Car_pediction.ipynb

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        "id": "39_homdTpBRq"
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      "source": [
        "import pandas as pd"
      ]
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      "cell_type": "code",
      "source": [
        "car=pd.read_csv('/content/car data.csv')"
      ],
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      "execution_count": 4,
      "outputs": []
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      "source": [
        "car.head()"
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            "text/plain": [
              "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
              "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
              "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
              "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
              "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
              "4    swift  2014           4.60           6.87       42450    Diesel   \n",
              "\n",
              "  Seller_Type Transmission  Owner  \n",
              "0      Dealer       Manual      0  \n",
              "1      Dealer       Manual      0  \n",
              "2      Dealer       Manual      0  \n",
              "3      Dealer       Manual      0  \n",
              "4      Dealer       Manual      0  "
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              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
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              "    <tr>\n",
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              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
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              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
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        "car.describe()"
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            "text/plain": [
              "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
              "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
              "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
              "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
              "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
              "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
              "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
              "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
              "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
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              "      <th>50%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>6.400000</td>\n",
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              "      <th>75%</th>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn import metrics"
      ],
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        "id": "GTkr7jFDqsIV"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car.shape"
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        "outputId": "09ba2e3c-a23d-4d27-e7c2-ab7969f1db79"
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      "execution_count": 10,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(301, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " car.info()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "hr_7LQdpjfIi",
        "outputId": "2b683fb8-b6b9-4450-d8b7-a7b0921e6015"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Car_Name       301 non-null    object \n",
            " 1   Year           301 non-null    int64  \n",
            " 2   Selling_Price  301 non-null    float64\n",
            " 3   Present_Price  301 non-null    float64\n",
            " 4   Kms_Driven     301 non-null    int64  \n",
            " 5   Fuel_Type      301 non-null    object \n",
            " 6   Seller_Type    301 non-null    object \n",
            " 7   Transmission   301 non-null    object \n",
            " 8   Owner          301 non-null    int64  \n",
            "dtypes: float64(2), int64(3), object(4)\n",
            "memory usage: 21.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30B0qGhaj0IH",
        "outputId": "dbca1f27-1b72-4ab5-80ef-ff1d3b756347"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Car_Name         0\n",
              "Year             0\n",
              "Selling_Price    0\n",
              "Present_Price    0\n",
              "Kms_Driven       0\n",
              "Fuel_Type        0\n",
              "Seller_Type      0\n",
              "Transmission     0\n",
              "Owner            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking distribution of categorical data"
      ],
      "metadata": {
        "id": "GyfJxd_xj-gd"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "car.Seller_Type.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3b4ffB_kLcE",
        "outputId": "ae76daa0-60ed-44bf-e2f9-58a64876d7a7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    195\n",
              "1    106\n",
              "Name: Seller_Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car.Fuel_Type.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYK65etTkjqB",
        "outputId": "2ba9f0fa-3206-4070-f317-14e78d6c059c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    239\n",
              "1     60\n",
              "0      2\n",
              "Name: Fuel_Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car.Transmission.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p68vINKckpR-",
        "outputId": "a5d41638-4393-4aed-fc53-2fad4ea0958e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    261\n",
              "0     40\n",
              "Name: Transmission, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "lb = LabelEncoder()\n",
        "car['Fuel_Type'] = lb.fit_transform(car['Fuel_Type'] )\n",
        "car['Transmission']=lb.fit_transform(car['Transmission'] )\n",
        "car['Seller_Type']=lb.fit_transform(car['Seller_Type'] )"
      ],
      "metadata": {
        "id": "HbaO-F4Mk0cr"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car.Fuel_Type"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SREMYMDmUbH",
        "outputId": "42df5719-c863-4b74-b549-231ffea37238"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2\n",
              "1      1\n",
              "2      2\n",
              "3      2\n",
              "4      1\n",
              "      ..\n",
              "296    1\n",
              "297    2\n",
              "298    2\n",
              "299    1\n",
              "300    2\n",
              "Name: Fuel_Type, Length: 301, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqCvRkhUmnWr",
        "outputId": "ed2c319a-bbb6-4626-b07f-5febe5bc3cd4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Car_Name       301 non-null    object \n",
            " 1   Year           301 non-null    int64  \n",
            " 2   Selling_Price  301 non-null    float64\n",
            " 3   Present_Price  301 non-null    float64\n",
            " 4   Kms_Driven     301 non-null    int64  \n",
            " 5   Fuel_Type      301 non-null    int64  \n",
            " 6   Seller_Type    301 non-null    int64  \n",
            " 7   Transmission   301 non-null    int64  \n",
            " 8   Owner          301 non-null    int64  \n",
            "dtypes: float64(2), int64(6), object(1)\n",
            "memory usage: 21.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=car.drop(['Car_Name','Selling_Price'],axis=1)"
