HW2_GradientDesecnt.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/negarhonarvar/Machine-Learning/blob/main/HW2_GradientDesecnt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialization"
      ],
      "metadata": {
        "id": "4XSzE2g9nCvG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-67_9T9Mm0Ro"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import preprocessing\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLgfl7GnnMg9",
        "outputId": "ce22d5a2-193e-4b79-80ae-eee9b7750cea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/insurance.csv')\n",
        "data"
      ],
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "_efMZYX2ngC9",
        "outputId": "badd327f-762b-4173-fa1b-cbf32e626903"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age     sex     bmi  children smoker     region      charges\n",
              "0      19  female  27.900         0    yes  southwest  16884.92400\n",
              "1      18    male  33.770         1     no  southeast   1725.55230\n",
              "2      28    male  33.000         3     no  southeast   4449.46200\n",
              "3      33    male  22.705         0     no  northwest  21984.47061\n",
              "4      32    male  28.880         0     no  northwest   3866.85520\n",
              "...   ...     ...     ...       ...    ...        ...          ...\n",
              "1333   50    male  30.970         3     no  northwest  10600.54830\n",
              "1334   18  female  31.920         0     no  northeast   2205.98080\n",
              "1335   18  female  36.850         0     no  southeast   1629.83350\n",
              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
              "\n",
              "[1338 rows x 7 columns]"
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              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83350</td>\n",
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              "      <th>1336</th>\n",
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              "      <td>female</td>\n",
              "      <td>25.800</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Experimenting on Data"
      ],
      "metadata": {
        "id": "gqyl7HL2prb-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "in first step we need to check if our data has samples with missing values so we take care of the missings before continuing to next steps"
      ],
      "metadata": {
        "id": "QlYuphcKqbWE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(data['age'].isna().sum(), data['age'].unique())\n",
        "print(data['sex'].isna().sum(), data['sex'].unique())\n",
        "print(data['bmi'].isna().sum(), data['bmi'].unique())\n",
        "print(data['children'].isna().sum(), data['children'].unique())\n",
        "print(data['smoker'].isna().sum(), data['smoker'].unique())\n",
        "print(data['region'].isna().sum(),data['region'].unique())\n",
        "print(data['charges'].isna().sum(), data['charges'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6wd5p7CpqwB",
        "outputId": "4545ec1f-7bdc-4c2e-b447-2f7c8e9e4311"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "0 [19 18 28 33 32 31 46 37 60 25 62 23 56 27 52 30 34 59 63 55 22 26 35 24\n",
            " 41 38 36 21 48 40 58 53 43 64 20 61 44 57 29 45 54 49 47 51 42 50 39]\n",
            "0 ['female' 'male']\n",
            "0 [27.9   33.77  33.    22.705 28.88  25.74  33.44  27.74  29.83  25.84\n",
            " 26.22  26.29  34.4   39.82  42.13  24.6   30.78  23.845 40.3   35.3\n",
            " 36.005 32.4   34.1   31.92  28.025 27.72  23.085 32.775 17.385 36.3\n",
            " 35.6   26.315 28.6   28.31  36.4   20.425 32.965 20.8   36.67  39.9\n",
            " 26.6   36.63  21.78  30.8   37.05  37.3   38.665 34.77  24.53  35.2\n",
            " 35.625 33.63  28.    34.43  28.69  36.955 31.825 31.68  22.88  37.335\n",
            " 27.36  33.66  24.7   25.935 22.42  28.9   39.1   36.19  23.98  24.75\n",
            " 28.5   28.1   32.01  27.4   34.01  29.59  35.53  39.805 26.885 38.285\n",
            " 37.62  41.23  34.8   22.895 31.16  27.2   26.98  39.49  24.795 31.3\n",
            " 38.28  19.95  19.3   31.6   25.46  30.115 29.92  27.5   28.4   30.875\n",
            " 27.94  35.09  29.7   35.72  32.205 28.595 49.06  27.17  23.37  37.1\n",
            " 23.75  28.975 31.35  33.915 28.785 28.3   37.4   17.765 34.7   26.505\n",
