1419_Linear_Regression.ipynb

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      "name": "1419_Linear_Regression.ipynb",
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        "<a href=\"https://colab.research.google.com/github/Minhaz78/Machine-Learning-Practice/blob/main/1419_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "metadata": {
        "id": "29QR0Uee8b7e"
      },
      "source": [
        ""
      ],
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFDhEQ3W8c9s"
      },
      "source": [
        "# Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NPZiutpCYk7"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUVXJMAWyxKM"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vNf7qablC8dI"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kN2hvxTtCkqx"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5l3a3t4jxnO",
        "outputId": "47ab4866-a253-48e6-a8f6-3ffc360b0872"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAD-aYm5zaCQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a8b615b8-1112-4919-bdb3-dc428ee526b4"
      },
      "source": [
        "dataset=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/dataset/dataset/50_Startups.csv')\n",
        "\n",
        "dataset.head()"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
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            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
              "0  165349.20       136897.80        471784.10    New York  192261.83\n",
              "1  162597.70       151377.59        443898.53  California  191792.06\n",
              "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
              "3  144372.41       118671.85        383199.62    New York  182901.99\n",
              "4  142107.34        91391.77        366168.42     Florida  166187.94"
            ]
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          "metadata": {},
          "execution_count": 178
        }
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    {
      "cell_type": "code",
      "source": [
        "X = dataset.iloc[:, 2].values\n",
        "y = dataset.iloc[:,4].values\n",
        "print(X)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKCwGE1vY76m",
        "outputId": "bf72e999-bb7a-4016-df2f-95e7982ee681"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[471784.1  443898.53 407934.54 383199.62 366168.42 362861.36 127716.82\n",
            " 323876.68 311613.29 304981.62 229160.95 249744.55 249839.44 252664.93\n",
            " 256512.92 261776.23 264346.06 282574.31 294919.57      0.   298664.47\n",
            " 299737.29 303319.26 304768.73 140574.81 137962.62 134050.07 353183.81\n",
            " 118148.2  107138.38  91131.24  88218.23  46085.25 214634.81 210797.67\n",
            " 205517.64 201126.82 197029.42 185265.1  174999.3  172795.67 164470.71\n",
            " 148001.11  35534.17  28334.72   1903.93 297114.46      0.        0.\n",
            "  45173.06]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIsUAgYJ7X25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f16f5f6-ae10-46d3-8e57-ec17fdaf11d9"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[192261.83 191792.06 191050.39 182901.99 166187.94 156991.12 156122.51\n",
            " 155752.6  152211.77 149759.96 146121.95 144259.4  141585.52 134307.35\n",
            " 132602.65 129917.04 126992.93 125370.37 124266.9  122776.86 118474.03\n",
            " 111313.02 110352.25 108733.99 108552.04 107404.34 105733.54 105008.31\n",
            " 103282.38 101004.64  99937.59  97483.56  97427.84  96778.92  96712.8\n",
            "  96479.51  90708.19  89949.14  81229.06  81005.76  78239.91  77798.83\n",
            "  71498.49  69758.98  65200.33  64926.08  49490.75  42559.73  35673.41\n",
            "  14681.4 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAHgcO2OC_W4"
      },
      "source": [
        "## Taking care of missing data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEtP-fkfDGQw"
      },
      "source": [
        "## Encoding categorical data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y114Pye9DO_J"
      },
      "source": [
        "### Encoding the Independent Variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biFO3cRADRTQ"
      },
      "source": [
        "### Encoding the Dependent Variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnfofU7qDbjw"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84oYm-TO7k1b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7957db8c-7ce0-4934-c0fc-d7319a3a8792"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
        "\n",
        "print(X_train)\n",
