(SEC1) 4 - Classwork - Santiago Naranjo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34a263dd",
   "metadata": {},
   "source": [
    "# HR ATTRIBUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5b13005",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, f1_score\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import tree  \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04a8026",
   "metadata": {},
   "source": [
    "# 1.) Import, split data into X/y, plot y data as bar charts, turn X categorical variables binary and tts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93645fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"HR_Analytics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8e2e640",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[[\"Attrition\"]].copy()\n",
    "X = df.drop(\"Attrition\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7c91f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y[\"Attrition\"] = [1 if i == \"Yes\" else 0 for i in y[\"Attrition\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b55847f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_counts = y.value_counts()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "class_counts.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Class Distribution')\n",
    "plt.xticks(rotation=0)  # Remove rotation of x-axis labels\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e2008dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Identify string columns\n",
    "string_columns = X.columns[X.dtypes == 'object']\n",
    "\n",
    "# Step 2: Convert string columns to categorical\n",
    "for col in string_columns:\n",
    "    X[col] = pd.Categorical(X[col])\n",
    "\n",
    "# Step 3: Create dummy columns\n",
    "X = pd.get_dummies(X, columns=string_columns, prefix=string_columns,drop_first=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6e531a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,\n",
    " y, test_size=0.20, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "630a63d0",
   "metadata": {},
   "source": [
    "# 2.) Using the default Decision Tree. What is the IN/Out of Sample accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "750e8a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IN SAMPLE ACCURACY :  1.0\n",
      "OUT OF SAMPLE ACCURACY :  0.8\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(x_train,y_train)\n",
    "y_pred=clf.predict(x_train)\n",
    "acc=accuracy_score(y_train,y_pred)\n",
    "print(\"IN SAMPLE ACCURACY : \" , round(acc,2))\n",
    "\n",
    "y_pred=clf.predict(x_test)\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "print(\"OUT OF SAMPLE ACCURACY : \" , round(acc,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee363c6",
   "metadata": {},
   "source": [
    "# 3.) Run a grid search cross validation using F1 score to find the best metrics. What is the In and Out of Sample now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b52348c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'criterion': 'gini', 'max_depth': 6, 'min_samples_leaf': 2, 'min_samples_split': 2}\n",
      "Best F1-Score: 0.8214764475510983\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define the hyperparameter grid to search through\n",
    "param_grid = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': np.arange(1, 11),  # Range of max_depth values to try\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "\n",
    "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "scoring = make_scorer(f1_score, average='weighted')\n",
    "\n",
    "grid_search = GridSearchCV(estimator=dt_classifier, param_grid=param_grid, scoring=scoring, cv=5)\n",
    "\n",
    "grid_search.fit(x_train, y_train)\n",
    "\n",
    "# Get the best parameters and the best score\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "print(\"Best Parameters:\", best_params)\n",
    "print(\"Best F1-Score:\", best_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15dabc60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IN SAMPLE ACCURACY :  0.91\n",
      "OUT OF SAMPLE ACCURACY :  0.83\n"
     ]
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(**best_params, random_state =42)\n",
    "clf.fit(x_train,y_train)\n",
    "y_pred=clf.predict(x_train)\n",
    "acc=accuracy_score(y_train,y_pred)\n",
    "print(\"IN SAMPLE ACCURACY : \" , round(acc,2))\n",
    "\n",
    "y_pred=clf.predict(x_test)\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "print(\"OUT OF SAMPLE ACCURACY : \" , round(acc,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464d3b86",
   "metadata": {},
   "source": [
