car_price_pred.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "car_price_pred.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNMK/+Ne1zX3ybGDWYeo2vO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mbilalhameed/demo-public-repo/blob/master/car_price_pred.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgnUG-F1_3CZ"
      },
      "source": [
        "# Car Price Prediction Data:\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SL0mXS-YEZja"
      },
      "source": [
        "**Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JG6ols-eEgVN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "dd19f6fa-1bfa-42ef-b970-3379ce87bed9"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import io\n",
        "import missingno\n",
        "sns.set(color_codes=True)\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKtdIeq1HdXv"
      },
      "source": [
        "**Load Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_RKO_NfHhaw",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "1917283d-a2cf-431a-db57-f5efca672c67"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3f349246-7b58-4d56-b457-d38a5249115c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3f349246-7b58-4d56-b457-d38a5249115c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving car_sales.xlsx to car_sales.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWnsOZa5IUZ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fa345661-c39c-4147-d8cd-1a8d7f4ca5c2"
      },
      "source": [
        "df = pd.read_excel(io.BytesIO(uploaded['car_sales.xlsx'])) \n",
        "df.head()\n"
      ],
      "execution_count": null,
      "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>manufact</th>\n",
              "      <th>model</th>\n",
              "      <th>type</th>\n",
              "      <th>engine_s</th>\n",
              "      <th>horsepow</th>\n",
              "      <th>wheelbas</th>\n",
              "      <th>width</th>\n",
              "      <th>length</th>\n",
              "      <th>curb_wgt</th>\n",
              "      <th>fuel_cap</th>\n",
              "      <th>mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>Integra</td>\n",
              "      <td>0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>140</td>\n",
              "      <td>101.2</td>\n",
              "      <td>67.3</td>\n",
              "      <td>172.4</td>\n",
              "      <td>2.639</td>\n",
              "      <td>13.2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>21.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acura</td>\n",
              "      <td>CL</td>\n",
              "      <td>0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>225</td>\n",
              "      <td>106.9</td>\n",
              "      <td>70.6</td>\n",
              "      <td>192.0</td>\n",
              "      <td>3.470</td>\n",
              "      <td>17.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acura</td>\n",
              "      <td>TL</td>\n",
              "      <td>0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>225</td>\n",
              "      <td>108.1</td>\n",
              "      <td>70.3</td>\n",
              "      <td>192.9</td>\n",
              "      <td>3.517</td>\n",
              "      <td>17.2</td>\n",
              "      <td>25.0</td>\n",
              "      <td>28.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acura</td>\n",
              "      <td>RL</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>210</td>\n",
              "      <td>114.6</td>\n",
              "      <td>71.4</td>\n",
              "      <td>196.6</td>\n",
              "      <td>3.850</td>\n",
              "      <td>18.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>42.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Audi</td>\n",
              "      <td>A4</td>\n",
              "      <td>0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>150</td>\n",
              "      <td>102.6</td>\n",
              "      <td>68.2</td>\n",
              "      <td>178.0</td>\n",
              "      <td>2.998</td>\n",
              "      <td>16.4</td>\n",
              "      <td>27.0</td>\n",
              "      <td>23.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  manufact    model  type  engine_s  ...  curb_wgt  fuel_cap   mpg  price\n",
              "0    Acura  Integra     0       1.8  ...     2.639      13.2  28.0  21.50\n",
              "1    Acura       CL     0       3.2  ...     3.470      17.2  26.0    NaN\n",
              "2    Acura       TL     0       3.2  ...     3.517      17.2  25.0  28.40\n",
              "3    Acura       RL     0       3.5  ...     3.850      18.0  22.0  42.00\n",
              "4     Audi       A4     0       1.8  ...     2.998      16.4  27.0  23.99\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qbqVcr5OFqv"
      },
      "source": [
        "**Number of Entries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_w2g6_nOKMC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c95a1c0e-591b-46e3-cea5-c75c3013e720"
      },
      "source": [
        "print(\"Number of entries\",len(df))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of entries 153\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZL44IC1ORaw"
      },
      "source": [
        "##**Data Description**\n",
        "#### **Columns:**\n",
        "\n",
        "* **manufact** : Manufacturer Name\n",
        "* **model**    : Model of car\n",
        "* **price**    : Price of car\n",
        "* **type**     : 1=SUV , 0=Non-SUV\n",
        "* **engine_s** : total volume of air and fuel that's pushed through the engine by its cylinders\n",
