Overfittting_and_underfitting.ipynb

overfittting_and_underfitting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Overfittting_and_underfitting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNH/6aBM+n7SsyprDqH7GwT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/ashwath007/cea49594c21f0f8b4b19c2eb60f8ad9d/overfittting_and_underfitting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Iooh-Z4omjN"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJshztlbouHk"
      },
      "source": [
        "**Importing our Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uObnAHGSoytM"
      },
      "source": [
        "dataset = pd.read_csv('Data.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QtGhOtJ0pB2k"
      },
      "source": [
        "**View and Pollting the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "IdOKRijMpTJs",
        "outputId": "c0273ae2-3c62-4334-d56b-37c72b853849"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 18,
      "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>Position</th>\n",
              "      <th>Level</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Data Analyst</td>\n",
              "      <td>1</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SDE</td>\n",
              "      <td>2</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SDE 1</td>\n",
              "      <td>3</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Team Lead</td>\n",
              "      <td>4</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HR</td>\n",
              "      <td>5</td>\n",
              "      <td>110000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Position  Level  Salary\n",
              "0  Data Analyst      1   45000\n",
              "1           SDE      2   50000\n",
              "2         SDE 1      3   60000\n",
              "3     Team Lead      4   80000\n",
              "4            HR      5  110000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "HeuNo19gpE2j",
        "outputId": "507d1f30-fe6a-4722-8a3a-5dfa52877939"
      },
      "source": [
        "plt.scatter(X, y, color = 'red')\n",
        "plt.title('Thedot - in')\n",
        "plt.xlabel('Position Level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()  "
      ],
      "execution_count": 57,
      "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": "93HzgC7iriWO"
      },
      "source": [
        "**Training the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3l2m5-NjqxsO",
        "outputId": "d2e45a0f-3d83-4e66-a18b-ad5e3b5990c8"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, y)\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 20)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "lin_reg_2 = LinearRegression()\n",
        "lin_reg_2.fit(X_poly, y)\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-9X5aoIrqCN"
      },
      "source": [
        "**Trained Polynomial Regression with higher degrees**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gFpKVAXjq8rN",
        "outputId": "5e9284f9-9c25-4a59-e1e6-c2fdcd0c8d35"
      },
      "source": [
        "plt.scatter(X, y, color = 'red')\n",
        "plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
        "plt.title('Thedot - in')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "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": "vZoZlEGSsmCM",
        "outputId": "1ed0e31b-c289-415e-a3f0-b22fa2cd6925"
      },
      "source": [
        "lin_reg_2.predict(poly_reg.fit_transform([[8.5]]))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([349013.934707])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGb3NwLjsE4d"
      },
      "source": [
        "**Trained Polynomial Regression with lower degrees**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dx5eZ9QesI7G",
        "outputId": "70f296d9-5258-4846-a8c1-45196f2d99e4"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, y)\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 2)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "lin_reg_2 = LinearRegression()\n",
        "lin_reg_2.fit(X_poly, y)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "erOIBuw9sNX0",
        "outputId": "e23314b1-3fd5-4b13-f486-3f3a2c883bf8"
      },
      "source": [
        "plt.scatter(X, y, color = 'red')\n",
        "plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
        "plt.title('Thedot - in')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "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": "PqOHEqRnsesG"
      },
      "source": [
        "**Let's us Test the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwpELH9_sh1m",
        "outputId": "95b4e29e-8ca4-4b08-946c-fa4d18105b38"
      },
      "source": [
        "lin_reg_2.predict(poly_reg.fit_transform([[8.5]]))"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([327206.85564436])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9IGynkE1RKO"
      },
      "source": [
        "**Trained Polynomial Regression with correct degrees**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NoeqPEb01UNm",
        "outputId": "845d6b01-c4b8-4940-f433-d2c732c7c964"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, y)\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 5)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "lin_reg_2 = LinearRegression()\n",
        "lin_reg_2.fit(X_poly, y)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zI91f8ju1XAf",
        "outputId": "82231b02-1c68-4795-fc39-891082ccab7c"
      },
      "source": [
        "plt.scatter(X, y, color = 'red')\n",
        "plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
        "plt.title('Thedot - in')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 65,
      "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"
          }
        }
      ]
    }
  ]
}