            " 22.04  35.9   25.555 28.05  25.175 31.9   36.    32.49  25.3   29.735\n",
            " 38.83  30.495 37.73  37.43  24.13  37.145 39.52  24.42  27.83  36.85\n",
            " 39.6   29.8   29.64  28.215 37.    33.155 18.905 41.47  30.3   15.96\n",
            " 33.345 37.7   27.835 29.2   26.41  30.69  41.895 30.9   32.2   32.11\n",
            " 31.57  26.2   30.59  32.8   18.05  39.33  32.23  24.035 36.08  22.3\n",
            " 26.4   31.8   26.73  23.1   23.21  33.7   33.25  24.64  33.88  38.06\n",
            " 41.91  31.635 36.195 17.8   24.51  22.22  38.39  29.07  22.135 26.8\n",
            " 30.02  35.86  20.9   17.29  34.21  25.365 40.15  24.415 25.2   26.84\n",
            " 24.32  42.35  19.8   32.395 30.2   29.37  34.2   27.455 27.55  20.615\n",
            " 24.3   31.79  21.56  28.12  40.565 27.645 31.2   26.62  48.07  36.765\n",
            " 33.4   45.54  28.82  22.99  27.7   25.41  34.39  22.61  37.51  38.\n",
            " 33.33  34.865 33.06  35.97  31.4   25.27  40.945 34.105 36.48  33.8\n",
            " 36.7   36.385 34.5   32.3   27.6   29.26  35.75  23.18  25.6   35.245\n",
            " 43.89  20.79  30.5   21.7   21.89  24.985 32.015 30.4   21.09  22.23\n",
            " 32.9   24.89  31.46  17.955 30.685 43.34  39.05  30.21  31.445 19.855\n",
            " 31.02  38.17  20.6   47.52  20.4   38.38  24.31  23.6   21.12  30.03\n",
            " 17.48  20.235 17.195 23.9   35.15  35.64  22.6   39.16  27.265 29.165\n",
            " 16.815 33.1   26.9   33.11  31.73  46.75  29.45  32.68  33.5   43.01\n",
            " 36.52  26.695 25.65  29.6   38.6   23.4   46.53  30.14  30.    38.095\n",
            " 28.38  28.7   33.82  24.09  32.67  25.1   32.56  41.325 39.5   34.3\n",
            " 31.065 21.47  25.08  43.4   25.7   27.93  39.2   26.03  30.25  28.93\n",
            " 35.7   35.31  31.    44.22  26.07  25.8   39.425 40.48  38.9   47.41\n",
            " 35.435 46.7   46.2   21.4   23.8   44.77  32.12  29.1   37.29  43.12\n",
            " 36.86  34.295 23.465 45.43  23.65  20.7   28.27  35.91  29.    19.57\n",
            " 31.13  21.85  40.26  33.725 29.48  32.6   37.525 23.655 37.8   19.\n",
            " 21.3   33.535 42.46  38.95  36.1   29.3   39.7   38.19  42.4   34.96\n",
            " 42.68  31.54  29.81  21.375 40.81  17.4   20.3   18.5   26.125 41.69\n",
            " 24.1   36.2   40.185 39.27  34.87  44.745 29.545 23.54  40.47  40.66\n",
            " 36.6   35.4   27.075 28.405 21.755 40.28  30.1   32.1   23.7   35.5\n",
            " 29.15  27.    37.905 22.77  22.8   34.58  27.1   19.475 26.7   34.32\n",
            " 24.4   41.14  22.515 41.8   26.18  42.24  26.51  35.815 41.42  36.575\n",
            " 42.94  21.01  24.225 17.67  31.5   31.1   32.78  32.45  50.38  47.6\n",
            " 25.4   29.9   43.7   24.86  28.8   29.5   29.04  38.94  44.    20.045\n",
            " 40.92  35.1   29.355 32.585 32.34  39.8   24.605 33.99  28.2   25.\n",
            " 33.2   23.2   20.1   32.5   37.18  46.09  39.93  35.8   31.255 18.335\n",
            " 42.9   26.79  39.615 25.9   25.745 28.16  23.56  40.5   35.42  39.995\n",
            " 34.675 20.52  23.275 36.29  32.7   19.19  20.13  23.32  45.32  34.6\n",
            " 18.715 21.565 23.    37.07  52.58  42.655 21.66  32.    18.3   47.74\n",
            " 22.1   19.095 31.24  29.925 20.35  25.85  42.75  18.6   23.87  45.9\n",
            " 21.5   30.305 44.88  41.1   40.37  28.49  33.55  40.375 27.28  17.86\n",
            " 33.3   39.14  21.945 24.97  23.94  34.485 21.8   23.3   36.96  21.28\n",
            " 29.4   27.3   37.9   37.715 23.76  25.52  27.61  27.06  39.4   34.9\n",
            " 22.    30.36  27.8   53.13  39.71  32.87  44.7   30.97 ]\n",
            "0 [0 1 3 2 5 4]\n",
            "0 ['yes' 'no']\n",
            "0 ['southwest' 'southeast' 'northwest' 'northeast']\n",
            "0 [16884.924   1725.5523  4449.462  ...  1629.8335  2007.945  29141.3603]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering"
      ],
      "metadata": {
        "id": "oJmN5cZ4ranI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "clustring age data in 4 ranges"
      ],
      "metadata": {
        "id": "LJW6XMQTwOpd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for index, row in data.iterrows():\n",
        "    if row['age'] < 20:\n",
        "        data.at[index, 'age'] = 'teen'\n",