    "# 4.) Plot ......"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3619b5a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "InvalidParameterError",
     "evalue": "The 'feature_names' parameter of plot_tree must be an instance of 'list' or None. Got Index(['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EmployeeCount',\n       'EmployeeNumber', 'EnvironmentSatisfaction', 'HourlyRate',\n       'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome',\n       'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike',\n       'PerformanceRating', 'RelationshipSatisfaction', 'StandardHours',\n       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n       'YearsSinceLastPromotion', 'YearsWithCurrManager',\n       'BusinessTravel_Travel_Frequently', 'BusinessTravel_Travel_Rarely',\n       'Department_Research & Development', 'Department_Sales',\n       'EducationField_Life Sciences', 'EducationField_Marketing',\n       'EducationField_Medical', 'EducationField_Other',\n       'EducationField_Technical Degree', 'Gender_Male',\n       'JobRole_Human Resources', 'JobRole_Laboratory Technician',\n       'JobRole_Manager', 'JobRole_Manufacturing Director',\n       'JobRole_Research Director', 'JobRole_Research Scientist',\n       'JobRole_Sales Executive', 'JobRole_Sales Representative',\n       'MaritalStatus_Married', 'MaritalStatus_Single', 'OverTime_Yes'],\n      dtype='object') instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 42\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;66;03m# Plot the Decision Tree for better visualization of the selected features\u001b[39;00m\n\u001b[1;32m     41\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m---> 42\u001b[0m plot_tree(clf, filled\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, feature_names\u001b[38;5;241m=\u001b[39mX\u001b[38;5;241m.\u001b[39mcolumns, class_names\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYes\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo\u001b[39m\u001b[38;5;124m\"\u001b[39m], rounded\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m7\u001b[39m)\n\u001b[1;32m     43\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDecision Tree Classifier\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     44\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:201\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m to_ignore \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcls\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    199\u001b[0m params \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m params\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m to_ignore}\n\u001b[0;32m--> 201\u001b[0m validate_parameter_constraints(\n\u001b[1;32m    202\u001b[0m     parameter_constraints, params, caller_name\u001b[38;5;241m=\u001b[39mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[1;32m    203\u001b[0m )\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    206\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    207\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    208\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    209\u001b[0m         )\n\u001b[1;32m    210\u001b[0m     ):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     90\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     91\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     93\u001b[0m     )\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     98\u001b[0m )\n",
      "\u001b[0;31mInvalidParameterError\u001b[0m: The 'feature_names' parameter of plot_tree must be an instance of 'list' or None. Got Index(['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EmployeeCount',\n       'EmployeeNumber', 'EnvironmentSatisfaction', 'HourlyRate',\n       'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome',\n       'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike',\n       'PerformanceRating', 'RelationshipSatisfaction', 'StandardHours',\n       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n       'YearsSinceLastPromotion', 'YearsWithCurrManager',\n       'BusinessTravel_Travel_Frequently', 'BusinessTravel_Travel_Rarely',\n       'Department_Research & Development', 'Department_Sales',\n       'EducationField_Life Sciences', 'EducationField_Marketing',\n       'EducationField_Medical', 'EducationField_Other',\n       'EducationField_Technical Degree', 'Gender_Male',\n       'JobRole_Human Resources', 'JobRole_Laboratory Technician',\n       'JobRole_Manager', 'JobRole_Manufacturing Director',\n       'JobRole_Research Director', 'JobRole_Research Scientist',\n       'JobRole_Sales Executive', 'JobRole_Sales Representative',\n       'MaritalStatus_Married', 'MaritalStatus_Single', 'OverTime_Yes'],\n      dtype='object') instead."
