diff --git a/TP1.ipynb b/TP1.ipynb
index 2f38326..6e1f4c7 100644
--- a/TP1.ipynb
+++ b/TP1.ipynb
@@ -110,6 +110,77 @@
     "sns.heatmap(df.corr(numeric_only=True),annot=True,fmt=\".2f\")\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[10  0  0]\n",
+      " [ 0  9  0]\n",
+      " [ 0  0 11]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        10\n",
+      "  versicolor       1.00      1.00      1.00         9\n",
+      "   virginica       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = df.drop('species',axis=1)\n",
+    "y = df[\"species\"]\n",
+    "\n",
+    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import confusion_matrix,classification_report\n",
+    "\n",
+    "model = LogisticRegression(max_iter=200)\n",
+    "model.fit(X_train,y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "print(confusion_matrix(y_test,y_pred))\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Précision moyenn : 0.9733333333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "scores = cross_val_score(model,X,y,cv=5)\n",
+    "print(\"Précision moyenn :\",scores.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {