LinearRegression.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "LdFhE0NP4Cej"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "4U--yrM84Chz"
      },
      "outputs": [],
      "source": [
        "df =  pd.read_csv(\"diabetes.csv\", usecols = ['Glucose','BloodPressure','Outcome'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "D_jIK3FD4Ckt",
        "outputId": "e3a5121b-7b9b-40ed-b235-3c8567e46854"
      },
      "outputs": [
        {
          "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>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Glucose  BloodPressure  Outcome\n",
              "0        148             72        1\n",
              "1         85             66        0\n",
              "2        183             64        1\n",
              "3         89             66        0\n",
              "4        137             40        1\n",
              "..       ...            ...      ...\n",
              "763      101             76        0\n",
              "764      122             70        0\n",
              "765      121             72        0\n",
              "766      126             60        1\n",
              "767       93             70        0\n",
              "\n",
              "[768 rows x 3 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "E9JqAg0Q4qti",
        "outputId": "575389e2-bc24-4315-ad31-64b6c9695a4d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'BloodPressure')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(df['Glucose'],df['BloodPressure'])\n",
        "plt.xlabel('Glucose')\n",
        "plt.ylabel('BloodPressure')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "4IeszF90Dr8U"
      },
      "outputs": [],
      "source": [
        "x = df.iloc[:,0:1]\n",
        "y = df.iloc[:,-2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75fFzi2yD1FL",
        "outputId": "e99d1836-8683-4604-8be9-7824e10a4277"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      72\n",
              "1      66\n",
              "2      64\n",
              "3      66\n",
              "4      40\n",
              "       ..\n",
              "763    76\n",
              "764    70\n",
              "765    72\n",
              "766    60\n",
              "767    70\n",
              "Name: BloodPressure, Length: 768, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ZnkMKtM-D4ah"
      },
      "outputs": [],
      "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=0.2,random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "s1QIrTeKD-dx"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "hcEbCKK_ED_L",
        "outputId": "504a63e7-516f-441c-ee75-dc33c34da664"
      },
      "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": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FaDQMoi7EeEV",
        "outputId": "6c945928-2b4c-4b9a-bb1d-f9ebf41e051e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "158     74\n",
              "251     84\n",
              "631     78\n",
              "757     72\n",
              "689     82\n",
              "      ... \n",
              "733     56\n",
              "441     66\n",
              "627     78\n",
              "84     108\n",
              "55      50\n",
              "Name: BloodPressure, Length: 154, dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_test\n",
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0kBxgmBEl5L",
        "outputId": "af36a7f9-bb58-4a1f-93ef-7329d7eaedba"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\karan\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([65.99943158])"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr.predict(x_test.iloc[0].values.reshape(1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "TgzAVYzCErEy",
        "outputId": "3bd16575-80ad-4b4f-d36b-af1c3cf989bc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x1d2aa7721d0>]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(df['Glucose'],df['BloodPressure'])\n",
        "plt.plot(x_train,lr.predict(x_train),color='red')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "pTfHPX8UE1C1"
      },
      "outputs": [],
      "source": [
        "m = lr.coef_\n",
        "b = lr.intercept_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtVhHmjQE9LX",
        "outputId": "729704c9-0800-47f2-bba5-2c4ce3c23e26"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([65.99943158])"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m * 88 + b"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}