Created using Colab

Author: LatiefDataVisionary

Tugas_EDA_week_8.ipynb

@@ -3944,12 +3944,12 @@
         "model.fit(X_train, y_train)"
       ],
       "metadata": {
-        "id": "X3N9d8Q06j92",
-        "outputId": "d7ea9be2-112f-4928-cc61-cf9caa9ebec5",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 80
-        }
+        },
+        "id": "X3N9d8Q06j92",
+        "outputId": "d7ea9be2-112f-4928-cc61-cf9caa9ebec5"
       },
       "execution_count": 34,
       "outputs": [
@@ -4371,6 +4371,86 @@
           "execution_count": 34
         }
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **3. Evaluasi Model**\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "YTlTotho7cvw"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Lakukan prediksi pada data testing:**\n"
+      ],
+      "metadata": {
+        "id": "5xBqbCvU7kGy"
+      }
+    },
+    {
+      "source": [
+        "y_pred = model.predict(X_test)"
+      ],
+      "cell_type": "code",
+      "metadata": {
+        "id": "C6Amb2vh7ifF"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Hitung metrik evaluasi:**\n"
+      ],
+      "metadata": {
+        "id": "nCU0Wit-7oAC"
+      }
+    },
+    {
+      "source": [
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f\"Mean Squared Error (MSE): {mse}\")\n",
+        "print(f\"R-squared (R2): {r2}\")"
+      ],
+      "cell_type": "code",
+      "metadata": {
+        "id": "bElQJX_27p17",
+        "outputId": "d98974f1-e7c0-4e8d-c053-e523b9de2e08",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Squared Error (MSE): 3560172.059583262\n",
+            "R-squared (R2): 0.7515297801671946\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Alasan Memilih Linear Regression:**\n",
+        "\n",
+        "* **Sederhana dan mudah diinterpretasi**: Linear Regression adalah algoritma yang relatif sederhana dan mudah dipahami, cocok untuk implementasi awal.\n",
+        "* **Data memiliki hubungan linear**: Dari EDA, terlihat bahwa beberapa fitur memiliki korelasi linear dengan harga, sehingga Linear Regression bisa menjadi pilihan yang baik.\n",
+        "* **Performa yang cukup baik untuk data ini**: Linear Regression seringkali memberikan performa yang cukup baik untuk kasus prediksi harga, terutama jika data memiliki hubungan linear."
+      ],
+      "metadata": {
+        "id": "bD78h31s7z3O"
+      }
     }
   ]
 }