        "* **horsepow** : Engine power\n",
        "* **wheelbas** : distance between front and rear wheels\n",
        "* **width**    : Width of car\n",
        "* **length**   : Length of car\n",
        "* **curb_wgt** : Weight of a car without occupants or baggage.\n",
        "* **fuel_cap** : Fuel capacity\n",
        "* **mpg**      : Miles per gallon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAgW06oQO4Ii",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8301417d-1445-4bfc-a17c-af001193d723"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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>type</th>\n",
              "      <th>engine_s</th>\n",
              "      <th>horsepow</th>\n",
              "      <th>wheelbas</th>\n",
              "      <th>width</th>\n",
              "      <th>length</th>\n",
              "      <th>curb_wgt</th>\n",
              "      <th>fuel_cap</th>\n",
              "      <th>mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.261438</td>\n",
              "      <td>3.050327</td>\n",
              "      <td>185.071895</td>\n",
              "      <td>107.410458</td>\n",
              "      <td>71.085621</td>\n",
              "      <td>187.091503</td>\n",
              "      <td>3.376797</td>\n",
              "      <td>17.954248</td>\n",
              "      <td>23.855556</td>\n",
              "      <td>27.331822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.440861</td>\n",
              "      <td>1.046430</td>\n",
              "      <td>56.729054</td>\n",
              "      <td>7.692522</td>\n",
              "      <td>3.453477</td>\n",
              "      <td>13.432800</td>\n",
              "      <td>0.634540</td>\n",
              "      <td>3.925088</td>\n",
              "      <td>4.294165</td>\n",
              "      <td>14.418669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>92.600000</td>\n",
              "      <td>62.600000</td>\n",
              "      <td>149.400000</td>\n",
              "      <td>1.895000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>9.235000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>148.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>68.400000</td>\n",
              "      <td>177.500000</td>\n",
              "      <td>2.967000</td>\n",
              "      <td>15.800000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>17.888750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>70.400000</td>\n",
              "      <td>186.700000</td>\n",
              "      <td>3.340000</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>22.747000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>112.200000</td>\n",
              "      <td>73.100000</td>\n",
              "      <td>194.800000</td>\n",
              "      <td>3.821000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>31.938750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>138.700000</td>\n",
              "      <td>79.900000</td>\n",
              "      <td>224.500000</td>\n",
              "      <td>5.572000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>85.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             type    engine_s    horsepow  ...    fuel_cap         mpg       price\n",
              "count  153.000000  153.000000  153.000000  ...  153.000000  153.000000  152.000000\n",
              "mean     0.261438    3.050327  185.071895  ...   17.954248   23.855556   27.331822\n",
              "std      0.440861    1.046430   56.729054  ...    3.925088    4.294165   14.418669\n",
              "min      0.000000    1.000000   55.000000  ...   10.300000   15.000000    9.235000\n",
              "25%      0.000000    2.300000  148.000000  ...   15.800000   21.000000   17.888750\n",
              "50%      0.000000    3.000000  175.000000  ...   17.200000   24.000000   22.747000\n",
              "75%      1.000000    3.500000  215.000000  ...   19.800000   26.000000   31.938750\n",
              "max      1.000000    8.000000  450.000000  ...   32.000000   45.000000   85.500000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7TEhCOuyPb7-"
      },
      "source": [
        "**Drop Missing Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFY9T07zPiyA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "79891bc1-f0bb-45e0-c6b0-1b86f875eee6"
      },
      "source": [
        "df.manufact.value_counts() #number of entries by manufecturer\n",
        "df.isnull().sum() # show number of missing values in each column\n",
        "df.dropna(axis=0,inplace=True)\n",
        "print(\"Number of entries\",len(df))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of entries 152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQqaq9Zqnfav"
      },
      "source": [
        "**Drop Duplicated rows**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdPYJpSXnjee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b77d8481-a372-4343-ce0d-8cf0965d4942"
      },
      "source": [
        "duplicate_rows_df = df[df.duplicated()]\n",
        "print(\"Number of duplicates: \", duplicate_rows_df.shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of duplicates:  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mGactgSWiOr"
      },
      "source": [
        "**Detecting Outliers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIDPc-tGWmbK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "4c7b1a15-bf7d-4bc3-f0e9-77bb6fc03b5c"
      },
      "source": [
