ml_lesson_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "data_path = ['../data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1\n",
    "\n",
    "* Begin by importing the data. Examine the columns and data.\n",
    "* Notice that the data contains a state, area code, and phone number.\n",
    "\n",
    "We will not be using them, so they can be dropped from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "        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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_length</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_code</th>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phone_number</th>\n",
       "      <td>382-4657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intl_plan</th>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_minutes</th>\n",
       "      <td>265.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_calls</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_charge</th>\n",
       "      <td>45.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <td>197.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_calls</th>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_charge</th>\n",
       "      <td>16.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_minutes</th>\n",
       "      <td>244.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_calls</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_charge</th>\n",
       "      <td>11.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_calls</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_charge</th>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churned</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0\n",
       "state                                KS\n",
       "account_length                      128\n",
       "area_code                           415\n",
       "phone_number                   382-4657\n",
       "intl_plan                            no\n",
       "voice_mail_plan                     yes\n",
       "number_vmail_messages                25\n",
       "total_day_minutes                 265.1\n",
       "total_day_calls                     110\n",
       "total_day_charge                  45.07\n",
       "total_eve_minutes                 197.4\n",
       "total_eve_calls                      99\n",
       "total_eve_charge                  16.78\n",
       "total_night_minutes               244.7\n",
       "total_night_calls                    91\n",
       "total_night_charge                11.01\n",
       "total_intl_minutes                   10\n",
       "total_intl_calls                      3\n",
       "total_intl_charge                   2.7\n",
       "number_customer_service_calls         1\n",
       "churned                           False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "fileName = ['Orange_Telecom_Churn_Data.csv']\n",
    "fileurl = os.sep.join(data_path+fileName)\n",
    "df = pd.read_csv(fileurl)\n",
    "df.head(1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['account_length', 'intl_plan', 'voice_mail_plan',\n",
       "       'number_vmail_messages', 'total_day_minutes', 'total_day_calls',\n",
       "       'total_day_charge', 'total_eve_minutes', 'total_eve_calls',\n",
       "       'total_eve_charge', 'total_night_minutes', 'total_night_calls',\n",
       "       'total_night_charge', 'total_intl_minutes', 'total_intl_calls',\n",
       "       'total_intl_charge', 'number_customer_service_calls', 'churned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['state','phone_number','area_code'],axis=1,inplace=True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2\n",
    "Notice that some of the columns are categorical data and some are floats. These features will need to be numerically encoded using one of the methods odf sklearn.\n",
    "Finally, K-nearest neighbors requires scaled data. Scale the data using one of the scaling methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>account_length</th>\n",
       "      <td>128.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intl_plan</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_minutes</th>\n",
       "      <td>265.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_calls</th>\n",
       "      <td>110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_charge</th>\n",
       "      <td>45.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <td>197.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_calls</th>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_charge</th>\n",
       "      <td>16.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_minutes</th>\n",