        "    if row['age'] >= 20 and row['age'] < 40:\n",
        "        data.at[index , 'age'] = 'young adults'\n",
        "    if row['age'] >= 40 and row['age'] <60:\n",
        "        data.at[index, 'age'] = 'adults'\n",
        "    if row['age'] >= 60 :\n",
        "        data.at[index, 'age'] = 'middle age adults'\n",
        "data['age'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GA0SsfgEqpDr",
        "outputId": "e9979f8b-d70d-431d-c361-aa407f783514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "adults               550\n",
              "young adults         537\n",
              "teen                 137\n",
              "middle age adults    114\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "changing 2 option variables to binary"
      ],
      "metadata": {
        "id": "K-CCGdhDwVWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lblenc=LabelEncoder()\n",
        "zero_one_valued = ['sex','smoker']\n",
        "for col in zero_one_valued:\n",
        "  data[col] = lblenc.fit_transform(data[col])\n",
        "# for No and Rural its 0 and for yes and Urban is 1\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "j09Y-TVGr8S9",
        "outputId": "6564bfb4-9550-45d3-aeef-6699f6a9a9d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            age  sex     bmi  children  smoker     region      charges\n",
              "0          teen    0  27.900         0       1  southwest  16884.92400\n",
              "1          teen    1  33.770         1       0  southeast   1725.55230\n",
              "2  young adults    1  33.000         3       0  southeast   4449.46200\n",
              "3  young adults    1  22.705         0       0  northwest  21984.47061\n",
              "4  young adults    1  28.880         0       0  northwest   3866.85520"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OneHot Encoding region and age"
      ],
      "metadata": {
        "id": "VI_pQB30wbgo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = data\n",
        "enc = OneHotEncoder(sparse=False)\n",
        "age = enc.fit_transform(data[['age']])\n",
        "enc_data1 = pd.DataFrame(age, columns=enc.get_feature_names_out(['age']))\n",
        "tmp.drop(columns='age', inplace=True )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpoAnGFXsU1D",
        "outputId": "c9f900cb-1adf-42ba-dd62-23e88dfb0491"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "region = enc.fit_transform(data[['region']])\n",
        "enc_data2 = pd.DataFrame(region, columns=enc.get_feature_names_out(['region']))\n",
        "tmp.drop(columns='region', inplace=True )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tza6VYrTvOCH",
        "outputId": "ff6a0b18-6083-4cc6-8dd3-e2f60fa9f684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "OneHotData = tmp.join(enc_data1)\n",
        "OneHotData = OneHotData.join(enc_data2)\n",
        "OneHotData"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "90uukdn1sxKw",
        "outputId": "3b74880e-55a0-4787-ccb1-7e3c2a8536a5"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "      sex     bmi  children  smoker      charges  age_adults  \\\n",
              "0       0  27.900         0       1  16884.92400         0.0   \n",
              "1       1  33.770         1       0   1725.55230         0.0   \n",
              "2       1  33.000         3       0   4449.46200         0.0   \n",
              "3       1  22.705         0       0  21984.47061         0.0   \n",
              "4       1  28.880         0       0   3866.85520         0.0   \n",
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              "1333    1  30.970         3       0  10600.54830         1.0   \n",
              "1334    0  31.920         0       0   2205.98080         0.0   \n",
              "1335    0  36.850         0       0   1629.83350         0.0   \n",
              "1336    0  25.800         0       0   2007.94500         0.0   \n",
              "1337    0  29.070         0       1  29141.36030         0.0   \n",
              "\n",
              "      age_middle age adults  age_teen  age_young adults  region_northeast  \\\n",
              "0                       0.0       1.0               0.0               0.0   \n",
              "1                       0.0       1.0               0.0               0.0   \n",
