Polynomial_Regression_(Position_Salaries)(Task-5).ipynb

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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "#IMPORT INDEPENDENCIES AND MOUNT DRIVE\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
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        },
        "id": "iOu9FRqRLJWd",
        "outputId": "f05d94b1-1dcf-41eb-bb0e-0cd7171a46ae"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Position</th>\n",
              "      <th>Level</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Business Analyst</td>\n",
              "      <td>1</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Junior Consultant</td>\n",
              "      <td>2</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Senior Consultant</td>\n",
              "      <td>3</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Manager</td>\n",
              "      <td>4</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Country Manager</td>\n",
              "      <td>5</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Region Manager</td>\n",
              "      <td>6</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Partner</td>\n",
              "      <td>7</td>\n",
              "      <td>200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Senior Partner</td>\n",
              "      <td>8</td>\n",
              "      <td>300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>C-level</td>\n",
              "      <td>9</td>\n",
              "      <td>500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>CEO</td>\n",
              "      <td>10</td>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "            Position  Level   Salary\n",
              "0   Business Analyst      1    45000\n",
              "1  Junior Consultant      2    50000\n",
              "2  Senior Consultant      3    60000\n",
              "3            Manager      4    80000\n",
              "4    Country Manager      5   110000\n",
              "5     Region Manager      6   150000\n",
              "6            Partner      7   200000\n",
              "7     Senior Partner      8   300000\n",
              "8            C-level      9   500000\n",
              "9                CEO     10  1000000"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#READ DATA FROM PANDAS LIBRARY\n",
        "dataset = pd.read_csv('/content/drive/MyDrive/Datasets/Datasets/Position_Salaries.csv')\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7ov_cxa2XGPH"
      },
      "outputs": [],
      "source": [
        "# Importing the dataset\n",
        "X = dataset.iloc[:, 1:2].values\n",
        "y = dataset.iloc[:, 2].values\n",
        "\n",
        "# Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)\n"
      ]
    },
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      "execution_count": 4,
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          "height": 306
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        "id": "Ct3kSHv1XIZd",
        "outputId": "c67f9924-14b3-4b99-f604-645be1113543"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Fitting the Linear Regression to the dataset\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model= LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "#Visualizing the Linear Regression\n",
        "plt.scatter(X_train,y_train, color='red')\n",
        "plt.plot(X_train,model.predict(X_train),color='blue')\n",
        "plt.title('Predicting the salary based on Position levels (Linear Regression)')\n",
        "plt.xlabel('Position Level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "P90PXi9SLYvV",
        "outputId": "95b74b81-5e9c-4688-80c8-6f5c907ac36a"
      },
      "outputs": [
        {
          "data": {
            "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>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Fitting Polynomial Regression to the dataset (Adding that curve in line)\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree=4)\n",
        "X_poly = poly_reg.fit_transform(X_train)\n",
        "poly_model = LinearRegression()\n",
        "poly_model.fit(X_poly,y_train)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OjxJHmelHD0s",
        "outputId": "f88fb1ac-af90-4972-c3e7-836c9bd39a7d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1.000e+00, 3.000e+00, 9.000e+00, 2.700e+01, 8.100e+01],\n",
              "       [1.000e+00, 9.000e+00, 8.100e+01, 7.290e+02, 6.561e+03],\n",
              "       [1.000e+00, 5.000e+00, 2.500e+01, 1.250e+02, 6.250e+02]])"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "poly_reg.fit_transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhgHCEKuGavv",
        "outputId": "297dde1b-d074-4892-d0bd-90e819acd66d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 63555.47530091, 552670.08013598, 117231.50005642])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Predicting the model\n",
        "prediction = poly_model.predict(poly_reg.fit_transform(X_test))\n",
        "prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1q-omv8pHgcI",
        "outputId": "59e54fdd-6211-4595-f964-98f5000d035a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 60000, 500000, 110000])"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJdstQucHSKw",
        "outputId": "00bff839-2262-4081-dce5-27294bf8304a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score is:  0.9755392877150003\n",
            "R2 score is:  0.9755392877150003\n"
          ]
        }
      ],
      "source": [
        "#Checking the accuracy\n",
        "acc = poly_model.score(poly_reg.fit_transform(X_test),y_test)\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_test, prediction)\n",
        "print(\"Accuracy score is: \", acc)\n",
        "print(\"R2 score is: \", r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "jxUDjrwnEzrr",
        "outputId": "5a2bf79d-365d-49cf-94e4-02c467bd5a6a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Visualizing the Polynomial Regression\n",
        "X_grid= np.arange(min(X_train),max(X_train),0.1)\n",
        "X_grid= X_grid.reshape(len(X_grid),1)\n",
        "plt.scatter(X,y, color='red')\n",
        "plt.plot(X_grid, poly_model.predict(poly_reg.fit_transform(X_grid)),color='blue')\n",
        "plt.title('Predicting the salary based on Position levels (Polynomial Regression)')\n",
        "plt.xlabel('Position Level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "a-4QJsoPMSLm",
        "outputId": "b0bbef52-4517-4bd8-9492-05b91e07b6b6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#PREDICTING ON TEST DATASET\n",
        "X_grid= np.arange(min(X_test),max(X_test),0.1)\n",
        "X_grid= X_grid.reshape(len(X_grid),1)\n",
        "plt.scatter(X_test,y_test, color='red')\n",
        "plt.plot(X_grid,poly_model.predict(poly_reg.fit_transform(X_grid)),color='blue')\n",
        "plt.title('Predicting the salary based on Position levels(Polynomial Regression)')\n",
        "plt.xlabel('Position Level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOljosV4ZeQw",
        "outputId": "680b899e-7144-4a95-ff92-c582fe8e1d04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "So predicted salary is:  63555.475300909835\n"
          ]
        }
      ],
      "source": [
        "#PREDICT MODEL MANUALLY\n",
        "pred = poly_model.predict(poly_reg.fit_transform([[3]]))\n",
        "print(\"So predicted salary is: \", pred[0])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ZhjCjDKAZlVl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "E9MEuje9Fcyx"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}