Simple_Linear_Regression.ipynb

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    "colab": {
      "name": "Simple Linear Regression.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zohaib304/MachineLearning/blob/main/Simple_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_LulNCC8z96"
      },
      "source": [
        "# Simple Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RB39Y7DQ80Co"
      },
      "source": [
        "Dataset -> [Download](https://drive.google.com/file/d/1J1WVGEluZrfseWKiykcEfpsMNweuy9n-/view?usp=sharing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpXdowrE9DxW"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhKd4hWx9GFt"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JhpWJi59J1p"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8GfYDZ59O18"
      },
      "source": [
        "dataset = pd.read_csv('Salary_Data.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "473k7qoa8Y38",
        "outputId": "32060478-3e70-4780-82ca-87d460a835df"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\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>YearsExperience</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.1</td>\n",
              "      <td>39343.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.3</td>\n",
              "      <td>46205.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.5</td>\n",
              "      <td>37731.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>43525.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.2</td>\n",
              "      <td>39891.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   YearsExperience   Salary\n",
              "0              1.1  39343.0\n",
              "1              1.3  46205.0\n",
              "2              1.5  37731.0\n",
              "3              2.0  43525.0\n",
              "4              2.2  39891.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Nbn-rk48Y39"
      },
      "source": [
        ""
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AyhQaTwP9RzG"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxOOauiN9VpC"
      },
      "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 = 1/3, random_state = 0)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5Qilg-m8Y3-",
        "outputId": "5a743a4c-534b-48ee-ec74-40d9409b2a7c"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.9],\n",
              "       [ 5.1],\n",
              "       [ 3.2],\n",
              "       [ 4.5],\n",
              "       [ 8.2],\n",
              "       [ 6.8],\n",
              "       [ 1.3],\n",
              "       [10.5],\n",
              "       [ 3. ],\n",
              "       [ 2.2],\n",
              "       [ 5.9],\n",
              "       [ 6. ],\n",
              "       [ 3.7],\n",
              "       [ 3.2],\n",
              "       [ 9. ],\n",
              "       [ 2. ],\n",
              "       [ 1.1],\n",
              "       [ 7.1],\n",
              "       [ 4.9],\n",
              "       [ 4. ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KJmHfBL8Y3-",
        "outputId": "979ea815-d7fa-4857-de89-43dca41a5f75"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.5],\n",
              "       [10.3],\n",
              "       [ 4.1],\n",
              "       [ 3.9],\n",
              "       [ 9.5],\n",
              "       [ 8.7],\n",
              "       [ 9.6],\n",
              "       [ 4. ],\n",
              "       [ 5.3],\n",
              "       [ 7.9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vNzWt0Q8Y3_",
        "outputId": "d9c9b8aa-fdcc-48b2-eec2-e57db76f5561"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 56642.,  66029.,  64445.,  61111., 113812.,  91738.,  46205.,\n",
              "       121872.,  60150.,  39891.,  81363.,  93940.,  57189.,  54445.,\n",
              "       105582.,  43525.,  39343.,  98273.,  67938.,  56957.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgX3j7t_8Y3_",
        "outputId": "02f6324d-b636-49a9-fcd7-5328c248ee9f"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
              "        55794.,  83088., 101302.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZijQwFMQ9itx"
      },
      "source": [
        "## Training the Simple Linear Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4Aj_8YJ9l7J",
        "outputId": "f43e4b18-2638-42bf-9582-cc804d4feaa0"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wa2T1Lq89o5H"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rn_sTJ2o9smm"
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zSoMZ-P9v8t"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "IAePn_u-93tI",
        "outputId": "8a7cfa4d-5be5-4851-e4c7-bf25711ba361"
      },
      "source": [
        "plt.scatter(X_train, y_train, color = 'red')\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
        "plt.title('Salary vs Experience (Training set)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUX1Vhsv97ZT"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Ze9vpBTf-Bol",
        "outputId": "5288baeb-9fc3-4e4c-89c7-82e5213e1a26"
      },
      "source": [
        "plt.scatter(X_test, y_test, color = 'red')\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
        "plt.title('Salary vs Experience (Test set)')\n",
        "plt.xlabel('Years of Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXfh8SPP8Y4B",
        "outputId": "63041439-9755-48f3-aff1-5539388796a7"
      },
      "source": [
        "regressor.score(X_test, y_test)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9749154407708353"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-udcdHkn8Y4B"
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    }
  ]
}