        "sns.boxplot(x=df.price)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3fb4272b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Mrm0BD5lTH1XP2Xs/RHxT30eRWqcR7qSlMjoSlIiTy9IUiKPdCUpkdGVpERGV5ISGV1JSmR0JSmR0ZWkRP8Prrq3Llxm2KQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyvIsZicWuM7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a8f92cd7-239f-49ca-fd89-0c0254ee899b"
      },
      "source": [
        "Q1 = df.quantile(0.25)\n",
        "Q3 = df.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "type         1.00000\n",
            "engine_s     1.27500\n",
            "horsepow    63.75000\n",
            "wheelbas     9.30000\n",
            "width        4.72500\n",
            "length      17.65000\n",
            "curb_wgt     0.85675\n",
            "fuel_cap     4.02500\n",
            "mpg          5.00000\n",
            "price       14.05000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3dZWPtVW02X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "263a7829-2d97-4b0c-8de4-c98a55275560"
      },
      "source": [
        "df = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-o-sIXes4oR"
      },
      "source": [
        "**Drop Irrelevent Columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoEEMBDXtVTc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "924e4ae8-b6c8-4203-cd9d-f214a4fce259"
      },
      "source": [
        "df.drop(['model','manufact'],axis=1,inplace=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "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>type</th>\n",
              "      <th>engine_s</th>\n",
              "      <th>horsepow</th>\n",
              "      <th>wheelbas</th>\n",
              "      <th>width</th>\n",
              "      <th>length</th>\n",
              "      <th>curb_wgt</th>\n",
              "      <th>fuel_cap</th>\n",
              "      <th>mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>140</td>\n",
              "      <td>101.2</td>\n",
              "      <td>67.3</td>\n",
              "      <td>172.4</td>\n",
              "      <td>2.639</td>\n",
              "      <td>13.2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>21.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>225</td>\n",
              "      <td>108.1</td>\n",
              "      <td>70.3</td>\n",
              "      <td>192.9</td>\n",
              "      <td>3.517</td>\n",
              "      <td>17.2</td>\n",
              "      <td>25.0</td>\n",
              "      <td>28.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>210</td>\n",
              "      <td>114.6</td>\n",
              "      <td>71.4</td>\n",
              "      <td>196.6</td>\n",
              "      <td>3.850</td>\n",
              "      <td>18.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>42.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>150</td>\n",
              "      <td>102.6</td>\n",
              "      <td>68.2</td>\n",
              "      <td>178.0</td>\n",
              "      <td>2.998</td>\n",
              "      <td>16.4</td>\n",
              "      <td>27.0</td>\n",
              "      <td>23.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>200</td>\n",
              "      <td>108.7</td>\n",
              "      <td>76.1</td>\n",
              "      <td>192.0</td>\n",
              "      <td>3.561</td>\n",
              "      <td>18.5</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   type  engine_s  horsepow  wheelbas  ...  curb_wgt  fuel_cap   mpg  price\n",
              "0     0       1.8       140     101.2  ...     2.639      13.2  28.0  21.50\n",
              "2     0       3.2       225     108.1  ...     3.517      17.2  25.0  28.40\n",
              "3     0       3.5       210     114.6  ...     3.850      18.0  22.0  42.00\n",
              "4     0       1.8       150     102.6  ...     2.998      16.4  27.0  23.99\n",
              "5     0       2.8       200     108.7  ...     3.561      18.5  22.0  33.95\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dD5Y4-Ehsl9E"
      },
      "source": [
        "**Heatmap to find relation between Features**\n",
        "\n",
        "\n",
        "we can see that prices depend mainly on engine size and HP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-_3yhxAsthl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "outputId": "ac5d723f-3ade-43e2-e9ef-20aa37221be9"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "c= df.corr()\n",
        "sns.heatmap(c,cmap=\"BrBG\",annot=True)\n",
        "c"
      ],
      "execution_count": null,
      "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>type</th>\n",
              "      <th>engine_s</th>\n",
              "      <th>horsepow</th>\n",
              "      <th>wheelbas</th>\n",
              "      <th>width</th>\n",
              "      <th>length</th>\n",
              "      <th>curb_wgt</th>\n",
              "      <th>fuel_cap</th>\n",
              "      <th>mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.239177</td>\n",
              "      <td>0.032612</td>\n",
              "      <td>0.221675</td>\n",
              "      <td>0.037472</td>\n",
              "      <td>-0.050599</td>\n",
              "      <td>0.434960</td>\n",
              "      <td>0.542801</td>\n",
              "      <td>-0.561529</td>\n",
              "      <td>-0.020676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>engine_s</th>\n",
              "      <td>0.239177</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.780235</td>\n",
              "      <td>0.608002</td>\n",
              "      <td>0.643561</td>\n",
              "      <td>0.637356</td>\n",
              "      <td>0.801820</td>\n",
              "      <td>0.646784</td>\n",
              "      <td>-0.699813</td>\n",