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              "3                       0.0       0.0               1.0               0.0   \n",
              "4                       0.0       0.0               1.0               0.0   \n",
              "...                     ...       ...               ...               ...   \n",
              "1333                    0.0       0.0               0.0               0.0   \n",
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              "1337                    1.0       0.0               0.0               0.0   \n",
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              "1336               0.0               0.0               1.0  \n",
              "1337               1.0               0.0               0.0  \n",
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              "      <th>1334</th>\n",
              "      <td>0</td>\n",
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              "      buttonEl.style.display =\n",
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              "                                                    [key], {});\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79876b85-7b5a-4e10-aca5-c056238470cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Moving result to last column"
      ],
      "metadata": {
        "id": "yHMboTWewgNU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "first_column = OneHotData.pop('charges')\n",
        "OneHotData.insert(12, 'charges', first_column)\n",
        "OneHotData"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "Z3ECWxlKtu_4",
        "outputId": "7204c3d6-d960-435d-bb21-500545ee921a"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      sex     bmi  children  smoker  age_adults  age_middle age adults  \\\n",
              "0       0  27.900         0       1         0.0                    0.0   \n",
              "1       1  33.770         1       0         0.0                    0.0   \n",
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              "3       1  22.705         0       0         0.0                    0.0   \n",
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              "1336    0  25.800         0       0         0.0                    0.0   \n",
              "1337    0  29.070         0       1         0.0                    1.0   \n",
              "\n",
              "      age_teen  age_young adults  region_northeast  region_northwest  \\\n",
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              "1336       0.0               1.0               0.0               0.0   \n",
              "1337       0.0               0.0               0.0               1.0   \n",
              "\n",
              "      region_southeast  region_southwest      charges  \n",
              "0                  0.0               1.0  16884.92400  \n",
              "1                  1.0               0.0   1725.55230  \n",
              "2                  1.0               0.0   4449.46200  \n",
              "3                  0.0               0.0  21984.47061  \n",
              "4                  0.0               0.0   3866.85520  \n",
              "...                ...               ...          ...  \n",
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              "1334               0.0               0.0   2205.98080  \n",
              "1335               1.0               0.0   1629.83350  \n",
              "1336               0.0               1.0   2007.94500  \n",
              "1337               0.0               0.0  29141.36030  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we shall normalize the data"
      ],
      "metadata": {
        "id": "9H5Tg6p1wKD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = preprocessing.normalize(OneHotData.iloc[:,0:12])\n",
        "scaled_df = pd.DataFrame(d, columns=OneHotData.iloc[:,0:12].columns)\n",
        "scaled_df.insert(12, 'charges', first_column)\n",
        "scaled_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "qiQRPDMowJw-",
        "outputId": "3f3abb56-cdf9-41c2-de46-167a71ff0a21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           sex       bmi  children    smoker  age_adults  \\\n",
              "0     0.000000  0.998079  0.000000  0.035773    0.000000   \n",
              "1     0.029560  0.998251  0.029560  0.000000    0.000000   \n",
