Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from pprint import pprint\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('training.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>satisfaction_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11999.000000</td>\n",
       "      <td>11999.000000</td>\n",
       "      <td>11999.000000</td>\n",
       "      <td>11999.000000</td>\n",
       "      <td>11999.000000</td>\n",
       "      <td>11999.000000</td>\n",
       "      <td>11999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.715750</td>\n",
       "      <td>3.795816</td>\n",
       "      <td>202.201511</td>\n",
       "      <td>3.500875</td>\n",
       "      <td>0.145179</td>\n",
       "      <td>0.020585</td>\n",
       "      <td>0.613853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.170733</td>\n",
       "      <td>1.229931</td>\n",
       "      <td>49.887899</td>\n",
       "      <td>1.464127</td>\n",
       "      <td>0.352296</td>\n",
       "      <td>0.141996</td>\n",
       "      <td>0.249038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>95.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>157.135000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.439000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>201.020000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>246.020000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.817000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>313.110000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       last_evaluation  number_project  average_monthly_hours  \\\n",
       "count     11999.000000    11999.000000           11999.000000   \n",
       "mean          0.715750        3.795816             202.201511   \n",
       "std           0.170733        1.229931              49.887899   \n",
       "min           0.350000        2.000000              95.100000   \n",
       "25%           0.560000        3.000000             157.135000   \n",
       "50%           0.720000        4.000000             201.020000   \n",
       "75%           0.870000        5.000000             246.020000   \n",
       "max           1.000000        7.000000             313.110000   \n",
       "\n",
       "       time_spend_company  Work_accident  promotion_last_5years  \\\n",
       "count        11999.000000   11999.000000           11999.000000   \n",
       "mean             3.500875       0.145179               0.020585   \n",
       "std              1.464127       0.352296               0.141996   \n",
       "min              2.000000       0.000000               0.000000   \n",
       "25%              3.000000       0.000000               0.000000   \n",
       "50%              3.000000       0.000000               0.000000   \n",
       "75%              4.000000       0.000000               0.000000   \n",
       "max             10.000000       1.000000               1.000000   \n",
       "\n",
       "       satisfaction_level  \n",
       "count        11999.000000  \n",
       "mean             0.613853  \n",
       "std              0.249038  \n",
       "min              0.076000  \n",
       "25%              0.439000  \n",
       "50%              0.646000  \n",
       "75%              0.817000  \n",
       "max              1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11999, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=dataset.copy()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11999 entries, 0 to 11998\n",
      "Data columns (total 10 columns):\n",
      "last_evaluation          11999 non-null float64\n",
      "number_project           11999 non-null int64\n",
      "average_monthly_hours    11999 non-null float64\n",
      "time_spend_company       11999 non-null int64\n",
      "Work_accident            11999 non-null int64\n",
      "package                  11999 non-null object\n",
      "promotion_last_5years    11999 non-null int64\n",
      "division                 11999 non-null object\n",
      "salary                   11999 non-null object\n",
      "satisfaction_level       11999 non-null float64\n",
      "dtypes: float64(3), int64(4), object(3)\n",
      "memory usage: 937.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def factorize_col(data,col):\n",
    "    data[col]=list(data[col].factorize())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "factorize_col(data,'division')\n",
    "factorize_col(data,'salary')\n",
    "factorize_col(data, 'package')\n",
    "factorize_col(data,'promotion_last_5years')\n",
    "factorize_col(data,'Work_accident')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.iloc[:,0:9]\n",
    "y=data[\"satisfaction_level\"].values\n",
    "## X represents the training set with all features\n",
    "## y is the conversion rate, something we want to predict\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yaoli\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "regressor = RandomForestRegressor(random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.035886172595833335"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv(\"testing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list=dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: number_project       Importance: 0.36\n",
      "Variable: average_monthly_hours Importance: 0.26\n",
      "Variable: last_evaluation      Importance: 0.15\n",
      "Variable: division             Importance: 0.07\n",
      "Variable: time_spend_company   Importance: 0.06\n",
      "Variable: package              Importance: 0.05\n",
      "Variable: salary               Importance: 0.03\n",
      "Variable: Work_accident        Importance: 0.01\n",
      "Variable: promotion_last_5years Importance: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Get numerical feature importances\n",
    "importances = list(regressor.feature_importances_)\n",
    "# List of tuples with variable and importance\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
    "# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "# Print out the feature and importances \n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X=data[[\"number_project\",\"average_monthly_hours\",\"last_evaluation\",\"division\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11999, 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X_train, new_X_test,new_y_train,new_y_test=train_test_split(new_X,y,test_size=0.2,random_state=0)                                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yaoli\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "new_regressor = RandomForestRegressor(random_state=0)\n",
    "new_regressor.fit(new_X_train, new_y_train)\n",
    "new_y_pred = new_regressor.predict(new_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04158792113509838"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(new_y_test, new_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = new_X_train.corr()\n",
    "ax = sns.heatmap(\n",
    "    corr, \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': [True, False],\n",
      " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],\n",
      " 'max_features': ['auto', 'sqrt'],\n",
      " 'min_samples_leaf': [1, 2, 4],\n",
      " 'min_samples_split': [2, 5, 10],\n",
      " 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}\n"
     ]
    }
   ],
   "source": [
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_random = RandomizedSearchCV(estimator = regressor, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed: 10.3min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed: 22.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=10, n_jobs=None,\n",
       "                                                   oob_score=False,\n",
       "                                                   random_state=0...\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [200, 400, 600, 800,\n",
       "                                                         1000, 1200, 1400, 1600,\n",
       "                                                         1800, 2000]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, test_features, test_labels):\n",
    "    y_pred = model.predict(test_features)\n",
    "    mse=mean_squared_error(test_labels, y_pred)\n",
    "    print('Model Performance')\n",
    "    print('Mean_Squared_Error = {:0.5f}.'.format(mse))\n",
    "    \n",
    "    return mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "Mean_Squared_Error = 0.03093.\n"
     ]
    }
   ],
   "source": [
    "best_random = rf_random.best_estimator_\n",
    "random_accuracy = evaluate(best_random, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_1=test_data.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 9)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "factorize_col(test_data_1,'division')\n",
    "factorize_col(test_data_1,'salary')\n",
    "factorize_col(test_data_1, 'package')\n",
    "factorize_col(test_data_1,'promotion_last_5years')\n",
    "factorize_col(test_data_1,'Work_accident')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['satisfaction_level']=best_random.predict(test_data_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_data=test_data[[\"id\",\"satisfaction_level\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_data.to_csv('submission.csv',index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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