{ "cells": [ { "cell_type": "markdown", "id": "cc4373ab", "metadata": {}, "source": [ "## About the Data: \n", "\n", "This rich data set provides a comprehensive window into Germany's used automobile market, spanning over 400,000 online sales listings from 2014-2016. \n", "\n", "- The contents of the data was in German, so some transalations and modifications (replacements of words) were made on the relevant fields.\n", "\n", "- Data Source is from [Data World](https://data.world/data-society/used-cars-data). However, the data was scrapped from eBay\n", "\n", "- Data was visualised using the Matplotlib library.\n", "\n", "- This data was from 2014 - 2016\n", "\n", "- Business problems intended to be analysed and identified from tyhis data were:\n", " - understanding the most used brands and their relationship with the vehicle types\n", " - undersatnding how prices affect the brands and vehicle types\n", " - understand consuer's preference for fuel type\n", " - Understand the sdvertisement trend on ebay (for cars)\n", " - identiffy the seller category with the most advertised cars on ebay.\n", " - Comparisms between high-end luxury vehicles versus their affordability\n", " - other metrics like pricing, brands, models, mileage, repairs and modifications were uncovered; revealling an intriguing demand patterns and shifts within this vast market.\n", "\n", "\n", "\n", "A few major dimensions stand out as offering meaningful insights. These analyses revealed how the ebay platform performed and insights to help them understand conusmers' preferences. \n", "\n", "Hopefully, this informs listings their sales team would be more on the lookout for (cosidering their increasing customer demands).\n", "\n", "----------------------------------------------------------\n", "**Data fields**:\n", "- dateCrawled : when this ad was first crawled, all field-values are taken from this date\n", "- name : \"name\" of the car \n", "- seller : private or dealer \n", "- offerType *(in German)*\n", "- price : the price on the ad to sell the car\n", "- abtest\n", "- vehicleType *(in German)*\n", "- yearOfRegistration : at which year the car was first registered\n", "- gearbox *(in German)*\n", "- powerPS : power of the car in PS\n", "- model *(in German)*\n", "- kilometer : how many kilometers the car has driven\n", "- monthOfRegistration : at which month the car was first registered\n", "- fuelType *(in German)*\n", "- brand\n", "- notRepairedDamage : if the car has a damage which is not repaired yet *(in German)*\n", "- dateCreated : the date for which the ad at ebay was created\n", "- nrOfPictures : number of pictures in the ad\n", "- postalCode\n", "- lastSeenOnline : when the crawler saw this ad last online\n" ] }, { "cell_type": "markdown", "id": "19993904", "metadata": {}, "source": [ "\n", "### Import the Data " ] }, { "cell_type": "code", "execution_count": 1, "id": "744bd101", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "e1649f97", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(r\"C:\\Users\\Teni\\Desktop\\Git-Github\\Online Datasets\\autos.csv\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "092b2ebe", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreatednrofpicturespostalcodelastseen
02016-03-24T11:52:17Golf_3_1.6privatAngebot480testNaN1993manuell0golf1500000benzinvolkswagenNaN2016-03-24T00:00:00False704352016-04-07T03:16:57
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivatAngebot18300testcoupe2011manuell190NaN1250005dieselaudija2016-03-24T00:00:00False669542016-04-07T01:46:50
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privatAngebot9800testsuv2004automatik163grand1250008dieseljeepNaN2016-03-14T00:00:00False904802016-04-05T12:47:46
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivatAngebot1500testkleinwagen2001manuell75golf1500006benzinvolkswagennein2016-03-17T00:00:00False910742016-03-17T17:40:17
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivatAngebot3600testkleinwagen2008manuell69fabia900007dieselskodanein2016-03-31T00:00:00False604372016-04-06T10:17:21
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3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivatAngebot2200testNaN2005NaN0NaN200001NaNsonstige_autosNaN2016-03-14T00:00:00False395762016-04-06T00:46:52
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivatAngebot1199testcabrio2000automatik101fortwo1250003benzinsmartnein2016-03-05T00:00:00False261352016-03-11T18:17:12