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions on the test data\n",
    "y_pred = clf.predict(x_test)\n",
    "y_prob = clf.predict_proba(x_test)[:, 1]\n",
    "\n",
    "# Calculate the confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(len(conf_matrix))\n",
    "plt.xticks(tick_marks, ['Class 0', 'Class 1'], rotation=45)\n",
    "plt.yticks(tick_marks, ['Class 0', 'Class 1'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "feature_importance = clf.feature_importances_\n",
    "\n",
    "# Sort features by importance and select the top 10\n",
    "top_n = 10\n",
    "top_feature_indices = np.argsort(feature_importance)[::-1][:top_n]\n",
    "top_feature_names = X.columns[top_feature_indices]\n",
    "top_feature_importance = feature_importance[top_feature_indices]\n",
    "\n",
    "# Plot the top 10 most important features\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(top_feature_names, top_feature_importance)\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Importance Score')\n",
    "plt.title('Top 10 Most Important Features - Decision Tree')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "# Plot the Decision Tree for better visualization of the selected features\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_tree(clf, filled=True, feature_names=X.columns, class_names=[\"Yes\", \"No\"], rounded=True, fontsize=7)\n",
    "plt.title('Decision Tree Classifier')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62913bb0",
   "metadata": {},
   "source": [
    "# 5.) Looking at the graphs. what would be your suggestions to try to improve customer retention? What additional information would you need for a better plan. Plot anything you think would assist in your assessment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0a8f98",
   "metadata": {},
   "source": [
    "## ANSWER :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85662e02",
   "metadata": {},
   "source": [
    "It would be neccesary to know more about the correlation between atrittion and OverTime_Yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "658df2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5f93dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_correlation(X, feature_name, y):\n",
    "        feature= X[feature_name]\n",
    "        coef, _= pearsonr(feature,y)\n",
    "        return coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e1a240ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.24611799],\n",
       "       [0.24611799, 1.        ]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(np.array(X[\"OverTime_Yes\"]), np.array(y[\"Attrition\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe22a6d",
   "metadata": {},
   "source": [
    "\n",
    "My suggestions encompass a comprehensive approach to managing overtime and promoting a healthy work-life balance. Firstly, a thorough review of overtime policies is warranted due to a moderately positive correlation observed, indicating the need for clearer guidelines to minimize unnecessary overtime and optimize workforce efficiency. Implementing or enhancing work-life balance programs, such as flexible working hours and mental health support, is crucial to counterbalance the effects of any required overtime. Additionally, regular monitoring and management of employee workloads are essential to prevent burnout and diminish the dependence on overtime, using tools and strategies that ensure a balanced distribution of work.\n",
    "\n",
    "Secondly, improving communication and feedback mechanisms between management and staff can aid in early identification of overtime and workload issues, fostering an environment where employees feel valued and heard. Offering alternatives to traditional overtime—like time off in lieu, performance-based bonuses, or professional development opportunities—provides employees with options that better align with their personal and professional objectives. These initiatives aim to create a more harmonious work environment where employee well-being is a priority, and overtime is efficiently managed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "493731dc",
   "metadata": {},
   "source": [
    "# 6.) Using the Training Data, if they made everyone work overtime. What would have been the expected difference in client retention?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c4db71e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_experiment = x_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "60996cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_experiment[\"OverTime_Yes\"] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8cf392c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_train)\n",
    "y_pred_experiment = clf.predict(x_train_experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8de5a437",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change fro, ..... 59\n"
     ]
    }
   ],
   "source": [
    "diff = sum(y_pred - y_pred_experiment)\n",
    "\n",
    "print (\"Change fro, .....\", diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d844ba",
   "metadata": {},
   "source": [
    "# 7.) If they company loses an employee, there is a cost to train a new employee for a role ~2.8 * their monthly income.\n",
    "# To make someone not work overtime costs the company 2K per person.\n",
    "# Is it profitable for the company to remove overtime? If so/not by how much? \n",
    "# What do you suggest to maximize company profits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "950a156f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-117593.99999999977"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_experiment[\"Y\"] = y_pred\n",