              "      <td>0.528601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>horsepow</th>\n",
              "      <td>0.032612</td>\n",
              "      <td>0.780235</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.526897</td>\n",
              "      <td>0.561939</td>\n",
              "      <td>0.528882</td>\n",
              "      <td>0.733183</td>\n",
              "      <td>0.572672</td>\n",
              "      <td>-0.615674</td>\n",
              "      <td>0.827566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wheelbas</th>\n",
              "      <td>0.221675</td>\n",
              "      <td>0.608002</td>\n",
              "      <td>0.526897</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.659359</td>\n",
              "      <td>0.802333</td>\n",
              "      <td>0.674301</td>\n",
              "      <td>0.599388</td>\n",
              "      <td>-0.433867</td>\n",
              "      <td>0.335369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>width</th>\n",
              "      <td>0.037472</td>\n",
              "      <td>0.643561</td>\n",
              "      <td>0.561939</td>\n",
              "      <td>0.659359</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.671299</td>\n",
              "      <td>0.621092</td>\n",
              "      <td>0.447833</td>\n",
              "      <td>-0.403567</td>\n",
              "      <td>0.360608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>length</th>\n",
              "      <td>-0.050599</td>\n",
              "      <td>0.637356</td>\n",
              "      <td>0.528882</td>\n",
              "      <td>0.802333</td>\n",
              "      <td>0.671299</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.608179</td>\n",
              "      <td>0.487476</td>\n",
              "      <td>-0.306701</td>\n",
              "      <td>0.253034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>curb_wgt</th>\n",
              "      <td>0.434960</td>\n",
              "      <td>0.801820</td>\n",
              "      <td>0.733183</td>\n",
              "      <td>0.674301</td>\n",
              "      <td>0.621092</td>\n",
              "      <td>0.608179</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.840116</td>\n",
              "      <td>-0.788092</td>\n",
              "      <td>0.630318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_cap</th>\n",
              "      <td>0.542801</td>\n",
              "      <td>0.646784</td>\n",
              "      <td>0.572672</td>\n",
              "      <td>0.599388</td>\n",
              "      <td>0.447833</td>\n",
              "      <td>0.487476</td>\n",
              "      <td>0.840116</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.767556</td>\n",
              "      <td>0.484777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mpg</th>\n",
              "      <td>-0.561529</td>\n",
              "      <td>-0.699813</td>\n",
              "      <td>-0.615674</td>\n",
              "      <td>-0.433867</td>\n",
              "      <td>-0.403567</td>\n",
              "      <td>-0.306701</td>\n",
              "      <td>-0.788092</td>\n",
              "      <td>-0.767556</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.513747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>-0.020676</td>\n",
              "      <td>0.528601</td>\n",
              "      <td>0.827566</td>\n",
              "      <td>0.335369</td>\n",
              "      <td>0.360608</td>\n",
              "      <td>0.253034</td>\n",
              "      <td>0.630318</td>\n",
              "      <td>0.484777</td>\n",
              "      <td>-0.513747</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              type  engine_s  horsepow  ...  fuel_cap       mpg     price\n",
              "type      1.000000  0.239177  0.032612  ...  0.542801 -0.561529 -0.020676\n",
              "engine_s  0.239177  1.000000  0.780235  ...  0.646784 -0.699813  0.528601\n",
              "horsepow  0.032612  0.780235  1.000000  ...  0.572672 -0.615674  0.827566\n",
              "wheelbas  0.221675  0.608002  0.526897  ...  0.599388 -0.433867  0.335369\n",
              "width     0.037472  0.643561  0.561939  ...  0.447833 -0.403567  0.360608\n",
              "length   -0.050599  0.637356  0.528882  ...  0.487476 -0.306701  0.253034\n",
              "curb_wgt  0.434960  0.801820  0.733183  ...  0.840116 -0.788092  0.630318\n",
              "fuel_cap  0.542801  0.646784  0.572672  ...  1.000000 -0.767556  0.484777\n",
              "mpg      -0.561529 -0.699813 -0.615674  ... -0.767556  1.000000 -0.513747\n",
              "price    -0.020676  0.528601  0.827566  ...  0.484777 -0.513747  1.000000\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFlYeb3ixrEl"
      },
      "source": [
        "**Scaterplot to demonstrait correlation between price and HP**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRihu2v0x2A1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "43092475-ef59-483f-a2db-64c6f03e4753"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8,5))\n",
        "ax.scatter(df.horsepow, df.price)\n",
        "ax.set_xlabel('HP')\n",
        "ax.set_ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMt5ZMEb5kb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "709a272f-1854-49cd-976f-7d6883fb112e"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8,5))\n",
        "ax.scatter(df.engine_s, df.price)\n",
        "ax.set_xlabel('Engine Size')\n",
        "ax.set_ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJF2UHpI5xXw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "1f1da323-a354-45cf-96d2-e73d3c6b4c35"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8,5))\n",