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privatAngebot9200testbus1996manuell102transporter1500003dieselvolkswagennein2016-03-19T00:00:00False874392016-04-07T07:15:26
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivatAngebot3400testkombi2002manuell100golf1500006dieselvolkswagenNaN2016-03-20T00:00:00False407642016-03-24T12:45:21
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivatAngebot28990controllimousine2013manuell320m_reihe500008benzinbmwnein2016-03-07T00:00:00False733262016-03-22T03:17:10
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371528 rows × 20 columns

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" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 privat Angebot 480 test NaN 1993 \n", "1 privat Angebot 18300 test coupe 2011 \n", "2 privat Angebot 9800 test suv 2004 \n", "3 privat Angebot 1500 test kleinwagen 2001 \n", "4 privat Angebot 3600 test kleinwagen 2008 \n", "... ... ... ... ... ... ... \n", "371523 privat Angebot 2200 test NaN 2005 \n", "371524 privat Angebot 1199 test cabrio 2000 \n", "371525 privat Angebot 9200 test bus 1996 \n", "371526 privat Angebot 3400 test kombi 2002 \n", "371527 privat Angebot 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 manuell 0 golf 150000 0 \n", "1 manuell 190 NaN 125000 5 \n", "2 automatik 163 grand 125000 8 \n", "3 manuell 75 golf 150000 6 \n", "4 manuell 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 NaN 0 NaN 20000 1 \n", "371524 automatik 101 fortwo 125000 3 \n", "371525 manuell 102 transporter 150000 3 \n", "371526 manuell 100 golf 150000 6 \n", "371527 manuell 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \\\n", "0 benzin volkswagen NaN 2016-03-24T00:00:00 \n", "1 diesel audi ja 2016-03-24T00:00:00 \n", "2 diesel jeep NaN 2016-03-14T00:00:00 \n", "3 benzin volkswagen nein 2016-03-17T00:00:00 \n", "4 diesel skoda nein 2016-03-31T00:00:00 \n", "... ... ... ... ... \n", "371523 NaN sonstige_autos NaN 2016-03-14T00:00:00 \n", "371524 benzin smart nein 2016-03-05T00:00:00 \n", "371525 diesel volkswagen nein 2016-03-19T00:00:00 \n", "371526 diesel volkswagen NaN 2016-03-20T00:00:00 \n", "371527 benzin bmw nein 2016-03-07T00:00:00 \n", "\n", " nrofpictures postalcode lastseen \n", "0 False 70435 2016-04-07T03:16:57 \n", "1 False 66954 2016-04-07T01:46:50 \n", "2 False 90480 2016-04-05T12:47:46 \n", "3 False 91074 2016-03-17T17:40:17 \n", "4 False 60437 2016-04-06T10:17:21 \n", "... ... ... ... \n", "371523 False 39576 2016-04-06T00:46:52 \n", "371524 False 26135 2016-03-11T18:17:12 \n", "371525 False 87439 2016-04-07T07:15:26 \n", "371526 False 40764 2016-03-24T12:45:21 \n", "371527 False 73326 2016-03-22T03:17:10 \n", "\n", "[371528 rows x 20 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df\n", "\n", "# The original dataset" ] }, { "cell_type": "code", "execution_count": 5, "id": "183fcff3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 371528 entries, 0 to 371527\n", "Data columns (total 20 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 datecrawled 371528 non-null object\n", " 1 name 371528 non-null object\n", " 2 seller 371528 non-null object\n", " 3 offertype 371528 non-null object\n", " 4 price 371528 non-null int64 \n", " 5 abtest 371528 non-null object\n", " 6 vehicletype 333659 non-null object\n", " 7 yearofregistration 371528 non-null int64 \n", " 8 gearbox 351319 non-null object\n", " 9 powerps 371528 non-null int64 \n", " 10 model 351044 non-null object\n", " 11 kilometer 371528 non-null int64 \n", " 12 monthofregistration 371528 non-null int64 \n", " 13 fueltype 338142 non-null object\n", " 14 brand 371528 non-null object\n", " 15 notrepaireddamage 299468 non-null object\n", " 16 datecreated 371528 non-null object\n", " 17 nrofpictures 371528 non-null bool \n", " 18 postalcode 371528 non-null int64 \n", " 19 lastseen 371528 non-null object\n", "dtypes: bool(1), int64(6), object(13)\n", "memory usage: 54.2+ MB\n" ] } ], "source": [ "# To get the summary, and datatype, of the fields.\n", "\n", "df.info()" ] }, { "cell_type": "markdown", "id": "797dc553", "metadata": {}, "source": [ "#### Based on the highlighted business problems to answer: \n", "To solve the business questions on:\n", " - most advertised brands and vehicle types\n", " - how the price affects the influx of cars advertised (are there more 'less priced' vehicles on the platform or more' pricey' vehicles?)