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cars.ipynb

@@ -0,0 +1,1452 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "RdGj5r4ilCXW"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import r2_score,mean_squared_error\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+        "from sklearn.pipeline import Pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "import pandas as pd = Verileri tablolama ve ön işleme aşamalarında kullanıldı.from sklearn.model_selection import train_test_split: Ana veri setini eğitim ve test verilerine ayırmak için kullanıldı.\n",
+        "from sklearn.linear_model import LinearRegression : Doğrusal regresyon\n",
+        "from sklearn.metrics import r2_score,mean_squared_error : modelimizin performansını ölçmek için\n",
+        "from sklearn.compose import ColumnTransformer :Sütun dönüşüm işlemleri\n",
+        "from sklearn.preprocessing import OneHotEncoder, StandardScaler : kategori - sayısal dönüşüm  ve ölçeklendirme\n",
+        "from sklearn.pipeline import Pipeline : Veri işleme hattı"
+      ],
+      "metadata": {
+        "id": "xOSP5tvYlhT8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install xldr"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "RB7HDScwl5fB",
+        "outputId": "33075e92-e29d-4ad6-a849-474362666f11"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[31mERROR: Could not find a version that satisfies the requirement xldr (from versions: none)\u001b[0m\u001b[31m\n",
+            "\u001b[0m\u001b[31mERROR: No matching distribution found for xldr\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Proje excel dosyası olduğu için"
+      ],
+      "metadata": {
+        "id": "kj4-OtBBmFuG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=pd.read_excel('cars.xls')\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "95ASq-kSnjIB",
+        "outputId": "3a194963-1a64-4919-c089-3611fd6402de"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Price  Mileage    Make     Model           Trim   Type  Cylinder  \\\n",
+              "0    17314.103129     8221   Buick   Century       Sedan 4D  Sedan         6   \n",
+              "1    17542.036083     9135   Buick   Century       Sedan 4D  Sedan         6   \n",
+              "2    16218.847862    13196   Buick   Century       Sedan 4D  Sedan         6   \n",
+              "3    16336.913140    16342   Buick   Century       Sedan 4D  Sedan         6   \n",
+              "4    16339.170324    19832   Buick   Century       Sedan 4D  Sedan         6   \n",
+              "..            ...      ...     ...       ...            ...    ...       ...   \n",
+              "799  16507.070267    16229  Saturn  L Series  L300 Sedan 4D  Sedan         6   \n",
+              "800  16175.957604    19095  Saturn  L Series  L300 Sedan 4D  Sedan         6   \n",
+              "801  15731.132897    20484  Saturn  L Series  L300 Sedan 4D  Sedan         6   \n",
+              "802  15118.893228    25979  Saturn  L Series  L300 Sedan 4D  Sedan         6   \n",
+              "803  13585.636802    35662  Saturn  L Series  L300 Sedan 4D  Sedan         6   \n",
+              "\n",
+              "     Liter  Doors  Cruise  Sound  Leather  \n",
+              "0      3.1      4       1      1        1  \n",
+              "1      3.1      4       1      1        0  \n",
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+              "3      3.1      4       1      0        0  \n",
+              "4      3.1      4       1      0        1  \n",
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+              "803    3.0      4       1      0        0  \n",
+              "\n",
+              "[804 rows x 12 columns]"
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+              "\n",
+              "  <div id=\"df-3cc9f608-874f-44c8-9e7f-2a19395206c5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Make</th>\n",
+              "      <th>Model</th>\n",
+              "      <th>Trim</th>\n",
+              "      <th>Type</th>\n",
+              "      <th>Cylinder</th>\n",
+              "      <th>Liter</th>\n",
+              "      <th>Doors</th>\n",
+              "      <th>Cruise</th>\n",
+              "      <th>Sound</th>\n",
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+              "      <td>17314.103129</td>\n",
+              "      <td>8221</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
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+              "      <td>13196</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>16336.913140</td>\n",
+              "      <td>16342</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
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+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
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+              "      <td>L300 Sedan 4D</td>\n",
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+              "      <td>Sedan</td>\n",
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+              "      <td>L300 Sedan 4D</td>\n",
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+              "      <td>L Series</td>\n",
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+              "<p>804 rows × 12 columns</p>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-6299111d-1cc8-4e91-adc7-252cf17863c3 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_d344df63-81a5-4d4a-b319-bdbbb7b95641\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d344df63-81a5-4d4a-b319-bdbbb7b95641 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 804,\n  \"fields\": [\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9884.852800898008,\n        \"min\": 8638.930895260657,\n        \"max\": 70755.46671654288,\n        \"num_unique_values\": 798,\n        \"samples\": [\n          28432.824212532152,\n          24852.495280683135,\n          22661.048485078372\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8196,\n        \"min\": 266,\n        \"max\": 50387,\n        \"num_unique_values\": 791,\n        \"samples\": [\n          21386,\n          29649,\n          29368\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Make\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Buick\",\n          \"Cadillac\",\n          \"Saturn\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"9-2X AWD\",\n          \"Impala\",\n          \"Vibe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trim\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"GXP Sedan 4D\",\n          \"Aero Sedan 4D\",\n          \"SS Coupe 2D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Convertible\",\n          \"Wagon\",\n          \"Hatchback\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cylinder\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          8,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Liter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1055619585094583,\n        \"min\": 1.6,\n        \"max\": 6.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          3.1,\n          3.6,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Doors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cruise\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sound\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Leather\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Proje yüklendi."
