Prediction_using_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Prediction_using_Linear_Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP2c60w0JAVg0MFjO/2MrSJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shajohn4/Portfolio/blob/main/Prediction_using_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing required libraries"
      ],
      "metadata": {
        "id": "i_YCN4MeySOD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "TuMTE-HrnFO7"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model  \n",
        "from word2number import w2n\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install word2number"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjXOJz3KCcIT",
        "outputId": "85d84607-84f6-4913-eb58-d688f47fc311"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting word2number\n",
            "  Downloading word2number-1.1.zip (9.7 kB)\n",
            "Building wheels for collected packages: word2number\n",
            "  Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5582 sha256=d64e2a5783d755a940c53e355bc9c2d9ce450759e6fc303cbbecff7b5264f8b7\n",
            "  Stored in directory: /root/.cache/pip/wheels/4b/c3/77/a5f48aeb0d3efb7cd5ad61cbd3da30bbf9ffc9662b07c9f879\n",
            "Successfully built word2number\n",
            "Installing collected packages: word2number\n",
            "Successfully installed word2number-1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading the inputfile"
      ],
      "metadata": {
        "id": "5wQ-LaYryV5u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/homeprices.csv\")\n",
        "df"
      ],
      "metadata": {
        "id": "YpQwiIBkqdaV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Function to draw ScatterPlot"
      ],
      "metadata": {
        "id": "uKiIvYR4yY1g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def draw_scatterplot(X_Cordinate,Y_Cordinate,X_Label, Y_Label, M_Color, M_Marker):\n",
        "  %matplotlib inline\n",
        "  plt.xlabel(X_Label)\n",
        "  plt.ylabel(Y_Label)\n",
        "  plt.scatter(X_Cordinate, Y_Cordinate,color = M_Color, marker = M_Marker)"
      ],
      "metadata": {
        "id": "M94gMf9VsCGP"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calling Function to draw the scatterplot"
      ],
      "metadata": {
        "id": "Ej-mjRzQybpZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "draw_scatterplot(df.area,df.price, 'area(sqr ft)','prices(USD)','red','*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "8WP5wCNaqxt1",
        "outputId": "9eb67fdc-5e7a-445a-a390-700807b307de"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining the Linear Regression Model"
      ],
      "metadata": {
        "id": "AFjIdot2yhv3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()\n",
        "reg.fit(df[['area']],df.price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWsFul3mtAHR",
        "outputId": "b6796575-3df3-42ca-cdcc-69717142dae2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calling the Linear Regression Model to predict the values"
      ],
      "metadata": {
        "id": "aaXy0lcDyQvv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[3300]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3SwMMHvuPr5",
        "outputId": "363256f5-04c4-407a-a031-e4110ae5b95f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([628715.75342466])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cross-checking if the value predicted is accurate."
      ],
      "metadata": {
        "id": "SESFFsdwytB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# price=m*area + b where m is slope and b is the Y-intercept\n",
        "print('slope is ', reg.coef_)\n",
        "print('Y intercept is ',reg.intercept_)\n",
        "print('Y = mx + 2')\n",
        "print(reg.coef_*3300+reg.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Uwf1drGvUy9",
        "outputId": "33ed94cc-1ac9-40f0-bcd7-b3788a0e933b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "slope is  [135.78767123]\n",
            "Y intercept is  180616.43835616432\n",
            "Y = mx + 2\n",
            "[628715.75342466]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.read_csv('/areas.csv')\n",
        "d"
      ],
      "metadata": {
        "id": "lGt3_hI_wSeX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p = reg.predict(d)"
      ],
      "metadata": {
        "id": "e90pRmqzwnpp"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d['prices'] = p\n",
        "d.to_csv('/predictions.csv',index=False)\n",
        "\n",
        "draw_scatterplot(d.area,df.price, 'area(sqr ft','prices(USD)','red','*')"
      ],
      "metadata": {
        "id": "HZySZYBpwvfl"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/canada_per_capita_income.csv')\n",
        "df.head(5)"
      ],
      "metadata": {
        "id": "HyXZdGHyy01Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "draw_scatterplot(df.year,df['per capita income (US$)'],'year','Per Capita Income (US$)','red','*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "8C_aK4HkzAN1",
        "outputId": "f1d85a54-80f2-47ae-ad45-6f60369a6823"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()\n",
        "reg.fit(df[['year']],df['per capita income (US$)'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9upzuONz-8m",
        "outputId": "9365db47-cde1-477d-a6ae-b1c94e86db90"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([['2020']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2Wc7kHk0KV9",
        "outputId": "7759576a-b307-4e5d-cec0-8134773d8578"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([41288.69409442])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MultiClass linear regression"
      ],
      "metadata": {
        "id": "0mcYyAb79d28"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/homeprices_MV.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "8xibGjyx9g1Y",
        "outputId": "eee75804-3074-4e4d-f0c9-b54fc44eef5e"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1e4cde41-b850-4af9-bd5e-c996517b8b2e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>area</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>age</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2600</td>\n",