\n", " - what type of sellers do we have on the platfomr the most\n", " - how does milleage and powerps of the vehicles relate with the brand and vehicle types? Perhaps there's a consumer trend outside ebay we're not paying attention to, yet\n", "\n", "*The needed fields are: seller, price, vehicletype, gearbox, powerups, kilometer, datecreated, fueltype*" ] }, { "cell_type": "markdown", "id": "ea64ba54", "metadata": {}, "source": [ "\n", "### Clean the data \n" ] }, { "cell_type": "markdown", "id": "c358f085", "metadata": {}, "source": [ "** Translate needed columns to the English Language **" ] }, { "cell_type": "code", "execution_count": 6, "id": "7706273d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Angebot 371516\n", "Gesuch 12\n", "Name: offertype, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# summarizing the categorical data uder offertype. This is in German language; so the next code replaces the field values\n", "df['offertype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "id": "7e02766e", "metadata": {}, "outputs": [], "source": [ "df['offertype'] = df['offertype'].replace({'Angebot': 'Offer', 'Gesuch': 'Request'})" ] }, { "cell_type": "code", "execution_count": 8, "id": "024498af", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Offer 371516\n", "Request 12\n", "Name: offertype, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# updated field value replacement\n", "df['offertype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 9, "id": "d328d103", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "benzin 223857\n", "diesel 107746\n", "lpg 5378\n", "cng 571\n", "hybrid 278\n", "andere 208\n", "elektro 104\n", "Name: fueltype, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# summarizing the categorical data uder fueltype. This is in German language; so the next code replaces the field values\n", "df['fueltype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 10, "id": "84a14d66", "metadata": {}, "outputs": [], "source": [ "df['fueltype']= df['fueltype'].replace({'benzin':'Petrol', 'diesel':'Diesel','lpg':'Liquefied Petroleum Gas', 'cng':'Compressed Natural Gas', 'hybrid': 'Hybrid', 'andere': 'Others', 'elektro':'Electric'})\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "6f15a6ae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Petrol 223857\n", "Diesel 107746\n", "Liquefied Petroleum Gas 5378\n", "Compressed Natural Gas 571\n", "Hybrid 278\n", "Others 208\n", "Electric 104\n", "Name: fueltype, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# updated field replacement\n", "df['fueltype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 12, "id": "9c382f16", "metadata": {}, "outputs": [], "source": [ "df['gearbox']= df['gearbox'].replace({'manuell': 'Manual', 'automatik': 'Automatic'})" ] }, { "cell_type": "code", "execution_count": 13, "id": "6835562f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Manual 274214\n", "Automatic 77105\n", "Name: gearbox, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# updated field replacement f the gearbox attribute of the data\n", "df['gearbox'].value_counts()" ] }, { "cell_type": "code", "execution_count": 14, "id": "dbd33c06", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "test 192585\n", "control 178943\n", "Name: abtest, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['abtest'].value_counts()" ] }, { "cell_type": "code", "execution_count": 15, "id": "51ea3912", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "limousine 95894\n", "kleinwagen 80023\n", "kombi 67564\n", "bus 30201\n", "cabrio 22898\n", "coupe 19015\n", "suv 14707\n", "andere 3357\n", "Name: vehicletype, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['vehicletype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 16, "id": "fb2fad4a", "metadata": {}, "outputs": [], "source": [ "df['vehicletype']=df['vehicletype'].replace({'kleinwagen': 'small car', 'kombi': 'station wagon', 'cabrio': 'convertible', 'andere': 'other'})\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "ace03305", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "privat 371525\n", "gewerblich 3\n", "Name: seller, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['seller'].value_counts()" ] }, { "cell_type": "code", "execution_count": 18, "id": "3648e513", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "private 371525\n", "dealer 3\n", "Name: seller, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['seller'] = df['seller'].replace({'privat':'private', 'gewerblich':'dealer'})\n", "\n", "df['seller'].value_counts()" ] }, { "cell_type": "code", "execution_count": 19, "id": "1ebe377e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "nein 263182\n", "ja 36286\n", "Name: