VanduongLE

module3/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Topic 6: Around Simpson's Paradox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from statsmodels.formula.api import logit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the dataset\n",
+    "file_path = 'https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv?inline=false'\n",
+    "data = pd.read_csv(file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data preparation\n",
+    "data['Smoker'] = data['Smoker'].astype('category')\n",
+    "data['Status'] = data['Status'].astype('category')\n",
+    "data['Death'] = data['Status'].map({'Alive': 0, 'Dead': 1})\n",
+    "data['Smoker_Code'] = data['Smoker'].map({'Yes': 1, 'No': 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define age categories\n",
+    "bins = [18, 34, 54, 64, 100]\n",
+    "labels = ['18-34', '34-54', '54-64', '65+']\n",
+    "data['Age Group'] = pd.cut(data['Age'], bins=bins, labels=labels, right=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Analysis by Smoking Habit\n",
+    "smoking_status_counts = data.groupby(['Smoker', 'Status']).size().unstack()\n",
+    "mortality_rate = smoking_status_counts.apply(lambda x: x['Dead'] / x.sum(), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Analysis by Age Group\n",
+    "age_smoking_status_counts = data.groupby(['Age Group', 'Smoker', 'Status']).size().unstack()\n",
+    "age_group_mortality_rate = age_smoking_status_counts.apply(lambda x: x['Dead'] / x.sum(), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.381244\n",
+      "         Iterations 7\n",
+      "                           Logit Regression Results                           \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Death   No. Observations:                 1314\n",
+      "Model:                          Logit   Df Residuals:                     1311\n",
+      "Method:                           MLE   Df Model:                            2\n",
+      "Date:                Sun, 12 Nov 2023   Pseudo R-squ.:                  0.3579\n",
+      "Time:                        20:44:03   Log-Likelihood:                -500.95\n",
+      "converged:                       True   LL-Null:                       -780.16\n",
+      "                                        LLR p-value:                5.534e-122\n",
+      "===============================================================================\n",
+      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "-------------------------------------------------------------------------------\n",
+      "Intercept      -6.3519      0.360    -17.637      0.000      -7.058      -5.646\n",
+      "Age             0.0998      0.006     17.290      0.000       0.089       0.111\n",
+      "Smoker_Code     0.2787      0.165      1.689      0.091      -0.045       0.602\n",
+      "===============================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Logistic Regression\n",
+    "model = logit(\"Death ~ Age + Smoker_Code\", data).fit()\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualization\n",
+    "# Mortality Rate by Smoking Status\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x=smoking_status_counts.index, y=mortality_rate)\n",
+    "plt.title('Mortality Rate by Smoking Status')\n",
+    "plt.ylabel('Mortality Rate')\n",
+    "plt.xlabel('Smoker')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 720x432 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Mortality Rate by Age Group and Smoking Status\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "age_group_mortality_rate.unstack().plot(kind='bar', stacked=True)\n",
+    "plt.title('Mortality Rate by Age Group and Smoking Status')\n",
+    "plt.ylabel('Mortality Rate')\n",
+    "plt.xlabel('Age Group and Smoker')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predicted Probabilities from Logistic Regression\n",
+    "data['Predicted_Death_Prob'] = model.predict(data[['Age', 'Smoker_Code']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 593.359x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualization using lmplot\n",
+    "sns.lmplot(x='Age', y='Predicted_Death_Prob', hue='Smoker', data=data, logistic=True, aspect=1.5)\n",
+    "plt.title('Predicted Probability of Death by Age and Smoking Status')\n",
+    "plt.ylabel('Predicted Probability of Death')\n",
+    "plt.xlabel('Age')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +249,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-