       "      <td>244.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_calls</th>\n",
       "      <td>91.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_charge</th>\n",
       "      <td>11.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_calls</th>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_charge</th>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churned</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0\n",
       "account_length                 128.00\n",
       "intl_plan                        0.00\n",
       "voice_mail_plan                  1.00\n",
       "number_vmail_messages           25.00\n",
       "total_day_minutes              265.10\n",
       "total_day_calls                110.00\n",
       "total_day_charge                45.07\n",
       "total_eve_minutes              197.40\n",
       "total_eve_calls                 99.00\n",
       "total_eve_charge                16.78\n",
       "total_night_minutes            244.70\n",
       "total_night_calls               91.00\n",
       "total_night_charge              11.01\n",
       "total_intl_minutes              10.00\n",
       "total_intl_calls                 3.00\n",
       "total_intl_charge                2.70\n",
       "number_customer_service_calls    1.00\n",
       "churned                          0.00"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "lb = LabelBinarizer()\n",
    "for col in ['intl_plan', 'voice_mail_plan','churned']:\n",
    "    df[col] = lb.fit_transform(df[col])\n",
    "df.head(1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>account_length</th>\n",
       "      <td>0.524793</td>\n",
       "      <td>0.438017</td>\n",
       "      <td>0.561983</td>\n",
       "      <td>0.342975</td>\n",
       "      <td>0.305785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intl_plan</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <td>0.480769</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_minutes</th>\n",
       "      <td>0.754196</td>\n",
       "      <td>0.459744</td>\n",
       "      <td>0.692461</td>\n",
       "      <td>0.851778</td>\n",
       "      <td>0.474253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_calls</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.430303</td>\n",
       "      <td>0.684848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_day_charge</th>\n",
       "      <td>0.754183</td>\n",
       "      <td>0.459672</td>\n",
       "      <td>0.692436</td>\n",
       "      <td>0.851740</td>\n",
       "      <td>0.474230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <td>0.542755</td>\n",
       "      <td>0.537531</td>\n",
       "      <td>0.333242</td>\n",
       "      <td>0.170195</td>\n",
       "      <td>0.407754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_calls</th>\n",
       "      <td>0.582353</td>\n",
       "      <td>0.605882</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>0.717647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_eve_charge</th>\n",
       "      <td>0.542866</td>\n",
       "      <td>0.537690</td>\n",
       "      <td>0.333225</td>\n",
       "      <td>0.170171</td>\n",
       "      <td>0.407959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_minutes</th>\n",
       "      <td>0.619494</td>\n",
       "      <td>0.644051</td>\n",
       "      <td>0.411646</td>\n",
       "      <td>0.498481</td>\n",
       "      <td>0.473165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_calls</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.588571</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>0.508571</td>\n",
       "      <td>0.691429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_night_charge</th>\n",
       "      <td>0.619584</td>\n",
       "      <td>0.644344</td>\n",
       "      <td>0.411930</td>\n",
       "      <td>0.498593</td>\n",
       "      <td>0.473270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.685000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_calls</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_intl_charge</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.685185</td>\n",
       "      <td>0.609259</td>\n",
       "      <td>0.329630</td>\n",
       "      <td>0.505556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churned</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0         1         2         3  \\\n",
       "account_length                 0.524793  0.438017  0.561983  0.342975   \n",
       "intl_plan                      0.000000  0.000000  0.000000  1.000000   \n",