              "2     0.030137  0.994535  0.090412  0.000000    0.000000   \n",
              "3     0.043916  0.997103  0.000000  0.000000    0.000000   \n",
              "4     0.034564  0.998206  0.000000  0.000000    0.000000   \n",
              "...        ...       ...       ...       ...         ...   \n",
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              "\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting Data"
      ],
      "metadata": {
        "id": "q0J_NzT9xdPJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x= scaled_df.iloc[:,0:12].values\n",
        "y= scaled_df.iloc[:,12:13].values\n",
        "# choose the test size based on scikit learn documantation\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)"
      ],
      "metadata": {
        "id": "jqKj-iRcxCWN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = y_train.reshape(-1)\n",
        "y_test = y_test.reshape(-1)"
      ],
      "metadata": {
        "id": "PdHz0zYQbjrX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implementation of Gradient Desecnt"
      ],
      "metadata": {
        "id": "8Xb5rXgwC5FG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwOrxr9IcCty",
        "outputId": "61387b0b-0ba2-4160-bb9c-c0de308aea14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1003, 12)\n",
            "(1003,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize w and b\n",
        "w = np.random.randn(x_train.shape[1])\n",
        "b = np.random.randn()\n",
        "alpha = 0.03\n",
        "print(w.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmOCBh5nYTnG",
        "outputId": "b1817bb2-8be7-4e4b-f425-d6e8a0512c3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(12,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Cost(x , y , w , b):\n",
        "  predict = np.dot(x,w)+b\n",
        "  m = x_train.shape[0]\n",
        "  cost = np.sum((predict - y)**2)/(2*m)\n",
        "  return cost"
      ],
      "metadata": {
        "id": "lG2stmlsZLYT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gradient_descent (x , y , w_in , b_in) :\n",
        "    predict = np.dot(x,w_in)+b_in\n",
        "    m = x_train.shape[0]\n",
        "    dj_dw = np.dot((predict-y),x)/m\n",
        "    dj_db = np.sum(predict-y)/m\n",
        "    return dj_dw,dj_db"
      ],
      "metadata": {
        "id": "SQ7V6sXdQP2m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cost = []\n",
        "for j in range(1000):\n",
        "  # print(y_train.shape)\n",
        "  cost.append(Cost(x_train , y_train , w ,b))\n",
        "  dw,db = gradient_descent(x_train , y_train , w ,b)\n",
        "  w -= alpha * dw\n",
        "  b -= alpha * db"
      ],
      "metadata": {
        "id": "-jIq4WlBaiEB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(range(1000), cost)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "sGHI1iVYbuPX",
        "outputId": "49922b8f-54e1-4b41-8015-359f18e55e2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ea89a30f2b0>]"
            ]
          },
          "metadata": {},
          "execution_count": 163
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing diffrent Split sizes"
      ],
      "metadata": {
        "id": "0xjAZ-qsMvRj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = np.array(np.dot(x_train, w)+b)\n",
        "pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cT_BRO7if7Dn",
        "outputId": "02b55bc5-0c25-448c-d743-61eb261aefc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1003,)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train1, x_test1, y_train1, y_test1 = train_test_split(x, y, test_size=0.35)\n",
        "x_train2, x_test2, y_train2, y_test2 = train_test_split(x, y, test_size=0.5)\n",
        "x_train3, x_test3, y_train3, y_test3 = train_test_split(x, y, test_size=0.6)\n",
        "x_train4, x_test4, y_train4, y_test4 = train_test_split(x, y, test_size=0.75)\n",
        "x_train5, x_test5, y_train5, y_test5 = train_test_split(x, y, test_size=0.85)"
      ],
      "metadata": {
        "id": "rOpsJClJirrs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train1 = y_train1.reshape(-1)\n",
        "y_test1 = y_test1.reshape(-1)\n",
        "y_train2 = y_train2.reshape(-1)\n",
        "y_test2 = y_test2.reshape(-1)\n",
        "y_train3 = y_train3.reshape(-1)\n",
        "y_test3 = y_test3.reshape(-1)\n",
        "y_train4 = y_train4.reshape(-1)\n",
        "y_test4 = y_test4.reshape(-1)\n",
