Exercise-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# This notebook is for course four : Classification Analysis (Supervised Learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Data Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "os.getcwd()\n",
    "os.chdir(\"C:\\\\Users\\\\dell latitude\\\\Downloads\\\\Compressed\\\\Student Files\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRODUCT CODE</th>\n",
       "      <th>PRODUCT CATEGORY</th>\n",
       "      <th>UNIT LIST PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30001</td>\n",
       "      <td>HEALTH &amp; BEAUTY</td>\n",
       "      <td>$7.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30002</td>\n",
       "      <td>HEALTH &amp; BEAUTY</td>\n",
       "      <td>$5.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30003</td>\n",
       "      <td>HEALTH &amp; BEAUTY</td>\n",
       "      <td>$5.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30004</td>\n",
       "      <td>HEALTH &amp; BEAUTY</td>\n",
       "      <td>$6.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30005</td>\n",
       "      <td>HEALTH &amp; BEAUTY</td>\n",
       "      <td>$7.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT CODE PRODUCT CATEGORY UNIT LIST PRICE\n",
       "0         30001  HEALTH & BEAUTY          $7.45 \n",
       "1         30002  HEALTH & BEAUTY          $5.35 \n",
       "2         30003  HEALTH & BEAUTY          $5.49 \n",
       "3         30004  HEALTH & BEAUTY          $6.46 \n",
       "4         30005  HEALTH & BEAUTY          $7.33 "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Product DataSet here\n",
    "product_data=pd.read_csv(\"Product Data Set - Student 2 of 3.csv\", sep=\"|\")\n",
    "product_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUSTOMER NUM</th>\n",
       "      <th>PRODUCT NUM</th>\n",
       "      <th>QUANTITY PURCHASED</th>\n",
       "      <th>DISCOUNT TAKEN</th>\n",
       "      <th>TRANSACTION DATE</th>\n",
       "      <th>STOCKOUT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10114</td>\n",
       "      <td>30011</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10217</td>\n",
       "      <td>30016</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10224</td>\n",
       "      <td>30013</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10103</td>\n",
       "      <td>30012</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10037</td>\n",
       "      <td>30010</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CUSTOMER NUM  PRODUCT NUM  QUANTITY PURCHASED  DISCOUNT TAKEN  \\\n",
       "0         10114        30011                   4             0.0   \n",
       "1         10217        30016                   3             0.0   \n",
       "2         10224        30013                   4             0.0   \n",
       "3         10103        30012                   3             0.2   \n",
       "4         10037        30010                   8             0.0   \n",
       "\n",
       "  TRANSACTION DATE  STOCKOUT  \n",
       "0         1/2/2015         0  \n",
       "1         1/2/2015         0  \n",
       "2         1/2/2015         0  \n",
       "3         1/2/2015         0  \n",
       "4         1/2/2015         0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Transaction DataSet Here\n",
    "transactions_data=pd.read_csv(\"Transaction Data Set - Student 3 of 3.csv\", sep=\"|\")\n",
    "transactions_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUSTOMERID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>EXPERIENCE SCORE</th>\n",
       "      <th>LOYALTY GROUP</th>\n",
       "      <th>ENROLLMENT DATE</th>\n",
       "      <th>HOUSEHOLD SIZE</th>\n",
       "      <th>MARITAL STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>$133,498</td>\n",
       "      <td>5</td>\n",
       "      <td>enrolled</td>\n",
       "      <td>06-03-2013</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>$94,475</td>\n",
       "      <td>9</td>\n",
       "      <td>notenrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>Married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>$88,610</td>\n",
       "      <td>9</td>\n",
       "      <td>enrolled</td>\n",
       "      <td>02-09-2010</td>\n",
       "      <td>5</td>\n",
       "      <td>Married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>$84,313</td>\n",
       "      <td>8</td>\n",
       "      <td>enrolled</td>\n",
       "      <td>06-04-2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>$51,498</td>\n",
       "      <td>3</td>\n",
       "      <td>notenrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CUSTOMERID  GENDER  AGE    INCOME  EXPERIENCE SCORE LOYALTY GROUP  \\\n",
       "0       10001       0   64  $133,498                 5      enrolled   \n",
       "1       10002       0   42   $94,475                 9   notenrolled   \n",
       "2       10003       0   40   $88,610                 9      enrolled   \n",
       "3       10004       0   38   $84,313                 8      enrolled   \n",
       "4       10005       0   30   $51,498                 3   notenrolled   \n",
       "\n",
       "  ENROLLMENT DATE  HOUSEHOLD SIZE MARITAL STATUS  \n",
       "0      06-03-2013               4         Single  \n",
       "1             NaN               6        Married  \n",
       "2      02-09-2010               5        Married  \n",
       "3      06-04-2015               1         Single  \n",
       "4             NaN               1         Single  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Customer Dataset Here\n",