              "      <td>3.0</td>\n",
              "      <td>20</td>\n",
              "      <td>550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15</td>\n",
              "      <td>565000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18</td>\n",
              "      <td>610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30</td>\n",
              "      <td>595000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8</td>\n",
              "      <td>760000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4100</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8</td>\n",
              "      <td>810000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e4cde41-b850-4af9-bd5e-c996517b8b2e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e4cde41-b850-4af9-bd5e-c996517b8b2e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e4cde41-b850-4af9-bd5e-c996517b8b2e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   area  bedrooms  age   price\n",
              "0  2600       3.0   20  550000\n",
              "1  3000       4.0   15  565000\n",
              "2  3200       NaN   18  610000\n",
              "3  3600       3.0   30  595000\n",
              "4  4000       5.0    8  760000\n",
              "5  4100       6.0    8  810000"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "median_bedrooms = math.floor(df.bedrooms.median())\n",
        "df.bedrooms= df.bedrooms.fillna(median_bedrooms)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "k-khT2C198gs",
        "outputId": "483039c0-3153-4417-886c-34b485487011"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e28ee889-b04e-4c4d-aa76-6cb598937f74\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>area</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>age</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2600</td>\n",
              "      <td>3.0</td>\n",
              "      <td>20</td>\n",
              "      <td>550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15</td>\n",
              "      <td>565000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "      <td>4.0</td>\n",
              "      <td>18</td>\n",
              "      <td>610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30</td>\n",
              "      <td>595000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8</td>\n",
              "      <td>760000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4100</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8</td>\n",
              "      <td>810000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e28ee889-b04e-4c4d-aa76-6cb598937f74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e28ee889-b04e-4c4d-aa76-6cb598937f74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e28ee889-b04e-4c4d-aa76-6cb598937f74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   area  bedrooms  age   price\n",
              "0  2600       3.0   20  550000\n",
              "1  3000       4.0   15  565000\n",
              "2  3200       4.0   18  610000\n",
              "3  3600       3.0   30  595000\n",
              "4  4000       5.0    8  760000\n",
              "5  4100       6.0    8  810000"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()\n",
        "reg.fit(df[['area','bedrooms','age']],df.price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3tA9zHW_DBR",
        "outputId": "3d03c9a0-06e1-4df1-b20c-8789973d51b9"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('coefficient', reg.coef_)\n",
        "print('Y-Intercept',reg.intercept_)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJgEsgPd_qL6",
        "outputId": "5cfb3db0-bccb-4e12-e7a3-d057d46e2680"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "coefficient [  112.06244194 23388.88007794 -3231.71790863]\n",
            "Y-Intercept 221323.00186540396\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[5000,6,50]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCr5D013_5Kr",
        "outputId": "eeee72f9-8ba4-412f-d986-512cc68cccd4"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([760382.59661206])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/hiring.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Isbi1rRiAdMp",
        "outputId": "709e530f-bc4a-4ee1-a1e8-94db52cc3cb1"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9cc4b07c-b411-4c8c-91ed-006fdc5adbd1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>experience</th>\n",
              "      <th>test_score(out of 10)</th>\n",
              "      <th>interview_score(out of 10)</th>\n",
              "      <th>salary($)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>five</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>two</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10</td>\n",
              "      <td>65000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>seven</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6</td>\n",
              "      <td>70000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>three</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10</td>\n",
              "      <td>62000</td>\n",
              "    </tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>eleven</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9cc4b07c-b411-4c8c-91ed-006fdc5adbd1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9cc4b07c-b411-4c8c-91ed-006fdc5adbd1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
              "0        NaN                    8.0                           9      50000\n",
              "1        NaN                    8.0                           6      45000\n",
              "2       five                    6.0                           7      60000\n",
              "3        two                   10.0                          10      65000\n",
              "4      seven                    9.0                           6      70000\n",
              "5      three                    7.0                          10      62000\n",
              "6        ten                    NaN                           7      72000\n",
              "7     eleven                    7.0                           8      80000"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.experience=df.experience.fillna('zero')\n",