notrepaireddamage, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['notrepaireddamage'].value_counts()" ] }, { "cell_type": "code", "execution_count": 20, "id": "b1c1006e", "metadata": {}, "outputs": [], "source": [ "df['notrepaireddamage'] = df['notrepaireddamage'].replace({'nein': 'No', 'ja': 'Yes'})" ] }, { "cell_type": "code", "execution_count": 21, "id": "13e55ceb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 golf\n", "1 NaN\n", "2 grand\n", "3 golf\n", "4 fabia\n", " ... \n", "95 a1\n", "96 insignia\n", "97 passat\n", "98 3er\n", "99 transporter\n", "Name: model, Length: 100, dtype: object" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['model'].head(100)" ] }, { "cell_type": "code", "execution_count": 22, "id": "1e1ae134", "metadata": {}, "outputs": [], "source": [ "df['model']= df['model'].replace({'andere': 'other', })" ] }, { "cell_type": "code", "execution_count": 23, "id": "c193e52c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreatednrofpicturespostalcodelastseen
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNaN1993Manual0golf1500000PetrolvolkswagenNaN2016-03-24T00:00:00False704352016-04-07T03:16:57
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190NaN1250005DieselaudiYes2016-03-24T00:00:00False669542016-04-07T01:46:50
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNaN2016-03-14T00:00:00False904802016-04-05T12:47:46
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006PetrolvolkswagenNo2016-03-17T00:00:00False910742016-03-17T17:40:17
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007DieselskodaNo2016-03-31T00:00:00False604372016-04-06T10:17:21
...............................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNaN2005NaN0NaN200001NaNsonstige_autosNaN2016-03-14T00:00:00False395762016-04-06T00:46:52
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003PetrolsmartNo2016-03-05T00:00:00False261352016-03-11T18:17:12
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003DieselvolkswagenNo2016-03-19T00:00:00False874392016-04-07T07:15:26
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNaN2016-03-20T00:00:00False407642016-03-24T12:45:21
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008PetrolbmwNo2016-03-07T00:00:00False733262016-03-22T03:17:10
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371528 rows × 20 columns

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" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test NaN 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test NaN 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 NaN 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 NaN 0 NaN 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \\\n", "0 Petrol volkswagen NaN 2016-03-24T00:00:00 \n", "1 Diesel audi Yes 2016-03-24T00:00:00 \n", "2 Diesel jeep NaN 2016-03-14T00:00:00 \n", "3 Petrol volkswagen No 2016-03-17T00:00:00 \n", "4 Diesel skoda No 2016-03-31T00:00:00 \n", "... ... ... ... ... \n", "371523 NaN sonstige_autos NaN 2016-03-14T00:00:00 \n", "371524 Petrol smart No 2016-03-05T00:00:00 \n", "371525 Diesel volkswagen No 2016-03-19T00:00:00 \n", "371526 Diesel volkswagen NaN 2016-03-20T00:00:00 \n", "371527 Petrol bmw No 2016-03-07T00:00:00 \n", "\n", " nrofpictures postalcode lastseen \n", "0 False 70435 2016-04-07T03:16:57 \n", "1 False 66954 2016-04-07T01:46:50 \n", "2 False 90480 2016-04-05T12:47:46 \n", "3 False 91074 2016-03-17T17:40:17 \n", "4 False 60437 2016-04-06T10:17:21 \n", "... ... ... ... \n", "371523 False 39576 2016-04-06T00:46:52 \n", "371524 False 26135 2016-03-11T18:17:12 \n", "371525 False 87439 2016-04-07T07:15:26 \n", "371526 False 40764 2016-03-24T12:45:21 \n", "371527 False 73326 2016-03-22T03:17:10 \n", "\n", "[371528 rows x 20 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "eb374027", "metadata": {}, "source": [ "** Drop irrelevant columns **\n", "\n", "*Some attributes are not needed in solving the business problem, so they'd all be dropped*" ] }, { "cell_type": "code", "execution_count": 24, "id": "18dcf5a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 371528\n", "Name: nrofpictures, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['nrofpictures'].value_counts()" ] }, { "cell_type": "code", "execution_count": 25, "id": "6cd93c97", "metadata": {}, "outputs": [], "source": [ "df = df.drop(['nrofpictures', 'lastseen', 'postalcode'], axis=1)" ] }, { "cell_type": "code", "execution_count": 26, "id": "21479642", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNaN1993Manual0golf1500000PetrolvolkswagenNaN2016-03-24T00:00:00
