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": "ABX9TyOzvdjWxoiILMu83hGM2yc6",
      "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/e69c48019557b4220343c3d18d6e75c0/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": "6b3ba4db-56a7-4428-d7cc-42621b837509"
      },
      "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": 19,
      "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": "849349ba-ffcc-4d4c-c437-95413643af1e"
      },
      "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": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "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": "8243d54e-fad1-49b7-add3-84496532ee97"
      },
      "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('Truth or Bluff (Polynomial Regression)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "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": "dfaa8f37-8115-4f6f-d24e-6e414b05eebf"
      },
      "source": [
        "lin_reg_2.predict(poly_reg.fit_transform([[8.5]]))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([349013.934707])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "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": "003875bc-7224-471e-926f-714a8699e79b"
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "erOIBuw9sNX0",
        "outputId": "619e6c28-0a7b-4c2e-a262-baa6a954ffd5"
      },
      "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('Truth or Bluff (Polynomial Regression)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "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": "1bcfbec3-61ec-4fd2-b70f-49f33c574fc8"
      },
      "source": [
        "lin_reg_2.predict(poly_reg.fit_transform([[8.5]]))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([362013.86991913])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    }
  ]
}