      ],
      "metadata": {
        "id": "LJfW23lenOLb"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=car['Selling_Price']"
      ],
      "metadata": {
        "id": "tbZg10Fwno3n"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "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": "_mcy4FPNnzwJ"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_rbLfUboGYk",
        "outputId": "161bdaa4-8c78-41e3-dce6-ae71e318e0c3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(240, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#linear reg.\n",
        "lr=LinearRegression()"
      ],
      "metadata": {
        "id": "II4QawYYoKYj"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkNMkTIGodBr",
        "outputId": "5554cd47-e4b3-42a6-d42a-0011f4d8df6b"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "traindatapred=lr.predict(x_train)"
      ],
      "metadata": {
        "id": "SDvTLVVuofqy"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#R squard error\n",
        "er_scr=metrics.r2_score(y_train,traindatapred)\n",
        "er_scr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzTw-evQozL9",
        "outputId": "902fda00-a634-4d2c-db2f-fe09312061d0"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8839793496750797"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testdatapred=lr.predict(x_test)"
      ],
      "metadata": {
        "id": "XI50MvJypNSK"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "er_scr=metrics.r2_score(y_test,testdatapred)\n",
        "er_scr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJV3h8t4pmqK",
        "outputId": "60c9ea63-addb-43af-9075-07888722a181"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8468053957655803"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize actual and predicted"
      ],
      "metadata": {
        "id": "gcl0nX4Nptn1"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_train,traindatapred)\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('predicted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "y3ja4Cn3p4BV",
        "outputId": "0da31ff3-2158-4926-c3ab-57e9a03a912d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,testdatapred)\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('predicted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "QtuYFWB-qJDY",
        "outputId": "90ba4eb1-d92b-424a-bb20-58a14d2487b1"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lasso reg.\n",
        "lr=Lasso()"
      ],
      "metadata": {
        "id": "iiDJH6XcqnGv"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eP6iAvTLrWhF",
        "outputId": "0fb36bb0-ce1f-457a-e7ad-c9abbea27f15"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven  Fuel_Type  \\\n",
              "0     ritz  2014           3.35           5.59       27000          2   \n",
              "1      sx4  2013           4.75           9.54       43000          1   \n",
              "2     ciaz  2017           7.25           9.85        6900          2   \n",
              "3  wagon r  2011           2.85           4.15        5200          2   \n",
              "4    swift  2014           4.60           6.87       42450          1   \n",
              "\n",
              "   Seller_Type  Transmission  Owner  \n",
              "0            0             1      0  \n",
              "1            0             1      0  \n",
              "2            0             1      0  \n",
              "3            0             1      0  \n",
              "4            0             1      0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-222df9e9-22f1-4f09-b587-e3f4224fc463\">\n",
              "    <div 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>Car_Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-222df9e9-22f1-4f09-b587-e3f4224fc463')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-222df9e9-22f1-4f09-b587-e3f4224fc463 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-222df9e9-22f1-4f09-b587-e3f4224fc463');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5ccebec-9854-4a40-952b-134685a11900",
        "id": "aqvTrx3Srcis"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso()"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "traindatapred=lr.predict(x_train)"
      ],
      "metadata": {
        "id": "Eqmbysaercis"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#R squard error\n",
        "er_scr=metrics.r2_score(y_train,traindatapred)\n",
        "er_scr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdfdae52-da0f-462f-f267-9ddd8276ac78",
        "id": "JHTT_yeRrcis"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.848030286813716"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testdatapred=lr.predict(x_test)"
      ],
      "metadata": {
        "id": "YA9nQXwfrcit"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "er_scr=metrics.r2_score(y_test,testdatapred)\n",
        "er_scr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f124ff8-57c0-4a79-ed66-f782068816ca",
        "id": "Vj0ptG3lrcit"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.798551246128469"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize actual and predicted"
      ],
      "metadata": {
        "id": "JBspBQgorcit"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_train,traindatapred)\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('predicted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d40599a7-914c-4560-d3ff-f00c91702301",
        "id": "sYIe0Hwbrcit"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,testdatapred)\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('predicted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "38c558e5-bc17-4dd8-97a8-1ee220f443b8",
        "id": "UNk2qMIdrcit"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.predict([[2017\t,9.85\t,6900\t,2\t,0\t,1\t,0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPGQJGqsrcit",
        "outputId": "8db567cd-162c-4975-b989-ba6cc10cb59a"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but Lasso was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.95150559])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
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      },
      "execution_count": 66,
      "outputs": [
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              "     Year  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  \\\n",
              "184  2008          0.750       26000          2            1             1   \n",
              "132  2017          0.950        3500          2            1             1   \n",
              "194  2008          0.787       50000          2            1             1   \n",
              "75   2015          6.800       36000          2            0             1   \n",
              "111  2016          1.500        8700          2            1             1   \n",
              "\n",
              "     Owner  \n",
              "184      1  \n",
              "132      0  \n",
              "194      0  \n",
              "75       0  \n",
              "111      0  "
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              "      <td>2016</td>\n",
              "      <td>1.500</td>\n",
              "      <td>8700</td>\n",
              "      <td>2</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      "metadata": {
        "id": "jY2UgAe2sdea"
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      "execution_count": null,
      "outputs": []
    }
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}