Ask_me_offers.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "pip install faker"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uIijdaO-puSX",
        "outputId": "857c49c2-dcc6-49ae-ef58-3ce11c59eea8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faker\n",
            "  Downloading Faker-25.0.1-py3-none-any.whl (1.8 MB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.8 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.2/1.8 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.7/1.8 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m1.5/1.8 MB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.4 in /usr/local/lib/python3.10/dist-packages (from faker) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.4->faker) (1.16.0)\n",
            "Installing collected packages: faker\n",
            "Successfully installed faker-25.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u6QgBJ-epKW6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "from faker import Faker"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fake = Faker()\n",
        "num_rows = 1000"
      ],
      "metadata": {
        "id": "XaVeidAKpTi-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_ids = random.sample(range(1, 10000), 1000)\n",
        "minimum_purchase = [round(random.uniform(1000, 10000), 2) for _ in range(num_rows)]\n",
        "coupons_offered = [random.randint(0, 10) for _ in range(num_rows)]\n",
        "redeemed_coupons = [random.randint(0, coupons_offered[i]) for i in range(num_rows)]\n",
        "user_location = [random.choice(['India','USA']) for _ in range(num_rows)]\n",
        "coupon_type = [random.choice(['Percentage', 'Fixed Amount']) for _ in range(num_rows)]\n",
        "coupon_usage_frequency = [random.randint(0, 10) for _ in range(num_rows)]\n",
        "website_visits = [random.randint(1, 100) for _ in range(num_rows)]\n",
        "time_spent_on_site = [random.randint(60, 1800) for _ in range(num_rows)]\n",
        "purchase_history = [[fake.word() for _ in range(random.randint(1, 5))] for _ in range(num_rows)]\n",
        "purchased_completed = [random.choice([True, False]) for _ in range(num_rows)]"
      ],
      "metadata": {
        "id": "zofvTyZ5p3Zf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'User ID': user_ids,\n",
        "    'Minimum Purchase': minimum_purchase,\n",
        "    'Coupons Offered': coupons_offered,\n",
        "    'Redeemed Coupons': redeemed_coupons,\n",
        "    'User Location': user_location,\n",
        "    'Coupon Type': coupon_type,\n",
        "    'Coupon Usage Frequency': coupon_usage_frequency,\n",
        "    'Website Visits': website_visits,\n",
        "    'Time Spent on Site (seconds)': time_spent_on_site,\n",
        "    'Purchase History': purchase_history,\n",
        "    'Purchased Completed': purchased_completed,\n",
        "}"
      ],
      "metadata": {
        "id": "hmQap1Tnqnzk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQfi796uvYU6",