        "y_train5 = y_train5.reshape(-1)\n",
        "y_test5 = y_test5.reshape(-1)"
      ],
      "metadata": {
        "id": "Te-1SQjEip16"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w1 = np.random.randn(x_train1.shape[1])\n",
        "w2 = np.random.randn(x_train2.shape[1])\n",
        "w3 = np.random.randn(x_train3.shape[1])\n",
        "w4 = np.random.randn(x_train4.shape[1])\n",
        "w5 = np.random.randn(x_train5.shape[1])\n",
        "cost1=[]\n",
        "cost2=[]\n",
        "cost3=[]\n",
        "cost4=[]\n",
        "cost5=[]\n",
        "for _ in range(1000):\n",
        "  # print(y_train.shape)\n",
        "  cost1.append(Cost(x_train1 , y_train1 , w1 ,b))\n",
        "  dw,db = gradient_descent(x_train1 , y_train1 , w1 ,b)\n",
        "  w1 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost2.append(Cost(x_train2 , y_train2 , w2 ,b))\n",
        "  dw,db = gradient_descent(x_train2 , y_train2 , w2 ,b)\n",
        "  w2 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost3.append(Cost(x_train3 , y_train3 , w3 ,b))\n",
        "  dw,db = gradient_descent(x_train3 , y_train3 , w3 ,b)\n",
        "  w3 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost4.append(Cost(x_train4 , y_train4 , w4 ,b))\n",
        "  dw,db = gradient_descent(x_train4 , y_train4 , w4 ,b)\n",
        "  w4 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost5.append(Cost(x_train5 , y_train5 , w5 ,b))\n",
        "  dw,db = gradient_descent(x_train5 , y_train5 , w5 ,b)\n",
        "  w5 -= alpha * dw\n",
        "  b -= alpha * db"
      ],
      "metadata": {
        "id": "CXTcyhqRfroE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For test size =0.25\n",
        "plt.plot(range(1000), cost,label = \"For test size = 0.25\")\n",
        "\n",
        "# For test size =0.35\n",
        "plt.plot(range(1000), cost1, color = 'r' , label =\"For test size = 0.35\")\n",
        "\n",
        "# For test size =0.5\n",
        "plt.plot(range(1000), cost2, color = 'g' , label = \"For test size = 0.5\")\n",
        "\n",
        "# For test size =0.6\n",
        "plt.plot(range(1000), cost3, color = 'yellow' , label =\"For test size = 0.6\")\n",
        "\n",
        "# For test size =0.75\n",
        "plt.plot(range(1000), cost4, color = 'purple' , label = \"For test size = 0.75\")\n",
        "\n",
        "# For test size =0.85\n",
        "plt.plot(range(1000), cost5 , color = 'pink' , label =\"For test size = 0.85\" )\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "weQ6Hc8ZgFxV",
        "outputId": "7559a731-c62b-4eff-f13d-ba9d527fba80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Polynominal"
      ],
      "metadata": {
        "id": "UxQDF3zGM-tD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the only paramether with values big enough to have an impact on polynominal is bmi , we shal consider bmi for our polynominal model"
      ],
      "metadata": {
        "id": "xMZA4vS8TUIN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df['Hyper_paramether'] = scaled_df['bmi']**2\n",
        "scaled_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "HHgRmTm8Ms-t",
        "outputId": "e440202d-3a19-4e74-b35e-d68169d52286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           sex       bmi  children    smoker  age_adults  \\\n",
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              "\n",
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              "1337          0.034339          0.000000          0.000000  29141.36030   \n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0faf68be-b14c-48e9-bb99-fdde021b993b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we need to insert this metric in its correct position and move charges column to the last column of dataframe"
      ],
      "metadata": {
        "id": "aCalBEMcUIAO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "first_column = scaled_df.pop('charges')\n",
        "scaled_df.insert(13, 'charges', first_column)\n",
        "scaled_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "O0WJjb0DUEMt",
        "outputId": "63cec446-af37-4776-b3a1-7325613de85f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           sex       bmi  children    smoker  age_adults  \\\n",
              "0     0.000000  0.998079  0.000000  0.035773    0.000000   \n",
              "1     0.029560  0.998251  0.029560  0.000000    0.000000   \n",
