classification_exercise.ipynb

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   "cell_type": "markdown",
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   "source": [
    "### 1. Load in the data using `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Turn the values into numerical values as such:\n",
    "\n",
    "* Change the `n`'s to 0.\n",
    "* Change the `y`'s to 1.\n",
    "* Change the `?`'s to 0.5."
   ]
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   "cell_type": "code",
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   "source": [
    "### 3. Make your `X` matrix and `y` vector:\n",
    "\n",
    "* Make a `y` vector to be 1 if the senator is republican and 0 if democrat. It should be a numpy array of length 435.\n",
    "* Make a `X` be all the remaining values. It should be a numpy array of dimensions 435 by 16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Run the commands `y.shape` and `X.shape` to verify that your vectors are the correct sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Use `sklearns`'s `Kfold` method.\n",
    "\n",
    "* You need to pass it the number of datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
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   "source": [
    "from sklearn.cross_validation import KFold\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We'll use the same `run_model` function as we did with the previous example. Code is copied here for your convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "def run_model(X, y, kfolds, Model):\n",
    "    accuracies = []\n",
    "    precisions = []\n",
    "    recalls = []\n",
    "    for train_index, test_index in kfolds:\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        model = Model()\n",
    "        model.fit(X_train, y_train)\n",
    "        y_predict = model.predict(X_test)\n",
    "        accuracies.append(accuracy_score(y_predict, y_test))\n",
    "        precisions.append(precision_score(y_predict, y_test))\n",
    "        recalls.append(recall_score(y_predict, y_test))\n",
    "\n",
    "    print \"accuracy:\", np.mean(accuracies)\n",
    "    print \"precision:\", np.mean(precisions)\n",
    "    print \"recall:\", np.mean(recalls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Build a logistic regression model on each of the folds and give the average values for accuracy, precision and recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Using the same kfolds, build a Decision Tree and Random Forest classifier to compare which is the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  }
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