        "outputId": "9924c29d-f34a-4cd2-af69-8a9207124e68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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1933.23, 1581.52, 4912.71, 8375.46, 6037.9, 3753.94, 6801.53, 1846.62, 2224.49, 3947.18, 9375.06, 4267.38, 6987.41, 4697.2, 5387.03, 6360.48, 4402.5, 6269.8, 8588.18, 2609.12, 4524.36, 8735.43, 3972.76, 1307.27, 6171.13, 9931.73, 9672.84, 6982.63, 3669.66, 3415.44, 3710.14, 3196.92, 3306.52, 7605.86, 7412.87, 1300.98, 5604.54, 9717.47, 6996.28, 2939.52, 6168.29, 7226.14, 3713.77, 7206.88, 4731.23, 5265.16, 6368.5, 7866.09, 6354.48, 1011.87, 3567.32, 3853.91, 9119.31, 3534.48, 4069.23, 4973.49, 4080.78, 5543.65, 3830.07, 6860.04, 1020.42, 4848.6, 9396.15, 7179.74, 1057.88, 5843.86, 2951.05, 9505.85, 7668.69, 8690.8, 7097.42, 4286.74, 6433.81, 4325.42, 5092.05, 1221.2, 2315.83, 1667.27, 2621.51, 4834.2, 6825.37, 1700.59, 3239.16, 4912.86, 3422.61, 8442.27, 3570.08, 8256.9, 5952.86, 4368.08, 2189.25, 4045.61, 8043.83, 2371.55, 4840.86, 2629.27, 1404.3, 9149.61], 'Coupons Offered': [9, 6, 2, 0, 3, 0, 7, 2, 3, 8, 5, 8, 10, 10, 7, 8, 3, 7, 8, 9, 5, 0, 3, 4, 4, 10, 7, 7, 9, 6, 3, 9, 6, 1, 6, 1, 5, 0, 4, 6, 1, 6, 3, 7, 0, 6, 4, 6, 5, 5, 8, 3, 1, 0, 8, 7, 8, 9, 9, 3, 0, 6, 2, 7, 2, 4, 7, 6, 2, 8, 1, 6, 8, 9, 5, 5, 8, 4, 10, 6, 0, 9, 2, 8, 7, 1, 5, 5, 6, 1, 4, 4, 10, 5, 2, 3, 1, 8, 10, 0, 7, 9, 0, 4, 9, 9, 0, 4, 3, 8, 3, 10, 2, 7, 10, 2, 9, 7, 8, 0, 3, 0, 0, 8, 3, 2, 8, 6, 5, 4, 9, 8, 5, 6, 6, 7, 4, 1, 0, 7, 6, 5, 7, 1, 8, 0, 8, 0, 5, 5, 7, 0, 10, 4, 4, 1, 9, 9, 5, 7, 8, 2, 9, 0, 0, 9, 10, 9, 9, 5, 6, 8, 1, 0, 8, 5, 4, 7, 5, 1, 10, 4, 9, 3, 8, 3, 2, 7, 8, 10, 2, 9, 7, 0, 9, 6, 6, 3, 6, 6, 8, 8, 6, 0, 5, 4, 6, 4, 6, 7, 9, 6, 7, 0, 3, 1, 4, 10, 10, 0, 2, 0, 9, 6, 4, 4, 3, 9, 5, 6, 6, 10, 10, 5, 2, 5, 10, 10, 7, 4, 7, 5, 1, 0, 4, 1, 9, 10, 2, 8, 8, 3, 10, 6, 3, 7, 9, 7, 3, 7, 4, 6, 8, 5, 3, 3, 5, 5, 2, 6, 0, 4, 5, 9, 7, 9, 10, 8, 4, 7, 2, 6, 10, 7, 4, 7, 6, 6, 8, 6, 3, 1, 4, 0, 5, 9, 0, 2, 9, 10, 2, 9, 4, 6, 5, 1, 8, 9, 5, 6, 10, 2, 6, 0, 5, 9, 6, 5, 5, 8, 5, 3, 10, 10, 8, 1, 6, 10, 8, 9, 0, 7, 6, 10, 9, 10, 5, 10, 4, 9, 9, 9, 4, 4, 0, 4, 4, 3, 0, 2, 3, 9, 3, 8, 6, 1, 6, 5, 10, 10, 7, 4, 4, 6, 2, 4, 0, 2, 9, 8, 0, 9, 1, 7, 4, 1, 6, 4, 9, 10, 2, 4, 5, 6, 5, 2, 4, 10, 0, 3, 8, 10, 4, 7, 7, 0, 2, 5, 8, 7, 5, 2, 0, 8, 1, 7, 10, 2, 10, 3, 3, 7, 2, 10, 3, 7, 2, 8, 3, 1, 1, 1, 9, 1, 9, 4, 6, 3, 6, 3, 7, 3, 4, 3, 2, 5, 0, 3, 10, 0, 3, 8, 7, 2, 7, 2, 0, 1, 1, 1, 6, 2, 2, 7, 7, 9, 3, 2, 6, 2, 0, 8, 9, 1, 5, 5, 7, 4, 9, 4, 0, 0, 5, 6, 8, 9, 10, 10, 1, 6, 0, 1, 8, 6, 7, 2, 6, 10, 9, 6, 4, 10, 10, 5, 9, 9, 1, 2, 2, 10, 5, 10, 9, 6, 8, 0, 8, 4, 8, 8, 6, 5, 1, 5, 4, 5, 7, 5, 1, 7, 2, 10, 10, 5, 6, 2, 6, 3, 8, 5, 5, 7, 4, 7, 6, 10, 7, 4, 4, 9, 3, 10, 2, 6, 6, 6, 1, 1, 9, 9, 3, 5, 0, 2, 9, 3, 8, 3, 9, 6, 9, 8, 9, 3, 6, 5, 1, 7, 3, 2, 4, 1, 5, 9, 0, 5, 0, 5, 1, 7, 2, 8, 4, 0, 5, 10, 9, 2, 10, 9, 10, 5, 2, 6, 8, 10, 2, 10, 4, 3, 8, 10, 4, 8, 2, 2, 10, 10, 8, 9, 0, 1, 7, 4, 4, 3, 7, 9, 9, 0, 3, 9, 7, 5, 6, 4, 4, 6, 9, 1, 2, 0, 10, 5, 6, 9, 10, 7, 4, 2, 10, 4, 10, 0, 5, 0, 2, 6, 3, 8, 0, 1, 4, 5, 6, 9, 10, 8, 10, 0, 6, 4, 0, 0, 8, 7, 7, 5, 10, 7, 1, 2, 4, 0, 4, 6, 1, 8, 0, 0, 7, 6, 1, 8, 0, 4, 3, 4, 3, 0, 8, 8, 7, 1, 2, 2, 0, 7, 10, 8, 10, 4, 4, 3, 3, 0, 5, 9, 10, 2, 9, 1, 7, 1, 10, 8, 1, 9, 4, 9, 8, 2, 6, 2, 0, 1, 9, 3, 9, 10, 8, 8, 4, 2, 1, 0, 1, 