KNN_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier as Knn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\",index_col=0)\n",
    "test = pd.read_csv(\"test.csv\",index_col=0)\n",
    "\n",
    "train_size = len(train)\n",
    "test_size = len(test)\n",
    "\n",
    "data = pd.concat([train,test])\n",
    "data = data.where((pd.notnull(data)), 0)\n",
    "\n",
    "for f in [\"Sex\",\"Embarked\"]:\n",
    "    data[f] = data[f].astype('category')\n",
    "    data[f] = data[f].cat.codes\n",
    "    \n",
    "train = data[:train_size]\n",
    "test = data[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_train = train[\"Survived\"]\n",
    "Y_test = test[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "PassengerId                                                    \n",
       "1                 3    1  22.0      1      0   7.2500         3\n",
       "2                 1    0  38.0      1      0  71.2833         1\n",
       "3                 3    0  26.0      0      0   7.9250         3\n",
       "4                 1    0  35.0      1      0  53.1000         3\n",
       "5                 3    1  35.0      0      0   8.0500         3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [\"Pclass\",\"Sex\",\"Age\",\"SibSp\",\"Parch\",\"Fare\",\"Embarked\"]\n",
    "X_train = train.get(features)\n",
    "X_test = test.get(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "ks = range(1,len(X_train))\n",
    "n_ks = len(ks)\n",
    "\n",
    "for k in ks:\n",
    "#     print(k,end=\",\")\n",
    "    knn = Knn(n_neighbors=k)\n",
    "    knn.fit(X_train,Y_train)\n",
    "    scores.append(knn.score(X_test,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5wLdJ/sFYc8ckIv4i6U5gFsl/33MkbzB3p0aOh99oNjOzrFq5fGRmZkVwKJiZ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\nZmaW5VAwM7Msh4KZmWX9f+EGCk8p+SQPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e37bd1208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.plot(ks,scores,'b',linewidth=3)\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_xlabel('K Neighbors')\n",
    "# fig.set_figwidth(100)\n",
    "# fig.set_figheight(30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}