        "ax.scatter(df.mpg, df.price)\n",
        "ax.set_xlabel('MPG')\n",
        "ax.set_ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ttbGwWdlJSb"
      },
      "source": [
        "**Normalizing Features**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pG2NYymmGrPz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "6b6cc506-ca0d-46d2-d20d-126e8f7dd5fc"
      },
      "source": [
        "df.engine_s=pd.DataFrame((df.engine_s-df.engine_s.mean())/df.engine_s.std()).values\n",
        "df.horsepow=pd.DataFrame((df.horsepow-df.horsepow.mean())/df.horsepow.std()).values\n",
        "df.wheelbas=pd.DataFrame((df.wheelbas-df.wheelbas.mean())/df.wheelbas.std()).values\n",
        "df.width=pd.DataFrame((df.width-df.width.mean())/df.width.std()).values\n",
        "df.length=pd.DataFrame((df.length-df.length.mean())/df.length.std()).values\n",
        "df.curb_wgt=pd.DataFrame((df.curb_wgt-df.curb_wgt.mean())/df.curb_wgt.std()).values\n",
        "df.fuel_cap=pd.DataFrame((df.fuel_cap-df.fuel_cap.mean())/df.fuel_cap.std()).values\n",
        "df.mpg=pd.DataFrame((df.mpg-df.mpg.mean())/df.mpg.std()).values\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "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>type</th>\n",
              "      <th>engine_s</th>\n",
              "      <th>horsepow</th>\n",
              "      <th>wheelbas</th>\n",
              "      <th>width</th>\n",
              "      <th>length</th>\n",
              "      <th>curb_wgt</th>\n",
              "      <th>fuel_cap</th>\n",
              "      <th>mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.288337</td>\n",
              "      <td>-0.803575</td>\n",
              "      <td>-0.916791</td>\n",
              "      <td>-1.088298</td>\n",
              "      <td>-1.096822</td>\n",
              "      <td>-1.238410</td>\n",
              "      <td>-1.391732</td>\n",
              "      <td>1.012849</td>\n",
              "      <td>21.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.455434</td>\n",
              "      <td>1.186154</td>\n",
              "      <td>0.291560</td>\n",
              "      <td>-0.075508</td>\n",
              "      <td>0.576006</td>\n",
              "      <td>0.501670</td>\n",
              "      <td>0.053507</td>\n",
              "      <td>0.142034</td>\n",
              "      <td>28.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.829100</td>\n",
              "      <td>0.835025</td>\n",
              "      <td>1.429862</td>\n",
              "      <td>0.295848</td>\n",
              "      <td>0.877931</td>\n",
              "      <td>1.161631</td>\n",
              "      <td>0.342555</td>\n",
              "      <td>-0.728782</td>\n",
              "      <td>42.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.288337</td>\n",
              "      <td>-0.569490</td>\n",
              "      <td>-0.671618</td>\n",
              "      <td>-0.784461</td>\n",
              "      <td>-0.639855</td>\n",
              "      <td>-0.526919</td>\n",
              "      <td>-0.235541</td>\n",
              "      <td>0.722577</td>\n",
              "      <td>23.990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.042786</td>\n",
              "      <td>0.600939</td>\n",
              "      <td>0.396634</td>\n",
              "      <td>1.882552</td>\n",
              "      <td>0.502565</td>\n",
              "      <td>0.588872</td>\n",
              "      <td>0.523210</td>\n",
              "      <td>-0.728782</td>\n",
              "      <td>33.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.416451</td>\n",
              "      <td>-0.101318</td>\n",
              "      <td>0.151462</td>\n",
              "      <td>-0.716942</td>\n",
              "      <td>-0.803057</td>\n",
              "      <td>-0.168202</td>\n",
              "      <td>-0.163279</td>\n",
              "      <td>0.461333</td>\n",
              "      <td>26.990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.042786</td>\n",
              "      <td>0.437079</td>\n",
              "      <td>0.151462</td>\n",
              "      <td>-0.683182</td>\n",
              "      <td>-0.803057</td>\n",
              "      <td>-0.132528</td>\n",
              "      <td>-0.163279</td>\n",
              "      <td>-0.148238</td>\n",
              "      <td>33.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.042786</td>\n",
              "      <td>0.437079</td>\n",
              "      <td>0.869468</td>\n",
              "      <td>0.127050</td>\n",
              "      <td>0.176159</td>\n",
              "      <td>0.412486</td>\n",
              "      <td>0.523210</td>\n",
              "      <td>0.083979</td>\n",
              "      <td>38.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>0.330879</td>\n",
              "      <td>0.015725</td>\n",
              "      <td>0.449171</td>\n",
              "      <td>0.734724</td>\n",
              "      <td>0.714728</td>\n",
              "      <td>0.206371</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.142034</td>\n",
              "      <td>21.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>1.202765</td>\n",
              "      <td>1.537283</td>\n",
              "      <td>0.449171</td>\n",
              "      <td>0.734724</td>\n",
              "      <td>0.845291</td>\n",
              "      <td>0.553198</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>-0.438510</td>\n",
              "      <td>25.300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    type  engine_s  horsepow  wheelbas  ...  curb_wgt  fuel_cap       mpg   price\n",