1, 1, 3, 2, 8, 10, 4, 7, 7, 4, 0, 8, 2, 3, 7, 3, 10, 3, 6, 3, 9, 6, 0, 10, 0, 1, 0, 0, 4, 3, 6, 8, 3, 0, 1, 0, 2, 5, 5, 2, 2, 1, 10, 7, 2, 5, 7, 7, 5, 0, 6, 6, 1, 5, 3, 6, 0, 6, 6, 9, 1, 3, 10, 6, 7, 9, 9, 3, 2, 7, 7, 0, 4, 8, 0, 5, 7, 0, 3, 8, 7, 10, 5, 0, 6, 5, 9, 4, 6, 8, 1, 5, 3, 8, 10, 2, 6, 9, 8, 0, 5, 5, 0, 5, 8, 3, 10, 3, 0, 7, 4, 5, 6, 5, 9, 2, 6, 1, 4, 0, 4, 10, 6, 10, 1, 6, 2, 9, 9, 4, 4, 1, 2, 3, 4, 9, 9, 4, 0, 0, 9, 0, 2, 5, 2, 7, 0, 5, 2, 0, 5, 5, 4, 0, 6, 5, 1, 4, 6, 6, 2, 10, 10, 9, 1, 1, 1, 9, 2, 4, 6, 2, 0, 3, 10, 2, 2, 0, 2, 5, 1, 1, 3, 4, 6, 3, 10, 10, 0, 2, 0, 1, 6, 3, 7, 6, 7, 3, 4, 9, 7, 1, 9, 7, 0, 3, 10, 0, 6, 9, 6, 7, 1, 3, 8, 0, 7, 5, 9, 1, 0, 8, 2, 7, 8, 6, 10, 7, 10, 6, 5, 1, 10, 1, 7, 1, 6, 7, 8, 8, 5, 2, 3, 3, 8, 3, 4, 5, 10, 7, 0, 2, 4, 2, 10, 5, 10, 8, 0, 4, 8, 5, 10], 'Redeemed Coupons': [8, 5, 0, 0, 1, 0, 2, 2, 0, 0, 4, 5, 0, 10, 7, 0, 3, 5, 8, 5, 3, 0, 0, 0, 2, 9, 5, 7, 3, 4, 0, 2, 6, 0, 2, 0, 0, 0, 0, 3, 1, 5, 3, 4, 0, 4, 1, 0, 3, 5, 5, 1, 1, 0, 6, 5, 4, 0, 0, 3, 0, 5, 1, 3, 2, 4, 1, 2, 2, 2, 1, 1, 1, 8, 1, 3, 5, 0, 9, 3, 0, 0, 0, 7, 0, 0, 3, 4, 2, 1, 4, 4, 5, 3, 0, 3, 1, 7, 0, 0, 5, 2, 0, 0, 2, 2, 0, 2, 1, 8, 1, 8, 1, 0, 7, 1, 7, 0, 7, 0, 3, 0, 0, 5, 3, 1, 6, 2, 0, 3, 8, 6, 2, 4, 0, 5, 4, 1, 0, 1, 4, 5, 2, 0, 8, 0, 7, 0, 2, 0, 3, 0, 1, 1, 3, 0, 2, 7, 5, 2, 4, 1, 1, 0, 0, 0, 6, 8, 5, 5, 2, 6, 0, 0, 3, 0, 2, 0, 5, 1, 0, 4, 3, 0, 6, 3, 0, 7, 8, 0, 1, 0, 6, 0, 8, 6, 2, 1, 1, 1, 6, 0, 2, 0, 1, 3, 2, 2, 3, 0, 3, 6, 2, 0, 0, 1, 2, 7, 2, 0, 2, 0, 0, 5, 1, 0, 3, 5, 5, 5, 4, 9, 1, 2, 0, 4, 8, 1, 4, 1, 3, 0, 1, 0, 4, 1, 5, 0, 2, 0, 3, 1, 1, 3, 2, 5, 5, 7, 1, 0, 1, 2, 4, 5, 0, 1, 4, 2, 1, 6, 0, 1, 4, 9, 2, 8, 7, 7, 1, 4, 1, 5, 9, 5, 2, 6, 4, 3, 6, 0, 1, 0, 4, 0, 5, 4, 0, 1, 4, 5, 2, 7, 2, 6, 4, 0, 5, 7, 0, 4, 9, 1, 6, 0, 4, 0, 6, 1, 1, 8, 5, 0, 4, 3, 0, 1, 5, 4, 4, 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'expert'], ['defense', 'their', 'ask'], ['local', 'soldier', 'type', 'policy', 'society'], ['small', 'building', 'same'], ['who', 'wonder'], ['fear', 'create'], ['deal', 'despite', 'investment', 'image', 'mother'], ['likely', 'top'], ['city', 'road', 'than', 'religious'], ['real', 'star', 'son'], ['campaign', 'nearly', 'mouth', 'college', 'central'], ['magazine', 'go', 'enough', 'appear', 'sing'], ['turn'], ['quickly', 'third', 'during'], ['pick', 'hit'], ['above', 'ok', 'I', 'yourself'], ['animal', 'camera', 'west', 'policy'], ['court'], ['move'], ['early', 'specific', 'need', 'toward', 'market'], ['record', 'truth'], ['whom', 'book', 'century', 'itself', 'I'], ['suffer', 'out', 'around'], ['itself', 'yeah', 'reality'], ['eight', 'prove', 'alone', 'power', 'cell'], ['put'], ['thank'], ['important', 'current', 'draw', 'support', 'sister'], ['draw', 'then', 'and', 'natural'], ['picture', 'until', 'article', 'plan', 'agency'], ['including'], ['out', 'evening', 'enter'], ['here', 'fear', 'matter', 'doctor', 'voice'], ['debate', 'determine', 'then', 'cost', 'few'], ['step', 'page'], ['body'], ['on', 'majority'], ['close'], ['through'], ['idea', 'deal', 'even', 'collection', 'product'], ['bag', 'religious', 'Democrat', 'same', 'computer'], ['worker', 'give', 'prepare', 