    "x_train_experiment[\"Y_exp\"] = y_pred_experiment\n",
    "x_train_experiment[\"RetChange\"] = x_train_experiment [\"Y_exp\"] - x_train_experiment[\"Y\"]\n",
    "sav = sum(-2.8*x_train_experiment[\"RetChange\"]*x_train_experiment[\"MonthlyIncome\"])\n",
    "cost = len(x_train[x_train [\"OverTime_Yes\"] ==1])*2000\n",
    "sav-cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd9fea8",
   "metadata": {},
   "source": [
    "\n",
    "This indicates that the cost of eliminating overtime (paying 2000 dollars per employee) outweighs the savings from reduced attrition costs. This outcome suggests that instead of eliminating overtime, other measures could be taken, such as limiting them or only using them in certain sectors of the company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b0108b",
   "metadata": {},
   "source": [
    "## ANSWER : "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d23fd2",
   "metadata": {},
   "source": [
    "# 8.) Use your model and get the expected change in retention for raising and lowering peoples income. Plot the outcome of the experiment. Comment on the outcome of the experiment and your suggestions to maximize profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "18d2106c",
   "metadata": {},
   "outputs": [],
   "source": [
    "raise_amount= 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a0b383b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in attrition -16\n",
      "profits, 1087584.4\n",
      "Change in attrition -14\n",
      "profits, 979524.0\n",
      "Change in attrition -13\n",
      "profits, 864992.8\n",
      "Change in attrition -12\n",
      "profits, 750738.8\n",
      "Change in attrition -12\n",
      "profits, 629778.8\n",
      "Change in attrition -9\n",
      "profits, 530138.0\n",
      "Change in attrition -7\n",
      "profits, 424200.0\n",
      "Change in attrition -4\n",
      "profits, 326096.4\n",
      "Change in attrition -1\n",
      "profits, 228440.8\n",
      "Change in attrition -1\n",
      "profits, 110714.8\n",
      "Change in attrition 0\n",
      "profits, 0.0\n",
      "Change in attrition 6\n",
      "profits, -75328.40000000001\n",
      "Change in attrition 15\n",
      "profits, -127503.60000000002\n",
      "Change in attrition 15\n",
      "profits, -240914.8\n",
      "Change in attrition 21\n",
      "profits, -311586.80000000005\n",
      "Change in attrition 22\n",
      "profits, -416449.6000000001\n",
      "Change in attrition 22\n",
      "profits, -527889.6000000001\n",
      "Change in attrition 22\n",
      "profits, -639329.6000000001\n",
      "Change in attrition 22\n",
      "profits, -750769.6000000001\n",
      "Change in attrition 23\n",
      "profits, -854999.6000000001\n"
     ]
    }
   ],
   "source": [
    "profits = []\n",
    "for raise_amount in range(-1000, 1000,100):\n",
    "    x_train_experiment= x_train.copy()\n",
    "    x_train_experiment [\"MonthlyIncome\"] = x_train_experiment [\"MonthlyIncome\"]+ raise_amount \n",
    "\n",
    "    y_pred = clf.predict(x_train)\n",
    "    y_pred_experiment = clf.predict(x_train_experiment)\n",
    "\n",
    "    diff = sum(y_pred - y_pred_experiment)\n",
    "    print (\"Change in attrition\", diff)\n",
    "    x_train_experiment [\"Y\"] = y_pred\n",
    "    x_train_experiment [\"Y_exp\"] = y_pred_experiment\n",
    "\n",
    "    x_train_experiment[\"RetChange\"] = x_train_experiment [\"Y_exp\"] - x_train_experiment[\"Y\"]\n",
    "    sav = sum(-2.8*x_train_experiment[\"RetChange\"]*x_train_experiment[\"MonthlyIncome\"])\n",
    "    cost = len(x_train)*raise_amount\n",
    "    print(\"profits,\", sav-cost)\n",
    "    profits.append(sav-cost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "208fb221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(-1000, 1000, 100), profits)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b21a9e",
   "metadata": {},
   "source": [
    "## ANSWER : "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6e9194",
   "metadata": {},
   "source": [
    "\n",
    "Decreasing Benefits: Substantial salary increases result in decreased benefits regarding profits, as the higher expenses are not adequately compensated by a reduction in turnover."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f83b3e6",
   "metadata": {},
   "source": [
    "Suggestions:\n",
    "\n",
    "Optimize Salary Increases: Identify the salary increase point where the benefit in reduced attrition begins to outweigh the cost. This is the optimal salary adjustment that maximizes employee retention while minimizing unnecessary expenses.\n",
    "\n",
    "Targeted Adjustments: Rather than across-the-board raises, consider targeted increases for roles or departments with high attrition rates and where additional salary would be most effective in retaining talent.\n",
    "\n",
    "Monitor Attrition and Costs: Continuously monitor the impact of salary adjustments on attrition rates and overall company costs. This data-driven approach ensures adjustments are made as needed to maintain an optimal balance.\n",
    "\n",
    "Invest in Employee Well-being: Besides financial compensation, invest in programs that improve employee satisfaction and well-being. This can include flexible work arrangements, career development opportunities, and health and wellness programs, which can all contribute to employee retention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03718786",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}