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  "cells": [
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      "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"
      ],
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        "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",
              "2      3.1      4       1      1        0  \n",
              "3      3.1      4       1      0        0  \n",
              "4      3.1      4       1      0        1  \n",
              "..     ...    ...     ...    ...      ...  \n",
              "799    3.0      4       1      0        0  \n",
              "800    3.0      4       1      1        0  \n",
              "801    3.0      4       1      1        0  \n",
              "802    3.0      4       1      1        0  \n",
              "803    3.0      4       1      0        0  \n",
              "\n",
              "[804 rows x 12 columns]"
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              "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()"
      ],
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        },
        "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",
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              "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  "
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              "      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": [
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            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
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            "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"
          ]
        }
      ]
    }
  ]
}