Salary prediction using Random Forest .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a7565ba",
   "metadata": {},
   "source": [
    "# Random forest "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d8a0a4",
   "metadata": {},
   "source": [
    "#### salary pridiction   more than 1000k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb8d1f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sab\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f0c3b4",
   "metadata": {},
   "source": [
    "### github se data liya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0613ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary_more_then_100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company              job     degree  salary_more_then_100k\n",
       "0  google  sales executive  bachelors                      0\n",
       "1  google  sales executive    masters                      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/codebasics/py/master/ML/9_decision_tree/salaries.csv')\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c583bb57",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[: , :3]\n",
    "y = df['salary_more_then_100k']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55a7be7",
   "metadata": {},
   "source": [
    "## hum Label Encoder use karenge\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3958b0",
   "metadata": {},
   "source": [
    "#### Label Encoder table ki text values ko 0  1  2   me badalta hai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ea80395",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le_company = LabelEncoder()\n",
    "le_job = LabelEncoder()\n",
    "le_degree = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3a31b4",
   "metadata": {},
   "source": [
    "###### new columns create karenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b480af3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['company_n'] = le_company.fit_transform(df['company'])\n",
    "df['job_n'] = le_company.fit_transform(df['job'])\n",
    "df['degree_n'] = le_company.fit_transform(df['degree'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3c06269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary_more_then_100k</th>\n",
       "      <th>company_n</th>\n",
       "      <th>job_n</th>\n",
       "      <th>degree_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      company                  job     degree  salary_more_then_100k  \\\n",
       "0      google      sales executive  bachelors                      0   \n",
       "1      google      sales executive    masters                      0   \n",
       "2      google     business manager  bachelors                      1   \n",
       "3      google     business manager    masters                      1   \n",
       "4      google  computer programmer  bachelors                      0   \n",
       "5      google  computer programmer    masters                      1   \n",
       "6  abc pharma      sales executive    masters                      0   \n",
       "7  abc pharma  computer programmer  bachelors                      0   \n",
       "8  abc pharma     business manager  bachelors                      0   \n",
       "\n",
       "   company_n  job_n  degree_n  \n",
       "0          2      2         0  \n",
       "1          2      2         1  \n",
       "2          2      0         0  \n",
       "3          2      0         1  \n",
       "4          2      1         0  \n",
       "5          2      1         1  \n",
       "6          0      2         1  \n",
       "7          0      1         0  \n",
       "8          0      0         0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "566b4012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_n</th>\n",
       "      <th>job_n</th>\n",
       "      <th>degree_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   company_n  job_n  degree_n\n",
       "0          2      2         0\n",
       "1          2      2         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop(columns = ['company', 'job', 'degree', 'salary_more_then_100k'])\n",
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc43fae",
   "metadata": {},
   "source": [
    "# Importing Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4c6a32f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "model = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3c700fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(df1, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e31607b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(df1, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "730e5756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[2, 0,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6a95b4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e7576829",
   "metadata": {},
   "source": [
    "## Saving ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "856da2be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f1d45367",
   "metadata": {},
   "source": [
    "# "
   ]
  }
 ],
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