'resource'], ['great', 'character', 'natural'], ['born', 'approach', 'save', 'plan', 'expert'], ['they', 'few'], ['claim', 'truth'], ['couple', 'little', 'leg', 'hour'], ['fill'], ['deep', 'staff', 'continue', 'create', 'fund'], ['contain', 'mean', 'give'], ['market'], ['election', 'sign'], ['capital', 'agreement', 'bar'], ['special', 'every', 'nation', 'really'], ['star', 'tree', 'maintain', 'yard'], ['his'], ['ok'], ['their'], ['this'], ['future'], ['evening', 'or', 'age', 'put', 'agreement'], ['admit'], ['tend', 'human', 'forward'], ['something', 'bad'], ['down', 'similar', 'debate', 'interview', 'wait'], ['side'], ['to', 'lose', 'various', 'year', 'full'], ['cover', 'exactly', 'relationship'], ['important', 'against', 'tell', 'trip', 'hotel'], ['feel', 'by'], ['team', 'blood', 'election', 'firm', 'organization'], ['group'], ['energy'], ['eat', 'several'], ['hard', 'find'], ['catch', 'tell', 'only'], ['might', 'happen'], ['no', 'suffer'], ['else', 'friend', 'page', 'Congress'], ['break', 'environmental', 'operation', 'test'], ['so', 'might'], ['economy', 'woman', 'so', 'any'], ['method'], ['more', 'allow', 'property', 'someone', 'voice'], ['assume', 'better'], ['human', 'heavy'], ['compare', 'pull'], ['prepare', 'detail', 'personal', 'than'], ['million', 'ago', 'something', 'support'], ['foreign', 'when', 'arm'], ['child', 'third'], ['president'], ['tax', 'stage', 'send', 'yard'], ['popular'], ['result'], ['cost'], ['remain', 'while', 'election', 'certain', 'east'], ['successful', 'anyone'], ['traditional', 'go', 'scene'], ['surface', 'care', 'process', 'indicate'], ['should', 'low'], ['modern', 'friend', 'too'], ['capital'], ['service', 'hair', 'lead', 'audience', 'sure'], ['six', 'enter', 'our', 'along'], ['four', 'old', 'particularly', 'term'], ['common', 'different', 'product', 'information'], ['drug', 'power', 'generation'], ['face'], ['finish'], ['them', 'line', 'something', 'paper'], ['rate', 'each'], ['white', 'air', 'PM'], ['view', 'person', 'civil'], ['support', 'rock'], ['moment'], ['computer'], ['protect', 'born', 'tend', 'increase', 'cut'], ['citizen', 'score', 'camera'], ['adult', 'economic', 'skin'], ['the', 'head', 'manage', 'their'], ['growth', 'energy', 'everyone', 'bag'], ['eight', 'program', 'design', 'measure'], ['better', 'main', 'fish'], ['price'], ['skin'], ['throw', 'hand', 'tell', 'miss', 'give'], ['then', 'like', 'likely'], ['focus', 'always', 'compare', 'much', 'forward'], ['throw', 'common', 'air', 'toward'], ['accept', 'remain'], ['me', 'without', 'enough'], ['decide', 'property', 'over'], ['expect'], ['country', 'series', 'easy', 'challenge', 'purpose'], ['reach', 'drive'], ['necessary', 'say', 'nearly', 'trouble'], ['material'], ['former', 'north', 'per'], ['data', 'these'], ['contain', 'recent', 'ask', 'toward'], ['nature', 'join', 'two', 'five'], ['available', 'win'], ['sign', 'wonder'], ['about', 'conference', 'collection'], ['message', 'have', 'policy', 'eight'], ['government', 'author', 'position', 'yeah', 'owner'], ['music', 'behind', 'state', 'say', 'human'], ['window', 'recent', 'onto'], ['open'], ['recent', 'suddenly', 'six', 'admit', 'data'], ['difference', 'fire'], ['second', 