+      ],
+      "metadata": {
+        "id": "RIYLZX0KoufQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "V3ONZtVtovzg",
+        "outputId": "ba9d149d-dd08-4d15-a149-cf948fe3153a"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 804 entries, 0 to 803\n",
+            "Data columns (total 12 columns):\n",
+            " #   Column    Non-Null Count  Dtype  \n",
+            "---  ------    --------------  -----  \n",
+            " 0   Price     804 non-null    float64\n",
+            " 1   Mileage   804 non-null    int64  \n",
+            " 2   Make      804 non-null    object \n",
+            " 3   Model     804 non-null    object \n",
+            " 4   Trim      804 non-null    object \n",
+            " 5   Type      804 non-null    object \n",
+            " 6   Cylinder  804 non-null    int64  \n",
+            " 7   Liter     804 non-null    float64\n",
+            " 8   Doors     804 non-null    int64  \n",
+            " 9   Cruise    804 non-null    int64  \n",
+            " 10  Sound     804 non-null    int64  \n",
+            " 11  Leather   804 non-null    int64  \n",
+            "dtypes: float64(2), int64(6), object(4)\n",
+            "memory usage: 75.5+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Proje hakkında bilgi edinildi."
+      ],
+      "metadata": {
+        "id": "KMMxJh4epowO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head(5)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "4KR2FxY6qKgk",
+        "outputId": "1112a6b1-a1ea-49b7-e8b1-04c2f7e69350"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          Price  Mileage   Make    Model      Trim   Type  Cylinder  Liter  \\\n",
+              "0  17314.103129     8221  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+              "1  17542.036083     9135  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+              "2  16218.847862    13196  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+              "3  16336.913140    16342  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+              "4  16339.170324    19832  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+              "\n",
+              "   Doors  Cruise  Sound  Leather  \n",
+              "0      4       1      1        1  \n",
+              "1      4       1      1        0  \n",
+              "2      4       1      1        0  \n",
+              "3      4       1      0        0  \n",
+              "4      4       1      0        1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-651124a1-30aa-4000-a85f-e210b7ebab84\" class=\"colab-df-container\">\n",
+              "    <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>Price</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Make</th>\n",
+              "      <th>Model</th>\n",
+              "      <th>Trim</th>\n",
+              "      <th>Type</th>\n",
+              "      <th>Cylinder</th>\n",
+              "      <th>Liter</th>\n",
+              "      <th>Doors</th>\n",
+              "      <th>Cruise</th>\n",
+              "      <th>Sound</th>\n",
+              "      <th>Leather</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>17314.103129</td>\n",
+              "      <td>8221</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>17542.036083</td>\n",
+              "      <td>9135</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>16218.847862</td>\n",
+              "      <td>13196</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>16336.913140</td>\n",
+              "      <td>16342</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>16339.170324</td>\n",
+              "      <td>19832</td>\n",
+              "      <td>Buick</td>\n",
+              "      <td>Century</td>\n",
+              "      <td>Sedan 4D</td>\n",
+              "      <td>Sedan</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-651124a1-30aa-4000-a85f-e210b7ebab84')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-651124a1-30aa-4000-a85f-e210b7ebab84 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-651124a1-30aa-4000-a85f-e210b7ebab84');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f84499d9-ca47-46ab-b03f-e1b46127dadb\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f84499d9-ca47-46ab-b03f-e1b46127dadb')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f84499d9-ca47-46ab-b03f-e1b46127dadb button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 804,\n  \"fields\": [\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9884.852800898008,\n        \"min\": 8638.930895260657,\n        \"max\": 70755.46671654288,\n        \"num_unique_values\": 798,\n        \"samples\": [\n          28432.824212532152,\n          24852.495280683135,\n          22661.048485078372\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8196,\n        \"min\": 266,\n        \"max\": 50387,\n        \"num_unique_values\": 791,\n        \"samples\": [\n          21386,\n          29649,\n          29368\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Make\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Buick\",\n          \"Cadillac\",\n          \"Saturn\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"9-2X AWD\",\n          \"Impala\",\n          \"Vibe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trim\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"GXP Sedan 4D\",\n          \"Aero Sedan 4D\",\n          \"SS Coupe 2D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Convertible\",\n          \"Wagon\",\n          \"Hatchback\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cylinder\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          8,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Liter\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1055619585094583,\n        \"min\": 1.6,\n        \"max\": 6.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          3.1,\n          3.6,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Doors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cruise\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sound\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Leather\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Veri setinin ilk 5 satırını getirir ve verinin yapısı hakkında ön bilgi verir."