              "0      0 -1.288337 -0.803575 -0.916791  ... -1.238410 -1.391732  1.012849  21.500\n",
              "2      0  0.455434  1.186154  0.291560  ...  0.501670  0.053507  0.142034  28.400\n",
              "3      0  0.829100  0.835025  1.429862  ...  1.161631  0.342555 -0.728782  42.000\n",
              "4      0 -1.288337 -0.569490 -0.671618  ... -0.526919 -0.235541  0.722577  23.990\n",
              "5      0 -0.042786  0.600939  0.396634  ...  0.588872  0.523210 -0.728782  33.950\n",
              "7      0 -0.416451 -0.101318  0.151462  ... -0.168202 -0.163279  0.461333  26.990\n",
              "8      0 -0.042786  0.437079  0.151462  ... -0.132528 -0.163279 -0.148238  33.400\n",
              "9      0 -0.042786  0.437079  0.869468  ...  0.412486  0.523210  0.083979  38.900\n",
              "10     0  0.330879  0.015725  0.449171  ...  0.206371  0.161900  0.142034  21.975\n",
              "11     0  1.202765  1.537283  0.449171  ...  0.553198  0.161900 -0.438510  25.300\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZcH6JeW5fo2"
      },
      "source": [
        "**Splitting the dataset into the Training set and Test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajomS8Vq5gnu"
      },
      "source": [
        "X_tr=df.iloc[:100, :-1].values\n",
        "y_tr=df.iloc[:100, -2:-1].values\n",
        "X_tst=df.iloc[100:, :-1].values\n",
        "y_tst=df.iloc[100:, -2:-1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLKN4cOw-4mb"
      },
      "source": [
        "**Regression Class**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHOpmoFD-7nk"
      },
      "source": [
        "class LinearModel:\n",
        "    def __init__(self, num_features):\n",
        "        self.num_features = num_features\n",
        "        self.W = np.random.rand(num_features, 1)\n",
        "        self.b = np.random.rand()\n",
        "        \n",
        "    def forward_pass(self, X):\n",
        "        y = self.b + np.dot(X, self.W)\n",
        "        return y\n",
        "    \n",
        "    def compute_loss(self, y, y_true):\n",
        "        loss = np.sum(np.square(y - y_true))\n",
        "        return loss/(2*y.shape[0])\n",
        "    \n",
        "    def backward_pass(self, X, y_true, y_hat):\n",
        "        sns.scatterplot\n",
        "        m = y_hat.shape[0]\n",
        "        db = np.sum(y_hat - y_true)/m\n",
        "        dW = np.sum(np.dot(np.transpose(y_hat - y_true), X), axis=0)/m\n",
        "        return dW, db\n",
        "    \n",
        "    def update_params(self, dW, db, lr):\n",
        "        self.W = self.W - lr * np.reshape(dW, (self.num_features, 1))\n",
        "        self.b = self.b - lr * db\n",
        "        \n",
        "    def train(self, x_train, y_train, iterations, lr):\n",
        "        losses = []\n",
        "        for i in range(iterations):\n",
        "            y_hat = self.forward_pass(x_train)\n",
        "            dW, db = self.backward_pass(x_train, y_train, y_hat)\n",
        "            self.update_params(dW, db, lr)\n",
        "            loss = self.compute_loss(y_hat, y_train)\n",
        "            losses.append(loss)\n",
        "            if i % 500 == 0:\n",
        "                print('Iter: {}, Current loss: {:.4f}'.format(i, loss))\n",
        "        return losses"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1OLryjw6tvF"
      },
      "source": [
        "**Training Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSd3XwFA6y4b"
      },
      "source": [
        "myModel=LinearModel(9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJ_kdMBcULEf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "9d4c8e84-f8f8-4c85-d92e-b659f86f1279"
      },
      "source": [
        "losses= myModel.train(X_tr,y_tr,10000,0.0003)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iter: 0, Current loss: 7.7881\n",
            "Iter: 500, Current loss: 1.8395\n",
            "Iter: 1000, Current loss: 0.5615\n",
            "Iter: 1500, Current loss: 0.2548\n",
            "Iter: 2000, Current loss: 0.1584\n",
            "Iter: 2500, Current loss: 0.1134\n",
            "Iter: 3000, Current loss: 0.0853\n",
            "Iter: 3500, Current loss: 0.0653\n",
            "Iter: 4000, Current loss: 0.0504\n",
            "Iter: 4500, Current loss: 0.0393\n",
            "Iter: 5000, Current loss: 0.0308\n",
            "Iter: 5500, Current loss: 0.0244\n",
            "Iter: 6000, Current loss: 0.0195\n",
            "Iter: 6500, Current loss: 0.0157\n",
            "Iter: 7000, Current loss: 0.0128\n",
            "Iter: 7500, Current loss: 0.0106\n",
            "Iter: 8000, Current loss: 0.0088\n",
            "Iter: 8500, Current loss: 0.0074\n",
            "Iter: 9000, Current loss: 0.0063\n",
