150c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from ucimlrepo import fetch_ucirepo\n",
    "import pandas as pd\n",
    "\n",
    "# fetch dataset \n",
    "heart_disease = fetch_ucirepo(id=45) \n",
    "  \n",
    "# data (as pandas dataframes) \n",
    "X = heart_disease.data.features \n",
    "y = heart_disease.data.targets \n",
    "\n",
    "male=1\n",
    "female=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "ae26378f-c104-4664-a313-ed8d9edfed42",
   "metadata": {
    "tags": []
   },
   "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>goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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  slope  \\\n",
       "0   63    1   1       145   233    1        2      150      0      2.3      3   \n",
       "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
       "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
       "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
       "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
       "\n",
       "    ca  thal  goal  \n",
       "0  0.0   6.0     0  \n",
       "1  3.0   3.0     2  \n",
       "2  2.0   7.0     1  \n",
       "3  0.0   3.0     0  \n",
       "4  0.0   3.0     0  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([X, y], axis=1)\n",
    "df = df.rename(columns={'num':'goal'})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6b3e5424-4a7e-4e53-82b9-d78e38939834",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "counts_male = len(X[X['sex'] == male])\n",
    "counts_female = len(X[X['sex'] == female])\n",
    "\n",
    "plt.bar([male, female], [counts_male, counts_female])\n",
    "plt.xticks([0, 1],['female', 'male'])\n",
    "plt.title('Age distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "48fd2655-1dcc-41f6-9938-ef6ea937d52e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X['age'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('counts')\n",
    "plt.title('Age distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b9174a9d-6c8a-4915-9580-48f23cbdd038",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(X, x='sex', y='age')\n",
    "ax.set_xticklabels(['male', 'female'])\n",
    "plt.title('Age distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "522ff499-cd7f-4417-ae7d-d637402505b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.hist(X['chol'])\n",
    "plt.xlabel('Cholesterin')\n",
    "plt.ylabel('counts')\n",
    "plt.title('Cholesterin distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f220fadf-33ec-4bf6-a225-a2c874f02088",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X['trestbps'])\n",
    "plt.xlabel('Blood pressure (rest)')\n",
    "plt.ylabel('counts')\n",
    "plt.title('Blood pressure distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f568c911-d961-4d7f-87b1-f25dd5403cff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(X['trestbps'])\n",
    "plt.ylabel('Blood pressure (rest)')\n",
    "plt.title('Blood pressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5c174a9d-59b7-4efe-a0eb-a132388c1d2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Chol / Age split by sex')"
      ]
     },
     "execution_count": 92,
     "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": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "\n",
    "model = LinearRegression()\n",
    "x = np.array(X['age'])\n",
    "x = x[:, np.newaxis]\n",
    "reg = model.fit(x, X['chol'])\n",
    "pred = reg.predict(x)\n",
    "\n",
    "sick = np.array(y)[:,0] != 0\n",
    "\n",
    "sns.scatterplot(X, x='age', y='chol', hue='sex')\n",
    "plt.plot(x, pred, color='black')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Chol')\n",
    "plt.title('Chol / Age split by sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b3d627cf-3ec9-4cd9-bee6-5baeb9d1a22d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Blood pressure / Age split by sex')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "x = np.array(X['chol'])\n",
    "x = x[:, np.newaxis]\n",
    "reg = model.fit(x, X['trestbps'])\n",
    "pred = reg.predict(x)\n",
    "\n",
    "sick = np.array(y)[:,0] != 0\n",
    "\n",
    "sns.scatterplot(X, x='chol', y='trestbps', hue='sex')\n",
    "plt.plot(x, pred, color='black')\n",
    "plt.xlabel('Chol')\n",
    "plt.ylabel('Blood pressure (rest)')\n",
    "plt.title('Blood pressure / Age split by sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "3a6dc91a-f3e9-4d7e-9e4b-58c59d24463c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "\n",
    "sns.heatmap(corr)"
   ]
  }
 ],
 "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
}