Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Lean Six Sigma: Logistic Regression\n",
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "336 records\n"
     ]
    },
    {
     "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>Incentive</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Incentive  Target\n",
       "0         16       0\n",
       "1         10       0\n",
       "2         19       1\n",
       "3         12       0\n",
       "4         19       1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Data\n",
    "df_incentive = pd.read_excel('df_incentive.xlsx')\n",
    "\n",
    "# replace column names\n",
    "print(\"{:,} records\".format(len(df_incentive)))\n",
    "df_incentive.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box plot of the two values distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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e2UneB2yc5AXAl4BvDLYsSZIkzUYvIW8p8Cvgx8CbgVOB9w+yKEmSJM1OL7dQ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\nSS30/wEWaVUOTpJrvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "df_incentive.boxplot(figsize=(10,7), by=['Target'], column = ['Incentive'])\n",
    "plt.xlabel('Target Reach (1: True, 0: False)')\n",
    "plt.ylabel('Incentive (Euros/Day)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Incentive</th>\n",
       "      <th>Target</th>\n",
       "      <th>No Target</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Incentive  Target  No Target  Total\n",
       "0          1       1         18     19\n",
       "1          2       0         15     15\n",
       "2          3       2         10     12\n",
       "3          4       1         14     15\n",
       "4          5       5          9     14"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format \n",
    "df_calc = pd.DataFrame(df_incentive.groupby(['Incentive'])['Target'].sum())\n",
    "df_calc.columns = ['Target']\n",
    "# Count waves\n",
    "df_calc['No Target'] = df_incentive.groupby(['Incentive'])['Target'].count() - df_calc['Target']\n",
    "df_calc['Total'] = df_incentive.groupby(['Incentive'])['Target'].count()\n",
    "# Reset index and sort by number of lines\n",
    "df_calc.reset_index(inplace = True)\n",
    "df_calc.sort_values(['Incentive'], ascending = True, inplace = True)\n",
    "df_calc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 2.1327739857133364e-141\n"
     ]
    }
   ],
   "source": [
    "# Define X, y\n",
    "X = df_incentive[['Incentive']]\n",
    "y = df_incentive['Target']\n",
    "# Training/Test Sets \n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)\n",
    "# Instantiate the model\n",
    "log_regression = LogisticRegression()\n",
    "# Fit your model\n",
    "log_regression.fit(X_train,y_train)\n",
    "\n",
    "# Calculate p-value (source: https://gist.github.com/rspeare/77061e6e317896be29c6de9a85db301d)\n",
    "denom = (2.0*(1.0+np.cosh(log_regression.decision_function(X))))\n",
    "denom = np.tile(denom,(X.shape[1],1)).T\n",
    "# Fisher Information Matrix\n",
    "F_ij = np.dot((X/denom).T,X)\n",
    "## Inverse Information Matrix\n",
    "Cramer_Rao = np.linalg.inv(F_ij) \n",
    "sigma_estimates = np.sqrt(np.diagonal(Cramer_Rao))\n",
    "# z-score for eaach model coefficient\n",
    "z_scores = log_regression.coef_[0]/sigma_estimates \n",
    "# two tailed test for p-values\n",
    "p_values = [stat.norm.sf(abs(x))*2 for x in z_scores] \n",
    "\n",
    "print(\"p-value: {}\".format(p_values[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vca2JLZNSIWHbfMA7jbtnzSy8K92LiIicp2Q6S2t3ks6+dKlL6ZfKZPn5zuPc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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ax = plt.gca()\n",
    "sns.regplot(x='Incentive', y='Target', data=df_incentive, logistic=True, ax = ax)\n",
    "plt.xlabel('Productivity Incentive (Euros/Day)')\n",
    "plt.ylabel('Probability of meeting the productivity target')\n",
    "plt.show()"
   ]
  }
 ],
 "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",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}