            "Iter: 9500, Current loss: 0.0054\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3fb00fc4e0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD7CAYAAABzGc+QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbOElEQVR4nO3dfZAkd33f8Xf3zOzM7M7s48096+6kk+6nk3ToECICbEnBgCEqCbCTYKuMZEOlDLIrrkolKZskRAFTLhUmZRcWBJddVIQglMFRyeCUSw5VIiBhYU4gsHToJ+l0e3fSPWhvb+/2eXae8sf07O3u7UPP7PbOdPfnVbU1M909M9+vNPfZ3l//etqp1WqIiEj4uO0uQEREWqMAFxEJKQW4iEhIKcBFREJKAS4iElLJgF8/DbwVOANUAn4vEZGoSAA7gB8BxZU2CjrA3wp8P+D3EBGJqtuBp1Za6SvAjTF3A38ION7Pp6y1j/l46hmAsbEpqtXm55sPDeUYHZ1s+nlhpp7jIW49x61fWF/PruswMNADXoauZM0AN8Y4wKPA7dba540xbwKeNsY8bq2trvH0CkC1WmspwBvPjRv1HA9x6zlu/cKG9Lzq0LPfg5hVoM+73w+c8RHeIiISIMfPqfTGmHcBfwVMAXngLmvtMz5efx9wfD0FiojE2NXA8Eor/QyhJIFPAB+w1j5tjPkF4BvGmBustb4GeEZHJ1v6U6JQyDMyMtH088JMPcdD3HqOW7+wvp5d12FoKLf2dj5e6zCw01r7NIB3OwUcbKkyERHZEH4C/DVgtzHGABhjDgLbgGNBFiYiIqtbcwjFWnvWGPMA8NfGmMaBy49aay8EW5qIiKzG1zxwa+3XgK8FXMsif/m3R9m3q593v3nnZr6tiEhodOx3oZw+P4U9oZ18EZGVdGyAZ9NJpmfL7S5DRKRjdWyAZ7oSzBQV4CIiK+nYAK/vgZfaXYaISMfq3ADv0hCKiMhqOjbAM+kE08Uyfk71FxGJo44N8Gw6SbVaY66s78wSEVlO5wZ4VwKAWR3IFBFZVscGeCZdP8doZk5XYhMRWU7HBni2ywtw7YGLiCyrcwM8rSEUEZHVdGyAZ7w98OmihlBERJbTsQE+vwc+pz1wEZHldGyAzx/E1BCKiMiyOjbA5w9iahaKiMiyOjbAU0mXVNLVQUwRkRX4uajxPuDxBYv6gV5r7WBQRTV0Z5LaAxcRWYGfS6oNU7+wMQDGmD/187yN0J1OaQ9cRGQFTQWxMaYL+A3gvcGUs1g2k9RBTBGRFTQ7Bv5+4HVr7Y+DKGYpDaGIiKys2aGQjwJfbvZNhoZyzT4FqA+hTM+UKRTyLT0/rOLWL6jnOIhbvxB8z74D3BizC7gTuK/ZNxkdnaRabf57vbszSSami4yMTDT93LAqFPKx6hfUcxzErV9YX8+u6/ja8W1mCOU3gf9jrR1tqaIW1MfANYQiIrKcZgL8t2hh+GQ9utNJnUovIrIC30Mo1toDQRaynO5MinKlRqlcJZXs2HOORETaoqNTsTuj70MREVmJAlxEJKQ6PMBTAMxoHFxE5AodHeC5bD3Ap2YV4CIiS3V0gPd4AT6jABcRuUJHB3gu2wXA1GypzZWIiHSejg7wnmzjupjaAxcRWaqjAzybTuI6DtMaQhERuUJHB7jjOHRnkgpwEZFldHSAQ30uuIZQRESu1PkBnk7qIKaIyDI6P8AzSU0jFBFZRggCPKUhFBGRZXR+gKeTOhNTRGQZHR/gPZqFIiKyrI4P8O5MknKlylxJV+YREVnI1wUdjDEZ4E+AdwOzwD9Ya387yMIaGt9IOF0s05VKbMZbioiEgt898M9SD+4D1tpDwCeDK2mx7rR3Or2GUUREFllzD9wYkwPuB3Zba2sA1tpzQRfW0JNRgIuILMfPEMp+YBR40BjzTmAS+C/W2qcCrcyTbQR4USfziIgs5CfAE8A1wE+stf/RGHMb8G1jzLXW2nE/bzI0lGu5wN07+upFpJIUCvmWXydM4tLnQuo5+uLWLwTfs58APwmUga8DWGt/aIw5DxwAjvh5k9HRSarVWtPFFQp5itNzAJwdmWRkZKLp1wibQiEfiz4XUs/RF7d+YX09u67ja8d3zYOY1trzwJPAewCMMQeArcArLVXWpO6MvhNcRGQ5vqYRAh8HvmyM+e9ACbjPWnsxuLIuSyZculIu0/pCKxGRRXwFuLX2VeCfB1vKyrrTOhtTRGSpjj8TE6Ank1KAi4gsEZIATzI5oyEUEZGFwhHg2RSTGgMXEVkkFAGe705pD1xEZIlQBHhPNsXkdIlarfm55CIiURWKAM9lU1SqNWbn9JWyIiIN4Qhw7ytlpzSMIiIyLxwB3l0PcB3IFBG5LBwBnvUCfFoBLiLSEK4A1xCKiMi8UAR4jwJcROQK4Qhw7xsJFeAiIpeFIsATrqvT6UVElghFgIN3Mo8CXERkXmgCPJdNaR64iMgCoQrwyRl9payISEPIAnyu3WWIiHQMX1fkMcYMA7PeD8DvW2ufCKimZWkPXERkMb/XxAT4V9ba5wOrZA092RTFUoVSuUoqGZo/HEREAhOaJNTZmCIiizWzB/41Y4wDPAX8p826Kn1DfkGAD+TTm/nWIiIdyW+A326tPWWMSQN/CjwMfNjvmwwN5VqpDYBCIQ/A7vEiAIlUcn5ZVEW9v+Wo5+iLW78QfM++Atxae8q7LRpjvgh8q5k3GR2dpFpt/mo6hUKekZEJAKpz9QOYJ89cZOdApunXCouFPceFeo6+uPUL6+vZdR1fO75rjoEbY3qMMX3efQf4deC5lqpah96eLgDGpzQGLiIC/vbAtwH/2xiTABLAUeB3Aq1qGd2ZJAnXYWJac8FFRMBHgFtrXwXevAm1rMp1HPLdKS5NKcBFRCBE0wgBeru7mFCAi4gAYQvwni7GNYQiIgKELMDz3V06iCki4glVgPf2pJiYnqNWa35KoohI1IQswLuYK1eZnau0uxQRkbYLV4B31+eCayqhiEjYAlwn84iIzAtXgHt74JqJIiIStgCf3wNXgIuIhCrA8931r5TVHriISMgCPJlw6ckktQcuIkLIAhy8k3mmdRBTRCR0Ad7X08X4ZLHdZYiItF3oArw/n+bipIZQRETCF+C5Li5OFnU6vYjEXugCfCCXZq5cZaZYbncpIiJt1VSAG2MeNMbUjDE3BVXQWvq9K9KPTWgcXETizXeAG2NuAd4GnAiunLX15+oBrnFwEYk7XwFujEkDXwAeCLactfXn6mdjXtRMFBGJOb974J8GvmqtHQ6wFl8u74ErwEUk3ta8qLEx5u3ArcAftPomQ0O5Vp9KoZC/Ylkum2K2XFt2XRREta/VqOfoi1u/EHzPawY4cCdwEDhujAHYDTxhjPmItfbv/bzJ6Ogk1Wrz0/4KhTwjIxNXLO/r6eLMyOSy68JupZ6jTD1HX9z6hfX17LqOrx3fNQPcWvsQ8FDjsTFmGLjbWvt8S5VtgMZccBGROAvdPHBonI2pABeRePMzhLKItXZfAHU0pT+X5tLkHNVaDddx2l2OiEhbhHMPPJemUq0xoW8lFJEYC2WAD3hnY17U2ZgiEmOhDvALE7NtrkREpH1CGeBDvRkARi8pwEUkvkIZ4PnuFKmky4VxDaGISHyFMsAdx2GwN8P5ce2Bi0h8hTLAAbb0pjWEIiKxFtoAH+rLcEF74CISY6EN8MHeDJem5iiVK+0uRUSkLUIb4I2ZKDqQKSJxFdoA39JXD3AdyBSRuAptgM/vgetApojEVGgDvD+fxnFgVHvgIhJToQ3wZMKlP6ephCISX6ENcKhPJdQeuIjEVagDvNCXZeTiTLvLEBFpi1AH+LaBLBfGi5oLLiKx5OuKPMaYx4GrgSowCfxba+1zQRbmx9aBLDVg5OIsO7f0tLscEZFN5feSar9prb0EYIz5APBl4JbAqvJp60A3AG+MzSjARSR2fA2hNMLb00d9T7zttg5kAXhjbLrNlYiIbD7fFzU2xvwl8MuAA7yvmTcZGso1WdZlhUJ+5XVATzbFRLGy6nZhE6Ve/FLP0Re3fiH4nn0HuLX23wAYY+4D/hi4y+9zR0cnqVZrTRdXKOQZGZlYfZu+DCdOX1pzu7Dw03PUqOfoi1u/sL6eXdfxtePb9CwUa+2jwDuNMUOtFLbRtg5keWNMUwlFJH7WDHBjTM4Yc9WCx/cAF7yftts60M35S7OUKx0xLC8ismn8DKH0AN80xvQAFerBfY+1tvkxkQBsG8hSrdUYHZ9lmzcrRUQkDtYMcGvtOeBtm1BLSxozUc5dmFGAi0ishPpMTIAdQ/X532dGp9pciYjI5gp9gOeyKXq7U5w+rwAXkXgJfYAD7NzSw2ntgYtIzEQiwHds6eH0+WlqtY44rioisikiEeA7h3qYKZa5ODnX7lJERDZNNAJ8iw5kikj8RCPAh+rTB3UgU0TiJBIB3tvTRU8myelRfSuhiMRHJALccZz6gcyRyXaXIiKyaSIR4AC7CzlOjUxpJoqIxEZkAnzPthwzxTIjl3SVehGJh8gE+N5t9S9OP3k2Xt85LCLxFZkA313owXUcTr6hABeReIhMgKeSCXZs6ebkOR3IFJF4iEyAA+zZmufEOe2Bi0g8RCrA927LcWlyjktTOqVeRKJvzQs6eNe+fBTYD8wBLwMfs9aOBFxb0/Y0DmSem+DQNR1xyU4RkcD42QOvAZ+11hpr7SHgGPBQsGW1Zu/2PA7w6unxdpciIhI4P5dUuwB8d8GiZ4AHgipoPbLpJLsKPRx7/VK7SxERCVxTY+DGGJd6eH8rmHLWb/+uPo6dHqeqMzJFJOL8XJV+oT8DJoGHm3nS0FCuybe5rFDIN7X9YbON//fcaYpV2LO9ued2imZ7jgL1HH1x6xeC79l3gBtjPgdcB9xjra028yajo5NUq83vERcKeUZGmpsWuLW3C4AfPX+GbMJp+j3brZWew049R1/c+oX19ey6jq8dX19DKMaYPwLeAnzQWltsqaJNsn2wm55MUuPgIhJ5fqYR3gh8AngJ+IExBuC4tfZXAq6tJY7jsH9XH68owEUk4vzMQnkBCNVYhLmqn58dG+XiZJH+XLrd5YiIBCJSZ2I2HNw3AMCLJ8baXImISHAiGeB7tubpTic5qgAXkQiLZIC7rsP1ewf4+fCYrtAjIpEVyQAHOLh3gNHxWV2hR0QiK9IBDnD0+IU2VyIiEozIBviOoW629GX46Svn212KiEggIhvgjuNw+NotHD0xRnGu0u5yREQ2XGQDHODwdVsolascHdYwiohET6QD/MBV/WTTSX6iYRQRiaBIB3gy4XLomkF++sp5KtWmvn9LRKTjRTrAAW41W5mYLvFzndQjIhET+QC/+dohsukkz7xwrt2liIhsqMgHeCqZ4C2mwLMvjVAsaTaKiERH5AMc4O03bKM4V+G5l3UwU0SiIxYBbvYMMNib5vs/O93uUkRENkwsAtx1He68eSdHh8c4MzrV7nJERDbEmgFujPmcMea4MaZmjLlpM4oKwh2Hd5FwHZ78yevtLkVEZEP42QN/HLgDOBFwLYHq6+ni1uu38vQ/nWV2rtzuckRE1m3NALfWPmWtPbUZxQTt3bfuZqZY1l64iERCLMbAG/bv7OOGfQM88cOTzGlKoYiE3JoXNd4IQ0O5lp9bKOQ3sBK4764b+MQXn+bZY6O8//b9G/raG2Wjew4D9Rx9cesXgu95UwJ8dHSSarX5S5sVCnlGRiY2tJZtvWnMVf381d9bDl89SDa9Kf8JfAui506nnqMvbv3C+np2XcfXjm+shlAaPvRL1zI+XeJvfzDc7lJERFrmZxrh540xrwG7ge8YY14IvqxgXb2jl1+4aTv/98gpzo1Nt7scEZGWrDl+YK39PeD3NqGWTfWrd+7n2ZdGeOTvXuQ/3PtmXMdpd0kiIk2J5RAKwEA+za+/6zpePHmRJ3+saYUiEj6xDXCA29+0g5uuGeSbT77Ca29MtrscEZGmxDrAHcfho3cdJJtJ8vBj/8T0bKndJYmI+BbrAAfoz6X53Q8eYnR8lv/xNy9QrujSayISDrEPcIBrd/dx33sNLxy/wF98+2hLc9ZFRDZbZ53F0kZ33LyT6dky33jyFZIJh4/cdZBkQr/fRKRzKcAXeN9teyhXqjz2vVeZmC7xwAdv6rgzNUVEGrSLucTd79jHb/2L6zk6PMZnvnKE10Y0O0VEOpMCfBl33LyTf/9rNzM1W+YzjxzhO0dOaVxcRDqOAnwFB/cN8qmPvJUDe/r5X995mc985Qivnh5vd1kiIvMU4Kvoy6X5d//6Zj72/hu5MFHkM185wuf/+mcMn1WQi0j76QjdGhzH4bYbtvGm/UN859nXeOKHJ/n0/zzCtbv6eOctu7jVFEglE+0uU0RiSAHuUzad5J537ONdt+ziez89w3efe52/+PZRHn0iweHrtnCr2cqN+wZJdynMRWRzKMCb1J1J