    "\n",
    "customer_data=pd.read_csv(\"Customer Data Set - Student 1 of 3.csv\")\n",
    "customer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_data['INCOME']=customer_data['INCOME'].map(lambda x : x.replace('$',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_data['INCOME']=customer_data['INCOME'].map(lambda x : int(x.replace(',','')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Customer View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_products=transactions_data.merge(product_data,how='inner', left_on='PRODUCT NUM', right_on='PRODUCT CODE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_products['UNIT LIST PRICE']=trans_products['UNIT LIST PRICE'].map(lambda x : float(x.replace('$','')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_products['Total_Price']=trans_products['QUANTITY PURCHASED'] * trans_products['UNIT LIST PRICE'] * (1- trans_products['DISCOUNT TAKEN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_prod_categ=trans_products.groupby(['CUSTOMER NUM','PRODUCT CATEGORY']).agg({'Total_Price':'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_prod_categ=customer_prod_categ.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_pivot=customer_prod_categ.pivot(index='CUSTOMER NUM',columns='PRODUCT CATEGORY',values='Total_Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_total_spend=trans_products.groupby('CUSTOMER NUM').agg({'Total_Price':'sum'}).\\\n",
    "rename(columns={'Total_Price':'TOTAL SPENT'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_KPIs=customer_pivot.merge(trans_total_spend,how='inner',left_index=True, right_index=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_KPIs=customer_KPIs.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_all_view=customer_data.merge(customer_KPIs,how='inner', left_on='CUSTOMERID', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUSTOMERID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>EXPERIENCE SCORE</th>\n",
       "      <th>LOYALTY GROUP</th>\n",
       "      <th>ENROLLMENT DATE</th>\n",
       "      <th>HOUSEHOLD SIZE</th>\n",
       "      <th>MARITAL STATUS</th>\n",
       "      <th>APPAREL</th>\n",
       "      <th>ELECTRONICS</th>\n",
       "      <th>FOOD</th>\n",
       "      <th>HEALTH &amp; BEAUTY</th>\n",
       "      <th>TOTAL SPENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>133498</td>\n",
       "      <td>5</td>\n",
       "      <td>enrolled</td>\n",
       "      <td>06-03-2013</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>4022.430</td>\n",
       "      <td>1601.315</td>\n",
       "      <td>68.688</td>\n",
       "      <td>1134.337</td>\n",
       "      <td>6826.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>94475</td>\n",
       "      <td>9</td>\n",
       "      <td>notenrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>Married</td>\n",
       "      <td>2312.509</td>\n",
       "      <td>2473.163</td>\n",
       "      <td>276.779</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5062.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>88610</td>\n",
       "      <td>9</td>\n",
       "      <td>enrolled</td>\n",
       "      <td>02-09-2010</td>\n",
       "      <td>5</td>\n",
       "      <td>Married</td>\n",
       "      <td>2887.382</td>\n",
       "      <td>5414.418</td>\n",
       "      <td>260.640</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8562.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>84313</td>\n",
       "      <td>8</td>\n",
       "      <td>enrolled</td>\n",
       "      <td>06-04-2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>3637.213</td>\n",
       "      <td>1840.211</td>\n",
       "      <td>45.270</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5522.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>51498</td>\n",
       "      <td>3</td>\n",
       "      <td>notenrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>213.512</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>213.512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CUSTOMERID  GENDER  AGE  INCOME  EXPERIENCE SCORE LOYALTY GROUP  \\\n",
       "0       10001       0   64  133498                 5      enrolled   \n",
       "1       10002       0   42   94475                 9   notenrolled   \n",
       "2       10003       0   40   88610                 9      enrolled   \n",
       "3       10004       0   38   84313                 8      enrolled   \n",
       "4       10005       0   30   51498                 3   notenrolled   \n",
       "\n",
       "  ENROLLMENT DATE  HOUSEHOLD SIZE MARITAL STATUS   APPAREL  ELECTRONICS  \\\n",
       "0      06-03-2013               4         Single  4022.430     1601.315   \n",
       "1             NaN               6        Married  2312.509     2473.163   \n",
       "2      02-09-2010               5        Married  2887.382     5414.418   \n",
       "3      06-04-2015               1         Single  3637.213     1840.211   \n",
       "4             NaN               1         Single   213.512        0.000   \n",
       "\n",
       "      FOOD  HEALTH & BEAUTY  TOTAL SPENT  \n",
       "0   68.688         1134.337     6826.770  \n",
       "1  276.779            0.000     5062.451  \n",
       "2  260.640            0.000     8562.440  \n",
       "3   45.270            0.000     5522.694  \n",
       "4    0.000            0.000      213.512  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_all_view.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Begin Writing your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EXPERIENCE SCORE</th>\n",