'well', 'rule', 'song'], ['able', 'around', 'seek', 'kitchen'], ['business', 'worry', 'across', 'cost'], ['box', 'huge', 'wait', 'summer'], ['democratic', 'let', 'good', 'store', 'evidence'], ['local', 'some'], ['general', 'Mrs'], ['nice'], ['sit', 'difference', 'age', 'right'], ['foot', 'apply', 'according'], ['government', 'stage', 'human'], ['parent'], ['positive', 'surface'], ['fact', 'address', 'thus', 'place', 'improve'], ['table', 'argue', 'begin'], ['give', 'price', 'perhaps'], ['approach', 'maintain', 'find', 'Congress', 'various'], ['training', 'per', 'fill', 'lot', 'per'], ['line', 'thank', 'responsibility', 'list'], ['again', 'generation', 'prevent'], ['education'], ['east', 'majority', 'rise'], ['night', 'type'], ['cut', 'relationship', 'knowledge', 'radio', 'season'], ['approach', 'poor', 'design', 'growth', 'now'], ['factor', 'example', 'return', 'candidate', 'find'], ['important', 'only', 'eight'], ['opportunity', 'station', 'miss', 'them'], ['shake', 'sense', 'hotel'], ['think', 'meeting', 'pay', 'increase', 'lose'], ['energy', 'foot'], ['these', 'find', 'worker'], ['person', 'song', 'with', 'night'], ['science', 'inside'], ['family'], ['full', 'clearly', 'major', 'above'], ['skill'], ['some', 'I', 'short', 'trip'], ['base', 'student', 'week', 'strong'], ['fight'], ['success', 'here', 'front', 'student', 'many'], ['bed'], ['fall'], ['discover', 'form', 'seek'], ['no', 'statement', 'may', 'media'], ['employee', 'senior', 'eight', 'to'], ['TV'], ['save', 'drop', 'interview', 'relate'], ['sea', 'while'], ['yourself', 'vote', 'military'], ['maintain', 'fly', 'perform', 'theory'], ['how', 'pay', 'personal'], ['describe', 'huge', 'sometimes', 'just', 'job'], ['dinner'], ['life', 'feeling', 'try', 'receive', 'shoulder'], ['bill', 'better'], ['meet'], ['relate', 'run', 'record', 'career', 'those'], ['however'], ['garden', 'war', 'list', 'baby', 'among'], ['force', 'major'], ['mother', 'dinner', 'name'], ['front', 'shoulder', 'health', 'become', 'attack'], ['pretty', 'international', 'hard'], ['whose', 'news'], ['respond', 'president', 'game', 'TV', 'cause'], ['relationship', 'design', 'baby', 'personal'], ['significant', 'land', 'country'], ['against', 'where', 'think'], ['investment', 'process', 'stand', 'give'], ['his', 'here', 'material', 'response', 'act'], ['little', 'be', 'center'], ['life', 'institution'], ['miss', 'all', 'service', 'sign', 'outside'], ['thank', 'line', 'work', 'answer', 'anything'], ['ago', 'senior'], ['effort', 'rather', 'himself', 'tax', 'there'], ['nation', 'fight', 'behind', 'statement', 'girl'], ['upon', 'article', 'them', 'time'], ['central', 'message', 'recognize', 'exist'], ['a', 'result', 'clearly', 'food', 'force'], ['friend', 'Democrat'], ['group'], ['result'], ['sister'], ['group', 'pattern', 'business', 'allow', 'woman'], ['account'], ['born'], ['sure'], ['certainly', 'skill', 'computer'], ['include', 'deal', 'door'], ['say', 'audience', 'material', 'direction'], ['window'], ['run', 'risk', 'force', 'nothing'], ['house', 'fill', 'east', 'energy', 'represent'], ['issue', 'senior', 'beyond', 'president', 'free'], ['move', 'deep', 'field', 'through'], ['not', 'worry', 