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190NaN1250005DieselaudiYes2016-03-24T00:00:00
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNaN2016-03-14T00:00:00
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006PetrolvolkswagenNo2016-03-17T00:00:00
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007DieselskodaNo2016-03-31T00:00:00
......................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNaN2005NaN0NaN200001NaNsonstige_autosNaN2016-03-14T00:00:00
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003PetrolsmartNo2016-03-05T00:00:00
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003DieselvolkswagenNo2016-03-19T00:00:00
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNaN2016-03-20T00:00:00
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008PetrolbmwNo2016-03-07T00:00:00
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371528 rows × 17 columns

\n", "
" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test NaN 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test NaN 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 NaN 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 NaN 0 NaN 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen NaN 2016-03-24T00:00:00 \n", "1 Diesel audi Yes 2016-03-24T00:00:00 \n", "2 Diesel jeep NaN 2016-03-14T00:00:00 \n", "3 Petrol volkswagen No 2016-03-17T00:00:00 \n", "4 Diesel skoda No 2016-03-31T00:00:00 \n", "... ... ... ... ... \n", "371523 NaN sonstige_autos NaN 2016-03-14T00:00:00 \n", "371524 Petrol smart No 2016-03-05T00:00:00 \n", "371525 Diesel volkswagen No 2016-03-19T00:00:00 \n", "371526 Diesel volkswagen NaN 2016-03-20T00:00:00 \n", "371527 Petrol bmw No 2016-03-07T00:00:00 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "5a696a66", "metadata": {}, "source": [ "#### Trends to Uncover from the data :\n", "\n", "\n", "1. **Pricing Analysis**: Investigate the price distribution for different vehicle types, brands, or models to understand pricing trends and identify outliers. \n", "\n", "2. **Trend Analysis over Time**: Analyze the trends in the number of listings or sales over different months or years to understand seasonal patterns in the automotive market.\n", "\n", "3. **Brand Comparison**: Compare the popularity or sales of different car brands to understand which brands are more preferred in the market and how they perform in terms of pricing or demand.\n", "\n", "4. **Vehicle Condition and Price Correlation**: Examine how the condition of a vehicle (repaired damage or not) correlates with its pricing. Determine if repaired damage affects the selling price significantly.\n", "\n", "5. **Fuel Type Preference**: Analyze the preference for different fuel types among different vehicle types or brands. Understand if there's a shift in preference towards more eco-friendly options like hybrid or electric vehicles.\n", "\n", "6. **Feature Importance**: Analyze the impact of specific vehicle features (like power, gearbox type, vehicle type, etc.) on the pricing to identify which features drive higher prices or are more sought after by buyers.\n", "\n" ] }, { "cell_type": "markdown", "id": "28da248b", "metadata": {}, "source": [ "#### Task 1. \n", "**Pricing Analysis**: Investigate the price distribution for different vehicle types, brands, or models to understand pricing trends and identify outliers." ] }, { "cell_type": "code", "execution_count": 27, "id": "262e1fca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNaN1993Manual0golf1500000PetrolvolkswagenNaN2016-03-24T00:00:00
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190NaN1250005DieselaudiYes2016-03-24T00:00:00
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNaN2016-03-14T00:00:00
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006PetrolvolkswagenNo2016-03-17T00:00:00
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007DieselskodaNo2016-03-31T00:00:00
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3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNaN2005NaN0NaN200001NaNsonstige_autosNaN2016-03-14T00:00:00
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003PetrolsmartNo2016-03-05T00:00:00
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003DieselvolkswagenNo2016-03-19T00:00:00
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNaN2016-03-20T00:00:00
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008PetrolbmwNo2016-03-07T00:00:00
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371528 rows × 17 columns

