Module_1g_Exercise_LogisticRegression.ipynb

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    "# Exercise - Logistic Regression\n",
    "\n",
    "The client bank *XYZ* is running a direct marketing (phone calls) campaign. The classification goal is to predict if the client will subscribe a term deposit or not.\n",
    "\n",
    "The data is obtained from [UCI Machine Learning repository]( http://mlr.cs.umass.edu/ml/datasets/Bank+Marketing)\n",
    "\n",
    "### Attribute Information:\n",
    "\n",
    "#### bank client data:\n",
    "- **age**: (numeric)\n",
    "- **job** : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "- **marital** : marital status (categorical: 'divorced', 'married', 'single', 'unknown'; note: 'divorced' means divorced or widowed)\n",
    "- **education** (categorical: 'basic.4y', 'basic.6y', 'basic.9y','high.school', 'illiterate' ,'professional.course', 'university.degree', 'unknown')\n",
    "- **default**: has credit in default? (categorical: 'no','yes','unknown')\n",
    "- **housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "- **loan**: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "#### related with the last contact of the current campaign:\n",
    "- **contact**: contact communication type (categorical: 'cellular', 'telephone') \n",
    "- **month**: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "- **day**: last contact day of the month (numerical: 1, 2, 3, 4, ...)\n",
    "- **duration**: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "\n",
    "#### other attributes:\n",
    "- **campaign**: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "- **pdays**: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "- **previous**: number of contacts performed before this campaign and for this client (numeric)\n",
    "- **poutcome**: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "\n",
    "#### Output variable (desired target):\n",
    "- **deposit** - has the client subscribed a term deposit? (binary: 'yes','no')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#1. import libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#2. import data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#3. Display first few records\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#4. check column names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#5. do label encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#6. do some exploratory data analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#7. Build logistic regression model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#8. Build L2 logistic regression model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#9. Build L1 logistic regression model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#10. Find generalization error. Use 80/20 split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#11. report mis-classification rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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