       "      <th>TOTAL SPENT</th>\n",
       "      <th>LOYALTY GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133498</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>6826.770</td>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94475</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>5062.451</td>\n",
       "      <td>notenrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88610</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>8562.440</td>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84313</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>5522.694</td>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51498</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>213.512</td>\n",
       "      <td>notenrolled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INCOME  AGE  EXPERIENCE SCORE  TOTAL SPENT LOYALTY GROUP\n",
       "0  133498   64                 5     6826.770      enrolled\n",
       "1   94475   42                 9     5062.451   notenrolled\n",
       "2   88610   40                 9     8562.440      enrolled\n",
       "3   84313   38                 8     5522.694      enrolled\n",
       "4   51498   30                 3      213.512   notenrolled"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_classification=customer_all_view[['INCOME','AGE','EXPERIENCE SCORE', 'TOTAL SPENT', 'LOYALTY GROUP']]\n",
    "DF_classification.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOYALTY GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>notenrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>notenrolled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LOYALTY GROUP\n",
       "0      enrolled\n",
       "1   notenrolled\n",
       "2      enrolled\n",
       "3      enrolled\n",
       "4   notenrolled"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_feature = pd.DataFrame(DF_classification['LOYALTY GROUP'])\n",
    "print(type(target_feature))\n",
    "target_feature.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EXPERIENCE SCORE</th>\n",
       "      <th>TOTAL SPENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133498</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>6826.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94475</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>5062.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88610</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>8562.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84313</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>5522.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51498</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>213.512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INCOME  AGE  EXPERIENCE SCORE  TOTAL SPENT\n",
       "0  133498   64                 5     6826.770\n",
       "1   94475   42                 9     5062.451\n",
       "2   88610   40                 9     8562.440\n",
       "3   84313   38                 8     5522.694\n",
       "4   51498   30                 3      213.512"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_classification = DF_classification.drop(['LOYALTY GROUP'], axis=1)\n",
    "DF_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_classification_column_names = DF_classification.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_classification=preprocessing.minmax_scale(DF_classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EXPERIENCE SCORE</th>\n",
       "      <th>TOTAL SPENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.872818</td>\n",
       "      <td>0.638889</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.446532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.572046</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.330790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.526842</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.560394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493722</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.360983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.240799</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.012694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     INCOME       AGE  EXPERIENCE SCORE  TOTAL SPENT\n",
       "0  0.872818  0.638889          0.444444     0.446532\n",
       "1  0.572046  0.333333          0.888889     0.330790\n",
       "2  0.526842  0.305556          0.888889     0.560394\n",
       "3  0.493722  0.277778          0.777778     0.360983\n",
       "4  0.240799  0.166667          0.222222     0.012694"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_classification=pd.DataFrame(DF_classification, columns=DF_classification_column_names)\n",
    "DF_classification.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME</th>\n",
       "      <th>AGE</th>\n",
       "      <th>EXPERIENCE SCORE</th>\n",
       "      <th>TOTAL SPENT</th>\n",
       "      <th>LOYALTY GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.872818</td>\n",
       "      <td>0.638889</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.446532</td>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.572046</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.330790</td>\n",
       "      <td>notenrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.526842</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.560394</td>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493722</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.360983</td>\n",