+      ],
+      "metadata": {
+        "id": "0jgxLWZEqQi1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-SjR6JBUqVQC",
+        "outputId": "c19b8e18-355d-4f69-a1af-3e939fa981a6"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(804, 12)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Veri setinin kaç satır ve sütundan oluştuğunu gösterir.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TcvqOd2kqa7a"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X=df.drop('Price',axis=1)\n",
+        "y=df['Price']"
+      ],
+      "metadata": {
+        "id": "vTLh077kqfni"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Fiyat sütunuyla veri ön işleme başladı."
+      ],
+      "metadata": {
+        "id": "rsqvPvJ5q1rI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
+      ],
+      "metadata": {
+        "id": "RJGA1qKTsJST"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "preprocess=ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num',StandardScaler(),['Mileage', 'Cylinder','Liter','Doors']),\n",
+        "        ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])\n",
+        "    ]\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "wSwPfUZhshha"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cars_model= LinearRegression()"
+      ],
+      "metadata": {
+        "id": "YdOchgVqtvLc"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe=Pipeline(steps=[('preprocessor',preprocess),('model',cars_model)])"
+      ],
+      "metadata": {
+        "id": "-tpAChVrv8th"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Pipeline tanımlandı"
+      ],
+      "metadata": {
+        "id": "15e56QlVwC-q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe.fit(X_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 191
+        },
+        "id": "f0TO4rAEwOZG",
+        "outputId": "29cb6d66-f9ce-4b0a-d76a-443bc2523929"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('preprocessor',\n",
+              "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
+              "                                                  ['Mileage', 'Cylinder',\n",
+              "                                                   'Liter', 'Doors']),\n",
+              "                                                 ('cat', OneHotEncoder(),\n",
+              "                                                  ['Make', 'Model', 'Trim',\n",
+              "                                                   'Type'])])),\n",
+              "                ('model', LinearRegression())])"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+              "                                                  [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;,\n",
+              "                                                   &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
+              "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
+              "                                                  [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;,\n",
+              "                                                   &#x27;Type&#x27;])])),\n",
+              "                (&#x27;model&#x27;, LinearRegression())])</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 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 sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+              "                                                  [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;,\n",
+              "                                                   &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
+              "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
+              "                                                  [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;,\n",
+              "                                                   &#x27;Type&#x27;])])),\n",
+              "                (&#x27;model&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label 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 sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+              "                                 [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;, &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
+              "                                (&#x27;cat&#x27;, OneHotEncoder(),\n",
+              "                                 [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;, &#x27;Type&#x27;])])</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 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 sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;, &#x27;Liter&#x27;, &#x27;Doors&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label 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 sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;, &#x27;Type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred=pipe.predict(X_test)\n",
+        "print('RMSE',mean_squared_error(y_test,y_pred)**0.5)\n",
+        "print('R2',r2_score(y_test,y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "41ntEyZOwpMG",
+        "outputId": "719ead68-c3a8-458e-d09d-0f36d3138d95"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE 835.1007875600316\n",
+            "R2 0.9912072813963753\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Skorlar kaydedildi."
+      ],
+      "metadata": {
+        "id": "Vc9d9Ch4xEXe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe.fit(X,y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 191
+        },
+        "id": "-i3lnaj_xMNW",
+        "outputId": "b82306d3-50b0-4cad-b7a9-5128891be4c2"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('preprocessor',\n",
+              "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
+              "                                                  ['Mileage', 'Cylinder',\n",
+              "                                                   'Liter', 'Doors']),\n",
+              "                                                 ('cat', OneHotEncoder(),\n",
+              "                                                  ['Make', 'Model', 'Trim',\n",
+              "                                                   'Type'])])),\n",
+              "                ('model', LinearRegression())])"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+              "                                                  [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;,\n",
+              "                                                   &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
+              "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
+              "                                                  [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;,\n",
+              "                                                   &#x27;Type&#x27;])])),\n",
+              "                (&#x27;model&#x27;, LinearRegression())])</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 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 sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+              "                                                  [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;,\n",
+              "                                                   &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
+              "                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n",
+              "                                                  [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;,\n",
+              "                                                   &#x27;Type&#x27;])])),\n",
+              "                (&#x27;model&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label 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 sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+              "                                 [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;, &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
+              "                                (&#x27;cat&#x27;, OneHotEncoder(),\n",
+              "                                 [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;, &#x27;Type&#x27;])])</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;, &#x27;Liter&#x27;, &#x27;Doors&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;, &#x27;Type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Veri setiyle tekrar eğitim yapıldı."
