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  {
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   "source": [
    "# HW3: Task\n",
    "Predict the region and area (or areaname) of olive oils based on their fatty acid contents.source \n",
    "\n",
    "The task's source is http://www.ggobi.org/book/\n",
    "\n",
    "Fit and evaluate the classification predictions of LDA, LogisticRegression, SVM, k-NN, and \n",
    "Random Forests for both the region and area.\n",
    "\n",
    "### 20 pts for each classifier\n",
    "\n",
    "You may use the Classification Jupyter Notebook Dr. Kalpakis prepared for the class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset description and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(\"OliveOil2.pdf\", height=100, width=100, embed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dataset\n",
    "df=pd.read_csv(\"olive.csv\")\n",
    "df.rename(columns={df.columns[0]:'areaname'}, inplace=True)\n",
    "df.areaname=df.areaname.map(lambda x: x.split('.')[-1])\n",
    "df.sample(50).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. k-NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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