'dream'], ['respond', 'language', 'seven', 'you', 'water'], ['model', 'firm', 'former', 'herself'], ['history', 'child', 'model', 'summer'], ['drive'], ['our'], ['significant'], ['wait'], ['vote', 'such'], ['natural', 'onto'], ['spring'], ['example', 'hundred'], ['grow'], ['war', 'deal', 'research', 'son', 'between'], ['town', 'state', 'eight', 'peace', 'from'], ['heart'], ['little'], ['cut', 'want', 'line', 'early'], ['message', 'themselves', 'improve', 'the'], ['growth', 'PM', 'mouth'], ['hair', 'management'], ['rise', 'culture', 'four'], ['interesting', 'nature', 'act'], ['box', 'clearly'], ['best'], ['maintain', 'another', 'assume'], ['word'], ['little', 'attorney'], ['watch', 'cost', 'book', 'personal', 'man'], ['relationship', 'system', 'music', 'painting'], ['crime', 'line', 'bit', 'pressure', 'performance'], ['trouble'], ['choice', 'window'], ['professor', 'hundred', 'act'], ['likely', 'thousand', 'senior', 'mission'], ['American', 'care', 'choice', 'stage'], ['office', 'run', 'note', 'effect'], ['throw', 'drive'], ['example', 'politics', 'oil', 'wife'], ['room', 'ahead', 'order', 'few', 'difficult'], ['wall', 'detail'], ['dream', 'from'], ['upon'], ['poor', 'service'], ['seven'], ['forget', 'sell', 'particular', 'product'], ['power'], ['you'], ['home', 'debate', 'item'], ['possible'], ['within', 'myself', 'indicate', 'she'], ['like', 'hour', 'necessary', 'maintain'], ['event'], ['that', 'pick', 'administration', 'somebody', 'structure'], ['physical'], ['floor', 'sure'], ['like'], ['land', 'scene'], ['reason', 'fall', 'road', 'camera', 'five'], ['her', 'Mrs', 'save'], ['establish', 'relate'], ['time', 'vote', 'without', 'ago'], ['air', 'federal'], ['medical', 'word', 'place'], ['mind', 'news', 'capital', 'property', 'identify'], ['film', 'personal', 'campaign'], ['social', 'fact'], ['increase', 'father'], ['hot', 'between', 'less', 'pay'], ['model', 'science', 'second', 'sing', 'design'], ['argue', 'compare', 'save'], ['quality', 'trade', 'interesting'], ['similar', 'agree', 'never', 'peace', 'argue'], ['American', 'lead', 'stand'], ['any', 'condition', 'there', 'box']], 'Purchased Completed': [True, True, True, True, False, True, True, True, True, False, True, False, True, True, True, False, False, False, True, True, True, True, True, False, True, True, False, True, True, False, False, 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data)"
      ],
      "metadata": {
        "id": "2ByEsDTsvZSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('data.csv')"
      ],
      "metadata": {
        "id": "qer6Lvmvw3Ew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OujUfDalxcaA",
        "outputId": "2ed94a98-a1c8-4e2d-a62c-8340d4e46cde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 11 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   User ID                       1000 non-null   int64  \n",
            " 1   Minimum Purchase              1000 non-null   float64\n",
            " 2   Coupons Offered               1000 non-null   int64  \n",
            " 3   Redeemed Coupons              1000 non-null   int64  \n",
            " 4   User Location                 1000 non-null   object \n",