8b7b9vDL/+wqfn5ijH88eo4fvzTCMy+cI+E67N/Zy/V7BzB7Bti7LU93Rv+JRSQYSpcWuY7DjfsGuXHfIPe912BPXeTo8Qu8eHKMb/9gmG89PQzA1oEs+7bnuWprju2DPWwfzLJ1IKthFxFZNwX4Bkgm3PkwB5ieLXHs9DjDZyc4eXaCY6+P848/f2N+ewcY6stQ6M8y2JtmIJ9hMJ9mIJ9mf6lKtVQml0nhuvqOchFZmQI8AN2ZFIeuGeLQNUPzy6Zny5wbm+bchWnOej+jl2Y5OjzGxckitSXTzB2gJ5si350in02R6+6q3+9O0ZNJkU0n6U4nyc7/JOYfp5Iuji5QIRJ5vgLcGHMAeAQYAkaB+621LwdZWNR0Z5JcvaOXq3f0XrGuUq0yPlXiwvgsFcfl5OmLTM6UmJguMTE9x8R0iXMXpnnltTkmZkpXhP1SCdeZD/h0V4J0KkFXyiWdaty/clm6K0FXsv64qytBOpkglXRJJhxSSZdUMrHgvkvC1QxUkXbzuwf+JeAL1tqvGmM+DPw58EvBlRUvCddlwBtCKRTyHNiZX3Hbaq3GbLHCTLHMTLHMtHe7+PHl9bNzFebKFYqlCuNTJeZK9fuNn7V+GazEcaiHecIl6d2mFt4mXZLe/UTCJek6JFyHRMIh4brz95MJl3wuQ3F2jkTCW+46l5+zZPuEW/+l0tgm4Tq4joPrOrgO3q2D4zokFix3Gts5Dq5b/yXnzK/XXysSTmsGuDFmK3AL8B5v0deBh40xBWvtSJDFyZVcx6E7k9yQ2S21Wo1ypUaxVFkU7HOlKnOlCqVKlVK5/lNu3K9UKXu3pXKVcrlGqVLx1tXq68oVSpUaU7NlSuUqlWqVSqVGpVqlXK1592tUKtX6bQd8TUEj2N0Fob5c+Cdcx3tc37ae/d52jgMO8/frD71bx8GB+fvpdJJSqYLjvU6jhuW2dZZ9vYXb1d8fbxvX24YF2+O9Xr3aep31+5e3W7jN5fuNNUvXOfPPvfw6zvz9pdvmetJMTRUXvVfjKc6CB0tfc+FQ4LLbzte/ZNtF6xa/xlKLamXRg+XuLqhv9de9vlihNx3sZAU/KXAV8Lq1tgJgra0YY057yxXgIeY4DqlkfViEbKptdWzZkuPsuXEv1L2g924rCwN//hdBjXLl8rpqrUa16t3WatSq9b9UKtXG4xrVGou2qVYbz8HHc2tUveULn1sDarX6L8L520XL6q8PzN+vVWtUqVErlpnz/gKafz61JY8vPw9vWdW7Be/1rnjPZV6vvnm9DmoL7jP/WszfZ/7+0m2lOYO9GT73O+8I9D025SDm0FCu5ecWCisPJ0RVHHvesb2v3SWITwt/wXgL5u83fiHUFv2SuPxg0WNYtN3S11m6XY3Fr9OoZdHrrLDdwm0X97LgPoseLHf3ive78vUur+jPZ+jt6Vp+ww3iJ8BPAbuMMQlv7zsB7PSW+zI6OtnSt/kVCnlGRiaafl6Yqed4iFvPm9mvs8L9DX3xFV/48orenq6We3Zdx9eO75pTCay1bwDPAfd6i+4FfqLxbxGR9vI7hPJx4BFjzH8FxoD7gytJRET88BXg1toXgdsCrkVERJqgszFEREJKAS4iElIKcBGRkAp6HngCWNe36sXxG/nUczzEree49Qut97zgeaueyuksN7l9A/0i8P0g30BEJMJuB55aaWXQAZ4G3gqcASpBvpGISIQkgB3Aj4DiShsFHeAiIhIQHcQUEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQ25ZJqzTLGHAAeAYaAUeB+a+3L7a2qecaYIeBRYD8wB7wMfMxaO2KMeRvw50AWGAY+7F08g1bXdRJjzIPAfwMOWWufj3K/xpgM8CfAu4FZ4B+stb+92ue41XWdwhhzN/CHeNcTBj5lrX0sSj0bYz4H/EtgH97n2Fu+4T222n+n7oF/CfiCtfYA8AXq/4DDqAZ81lprrLWHgGPAQ8YYF/gq8Ltej98DHgJodV0nMcbcArwNOOE9jnS/wGepB/cB7//zJ73lq32OW13XdsYYh/qOyX3W2sPAfdQv+OISrZ4fB+7A+xwvEESPLfXfcQFujNkK3AJ83Vv0deAWY0yhfVW1xlp7wVr73QWLngH2Am8BZq21je84+BLwIe9+q+s6gjEmTf0D+MCCxVHuN0f9ClWftNbWAKy151b7HLe6bnM68q0KNK5E3U/96zK2EKGerbVPWWsXXfs3iP+v6+m/4wIcuAp43VpbAfBuT3vLQ8vbO3kA+BawhwW/1a215wHXGDO4jnWd4tPAV621wwuWRbnf/dT/5H3QGHPEGPNdY8wvsvrnuNV1HcH7RfUh4G+MMSeo76neT4R7XiCIHlvuvxMDPKr+DJgEHm53IUExxrwduBX4Yrtr2UQJ4BrqF/q+Ffh94DFg7UuKh5QxJgl8AviAtXYvcA/wDSLcc6fqxAA/BewyxiQAvNud3vJQ8g6GXAf8mrW2CpykPpTSWL8FqFprL6xjXSe4EzgIHDfGDAO7gSeAa4lmv1CvsYz356+19ofAeWCGlT/Hq33Gw/D5PwzstNY+DeDdTlE/DhDVnhta7SOQ/jsuwL0ZBs8B93qL7qW+dzPSvqpaZ4z5I+pjuR+01ja+FvJZIOv9qQ3wceCb61zXdtbah6y1O621+6y1+4DXgPcCf0wE+4X5YZ0ngffA/GyCrcBLrPA5Xu0zHpLP/2vAbmOMATDGHAS2UZ9lFdWegdXzKYh1a9XTkV8na4y5nvqUmgFgjPqUGtveqppnjLkReJ76P+YZb/Fxa+2vGGPeQf1Ic4bL0+POec9raV2n8fbC7/amEUa2X2PMNcCXqU8BKwH/2Vr7d6t9jltd1ymMMb8B/AH1g5kAD1prH49Sz8aYzwO/Cmyn/lfVqLX2xiB6bLX/jgxwERFZW8cNoYiIiD8KcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISCnARURC6v8D3TtsnOtazm8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atw0alYBHB7H"
      },
      "source": [
        "**Ploting Costs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ib8F5oJjHDGB"
      },
      "source": [
        "plt.plot(losses)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzbmZhGqHptu"
      },
      "source": [
        "**Forward pass on test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eS8UvkaU1uUb"
      },
      "source": [
        "y_test_pred=myModel.forward_pass(X_tst)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mT8-zW82Ony"
      },
      "source": [
        "**Ploting Predictions vs Actual Values for test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqUlhiky2Xbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "d036589a-618f-4a94-bb3a-ea0544550e90"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(y_tst,'rx',label='Actual Values')\n",
        "plt.plot(y_test_pred,'bo',label='Predictions')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5aPfpFc6T6V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8c6a882b-bd5d-4f37-f553-e093ec62b859"
      },
      "source": [
        "\n",
        "def pearsonr(X, Y):\n",
        "    ''' Takes X & Y as numpy array\n",
        "       returms Pearson Correlation Coefficient \n",
        "    '''\n",
        "    # Normalise X and Y\n",
        "    X -= X.mean(0)\n",
        "    Y -= Y.mean(0)\n",
        "    # Standardise X and Y\n",
        "    X /= X.std(0)\n",
        "    Y /= Y.std(0)\n",
        "    # Compute mean product\n",
        "    return np.mean(X*Y)\n",
        "print(\"Accuracy of the model\",pearsonr(y_tst, y_test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the model 0.9941748709261294\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "au5FHk-PH3t8"
      },
      "source": [
        "**Fetching learned weights of the model for future predictions**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8qLc8UyIKzu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "73f572bd-e7df-4685-f81e-f090e8e972fa"
      },
      "source": [
        "W=myModel.W\n",
        "b=myModel.b\n",
        "print(\"Value of Weights(W) \",W)\n",
        "print(\"Value of bias(b) \",b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Value of Weights(W)  [[ 0.17498029]\n",
            " [ 0.08711869]\n",
            " [-0.00960086]\n",
            " [-0.07116314]\n",
            " [ 0.03858008]\n",
            " [ 0.03372087]\n",
            " [-0.04257171]\n",
            " [-0.09552984]\n",
            " [ 0.98754257]]\n",
            "Value of bias(b)  -0.006185565562785028\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}