       "voice_mail_plan                1.000000  1.000000  0.000000  0.000000   \n",
       "number_vmail_messages          0.480769  0.500000  0.000000  0.000000   \n",
       "total_day_minutes              0.754196  0.459744  0.692461  0.851778   \n",
       "total_day_calls                0.666667  0.745455  0.690909  0.430303   \n",
       "total_day_charge               0.754183  0.459672  0.692436  0.851740   \n",
       "total_eve_minutes              0.542755  0.537531  0.333242  0.170195   \n",
       "total_eve_calls                0.582353  0.605882  0.647059  0.517647   \n",
       "total_eve_charge               0.542866  0.537690  0.333225  0.170171   \n",
       "total_night_minutes            0.619494  0.644051  0.411646  0.498481   \n",
       "total_night_calls              0.520000  0.588571  0.594286  0.508571   \n",
       "total_night_charge             0.619584  0.644344  0.411930  0.498593   \n",
       "total_intl_minutes             0.500000  0.685000  0.610000  0.330000   \n",
       "total_intl_calls               0.150000  0.150000  0.250000  0.350000   \n",
       "total_intl_charge              0.500000  0.685185  0.609259  0.329630   \n",
       "number_customer_service_calls  0.111111  0.111111  0.000000  0.222222   \n",
       "churned                        0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "                                      4  \n",
       "account_length                 0.305785  \n",
       "intl_plan                      1.000000  \n",
       "voice_mail_plan                0.000000  \n",
       "number_vmail_messages          0.000000  \n",
       "total_day_minutes              0.474253  \n",
       "total_day_calls                0.684848  \n",
       "total_day_charge               0.474230  \n",
       "total_eve_minutes              0.407754  \n",
       "total_eve_calls                0.717647  \n",
       "total_eve_charge               0.407959  \n",
       "total_night_minutes            0.473165  \n",
       "total_night_calls              0.691429  \n",
       "total_night_charge             0.473270  \n",
       "total_intl_minutes             0.505000  \n",
       "total_intl_calls               0.150000  \n",
       "total_intl_charge              0.505556  \n",
       "number_customer_service_calls  0.333333  \n",
       "churned                        0.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "minmax = MinMaxScaler()\n",
    "\n",
    "df = pd.DataFrame(minmax.fit_transform(df),columns=df.columns)\n",
    "df.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 4\n",
    "Ways to measure error haven't been discussed in class yet, but accuracy is an easy one to understand--it is simply the percent of labels that were correctly predicted (either true or false).\n",
    "\n",
    "Write a function to calculate accuracy using the actual and predicted labels.\n",
    "Using the function, calculate the accuracy of this K-nearest neighbors model on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accuracy(real,predicted):\n",
    "    return predicted.sum()/y_test.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 5\n",
    "\n",
    "* Fit the K-nearest neighbors model again with `n_neighbors=3` but this time use distance for the weights. Calculate the accuracy using the function you created above. \n",
    "* Fit another K-nearest neighbors model. This time use uniform weights but set the power parameter for the Minkowski distance metric to be 1 (`p=1`) i.e. Manhattan Distance.\n",
    "\n",
    "When weighted distances are used for part 1 of this question, a value of 1.0 should be returned for the accuracy. Why do you think this is? *Hint:* we are predicting on the data and with KNN the model *is* the data. We will learn how to avoid this pitfall in the next lecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.101481481481\n",
      "0.0962962962963\n",
      "\n",
      "\n",
      "\n",
      "Values on KFold Model  : \n",
      "MSE : \n",
      "0.099\n",
      "0.097\n",
      "Confusion Matrix :  : \n",
      "[[847  24]\n",
      " [ 75  54]]\n",
      "[[848  23]\n",
      " [ 74  55]]\n",
      "Test Score : \n",
      "0.901\n",
      "0.901\n",
      "\n",
      "\n",
      "\n",
      "Values on KFold Model  : \n",
      "MSE : \n",
      "0.102\n",
      "0.105\n",
      "Confusion Matrix :  : \n",
      "[[842  15]\n",
      " [ 87  56]]\n",
      "[[836  21]\n",
      " [ 84  59]]\n",
      "Test Score : \n",
      "0.898\n",
      "0.898\n",
      "\n",
      "\n",
      "\n",
      "Values on KFold Model  : \n",
      "MSE : \n",
      "0.116\n",
      "0.105\n",