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" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test NaN 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test NaN 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 NaN 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 NaN 0 NaN 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen NaN 2016-03-24T00:00:00 \n", "1 Diesel audi Yes 2016-03-24T00:00:00 \n", "2 Diesel jeep NaN 2016-03-14T00:00:00 \n", "3 Petrol volkswagen No 2016-03-17T00:00:00 \n", "4 Diesel skoda No 2016-03-31T00:00:00 \n", "... ... ... ... ... \n", "371523 NaN sonstige_autos NaN 2016-03-14T00:00:00 \n", "371524 Petrol smart No 2016-03-05T00:00:00 \n", "371525 Diesel volkswagen No 2016-03-19T00:00:00 \n", "371526 Diesel volkswagen NaN 2016-03-20T00:00:00 \n", "371527 Petrol bmw No 2016-03-07T00:00:00 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 28, "id": "010dfcab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 371528 entries, 0 to 371527\n", "Data columns (total 17 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 datecrawled 371528 non-null object\n", " 1 name 371528 non-null object\n", " 2 seller 371528 non-null object\n", " 3 offertype 371528 non-null object\n", " 4 price 371528 non-null int64 \n", " 5 abtest 371528 non-null object\n", " 6 vehicletype 333659 non-null object\n", " 7 yearofregistration 371528 non-null int64 \n", " 8 gearbox 351319 non-null object\n", " 9 powerps 371528 non-null int64 \n", " 10 model 351044 non-null object\n", " 11 kilometer 371528 non-null int64 \n", " 12 monthofregistration 371528 non-null int64 \n", " 13 fueltype 338142 non-null object\n", " 14 brand 371528 non-null object\n", " 15 notrepaireddamage 299468 non-null object\n", " 16 datecreated 371528 non-null object\n", "dtypes: int64(5), object(12)\n", "memory usage: 48.2+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 29, "id": "e821510f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "371523 False\n", "371524 False\n", "371525 False\n", "371526 False\n", "371527 False\n", "Name: price, Length: 371528, dtype: bool" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['price'].isnull()" ] }, { "cell_type": "code", "execution_count": 30, "id": "3151443c", "metadata": {}, "outputs": [], "source": [ "df = df.fillna('None')\n", "\n", "# Since there's no Null value in Price, it's safe to fill all null values in the dataframe with the string 'None' than the integer '0'" ] }, { "cell_type": "markdown", "id": "224057f4", "metadata": {}, "source": [ "#### Price and Brand Relationship \n" ] }, { "cell_type": "code", "execution_count": 31, "id": "dcd8a743", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNone1993Manual0golf1500000PetrolvolkswagenNone2016-03-24T00:00:00
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190None1250005DieselaudiYes2016-03-24T00:00:00
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNone2016-03-14T00:00:00
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006PetrolvolkswagenNo2016-03-17T00:00:00
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007DieselskodaNo2016-03-31T00:00:00
......................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNone2005None0None200001Nonesonstige_autosNone2016-03-14T00:00:00
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003PetrolsmartNo2016-03-05T00:00:00
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003DieselvolkswagenNo2016-03-19T00:00:00
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNone2016-03-20T00:00:00
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008PetrolbmwNo2016-03-07T00:00:00
\n", "

371528 rows × 17 columns

\n", "
" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test None 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test None 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 None 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 None 0 None 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen None 2016-03-24T00:00:00 \n", "1 Diesel audi Yes 2016-03-24T00:00:00 \n", "2 Diesel jeep None 2016-03-14T00:00:00 \n", "3 Petrol volkswagen No 2016-03-17T00:00:00 \n", "4 Diesel skoda No 2016-03-31T00:00:00 \n", "... ... ... ... ... \n", "371523 None sonstige_autos None 2016-03-14T00:00:00 \n", "371524 Petrol smart No 2016-03-05T00:00:00 \n", "371525 Diesel volkswagen No 2016-03-19T00:00:00 \n", "371526 Diesel volkswagen None 2016-03-20T00:00:00 \n", "371527 Petrol bmw No 2016-03-07T00:00:00 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 