            " 5   Coupon Type                   1000 non-null   object \n",
            " 6   Coupon Usage Frequency        1000 non-null   int64  \n",
            " 7   Website Visits                1000 non-null   int64  \n",
            " 8   Time Spent on Site (seconds)  1000 non-null   int64  \n",
            " 9   Purchase History              1000 non-null   object \n",
            " 10  Purchased Completed           1000 non-null   bool   \n",
            "dtypes: bool(1), float64(1), int64(6), object(3)\n",
            "memory usage: 79.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Coupon Usage Frequency'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_mZURoiydG3",
        "outputId": "8afe9232-0878-4a13-8918-f2ae64cb786a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Coupon Usage Frequency\n",
              "6     106\n",
              "3     100\n",
              "9      96\n",
              "8      93\n",
              "1      93\n",
              "4      92\n",
              "10     91\n",
              "0      86\n",
              "2      86\n",
              "5      81\n",
              "7      76\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['User Location'].map({'India':0, 'USA':1})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AByQLS3WyplA",
        "outputId": "aee32bac-d808-4df9-d6c5-9dc03c590e8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      0\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "995    0\n",
              "996    1\n",
              "997    0\n",
              "998    0\n",
              "999    1\n",
              "Name: User Location, Length: 1000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['User Location'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3JoMe4kz1uK",
        "outputId": "97816cb4-ab88-42db-e86f-6b270350b18d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User Location\n",
              "India    511\n",
              "USA      489\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Coupon Type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oTSd5OKzVIq",
        "outputId": "77b7b0ec-221f-4bbb-c35e-1df041077296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Coupon Type\n",
              "Fixed Amount    532\n",
              "Percentage      468\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Coupon Type'].map({'Percentage': 0, 'Fixed Amount': 1})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8euXytg6zfGK",
        "outputId": "7d696373-b847-462d-e38e-fe75c5c95763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "995    1\n",
              "996    1\n",
              "997    1\n",
              "998    0\n",
              "999    0\n",
              "Name: Coupon Type, Length: 1000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Purchase History'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBWvQKmC0IYQ",
        "outputId": "41405963-9181-4f79-e4bb-3287ce80eacb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Purchase History\n",
              "[group]                                             3\n",
              "[grow]                                              2\n",
