DSN_week_4_Assignment_.ipynb

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      "name": "DSN week 4 Assignment .ipynb",
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        "<a href=\"https://colab.research.google.com/github/dmarinere/ds/blob/master/DSN_week_4_Assignment_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "pwd"
      ],
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"C:\\\\Users\\\\Tayo Swavey\\\\Desktop\\\\Iyanu's File\""
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          "metadata": {
            "tags": []
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          "execution_count": 2
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "GGVyXD-cQM7e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import modules\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "5f2a1b28-3ede-405d-a2cb-46a555693bd2"
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      "source": [
        "data= pd.read_csv(\"Salary_Data.csv\")\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>YearsExperience</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>39343.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>46205.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>37731.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43525.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\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"
            ]
          },
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          "execution_count": 3
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      ]
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    {
      "cell_type": "code",
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        "id": "yRVieicDQM7n",
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        "colab": {},
        "outputId": "236b59d1-3811-46d9-f49f-4346feb7dd34"
      },
      "source": [
        "data.info()  #This proves there is no missing data in our dataset "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 2 columns):\n",
            "YearsExperience    30 non-null float64\n",
            "Salary             30 non-null float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 544.0 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAZ9UPxuQM7r",
        "colab_type": "text"
      },
      "source": [
        "### Question 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odV6XIeFQM7s",
        "colab_type": "text"
      },
      "source": [
        "In the assignment we were told to find the best Linear regression equation that would predict the Salary based on the experience of the individual.\n",
        "This would be gotten from the regression coefficient and the intercept."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Djf8TlYyQM7t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#This would convert the panda dataframe to numpy and also differentiate the data clearly , \n",
        "#X here is the Independent dataframe which is the Year of Experience and y is our dependent data in this data.\n",
        "X = data.iloc[:, :-1].values #All rows, all columns excluding the last column indexed -1\n",
        "y = data.iloc[:, 1].values #All rows, column 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gg4VICZmQM7x",
        "colab_type": "code",
        "colab": {},
        "outputId": "38d9123f-74aa-4812-be4f-c1ebac0d8596"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X, y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thoMNk5iQM71",
        "colab_type": "text"
      },
      "source": [
        "Next we would find the regression score\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2zXl79NQM72",
        "colab_type": "code",
        "colab": {},
        "outputId": "c54759cf-bedf-4110-c9ad-d510e694427e"
      },
      "source": [
        "print(\"The regression score is:\", regressor.score(X,y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The regression score is: 0.9569566641435084\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvltU1C9QM77",
        "colab_type": "code",
        "colab": {},
        "outputId": "7182a087-e305-44f1-9132-b97ec2887e23"
      },
      "source": [
        "print('The intercept for this equation is :', regressor.intercept_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The intercept for this equation is : 25792.200198668717\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VNWj1yqQM7_",
        "colab_type": "code",
        "colab": {},
        "outputId": "9adeb53c-ad63-4579-f1ba-b89c14e5f0d2"
      },
      "source": [
        "print(\"The Slope of the regression is: \", regressor.coef_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Slope of the regression is:  [9449.96232146]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrJV5q45QM8D",
        "colab_type": "text"
      },
      "source": [
        "### The equation for the linear regression here is Pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fg7528jyQM8D",
        "colab_type": "text"
      },
      "source": [
        "The linear equation for the predicted data frame is \n",
        "the co-efficient and the intercept which are\n",
        "#### Pred = 9449.96X + 25792.2 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZbbFH4z3QM8E",
        "colab_type": "text"
      },
      "source": [
        "## Question 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVNEf5uCQM8F",
        "colab_type": "text"
      },
      "source": [
        "#### What new value of y will the linear regression equation suggest in line with 20% split of the previous Salary?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00qPqV-vQM8F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "#we splitted our data by 20 percent here.\n",
        "x_train, x_test,y_train, y_test= train_test_split(X, y, test_size=0.2)b"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jxp-mgqYQM8J",
        "colab_type": "code",
        "colab": {},