+      ],
+      "metadata": {
+        "id": "bEKsz1pExU7o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install streamlit"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hP5OsYaZxa5I",
+        "outputId": "d32a7d22-551d-40b2-fdab-93c2f666dd44"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting streamlit\n",
+            "  Downloading streamlit-1.35.0-py2.py3-none-any.whl (8.6 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m46.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "Requirement already satisfied: typing-extensions<5,>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (4.12.2)\n",
+            "Collecting gitpython!=3.1.19,<4,>=3.0.7 (from streamlit)\n",
+            "  Downloading GitPython-3.1.43-py3-none-any.whl (207 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting pydeck<1,>=0.8.0b4 (from streamlit)\n",
+            "  Downloading pydeck-0.9.1-py2.py3-none-any.whl (6.9 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m88.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tornado<7,>=6.0.3 in /usr/local/lib/python3.10/dist-packages (from streamlit) (6.3.3)\n",
+            "Collecting watchdog>=2.1.5 (from streamlit)\n",
+            "  Downloading watchdog-4.0.1-py3-none-manylinux2014_x86_64.whl (83 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.0/83.0 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (0.4)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (3.1.4)\n",
+            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (4.19.2)\n",
+            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (0.12.1)\n",
+            "Collecting gitdb<5,>=4.0.1 (from gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
+            "  Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas<3,>=1.3.0->streamlit) (2.8.2)\n",
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+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->streamlit) (2024.6.2)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich<14,>=10.14.0->streamlit) (3.0.0)\n",
+            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich<14,>=10.14.0->streamlit) (2.16.1)\n",
+            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
+            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->altair<6,>=4.0->streamlit) (2.1.5)\n",
+            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (23.2.0)\n",
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+            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.35.1)\n",
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+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas<3,>=1.3.0->streamlit) (1.16.0)\n",
+            "Installing collected packages: watchdog, smmap, pydeck, gitdb, gitpython, streamlit\n",
+            "Successfully installed gitdb-4.0.11 gitpython-3.1.43 pydeck-0.9.1 smmap-5.0.1 streamlit-1.35.0 watchdog-4.0.1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import streamlit as st\n",
+        "\n",
+        "def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):\n",
+        "    input_data=pd.DataFrame({'Make':[make],\n",
+        "                             'Model':[model],\n",
+        "                             'Trim':[trim],\n",
+        "                             'Mileage':[mileage],\n",
+        "                             'Type':[car_type],\n",
+        "                             'Cylinder':[cylinder],\n",
+        "                             'Liter':[liter],\n",
+        "                             'Doors':[doors],\n",
+        "                             'Cruise':[cruise],\n",
+        "                             'Sound':[sound],\n",
+        "                             'Leather':[leather]})\n",
+        "    prediction=pipe.predict(input_data)[0]\n",
+        "    return prediction\n",
+        "st.title(\"II. El Araba Fiyatı Tahmin:red_car: @drmurataltun\")\n",
+        "st.write('Arabanın özelliklerini seçiniz')\n",
+        "make=st.selectbox('Marka',df['Make'].unique())\n",
+        "model=st.selectbox('Model',df[df['Make']==make]['Model'].unique())\n",
+        "trim=st.selectbox('Trim',df[(df['Make']==make) &(df['Model']==model)]['Trim'].unique())\n",
+        "mileage=st.number_input('Kilometre',100,200000)\n",
+        "car_type=st.selectbox('Araç Tipi',df[(df['Make']==make) &(df['Model']==model)&(df['Trim']==trim)]['Type'].unique())\n",
+        "cylinder=st.selectbox('Cylinder',df['Cylinder'].unique())\n",
+        "liter=st.number_input('Yakıt hacmi',1,10)\n",
+        "doors=st.selectbox('Kapı sayısı',df['Doors'].unique())\n",
+        "cruise=st.radio('Hız Sbt.',[True,False])\n",
+        "sound=st.radio('Ses Sis.',[True,False])\n",
+        "leather=st.radio('Deri döşeme.',[True,False])\n",
+        "if st.button('Tahmin'):\n",
+        "    pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)\n",
+        "    st.write('Fiyat:$', round(pred[0],2))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xC3w9NK_3Lmh",
+        "outputId": "f232a770-f806-4362-dce1-ea2ea73e79cf"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "2024-06-18 15:00:30.503 \n",
+            "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+            "  command:\n",
+            "\n",
+            "    streamlit run /usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py [ARGUMENTS]\n",
+            "2024-06-18 15:00:30.507 Session state does not function when running a script without `streamlit run`\n"
+          ]
+        }
+      ]
+    }
+  ]
+}