SVR.ipynb

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        "<a href=\"https://colab.research.google.com/github/Daksh-Raghuvanshi/Machine-Learnig-Projects/blob/main/SVR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Regression for Bnagalore House Prediction"
      ],
      "metadata": {
        "id": "vQi1G4bsYvVu"
      }
    },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/bangalore house price prediction OHE-data (1).csv')\n",
        "df.head()"
      ],
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          "height": 317
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              "   bath  balcony  price  total_sqft_int  bhk  price_per_sqft  \\\n",
              "0   3.0      2.0  150.0          1672.0    3     8971.291866   \n",
              "1   3.0      3.0  149.0          1750.0    3     8514.285714   \n",
              "2   3.0      2.0  150.0          1750.0    3     8571.428571   \n",
              "3   2.0      2.0   40.0          1250.0    2     3200.000000   \n",
              "4   2.0      2.0   83.0          1200.0    2     6916.666667   \n",
              "\n",
              "   area_typeSuper built-up  Area  area_typeBuilt-up  Area  \\\n",
              "0                              1                        0   \n",
              "1                              0                        1   \n",
              "2                              1                        0   \n",
              "3                              1                        0   \n",
              "4                              0                        0   \n",
              "\n",
              "   area_typePlot  Area  availability_Ready To Move  ...  \\\n",
              "0                    0                           1  ...   \n",
              "1                    0                           1  ...   \n",
              "2                    0                           1  ...   \n",
              "3                    0                           1  ...   \n",
              "4                    1                           1  ...   \n",
              "\n",
              "   location_Kalena Agrahara  location_Horamavu Agara  location_Vidyaranyapura  \\\n",
              "0                         0                        0                        0   \n",
              "1                         0                        0                        0   \n",
              "2                         0                        0                        0   \n",
              "3                         0                        0                        0   \n",
              "4                         0                        0                        0   \n",
              "\n",
              "   location_BTM 2nd Stage  location_Hebbal Kempapura  location_Hosur Road  \\\n",
              "0                       0                          0                    0   \n",
              "1                       0                          0                    0   \n",
              "2                       0                          0                    0   \n",
              "3                       0                          0                    0   \n",
              "4                       0                          0                    0   \n",
              "\n",
              "   location_Horamavu Banaswadi  location_Domlur  location_Mahadevpura  \\\n",
              "0                            0                0                     0   \n",
              "1                            0                0                     0   \n",
              "2                            0                0                     0   \n",
              "3                            0                0                     0   \n",
              "4                            0                0                     0   \n",
              "\n",
              "   location_Tumkur Road  \n",
              "0                     0  \n",
              "1                     0  \n",
              "2                     0  \n",
              "3                     0  \n",
              "4                     0  \n",
              "\n",
              "[5 rows x 108 columns]"
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          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('price' , axis = 1)\n",
        "y = df['price']"
      ],
      "metadata": {
        "id": "XzyjaDf1ZS3J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NN-0BkffZVaQ",
        "outputId": "2007cd55-fb0d-43dd-e2bc-d10a9e48129e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7120, 107)\n",
            "(7120,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train , X_test , y_train , y_test = train_test_split( X , y , test_size= 0.2 , random_state = 51)\n",
        "print(f\"{X_train.shape} ,{X_test.shape} , {y_train.shape} , {y_test.shape} \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70HXxu56ZXdZ",
        "outputId": "791ea2b8-d34b-4a4e-be3a-fc30efd589a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5696, 107) ,(1424, 107) , (5696,) , (1424,) \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.head()"
      ],
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        "colab": {
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        },
        "id": "fXw7gfYCt4dW",
        "outputId": "c4172210-d924-46ce-962a-5ec5f9225ee0"
      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "2435     80.0\n",
              "3113     40.0\n",
              "426     120.0\n",
              "1124     79.0\n",
              "1161     45.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc = StandardScaler()\n",
        "sc.fit(X_train)\n",
        "print(sc.mean_)\n",
        "print(sc.scale_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlnHjhXiZbI7",