        "outputId": "ab01d567-3ea0-44e1-f33f-ce726771d5e0"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor_2 = LinearRegression()\n",
        "regressor_2.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-1-hkH2QM8N",
        "colab_type": "code",
        "colab": {},
        "outputId": "369761f3-e240-4b9e-dc10-8769fc3ae799"
      },
      "source": [
        "regressor_2.score(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.967952568814689"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-GtBSp5tQM8R",
        "colab_type": "code",
        "colab": {},
        "outputId": "8948e22d-3077-4742-e4ad-e4c8618c199b"
      },
      "source": [
        "\n",
        "regressor_2.coef_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9455.89017044])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9z7985OHQM8U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = regressor_2.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JyzwKUZNQM8Y",
        "colab_type": "code",
        "colab": {},
        "outputId": "e03f49aa-5bda-4066-fcba-320f00fd7e87"
      },
      "source": [
        "y_pred #This are the new salary that the linear regression suggested "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 81769.77037357, 115810.97498717,  67585.93511791, 102572.72874855,\n",
              "        55293.27789633,  37327.08657249])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znt_SFTjQM8c",
        "colab_type": "code",
        "colab": {},
        "outputId": "9ca63fbc-1012-4906-8de4-643cb20b197c"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 93940., 112635.,  61111., 113812.,  54445.,  46205.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "y8NlCOIMQM8h",
        "colab_type": "code",
        "colab": {},
        "outputId": "81fb0be5-28dd-48b8-df35-2d4072fb0878"
      },
      "source": [
        "# Visualising the results\n",
        "plt.scatter(x_test, y_test, color = 'red') #Scatter plot of the X an y dataset\n",
        "\n",
        "plt.plot(x_test, regressor_2.predict(x_test), color = 'blue')#Line plot of X against predicted Y \n",
        "#(remember that output of regressor_2.predict is y_pred)\n",
        "\n",
        "plt.title('Simple Linear Regression of Predictor vs Dependent variable')\n",
        "plt.xlabel('x_test (Predictor)')\n",
        "plt.ylabel('y_test(Dependent Variable)')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "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": "ok-GBQ2OQM8l",
        "colab_type": "text"
      },
      "source": [
        "Question 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbToDBNUQM8l",
        "colab_type": "code",
        "colab": {},
        "outputId": "b9b96f06-c018-47e9-a9fc-0249226757f4"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 7131.100384466475\n",
            "Mean Squared Error: 67664038.58995993\n",
            "Root Mean Squared Error: 8225.81537538741\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtuoXOZvQM8p",
        "colab_type": "code",
        "colab": {},
        "outputId": "6ce0c5df-4ced-4c98-b4a4-4f0df8264427"
      },
      "source": [
        "#Importing Polynomial Feature class from SKLearn\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 6) #Degree depicts the Curves\n",
        "X_poly = poly_reg.fit_transform(x_train)\n",
        "poly_reg.fit(X_poly, y_train) #Transform X as polynomial feature"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PolynomialFeatures(degree=6, include_bias=True, interaction_only=False,\n",
              "                   order='C')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46rE9jgGQM8s",
        "colab_type": "code",
        "colab": {},
        "outputId": "a41b3e5c-f580-4fed-9af0-8754e8e3bc0f"
      },
      "source": [
        "regressor_3 = LinearRegression()\n",
        "regressor_3.fit(X_poly, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKpG_0AUQM8v",
        "colab_type": "code",
        "colab": {},
        "outputId": "b55c24bc-9f9a-4401-8157-d923ab8b3af9"
      },
      "source": [
        "regressor_3.score(X_poly, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9719531628109493"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AulIXTXKQM8y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Poly_pred = regressor_3.predict(poly_reg.fit_transform(x_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-bmIRhMQM82",
        "colab_type": "code",
        "colab": {},
        "outputId": "72455811-30d5-4037-ca4a-5c3279348d7f"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 93940., 112635.,  61111., 113812.,  54445.,  46205.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3r2JLA9QM86",
        "colab_type": "code",
        "colab": {},
        "outputId": "f621d644-0d51-49bb-a4d9-3ace40cb74a6"
      },
      "source": [
        "regressor_3.predict(poly_reg.fit_transform(x_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 81606.08119739, 113100.97174174,  65719.92435293, 106220.09335503,\n",
              "        55358.42889653,  38298.14076397])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vf98kN2QM8-",
        "colab_type": "code",
        "colab": {},
        "outputId": "1c2b6bdc-dc2e-4686-c57c-24b59c545d62"
      },
      "source": [
        " # Visualising the results\n",
        "plt.scatter(x_test, y_test, color = 'red') #Scatter plot of the X an y dataset\n",
        "\n",
        "plt.scatter(x_test,  regressor_3.predict(poly_reg.fit_transform(x_test)), color = 'blue')#Line plot of X against predicted Y \n",
        "#(remember that output of regressor_2.predict is y_pred)\n",
        "\n",
        "plt.title('Simple Linear Regression of Predictor vs Dependent variable')\n",
        "plt.xlabel('x_test (Predictor)')\n",
        "plt.ylabel('y_test(Dependent Variable)')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "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": {
        "id": "WVPiEKJzQM9D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}