              "[continue]                                          2\n",
              "[support]                                           2\n",
              "[at]                                                2\n",
              "                                                   ..\n",
              "[thousand]                                          1\n",
              "[significant, order, central, something, nation]    1\n",
              "[mother, decide]                                    1\n",
              "[power, fill, necessary]                            1\n",
              "[any, condition, there, box]                        1\n",
              "Name: count, Length: 982, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('Purchase History', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "yZkAkh2G0Qu0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jn0nDqey0j6X",
        "outputId": "915aa933-bf1d-4917-a021-298400880533"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 10 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   User ID                       1000 non-null   int64  \n",
            " 1   Minimum Purchase              1000 non-null   float64\n",
            " 2   Coupons Offered               1000 non-null   int64  \n",
            " 3   Redeemed Coupons              1000 non-null   int64  \n",
            " 4   User Location                 1000 non-null   object \n",
            " 5   Coupon Type                   1000 non-null   object \n",
            " 6   Coupon Usage Frequency        1000 non-null   int64  \n",
            " 7   Website Visits                1000 non-null   int64  \n",
            " 8   Time Spent on Site (seconds)  1000 non-null   int64  \n",
            " 9   Purchased Completed           1000 non-null   bool   \n",
            "dtypes: bool(1), float64(1), int64(6), object(2)\n",
            "memory usage: 71.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**As I've generated the dataset myself so there are no outliers to be concerned about, we can simplify the analysis to focus on basic descriptive statistics, distribution analysis, and frequency analysis.**"
      ],
      "metadata": {
        "id": "t-WzJVM05LR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().any()  #No missing value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhP7CTI-0lqt",
        "outputId": "1109818f-7781-4cad-8121-0106f2a94b7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User ID                         False\n",
              "Minimum Purchase                False\n",
              "Coupons Offered                 False\n",
              "Redeemed Coupons                False\n",
              "User Location                   False\n",
              "Coupon Type                     False\n",
              "Coupon Usage Frequency          False\n",
              "Website Visits                  False\n",
              "Time Spent on Site (seconds)    False\n",
              "Purchased Completed             False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IRZDlQH35fgT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}