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              "3     0.043916  0.997103  0.000000  0.000000    0.000000   \n",
              "4     0.034564  0.998206  0.000000  0.000000    0.000000   \n",
              "...        ...       ...       ...       ...         ...   \n",
              "1333  0.032089  0.993802  0.096268  0.000000    0.032089   \n",
              "1334  0.000000  0.999020  0.000000  0.000000    0.000000   \n",
              "1335  0.000000  0.999264  0.000000  0.000000    0.000000   \n",
              "1336  0.000000  0.998501  0.000000  0.000000    0.000000   \n",
              "1337  0.000000  0.998230  0.000000  0.034339    0.000000   \n",
              "\n",
              "      age_middle age adults  age_teen  age_young adults  region_northeast  \\\n",
              "0                  0.000000  0.035773          0.000000          0.000000   \n",
              "1                  0.000000  0.029560          0.000000          0.000000   \n",
              "2                  0.000000  0.000000          0.030137          0.000000   \n",
              "3                  0.000000  0.000000          0.043916          0.000000   \n",
              "4                  0.000000  0.000000          0.034564          0.000000   \n",
              "...                     ...       ...               ...               ...   \n",
              "1333               0.000000  0.000000          0.000000          0.000000   \n",
              "1334               0.000000  0.031298          0.000000          0.031298   \n",
              "1335               0.000000  0.027117          0.000000          0.000000   \n",
              "1336               0.000000  0.000000          0.038702          0.000000   \n",
              "1337               0.034339  0.000000          0.000000          0.000000   \n",
              "\n",
              "      region_northwest  region_southeast  region_southwest  Hyper_paramether  \\\n",
              "0             0.000000          0.000000          0.035773          0.996161   \n",
              "1             0.000000          0.029560          0.000000          0.996505   \n",
              "2             0.000000          0.030137          0.000000          0.989101   \n",
              "3             0.043916          0.000000          0.000000          0.994214   \n",
              "4             0.034564          0.000000          0.000000          0.996416   \n",
              "...                ...               ...               ...               ...   \n",
              "1333          0.032089          0.000000          0.000000          0.987643   \n",
              "1334          0.000000          0.000000          0.000000          0.998041   \n",
              "1335          0.000000          0.027117          0.000000          0.998529   \n",
              "1336          0.000000          0.000000          0.038702          0.997004   \n",
              "1337          0.034339          0.000000          0.000000          0.996463   \n",
              "\n",
              "          charges  \n",
              "0     16884.92400  \n",
              "1      1725.55230  \n",
              "2      4449.46200  \n",
              "3     21984.47061  \n",
              "4      3866.85520  \n",
              "...           ...  \n",
              "1333  10600.54830  \n",
              "1334   2205.98080  \n",
              "1335   1629.83350  \n",
              "1336   2007.94500  \n",
              "1337  29141.36030  \n",
              "\n",
              "[1338 rows x 14 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
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              "<p>1338 rows × 14 columns</p>\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3322abd7-20c0-4ae5-8f0c-ca806cdf586b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x= scaled_df.iloc[:,0:13].values\n",
        "y= scaled_df.iloc[:,13:14].values\n",
        "# choose the test size based on scikit learn documantation\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)\n",
        "y_train = y_train.reshape(-1)\n",
        "y_test = y_test.reshape(-1)"
      ],
      "metadata": {
        "id": "Pd39z_0VUgOZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "we define a loss function which is an error for each sample data"
      ],
      "metadata": {