        "outputId": "f31071c6-03ae-429f-d82c-d2eae2b30c0d"
      },
      "execution_count": null,
      "outputs": [
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            " 4.21348315e-03 3.86235955e-03 3.68679775e-03 3.51123596e-03\n",
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            " 3.16011236e-03 3.16011236e-03 2.63342697e-03]\n",
            "[8.65227177e-01 7.71370094e-01 7.75239330e+02 8.27588110e-01\n",
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            " 1.27398847e-01 1.21944067e-01 1.18398830e-01 1.13990181e-01\n",
            " 1.08607926e-01 1.07815958e-01 1.05402601e-01 1.10948250e-01\n",
            " 9.23500114e-02 8.55524728e-02 8.85317257e-02 8.95020864e-02\n",
            " 8.65573078e-02 8.95020864e-02 8.65573078e-02 8.14054140e-02\n",
            " 8.14054140e-02 7.58944941e-02 7.47423298e-02 8.03342472e-02\n",
            " 7.35717054e-02 7.92482144e-02 7.11714190e-02 7.47423298e-02\n",
            " 6.74075004e-02 8.03342472e-02 7.11714190e-02 7.81466956e-02\n",
            " 7.47423298e-02 7.81466956e-02 6.47744526e-02 7.47423298e-02\n",
            " 6.86855104e-02 7.47423298e-02 6.86855104e-02 6.99397307e-02\n",
            " 7.23817255e-02 6.99397307e-02 6.34162167e-02 6.20277497e-02\n",
            " 6.20277497e-02 6.61043208e-02 6.47744526e-02 5.91515611e-02\n",
            " 6.06069738e-02 6.61043208e-02 6.61043208e-02 6.20277497e-02\n",
            " 6.34162167e-02 5.91515611e-02 6.61043208e-02 6.34162167e-02\n",
            " 6.20277497e-02 6.20277497e-02 5.91515611e-02 5.91515611e-02\n",
            " 5.91515611e-02 5.61259837e-02 5.91515611e-02 5.76588886e-02\n",
            " 5.91515611e-02 5.91515611e-02 6.06069738e-02 6.06069738e-02\n",
            " 5.45494548e-02 6.06069738e-02 6.20277497e-02 5.91515611e-02\n",
            " 5.91515611e-02 6.20277497e-02 6.20277497e-02 5.76588886e-02\n",
            " 6.47744526e-02 6.20277497e-02 6.06069738e-02 5.91515611e-02\n",
            " 5.29254036e-02 5.61259837e-02 6.20277497e-02 5.45494548e-02\n",
            " 5.91515611e-02 5.45494548e-02 5.45494548e-02 5.12493125e-02\n",
            " 5.29254036e-02 5.29254036e-02 5.45494548e-02 5.45494548e-02\n",
            " 5.61259837e-02 5.61259837e-02 5.12493125e-02]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_sc = sc.transform(X_train)\n",
        "X_test_sc = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "D-5CXnt_ZeA8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating the Model for Support Vector Regression"
      ],
      "metadata": {
        "id": "g4tLiIZDZj_Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR"
      ],
      "metadata": {
        "id": "gEvBgLnuZhgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svr_rbf = SVR(kernel = 'rbf')\n",
        "svr_rbf.fit(X_train_sc , y_train)\n",
        "svr_rbf.score(X_test_sc , y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olwlBD-DZ5vy",
        "outputId": "281b9454-0389-423a-a916-64ce24388a5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.20638035840828173"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will try diffeent kernels for different Accuracy Scores"
      ],
      "metadata": {
        "id": "eyoKh-nob-UQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svr_linear = SVR(kernel = 'linear')\n",
        "svr_linear.fit(X_train_sc , y_train)\n",
        "svr_linear.score(X_test_sc , y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VF6bXsWtarAP",
        "outputId": "0baf01fa-364a-461a-a8dc-d8744cd2ddec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7962656700653191"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svr_poly = SVR(kernel = 'poly' , degree = 2)\n",
        "svr_poly.fit(X_train_sc , y_train)\n",
        "svr_poly.score(X_test_sc , y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjIns1aXbW3u",
        "outputId": "e4a16b21-9498-48c5-e6a4-27e6bb320679"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.18266215211874637"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svr_sigmoid = SVR(kernel = 'sigmoid')\n",
        "svr_sigmoid.fit(X_train_sc , y_train)\n",
        "svr_sigmoid.score(X_test_sc , y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xj_-M4YAbmSu",
        "outputId": "c7182653-0db0-4582-df7c-961fa7bf22ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4867409362089056"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence Linear model giving us the best Accuracy Score , we will go with that"
      ],
      "metadata": {
        "id": "5IUOj1XrcFcH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svr_linear.predict(X_test_sc)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1dQSXo-b7I_",
        "outputId": "955e6a71-add7-430f-a3c3-d199200e2e54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 86.53643947,  33.70344615, 122.48608154, ...,  26.23978561,\n",
              "        64.52625061, 176.17288975])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZPouHfVcZzt",
        "outputId": "71f440fc-c576-4276-d52d-04c4cf4d82a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2435     80.00\n",
              "3113     40.00\n",
              "426     120.00\n",
              "1124     79.00\n",
              "1161     45.00\n",
              "         ...  \n",
              "2078     28.34\n",
              "6855     84.00\n",
              "4381     32.00\n",
              "3862     63.00\n",
              "43      180.00\n",
              "Name: price, Length: 1424, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As in general we do naot use SVR for the formation of Regression model"
      ],
      "metadata": {
        "id": "pl9OQ7BgcqrM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EuNvryYlccGF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}