       "      <td>enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.240799</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.012694</td>\n",
       "      <td>notenrolled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     INCOME       AGE  EXPERIENCE SCORE  TOTAL SPENT LOYALTY GROUP\n",
       "0  0.872818  0.638889          0.444444     0.446532      enrolled\n",
       "1  0.572046  0.333333          0.888889     0.330790   notenrolled\n",
       "2  0.526842  0.305556          0.888889     0.560394      enrolled\n",
       "3  0.493722  0.277778          0.777778     0.360983      enrolled\n",
       "4  0.240799  0.166667          0.222222     0.012694   notenrolled"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_classification = pd.concat([DF_classification,target_feature], axis=1)\n",
    "DF_classification.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = \\\n",
    "train_test_split(DF_classification[['EXPERIENCE SCORE','TOTAL SPENT','INCOME']],DF_classification['LOYALTY GROUP'], test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "plt.scatter(customer_all_view['AGE'],customer_all_view['INCOME'])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Income\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training a K-Nearest Neighbor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_kNN = KNeighborsClassifier(n_neighbors=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=8, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=8, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_kNN.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = clf_kNN.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = metrics.accuracy_score(Y_test,predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 79.0%\n"
     ]
    }
   ],
   "source": [
    "print ('accuracy = '+str(acc*100)+'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    enrolled       0.73      0.92      0.82        51\n",
      " notenrolled       0.89      0.65      0.75        49\n",
      "\n",
      "    accuracy                           0.79       100\n",
      "   macro avg       0.81      0.79      0.79       100\n",
      "weighted avg       0.81      0.79      0.79       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (metrics.classification_report(Y_test,predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a Decision Trees Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_Tree=DecisionTreeClassifier(criterion='entropy', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 75.0%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    enrolled       0.74      0.78      0.76        51\n",
      " notenrolled       0.76      0.71      0.74        49\n",
      "\n",
      "    accuracy                           0.75       100\n",
      "   macro avg       0.75      0.75      0.75       100\n",
      "weighted avg       0.75      0.75      0.75       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf_Tree.fit(X_train, Y_train)\n",
    "predicted = clf_Tree .predict(X_test)\n",
    "acc = metrics.accuracy_score(Y_test,predicted)\n",
    "print ('accuracy = '+str(acc*100)+'%')\n",
    "print (metrics.classification_report(Y_test,predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.26796289, 0.39545388, 0.33658323])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_Tree.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EXPERIENCE SCORE</th>\n",
       "      <th>TOTAL SPENT</th>\n",
       "      <th>INCOME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.446532</td>\n",
       "      <td>0.872818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.330790</td>\n",
       "      <td>0.572046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.560394</td>\n",
       "      <td>0.526842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.360983</td>\n",
       "      <td>0.493722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.012694</td>\n",
       "      <td>0.240799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.556394</td>\n",
       "      <td>0.715792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.648380</td>\n",
       "      <td>0.754199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.408791</td>\n",
       "      <td>0.594229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.273034</td>\n",
       "      <td>0.422820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     EXPERIENCE SCORE  TOTAL SPENT    INCOME\n",
       "0            0.444444     0.446532  0.872818\n",
       "1            0.888889     0.330790  0.572046\n",
       "2            0.888889     0.560394  0.526842\n",
       "3            0.777778     0.360983  0.493722\n",
       "4            0.222222     0.012694  0.240799\n",
       "..                ...          ...       ...\n",
       "495          0.666667     0.556394  0.715792\n",
       "496          0.666667     0.648380  0.754199\n",
       "497          0.777778     0.408791  0.594229\n",
       "498          0.000000     0.000000  0.109200\n",
       "499          0.888889     0.273034  0.422820\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF_classification[['EXPERIENCE SCORE','TOTAL SPENT','INCOME']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and testing the model using k-fold cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Training_Testing_Accuracy_Only(model,train_data,train_labels,test_data,test_labels):\n",
    "    model.fit(train_data,train_labels)\n",
    "    predicted = model.predict(test_data)\n",
    "    acc = metrics.accuracy_score(test_labels,predicted)\n",