        "id": "K2PjEHkhVvMa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = []\n",
        "# initialize w and b\n",
        "w = np.random.randn(x_train.shape[1])\n",
        "b = np.random.randn()\n",
        "loss.append(Cost(x_train , y_train , w ,b))\n",
        "\n",
        "alpha = 0.01\n",
        "print(w.shape)\n",
        "\n",
        "tmp = []\n",
        "for _ in range(10000):\n",
        "    tmp.append(Cost(x_train, y_train, w, b))\n",
        "    dw, db = gradient_descent(x_train, y_train, w, b)\n",
        "    w -= alpha * dw\n",
        "    b -= alpha * db\n",
        "loss.append(tmp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_PtZybs7Vq3a",
        "outputId": "bfc99f59-1243-4cac-9b2d-30bfe78c37f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(13,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we shall draw plots for each test size to compare the results"
      ],
      "metadata": {
        "id": "ODVO0Z4rYKam"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train1, x_test1, y_train1, y_test1 = train_test_split(x, y, test_size=0.35)\n",
        "x_train2, x_test2, y_train2, y_test2 = train_test_split(x, y, test_size=0.5)\n",
        "x_train3, x_test3, y_train3, y_test3 = train_test_split(x, y, test_size=0.6)\n",
        "x_train4, x_test4, y_train4, y_test4 = train_test_split(x, y, test_size=0.75)\n",
        "x_train5, x_test5, y_train5, y_test5 = train_test_split(x, y, test_size=0.85)\n",
        "\n",
        "y_train1 = y_train1.reshape(-1)\n",
        "y_test1 = y_test1.reshape(-1)\n",
        "y_train2 = y_train2.reshape(-1)\n",
        "y_test2 = y_test2.reshape(-1)\n",
        "y_train3 = y_train3.reshape(-1)\n",
        "y_test3 = y_test3.reshape(-1)\n",
        "y_train4 = y_train4.reshape(-1)\n",
        "y_test4 = y_test4.reshape(-1)\n",
        "y_train5 = y_train5.reshape(-1)\n",
        "y_test5 = y_test5.reshape(-1)\n",
        "\n",
        "w1 = np.random.randn(x_train1.shape[1])\n",
        "w2 = np.random.randn(x_train2.shape[1])\n",
        "w3 = np.random.randn(x_train3.shape[1])\n",
        "w4 = np.random.randn(x_train4.shape[1])\n",
        "w5 = np.random.randn(x_train5.shape[1])\n",
        "cost1=[]\n",
        "cost2=[]\n",
        "cost3=[]\n",
        "cost4=[]\n",
        "cost5=[]\n",
        "for _ in range(1000):\n",
        "  # print(y_train.shape)\n",
        "  cost1.append(Cost(x_train1 , y_train1 , w1 ,b))\n",
        "  dw,db = gradient_descent(x_train1 , y_train1 , w1 ,b)\n",
        "  w1 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost2.append(Cost(x_train2 , y_train2 , w2 ,b))\n",
        "  dw,db = gradient_descent(x_train2 , y_train2 , w2 ,b)\n",
        "  w2 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost3.append(Cost(x_train3 , y_train3 , w3 ,b))\n",
        "  dw,db = gradient_descent(x_train3 , y_train3 , w3 ,b)\n",
        "  w3 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost4.append(Cost(x_train4 , y_train4 , w4 ,b))\n",
        "  dw,db = gradient_descent(x_train4 , y_train4 , w4 ,b)\n",
        "  w4 -= alpha * dw\n",
        "  b -= alpha * db\n",
        "  cost5.append(Cost(x_train5 , y_train5 , w5 ,b))\n",
        "  dw,db = gradient_descent(x_train5 , y_train5 , w5 ,b)\n",
        "  w5 -= alpha * dw\n",
        "  b -= alpha * db"
      ],
      "metadata": {
        "id": "Rw61TtGNYJFd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For test size =0.25\n",
        "plt.plot(range(1000), cost,label = \"For test size = 0.25\")\n",
        "\n",
        "# For test size =0.35\n",
        "plt.plot(range(1000), cost1, color = 'r' , label =\"For test size = 0.35\")\n",
        "\n",
        "# For test size =0.5\n",
        "plt.plot(range(1000), cost2, color = 'g' , label = \"For test size = 0.5\")\n",
        "\n",
        "# For test size =0.6\n",
        "plt.plot(range(1000), cost3, color = 'yellow' , label =\"For test size = 0.6\")\n",
        "\n",
        "# For test size =0.75\n",
        "plt.plot(range(1000), cost4, color = 'purple' , label = \"For test size = 0.75\")\n",
        "\n",
        "# For test size =0.85\n",
        "plt.plot(range(1000), cost5 , color = 'pink' , label =\"For test size = 0.85\" )\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "15a1d141-2a68-4cc6-ecab-949b082ccadf",
        "id": "YOxTsoApYpNy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}