Interpretation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "149cd309",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['figure.figsize']=15,8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2b64e38c",
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>Important_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chickie</td>\n",
       "      <td>Brister</td>\n",
       "      <td>Male</td>\n",
       "      <td>86</td>\n",
       "      <td>1957-07-12</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>45 Shopko Center</td>\n",
       "      <td>4500</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morly</td>\n",
       "      <td>Genery</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "      <td>1970-03-22</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16</td>\n",
       "      <td>14 Mccormick Park</td>\n",
       "      <td>2113</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ardelis</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1974-08-28</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>5 Colorado Crossing</td>\n",
       "      <td>3505</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lucine</td>\n",
       "      <td>Stutt</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1979-01-28</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>207 Annamark Plaza</td>\n",
       "      <td>4814</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Melinda</td>\n",
       "      <td>Hadlee</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1965-09-21</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19</td>\n",
       "      <td>115 Montana Place</td>\n",
       "      <td>2093</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  last_name  gender  past_3_years_bike_related_purchases  \\\n",
       "0    Chickie    Brister    Male                                   86   \n",
       "1      Morly     Genery    Male                                   69   \n",
       "2    Ardelis  Forrester  Female                                   10   \n",
       "3     Lucine      Stutt  Female                                   64   \n",
       "4    Melinda     Hadlee  Female                                   34   \n",
       "\n",
       "          DOB                   job_title job_industry_category  \\\n",
       "0  1957-07-12             General Manager         Manufacturing   \n",
       "1  1970-03-22         Structural Engineer              Property   \n",
       "2  1974-08-28      Senior Cost Accountant    Financial Services   \n",
       "3  1979-01-28  Account Representative III         Manufacturing   \n",
       "4  1965-09-21           Financial Analyst    Financial Services   \n",
       "\n",
       "      wealth_segment deceased_indicator owns_car  tenure              address  \\\n",
       "0      Mass Customer                  N      Yes      14     45 Shopko Center   \n",
       "1      Mass Customer                  N       No      16    14 Mccormick Park   \n",
       "2  Affluent Customer                  N       No      10  5 Colorado Crossing   \n",
       "3  Affluent Customer                  N      Yes       5   207 Annamark Plaza   \n",
       "4  Affluent Customer                  N       No      19    115 Montana Place   \n",
       "\n",
       "   postcode state    country  property_valuation  Important_Customer  \n",
       "0      4500   QLD  Australia                   6                   1  \n",
       "1      2113   NSW  Australia                  11                   0  \n",
       "2      3505   VIC  Australia                   5                   0  \n",
       "3      4814   QLD  Australia                   1                   0  \n",
       "4      2093   NSW  Australia                   9                   0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Predicted_File.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b99279ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shakti\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Important_Customer);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5091a6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='job_industry_category', ylabel='count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,7))\n",
    "sns.countplot(x=df.job_industry_category,hue=df.Important_Customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3b124d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='wealth_segment', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.wealth_segment,hue=df.Important_Customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4bd3b322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='property_valuation', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.property_valuation,hue=df.Important_Customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "71c1ee16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='state', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.state,hue=df.Important_Customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "db96fe95",
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>Important_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chickie</td>\n",
       "      <td>Brister</td>\n",
       "      <td>Male</td>\n",
       "      <td>86</td>\n",
       "      <td>1957-07-12</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>45 Shopko Center</td>\n",
       "      <td>4500</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morly</td>\n",
       "      <td>Genery</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "      <td>1970-03-22</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16</td>\n",
       "      <td>14 Mccormick Park</td>\n",
       "      <td>2113</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ardelis</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1974-08-28</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>5 Colorado Crossing</td>\n",
       "      <td>3505</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lucine</td>\n",
       "      <td>Stutt</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1979-01-28</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>207 Annamark Plaza</td>\n",
       "      <td>4814</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Melinda</td>\n",
       "      <td>Hadlee</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1965-09-21</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19</td>\n",
       "      <td>115 Montana Place</td>\n",
       "      <td>2093</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  last_name  gender  past_3_years_bike_related_purchases  \\\n",
       "0    Chickie    Brister    Male                                   86   \n",
       "1      Morly     Genery    Male                                   69   \n",
       "2    Ardelis  Forrester  Female                                   10   \n",
       "3     Lucine      Stutt  Female                                   64   \n",
       "4    Melinda     Hadlee  Female                                   34   \n",
       "\n",
       "          DOB                   job_title job_industry_category  \\\n",
       "0  1957-07-12             General Manager         Manufacturing   \n",
       "1  1970-03-22         Structural Engineer              Property   \n",
       "2  1974-08-28      Senior Cost Accountant    Financial Services   \n",
       "3  1979-01-28  Account Representative III         Manufacturing   \n",
       "4  1965-09-21           Financial Analyst    Financial Services   \n",
       "\n",
       "      wealth_segment deceased_indicator owns_car  tenure              address  \\\n",
       "0      Mass Customer                  N      Yes      14     45 Shopko Center   \n",
       "1      Mass Customer                  N       No      16    14 Mccormick Park   \n",
       "2  Affluent Customer                  N       No      10  5 Colorado Crossing   \n",
       "3  Affluent Customer                  N      Yes       5   207 Annamark Plaza   \n",
       "4  Affluent Customer                  N       No      19    115 Montana Place   \n",
       "\n",
       "   postcode state    country  property_valuation  Important_Customer  \n",
       "0      4500   QLD  Australia                   6                   1  \n",
       "1      2113   NSW  Australia                  11                   0  \n",
       "2      3505   VIC  Australia                   5                   0  \n",
       "3      4814   QLD  Australia                   1                   0  \n",
       "4      2093   NSW  Australia                   9                   0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2fd75ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Important_Customer', ylabel='past_3_years_bike_related_purchases'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df.Important_Customer,y=df.past_3_years_bike_related_purchases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "231f22c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shakti\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State', ylabel='count'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.read_csv('Cleaned_Data')\n",
    "sns.countplot(df.State,hue=df.Important_Customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d23a7f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Its a rough work. contact us for any concern."
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}