Salary_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Salary_Prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyORTfa7eVi+h5/KrH0hvJZe",
      "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/Chandradithya8/Machine_learning-Projects/blob/master/Salary_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpDoAs4rqOI0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "3f2f6b9a-4961-40a6-a248-96a050bf65e5"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn import linear_model\n",
        "df=pd.read_csv(\"/content/hiring.csv.txt\")\n",
        "df"
      ],
      "execution_count": 1,
      "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>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",
              "      <td>50000</td>\n",
              "    </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>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10</td>\n",
              "      <td>65000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6</td>\n",
              "      <td>70000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10</td>\n",
              "      <td>62000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>72000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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         5.0                    6.0                           7      60000\n",
              "3         2.0                   10.0                          10      65000\n",
              "4         7.0                    9.0                           6      70000\n",
              "5         3.0                    7.0                          10      62000\n",
              "6        10.0                    NaN                           7      72000\n",
              "7        11.0                    7.0                           8      80000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyLToyDBrZn2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "007e7eb0-a8d5-42e3-91ed-1297df63b92e"
      },
      "source": [
        "df=df.fillna({'experience':0})\n",
        "df"
      ],
      "execution_count": 2,
      "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>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>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10</td>\n",
              "      <td>65000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6</td>\n",
              "      <td>70000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10</td>\n",
              "      <td>62000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>72000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
              "0         0.0                    8.0                           9      50000\n",
              "1         0.0                    8.0                           6      45000\n",
              "2         5.0                    6.0                           7      60000\n",
              "3         2.0                   10.0                          10      65000\n",
              "4         7.0                    9.0                           6      70000\n",
              "5         3.0                    7.0                          10      62000\n",
              "6        10.0                    NaN                           7      72000\n",
              "7        11.0                    7.0                           8      80000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqj8TEx5roWi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7b9775d1-a950-4f15-dd28-fa43cc1ecfad"
      },
      "source": [
        "m=int(df['test_score(out of 10)'].mean())\n",
        "m"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOMto7hgsLNx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "30cc44e3-9954-4acc-edce-52bdeb27c73c"
      },
      "source": [
        "df=df.fillna(m)\n",
        "df"
      ],
      "execution_count": 4,
      "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>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>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6</td>\n",
              "      <td>45000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10</td>\n",
              "      <td>65000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6</td>\n",
              "      <td>70000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10</td>\n",
              "      <td>62000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7</td>\n",
              "      <td>72000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
              "0         0.0                    8.0                           9      50000\n",
              "1         0.0                    8.0                           6      45000\n",
              "2         5.0                    6.0                           7      60000\n",
              "3         2.0                   10.0                          10      65000\n",
              "4         7.0                    9.0                           6      70000\n",
              "5         3.0                    7.0                          10      62000\n",
              "6        10.0                    7.0                           7      72000\n",
              "7        11.0                    7.0                           8      80000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nAn_7YpsRMR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5c2187ca-50da-43fa-f009-cdfd245fb243"
      },
      "source": [
        "reg=linear_model.LinearRegression()\n",
        "reg.fit(df[['experience','test_score(out of 10)','interview_score(out of 10)']],df['salary($)'])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrOSoDLQtQqY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "07e90727-78d3-472f-9b57-c98894aef6f1"
      },
      "source": [
        "# Predicting salary of a person with 2yrs experience, 9 in test_score, and 6 in interview_score\n",
        "reg.predict([[2,9,6]])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([53713.86677124])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCp7_m9dusQp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6dac53d2-6df6-4f4d-c597-17b09d2f6c67"
      },
      "source": [
        "# Predicting salary of a person with 12yrs of experiance, 10 in test_score, and  10 in interview_score\n",
        "reg.predict([[12,10,10]])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([93747.79628651])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    }
  ]
}