    "    print ('accuracy = '+str(acc*100)+'%')\n",
    "    return(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and testing the decision tree model by using 10-fold cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_Tree=DecisionTreeClassifier(criterion='entropy')\n",
    "accuracy_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 76.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 76.0%\n",
      "overall_accuracy is % 73.8\n"
     ]
    }
   ],
   "source": [
    "for train_index, test_index in kf.split(DF_classification[['EXPERIENCE SCORE','TOTAL SPENT','INCOME']]):\n",
    "    X=DF_classification[['EXPERIENCE SCORE','TOTAL SPENT','INCOME']]\n",
    "    Y=DF_classification['LOYALTY GROUP']\n",
    "    X_train,X_test=X.iloc[train_index], X.iloc[test_index]\n",
    "    Y_train,Y_test=Y.iloc[train_index], Y.iloc[test_index]   \n",
    "    accuracy=Training_Testing_Accuracy_Only(clf_Tree,X_train,Y_train,X_test,Y_test)\n",
    "    accuracy_list.append(accuracy) \n",
    "print(\"overall_accuracy is %\",100 * sum(accuracy_list)/len(accuracy_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and testing the K-Nearest Neighbor classifier with a 10-fold cross-validation and finding the optimum K_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_list_for_each_K_neighbours=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 64.0%\n",
      "accuracy = 64.0%\n",
      "accuracy = 66.0%\n",
      "accuracy = 64.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 64.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 68.0%\n",
      "Overall Accuracy for K_neighbours= 1 is 68.6\n",
      "accuracy = 74.0%\n",
      "accuracy = 60.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 76.0%\n",
      "Overall Accuracy for K_neighbours= 2 is 73.2\n",
      "accuracy = 72.0%\n",
      "accuracy = 64.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 76.0%\n",
      "Overall Accuracy for K_neighbours= 3 is 72.20000000000002\n",
      "accuracy = 82.0%\n",
      "accuracy = 66.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 78.0%\n",
      "Overall Accuracy for K_neighbours= 4 is 75.80000000000001\n",
      "accuracy = 78.0%\n",
      "accuracy = 64.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 80.0%\n",
      "Overall Accuracy for K_neighbours= 5 is 74.99999999999999\n",
      "accuracy = 76.0%\n",
      "accuracy = 66.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 84.0%\n",
      "Overall Accuracy for K_neighbours= 6 is 75.4\n",
      "accuracy = 68.0%\n",
      "accuracy = 66.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 84.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 82.0%\n",
      "Overall Accuracy for K_neighbours= 7 is 75.6\n",
      "accuracy = 74.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 84.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 84.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 86.0%\n",
      "Overall Accuracy for K_neighbours= 8 is 77.39999999999999\n",
      "accuracy = 72.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 84.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 84.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 86.0%\n",
      "Overall Accuracy for K_neighbours= 9 is 76.99999999999999\n",
      "accuracy = 76.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 86.0%\n",
      "Overall Accuracy for K_neighbours= 10 is 77.60000000000001\n",
      "accuracy = 74.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 84.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 82.0%\n",
      "Overall Accuracy for K_neighbours= 11 is 77.0\n",
      "accuracy = 76.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 76.0%\n",
      "accuracy = 84.0%\n",
      "Overall Accuracy for K_neighbours= 12 is 77.0\n",
      "accuracy = 76.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 70.0%\n",
      "accuracy = 82.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 82.0%\n",
      "Overall Accuracy for K_neighbours= 13 is 77.4\n",
      "accuracy = 76.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 68.0%\n",
      "accuracy = 80.0%\n",
      "accuracy = 78.0%\n",
      "accuracy = 72.0%\n",
      "accuracy = 84.0%\n",
      "accuracy = 74.0%\n",
      "accuracy = 82.0%\n",
      "Overall Accuracy for K_neighbours= 14 is 76.20000000000002\n"
     ]
    }
   ],
   "source": [
    "for k_neighbours in range(1,15):\n",
    "    clf_NN = KNeighborsClassifier(n_neighbors=k_neighbours)\n",
    "    accuracy_list_k_fold = []\n",
    "    for train_index, test_index in kf.split(DF_classification[['EXPERIENCE SCORE','TOTAL SPENT','INCOME']]):\n",
    "        X=DF_classification[['EXPERIENCE SCORE','TOTAL SPENT','INCOME']]\n",
    "        Y=DF_classification['LOYALTY GROUP']\n",
    "        X_train,X_test=X.iloc[train_index], X.iloc[test_index]\n",
    "        Y_train,Y_test=Y.iloc[train_index], Y.iloc[test_index]    \n",
    "        accuracy=Training_Testing_Accuracy_Only(clf_NN,X_train,Y_train,X_test,Y_test)\n",
    "        accuracy_list_k_fold.append(accuracy)\n",
    "    accuracy_list_for_each_K_neighbours.append(100 * sum(accuracy_list_k_fold)/len(accuracy_list_k_fold))\n",
    "    print(\"Overall Accuracy for K_neighbours=\",k_neighbours,\"is\",accuracy_list_for_each_K_neighbours[k_neighbours-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,15),accuracy_list_for_each_K_neighbours)\n",
    "plt.legend(['Accuracy per K neighbours'], loc='upper left')\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"10 k-Fold Accuracy\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}