DS640 Assignment 4 -Manav Patel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Truth or Bluff Detector "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When HR planning indicates the need for additional labor, organizations have a number of choices to make. Careful HR planning must consider the overall growth prospects of the organization and accurate forecasting of future labor needs. Recruitment planning begins only when other alternatives have been considered and eliminated. When one finds a deserving candidate it must decide on the salary of the candidate as per the industry standards.  \n",
    "\n",
    "In this project we need to make a truth or bluff detector for the HR recruitment salary negotiation process to check if a candidate is giving us the true information about his/her last drawn salary, using the database Position_Salaries.csv. We have a specific case of a potential employee candidate tells he has 20+ years (Level 6.5) of experience and earned 160,000 annual salary in his previous company. Therefore, he is asking now for at least more than 160,000. We need to determine if he deserves it and if he is telling the truth using random forest regression model. \n",
    "\n",
    "Here we have 10 datapoints and three attributes in the dataset namely:\n",
    "\n",
    "Position (The position in the company)\n",
    "Level (The level of position as decided by the Company Policy)\n",
    "Salary (The salary drawn by personnel’s at different position)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration and preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the csv file  \n",
    "dataset = pd.read_csv('/Users/Lenovo/Desktop/Position_Salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level  Salary\n",
       "0   Business Analyst      1   45000\n",
       "1  Junior Consultant      2   50000\n",
       "2  Senior Consultant      3   60000\n",
       "3            Manager      4   80000\n",
       "4    Country Manager      5  110000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing first 5 elements of the dataset \n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preprocessing the dataset to create 2 arrays\n",
    "X = dataset.iloc[:, 1:2].values #The 'Level' feature of the dataset\n",
    "y = dataset.iloc[:, 2].values #The 'Salary' feature of the dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have a small dataset we do not split it into training and testing datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the X array\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  45000,   50000,   60000,   80000,  110000,  150000,  200000,\n",
       "        300000,  500000, 1000000], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing the y array \n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the random forest regression model with 10 tree (default value).\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=10,random_state=0)\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the Regression result \n",
    "X_grid = np.arange(min(X), max(X), 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color = 'blue')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'red')\n",
    "plt.title('Truth or Bluff Detector (Random Forest Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([167000.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting the result for Level 6.5 using the model with 10 trees. \n",
    "y_pred10 = regressor.predict(6.5)\n",
    "y_pred10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted value is close enough to what the candidate mentioned to the HR, but as we know from our previous project that the candidate was not bluffing about his previous salary using polynomial regression, we can increase the accuracy of the random forest model to get close to the 160000 mark but increasing the number of trees in the model. \n",
    "\n",
    "Hence we try to predict the salary using models with varying amount of trees before choosing the best fit model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the random forest regression model with 100 trees.\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=100,random_state=0)\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the Regression result with 100 trees \n",
    "X_grid = np.arange(min(X), max(X), 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
    "plt.title('Truth or Bluff Detector (Random Forest Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([160600.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting the result for Level 6.5 using the model with 100 trees \n",
    "y_pred100 = regressor.predict(6.5)\n",
    "y_pred100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the random forest regression model with 300 trees.\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=300,random_state=0)\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the Regression result with 300 trees \n",
    "X_grid = np.arange(min(X), max(X), 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1)) #To smoothen out vertical lines of the model  \n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
    "plt.title('Truth or Bluff Detector (Random Forest Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([160333.33333333])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting the result for Level 6.5 using the model with 300 trees \n",
    "y_pred300 = regressor.predict(6.5)\n",
    "y_pred300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the random forest regression model with 500 trees.\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=500,random_state=0)\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the Regression result with 500 trees \n",
    "X_grid = np.arange(min(X), max(X), 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1)) #To smoothen out vertical lines of the model  \n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
    "plt.title('Truth or Bluff Detector (Random Forest Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([160600.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting the result for Level 6.5 using the model with 500 trees\n",
    "y_pred500 = regressor.predict(6.5)\n",
    "y_pred500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After predicting results of Level 6.5 salary using random forest regression models with varying number of trees, we find that the predictions from models with 10, 100 and 500 trees were close enough but not accurate as the one with 300 trees.  \n",
    "\n",
    "Hence we choose the random forest regression model with 300 trees as our predictor model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per our model, we find that the salary of an employee at level 6.5 tends to be nearly 160000 as we found out using polynomial regression about the last drawn salary by the candidate. We revised our random forest regression model to be accurate with the beforehand known salary. Hence in conclusion we find that the candidate is not bluffing and we can justify the amount of salary stated by the candidate as true using the random forest model with more accuracy.    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}