        "df.experience\n",
        "df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "7j-J-pMLApsh",
        "outputId": "83461062-5466-4894-c3e3-b1320085c548"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>eleven</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
              "0       zero                    8.0                           9      50000\n",
              "1       zero                    8.0                           6      45000\n",
              "2       five                    6.0                           7      60000\n",
              "3        two                   10.0                          10      65000\n",
              "4      seven                    9.0                           6      70000\n",
              "5      three                    7.0                          10      62000\n",
              "6        ten                    NaN                           7      72000\n",
              "7     eleven                    7.0                           8      80000"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "median_test_score = math.floor( df['test_score(out of 10)'].median())\n",
        "df['test_score(out of 10)'] = df['test_score(out of 10)'].fillna(median_test_score)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Zzf5VLGEA1vP",
        "outputId": "13faf4b4-9a59-4497-d6c9-dfeb7f471f3e"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>experience</th>\n",
              "      <th>test_score(out of 10)</th>\n",
              "      <th>interview_score(out of 10)</th>\n",
              "      <th>salary($)</th>\n",
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              "      <th>3</th>\n",
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              "      <td>10</td>\n",
              "      <td>65000</td>\n",
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              "      <th>4</th>\n",
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              "      <td>62000</td>\n",
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              "      <th>6</th>\n",
              "      <td>10</td>\n",
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              "      <td>72000</td>\n",
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              "      <th>7</th>\n",
              "      <td>11</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8</td>\n",
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              "\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-dd85c81a-132d-4a54-a791-126b329935a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dd85c81a-132d-4a54-a791-126b329935a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
              "0           0                    8.0                           9      50000\n",
              "1           0                    8.0                           6      45000\n",
              "2           5                    6.0                           7      60000\n",
              "3           2                   10.0                          10      65000\n",
              "4           7                    9.0                           6      70000\n",
              "5           3                    7.0                          10      62000\n",
              "6          10                    8.0                           7      72000\n",
              "7          11                    7.0                           8      80000"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.experience = df.experience.apply(w2n.word_to_num)\n",
        "df.experience.astype(float)\n",
        "df.info()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4y5_z32LpB7",
        "outputId": "5f474c2b-956b-4677-8231-57c98df43f13"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8 entries, 0 to 7\n",
            "Data columns (total 4 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   experience                  8 non-null      int64  \n",
            " 1   test_score(out of 10)       8 non-null      float64\n",
            " 2   interview_score(out of 10)  8 non-null      int64  \n",
            " 3   salary($)                   8 non-null      int64  \n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 384.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = linear_model.LogisticRegression()\n",
        "reg.fit(df[['experience','test_score(out of 10)','interview_score(out of 10)']], df['salary($)'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YiPm5jtOMa19",
        "outputId": "123b9eaf-c2d7-4088-acf8-27fad4225d1b"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('coefficient :', reg.coef_)\n",
        "print('Y Intercept :', reg.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9xZhioBOMu0",
        "outputId": "cd43a7c7-c728-4205-8398-307e1c469699"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "coefficient : [[-0.60394051  0.03025633 -0.71997699]\n",
            " [-0.77855807 -0.07111001  0.23788777]\n",
            " [ 0.00291385 -0.72079529 -0.34878269]\n",
            " [-0.04109057 -0.36431254  0.67727445]\n",
            " [-0.12456105  0.78467449  0.49785354]\n",
            " [ 0.16638476  0.53267266 -0.53712487]\n",
            " [ 0.62121555  0.16170047 -0.14956357]\n",
            " [ 0.75763605 -0.35308611  0.34243236]]\n",
            "Y Intercept : [ 7.94386231  1.4676045   9.20377995 -1.63911276 -9.30656314  0.01278117\n",
            " -3.29293595 -4.38941607]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[2,9,6]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXpdY9qMOXkf",
        "outputId": "47380ab8-924d-41bf-d865-6a389e8a8890"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([45000])"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[12,10,10]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6TxBi8rzOdH-",
        "outputId": "0d5a96f3-defd-4445-d362-a32b5e77efbb"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "array([80000])"
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          "metadata": {},
          "execution_count": 129
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}