        "\n"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[214634.81 205517.64 134050.07 210797.67 294919.57 323876.68 256512.92\n",
            "   1903.93      0.   107138.38 261776.23  91131.24  46085.25 264346.06\n",
            " 148001.11 298664.47  35534.17 311613.29 252664.93 137962.62 362861.36\n",
            " 282574.31 172795.67  45173.06 443898.53 249839.44 197029.42 140574.81\n",
            " 127716.82 304768.73 201126.82 299737.29      0.   304981.62 174999.3\n",
            " 297114.46 383199.62 471784.1       0.    28334.72]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lp3hDddMDgfD"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOyg9-bz9SrK"
      },
      "source": [
        "## Training the Linear regression model with the training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UtsbLQcn9t4M",
        "outputId": "f9306163-abdd-45bb-c074-5e816d5720e9"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "#X_train = reshape((1,1))\n",
        "regressor.fit(X_train.reshape(-1,1), y_train.reshape(-1,1))"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tiaCe1IQ-M3_"
      },
      "source": [
        "## Predict test set result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYhBB02r-z1Y"
      },
      "source": [
        "y_pred = regressor.predict(X_test.reshape(-1,1))"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Making the Confusion Matrix"
      ],
      "metadata": {
        "id": "gskMIzb5rO9U"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCo2gg8i_I8t"
      },
      "source": [
        "## Visualise the training set result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rGD8_Nb_VJM"
      },
      "source": [
        "# plt.scatter(X_train, y_train, marker='*',color='g')\n",
        "# plt.plot(X_train, regressor.predict(X_train.reshape(-1,1)), color='blue')\n",
        "# plt.title('Profit vs R&D Spend\t (Training set)')\n",
        "# plt.xlabel('R&D Spend\t')\n",
        "# plt.ylabel('profit')\n",
        "# plt.show()\n"
      ],
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.scatter(X_train, y_train, marker='*',color='g')\n",
        "# plt.plot(X_train, regressor.predict(X_train.reshape(-1,1)), color='blue')\n",
        "# plt.title('Profit vs Marketing Spend\t(Training set)')\n",
        "# plt.xlabel('Marketing Spend\t\t')\n",
        "# plt.ylabel('profit')\n",
        "# plt.show()\n"
      ],
      "metadata": {
        "id": "NRqaeL3NjfOa"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_train, y_train, marker='*',color='g')\n",
        "plt.plot(X_train, regressor.predict(X_train.reshape(-1,1)), color='blue')\n",
        "plt.title('Profit vs Administration(Training set)')\n",
        "plt.xlabel('Administration')\n",
        "plt.ylabel('profit')\n",
        "# plt.show()\n"
      ],
      "metadata": {
        "id": "KJznHYZFdY04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "47603927-750d-4dce-b95d-93531b4fd14a"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'profit')"
            ]
          },
          "metadata": {},
          "execution_count": 186
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualise the test set result"
      ],
      "metadata": {
        "id": "359g2ILcz9hx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.scatter(X_train, y_train, color='red')\n",
        "# plt.scatter(X_test, y_test, color='blue')\n",
        "# plt.plot(X_test, regressor.predict(X_test.reshape(-1,1)), color = 'red')\n",
        "# plt.title('Profit vs Marketing Spend\t(Training set)')\n",
        "# plt.xlabel('Marketing Spend\t\t')\n",
        "# plt.ylabel('profit')\n",
        "\n",
        "# plt.show()\n"
      ],
      "metadata": {
        "id": "C2u9DQCpkkGU"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_train, y_train, color='red')\n",
        "plt.scatter(X_test, y_test, color='blue')\n",
        "plt.plot(X_test, regressor.predict(X_test.reshape(-1,1)), color = 'red')\n",
        "plt.title('Profit vs Administration(Training set)')\n",
        "plt.xlabel('Administration')\n",
        "plt.ylabel('profit')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "01zGhRNvyPua",
        "outputId": "9fcaeb8b-d4c5-4454-eafb-d1cac0a70107"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.scatter(X_train, y_train, color='red')\n",
        "# plt.scatter(X_test, y_test, color='blue')\n",
        "# plt.plot(X_test, regressor.predict(X_test.reshape(-1,1)), color = 'red')\n",
        "\n",
        "# plt.title('Profit vs R&D Spend\t (Training set)')\n",
        "# plt.xlabel('R&D Spend\t')\n",
        "# plt.ylabel('profit')\n",
        "# plt.show()\n"
      ],
      "metadata": {
        "id": "o8V-1ISAkkBL"
      },
      "execution_count": 189,
      "outputs": []
    }
  ]
}