      "Confusion Matrix :  : \n",
      "[[821  15]\n",
      " [101  63]]\n",
      "[[826  10]\n",
      " [ 95  69]]\n",
      "Test Score : \n",
      "0.884\n",
      "0.884\n",
      "\n",
      "\n",
      "\n",
      "Values on KFold Model  : \n",
      "MSE : \n",
      "0.091\n",
      "0.089\n",
      "Confusion Matrix :  : \n",
      "[[852  18]\n",
      " [ 73  57]]\n",
      "[[847  23]\n",
      " [ 66  64]]\n",
      "Test Score : \n",
      "0.909\n",
      "0.909\n",
      "\n",
      "\n",
      "\n",
      "Values on KFold Model  : \n",
      "MSE : \n",
      "0.096\n",
      "0.085\n",
      "Confusion Matrix :  : \n",
      "[[841  18]\n",
      " [ 78  63]]\n",
      "[[843  16]\n",
      " [ 69  72]]\n",
      "Test Score : \n",
      "0.904\n",
      "0.904\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import KFold\n",
    "clfknn = [KNeighborsClassifier(n_neighbors=3,weights='distance'),\n",
    "          KNeighborsClassifier(n_neighbors=3,weights='uniform',p=1)]\n",
    "X = df.ix[:,df.columns!='churned']\n",
    "Y = df.ix[:,df.columns=='churned']\n",
    "y = np.array(Y[:])\n",
    "x = np.array(X[:])\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=2,test_size=.27)\n",
    "model = [clfknn[0].fit(x_train,y_train),clfknn[1].fit(x_train,y_train)]\n",
    "predict1 = model[0].predict(x_test)\n",
    "predict2 = model[1].predict(x_test)\n",
    "\n",
    "print(mean_squared_error(y_test,predict1))\n",
    "print(mean_squared_error(y_test,predict2))\n",
    "\n",
    "kf = KFold(5,random_state=3)\n",
    "for train_index,test_index in kf.split(x):\n",
    "        model = [clfknn[0].fit(x[train_index],y[train_index]),clfknn[1].fit(x[train_index],y[train_index])]\n",
    "        \n",
    "        predict1 = model[0].predict(x[test_index])\n",
    "        predict2 = model[1].predict(x[test_index])\n",
    "        print(\"\\n\\n\\nValues on KFold Model  : \")\n",
    "        print(\"MSE : \")\n",
    "        print(mean_squared_error(y[test_index],predict1))\n",
    "        print(mean_squared_error(y[test_index],predict2))\n",
    "        print(\"Confusion Matrix :  : \")\n",
    "        print(confusion_matrix(y[test_index],predict1))\n",
    "        print(confusion_matrix(y[test_index],predict2))\n",
    "        print(\"Test Score : \")\n",
    "        print(model[0].score(x[test_index],y[test_index]))\n",
    "        print(model[0].score(x[test_index],y[test_index]))\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Question 6\n",
    "* Fit a K-nearest neighbors model using values of k (n_neighbors) ranging from 1 to 20. Use uniform weights (the default). The coefficient for the Minkowski distance (p) can be set to either 1 or 2--just be consistent. Store the accuracy and the value of k used from each of these fits in a list or dictionary.\n",
    "* Plot (or view the table of) the accuracy vs k. What do you notice happens when k=1? Why do you think this is? Hint: it's for the same reason discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ApAxBDwDQnH37pFOnTq/o9fVJhw4luy4AAPALBD0AQHPCDL36ip5EVQ8AgBQh6AEAmjM8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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f964c8f4198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
      "Wall time: 6.91 µs\n"
     ]
    },
    {
     "data": {
      "image/png": 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kb3wj/vN5j9q8Wfr856WxMeljH7M62nX9ug22J+hFi6CHRRH0AABAO1wHvbyO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\n7fkjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9643725e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "neighbors = np.arange(1,21,1)\n",
    "accuracy =[]\n",
    "errors = []\n",
    "neighbors\n",
    "for n in neighbors:\n",
    "    clfKnn = KNeighborsClassifier(n_neighbors=n,p=2)\n",
    "    model = clfKnn.fit(x_train,y_train)\n",
    "    \n",
    "    predicted = model.predict(x_test)\n",
    "    model_accuracy = model.score(x_test,y_test)\n",
    "    mse = mean_squared_error(predicted,y_test)\n",
    "    accuracy.append(model_accuracy)\n",
    "    errors.append(mse)\n",
    "\n",
    "plt.rcParams['figure.figsize']=(15,15)\n",
    "ax = plt.subplots()\n",
    "plt.plot(neighbors,accuracy,color='red',label='Accuracy Score')\n",
    "#plt.plot(neighbors,errors,color='blue',label='Mean Square Error')\n",
    "plt.title('N_neighbors vs (Accuracy score, MSE )')\n",
    "plt.xlabel('N-Neighbors')\n",
    "plt.xticks(neighbors)\n",
    "plt.show()\n",
    "plt.plot(neighbors,errors,color='blue',label='Mean Square Error')\n",
    "%time"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}