32, "id": "51d18360", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "\n", "# Calculate the average price for each brand (top 10)\n", "avg_prices = df.groupby('brand')['price'].mean().nlargest(10)\n", "\n", "# Get the number of brands for each category (top 10)\n", "brand_count = df['brand'].value_counts()[:10]\n", "\n", "# Using the add_axes because I'd like to display 2 related information in one plot\n", "fig = plt.figure(figsize=(22, 15)) # Adjust the figure size if needed\n", "ax1 = fig.add_axes([0, 0, 1, 1])\n", "\n", "# Plotting the Main graph for the Avg_prices for the top 10 brands\n", "ax1.bar(avg_prices.index,avg_prices, color='blue')\n", "\n", "# Defining the title of the main plot\n", "ax1.set_title('Prices of the top 10 Brands advertised on the platform', fontsize=20, fontweight='bold',color='blue')\n", "\n", "# Defining the x and y labels\n", "ax1.set_xlabel('Brand', fontsize=20,fontweight='bold')\n", "ax1.set_ylabel('Average Price', fontsize=20, fontweight='bold')\n", "\n", "# # Adjusting the x and y items\n", "ax1.tick_params(axis='x', rotation=45, labelsize=16) \n", "ax1.tick_params(axis='y', rotation=45, labelsize=16) \n", "\n", "\n", "\n", "\n", "# Plotting the inner graph for the count of the top 10 Brands\n", "ax2 = fig.add_axes([0.5, 0.5, 0.25, 0.25])\n", "ax2.bar(brand_count.index,brand_count, color='skyblue')\n", "\n", "# Defining the title of the main plot\n", "ax2.set_title('Top 10 advertised brands on the platform ', fontsize=20, fontweight='bold',color='skyblue')\n", "\n", "# Defining the x and y labels\n", "ax2.set_xlabel('Brand', fontsize=14,fontweight='bold')\n", "ax2.set_ylabel('Count', fontsize=14, fontweight='bold')\n", "\n", "# Adjusting the x and y items\n", "ax2.tick_params(axis='x', rotation=45, labelsize=15) \n", "ax2.tick_params(axis='y', labelsize=15) \n", "\n", "\n", "# Display the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6a86340f", "metadata": {}, "source": [ "### Possible reasons for the inverse relationship between the Brand price and the quantity:\n", " \n", "- The data reveals that the expensive cars do not meet the top 10 most sold or advertised cars on Ebay. Invariably, the less pricey cars are the most sold/advertised cars\n", " - This tells us that either the demand and supply power is in play. The less pricey the car, the more they are in quantity, and vice-versa. This would nt be restricted to the platfomr alone, but the automobile industry in general. \n", " - Most of the pricey cars are probably not sold on ebay (probaly there's a different market for those types of cars), and ebay is mostly popular for selling the less-pricey cars.\n", " - ebay doesn't have as much car sellers of whose who own/deal in luxury cars in comparism with those who own the less pricey cars (*data sufficiency wouldn't allow us deterine this*).\n" ] }, { "cell_type": "code", "execution_count": 33, "id": "8cfe49d1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "limousine 95894\n", "small car 80023\n", "station wagon 67564\n", "None 37869\n", "bus 30201\n", "convertible 22898\n", "coupe 19015\n", "suv 14707\n", "other 3357\n", "Name: vehicletype, dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['vehicletype'].value_counts()" ] }, { "cell_type": "code", "execution_count": 34, "id": "6e908d41", "metadata": {}, "outputs": [ { "data": { "image/png": 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vzPd335ozZ459/PHHzvMznSRu2bLFGjVqZDfffLM9++yz9s4771h0dLQFBQXZhg0bvJku4FWFub34Kxwb8k5BazM4l8idwt7nOF1R7ovS//JEUaqIyszMtB49eti1115rEydOzHEwuPXWWy0sLMz++c9/2v79+83sxDcf5cuXt/vuu8+OHj3qi7R9ZseOHR63Ln7jjTfMzGzEiBFWuXJlGzNmjNNIpKamWuPGjc3lctn//ve/AnHr4k2bNllcXJyZnWwQ+/fvb/fdd1+hvcvD35V9x6Dx48fbrl27PNZt2rTJKlWqZO3bt3fmDClI3+Zs2LDB6tevb/fff7/HyWTfvn2tdevWOW5pjr926onmqdatW2fVqlWzgQMHekxg+f3339udd95pjz/+uDVv3ty2bNnirVTPW1ZWlm3bts1atmxpN910k33++ece67Jlb/tPP/1kDz30kNWtW9fq1Kljt9xyS46OFVAYFeb24kw4Nvx9haHN4Fwidwp7nyMbfVH6X6eiKFWEZc/m37hxYxs/frwdPnzYWffEE09Yw4YNbdCgQR7DKteuXWu//PKLL9L1mYMHD1rTpk2tefPm9s4775jL5bKYmBhn/bPPPpujkUhJSbFrrrmmQDUO2bZs2WJPPvmkud1u564o8LR7925r2LCh/ec//zEzs2PHjtn+/fttwYIFzrDiTZs22SWXXGJ33HHHWU8u87Pvv//ewsPDrVq1atarVy/r27evlS9fvsjf/Sg3Tv39f/HFF/a///3PPvvsM+eYGxMTY6VLl7bo6Gh755137IcffrB27dpZv379bMOGDeZyuey9997zVfp/KS4uzu644w675ZZbLDY21ll+ps7n8ePH7fjx47Z//36PNgcorIpCe3E2HBtypzC1GZxLXJii1uegL3pSUe9/UZQqYjZu3Ghbt251hoUfO3bMORiMHTvWkpOTLSsry+655x773//+5zGRXGE6UboQ6enp9vXXX1tYWJiVLFnSuWXzqRX97EZi3Lhxtn37djMrmN90rlmzxu6++26rU6cOd0E5hwMHDtj1119vb775ph04cMCeeeYZu+GGGywkJMQuvfRS+9///mdmZlu3brWffvrJx9nm3oYNG6xGjRp2xRVX2JgxY+z333/3dUoFzqnHgccff9xq1Khh1apVsxYtWlhERISlpqaa2YnbZrds2dJCQkKsWrVq1qhRIzt27JgdOXLEGjRokGO+FV+bNm2a3X333c7zRYsWWefOnc/Z+TzTc6CwKyrtRTaODX9PYWwzOJc4f0Whz0FfNCf6XxSlipRhw4ZZzZo1LSwszEJCQpzbx6alpdnDDz9sjRo1smrVqlnjxo09brVZWA8A5yN7H2zevNmCg4OtQoUKHrdgPXXo6IgRIywgIMAmTZpkx48fL1ANRLYjR47Y119/XSC/bfGGXbt2WXp6uiUnJ1unTp3shhtusFKlSlmXLl1s8uTJ9tNPP9mtt95qjzzyiK9TzTNr1qyxtm3bOpeVIHfGjRtnoaGhtnz5cjMzGzlypLlcLrvuuuucW53v3r3bfvnlF1u/fr1z/PjXv/6V7+6kk5aWZs8//7zVqVPHHnroIWf5+XY+gaKgKLYXHBvyTmFqM8w4lzgfRaHPQV/0zOh/UZQqMsaPH2/ly5e3L774wr788kt7++23rWTJkta7d28zO3Ew+PDDD23kyJE2atQoZ3K1gnqrzbwUExNjd999ty1btsyWLFliV111lbVu3dpZf2oj8dJLLxWKbzqR09atW83Pz8+5tv+XX36xd955x9566y2PeRFuv/12e/rpp32V5kVRGK7b97ZTTxC3bdtmnTp1svnz55uZ2aeffmqXXnqpDR482OrWrWtNmzbNcYepFStWWNeuXS0kJCRfXuZw4MAB+89//mP169e3Pn36OMvpfAJFu73g2JA7hb3NMONc4nwU5j4HfVGcC0WpIiAzM9O6dOniVKOzffHFF+ZyuWzy5MlnfF1RPghknxz8+eef1rRpU3v55ZfNzCwjI8MWLlxoderUsTZt2jjx//nPf+zNN9/0Sa7wnoEDB1qpUqXs7bffzrHuwIEDNmzYMKtQoYL9+OOPPsgO+dm7775rO3bssNWrV1vlypXt9ddfN7MT3xq6XC6rWrWqR2f1wIED9sQTT9imTZt8lfJZZR8fDxw4YJMmTTpn5/PUCY6BoqQothccG/JOYWozcG5Foc9BXxR/haJUIbdv3z4zM6tXr57961//MrMTB7/s27AOGjTIbrnlFjt8+HCRu/XkX/n888+tb9++1r17d9u9e7ezPC0tzWkkateubX369DGXy1UkJ6UrzM727e1jjz1mJUqUsBkzZji36503b55FRkZalSpVCtSteHFx/ec//7G+fft6LBs7dqx1797dmR9i6tSpdtddd9mjjz6a4+Qrv44gODWv5ORkmzhx4hk7n3fccYc1atQoX81tAlwMtBcncGz4ewprm4G/Vpj7HPRFcT6KCYXWxIkT9eyzz2r37t2KjIzUe++9pzVr1sjlcql48eKSpEsvvVTFihVTqVKlnGU4ISUlRW+88YYWLlyoAwcOSJLMTCVLllRERIRmzZqlJk2aKDk5WevXr1d4eLhvE0aecrlcWrJkiZYsWeKxfOzYsXr00UfVp08fvfPOO5Kkpk2bqkmTJvriiy/UsGFDX6SLfMDMnJ8zMjLk5+enH374QcuWLXOW79ixQ/Hx8QoICNDx48cVGxurhg0bauLEifLz81NmZqYT63K5vJr/X8neviNHjkiSjh07pjJlyui+++7T/fffrxUrVqhv376SpDZt2ig6Olq1atVSjRo1fJYz4A1Fvb3g2JA7hb3NwPkrrH0O+qI4b76siOHieeyxx6xChQo2d+5c2759u8XHx1unTp2sQ4cOtmbNGjMzO3TokEVERFivXr18nG3+9fHHH1vJkiWtX79+duzYsTPGnG05Cr7IyEgrVqyYffHFFznWdevWzUJDQ23atGlmVvgnYcS5nfoNdfa3f1u2bLGbbrrJhgwZ4qz78ssv7eqrr7aqVataw4YNrU6dOs43g/n5W+7s3GJjY+3OO++0li1b2kMPPeTcPeePP/5wRkWcOsFxUb+1O4qOotpecGzIncLeZuDCFbY+B31RXAiKUoXQokWLrFq1arZs2TKP5R999JF17tzZAgMD7brrrrP69etbeHi40xgW5cYte9t//fVXW758ua1bt87++OMPMzsx1L548eI2dOhQZ1+ZFa6TSpxZZmamRUVFWZkyZWzJkiUe65588kkrV66chYSEOHfCAZ5//nlr166dJSQkmNmJIfkul8sWLlxoZifusPLFF1/YM888U+Am8vzwww8tMDDQnnrqKXv++eetQ4cOVqtWLecygj/++MNeeuklu/zyy23QoEFmVrTbFRQtRbm94NiQe4W5zcCZFYU+B31RXCiKUoXQW2+9ZfXq1bPk5GQz8zyQ/frrr/bZZ5/ZqFGj7LXXXnMat6J8DW/2AfD999+3WrVqWc2aNa1p06bWtGlT27p1q5mdmHCyRIkS9vjjj3s0Eig8Tp2gde/evR4nfN27d7eyZcvakiVLnG+pHnvsMfvyyy+dEwkgMzPT6tevby6Xyxo3bmxTpkyxn3/+2V544QW79tprbcuWLWd8XUHoXGzatMmuvvpqZ7LdXbt2WVhYmPPYsGGDmZnt3bvXXnnlFfv11199mS5wUdFenMSxIfcKc5uBMysqfQ76orhQzClViNj/X5t+7NixHNeYZz+Pj49XzZo19cwzz+jBBx9U8eLFlZmZWaSv4XW5XPrmm2/Us2dPDRo0SD/99JMGDBigVatWacGCBZKkO++8U3PnztW4ceP03HPP+Thj5DUzk8vl0kcffaTbb79d1157rbp27er8rmNiYtShQwfddtttioqK0h133KHXX39dYWFhKl++vI+zh6/YKfOBSFKxYsU0ffp03X777apSpYq2bNmiAQMGaMuWLapRo4Y+//xzZWVl5Xidn5+fN9POlbS0NF1//fV64IEHlJCQoJYtW+rWW2/Ve++9p0svvVRdu3bVunXrVKFCBT344IOqXr26r1MGLgraC08cG85fUWozcGaFvc9BXxS55bLTj3Qo8LZs2aL69evr6aef1ogRI5zlhw4d0j333KOIiAj179/fdwnmI1lZWSpWrJjGjh2r3377TVOnTtWuXbvUrFkzde7cWVOmTJEkHTx4UKVLl9aHH36o2rVrq06dOj7OHHktNjZWt99+u5599llVqFBB69ev16JFi3TTTTfpzTfflCS98MILWr9+vY4fP65nn31W9evX93HWyA8mTpyoOnXqqFGjRipXrpyefvppXXrppbr99tudTsa+fftUrlw5bdy4UaGhob5OOVd+//13Va1aVb1799bhw4c1a9YslShRQrfffrs+++wzVa9eXevWrZO/vz8T7qJQo73wxLHhwhSVNgOeilKfg74oLpivhmjh4po6daqVKFHCBg4caHFxcfbVV19ZRESEXX311QyPPIMBAwbYI488Yjt27LDLL7/c+vTp4wyxXbBggb388ssFanJBXJhjx45ZVFSUPfroo86y1NRUmzFjhtWsWdPGjh3rEc//EE515513Ws2aNe3++++37777zn766SerXr26ff7552Z2Yqh6r169rG3btgXisovsY9/evXtt586dHutSU1OtcePGNmnSJDM7cRlJdHS0zZo1y5KSkrydKuB1Rbm94NiQNwpbm4ELU1T6HPRFcSG4fK+Qio6O1rvvvqsPP/xQ999/v1ONXrNmjTNMsqhbtWqVPv74Y0lSpUqV9PXXX6tFixa69dZbNXXqVEknbtG7YMEC/fbbbzmGT6Pw8Pf3144dO7R3715nWenSpfWPf/xDLVq00Pfff+8RzxBjnOrdd9/VmDFjZGa65ZZbtGLFCt1+++0aMGCAfv/9d1WvXl2TJ0/W559/nuMW3vmRy+XSBx98oFtuuUVNmzZV7969tX79ekkn/i8qV66s2bNna/HixXrssce0ePFitWzZUhUrVvRx5sDFV5TbC44NeaOwtRn4a0Wxz0FfFBfEtzUxXGz79u2zX375xX766SdnkrmiXp3OysqyY8eOWZMmTSwyMtLMTnzzed1119mll15qGzZssPT0dDt8+LANGzbMKlWqdNbJJlE4HD9+3J544glr3769/fTTTx7rxo0bZ/Xq1bPU1FQfZYf87NTJO/fv329z5syxkJAQa9eunZUpU8ZGjRplR48edWIKwp1lNm/ebFWqVLHnn3/e3nzzTatSpYq1a9fOudX9V199ZTfffLNVqlTJ6tSpY/Hx8T7OGPCeotxecGz4+wpjm4Gzo89BXxTnhzmlipjs65khffbZZ7r77rsVExOj9u3ba/v27WrXrp2kE5NPXnHFFVq/fr0+/fRTNWzY0MfZIq/Y/09Sm5iYKH9/fwUEBCggIEBff/21/vGPf6hHjx4aMGCAatWqJUl66KGHtGvXLv3vf//TJZdc4uPskR9l/01l27Jli95880298soruvXWW/XBBx/4MLu/ln0akL0N27dv10svvaRJkyZJOjFfTJcuXVShQgWNHDlSzZs3V1pamrZt26by5curQoUKPssduJiKenvBseHiKOhtBi4cfY6T6IviTChKoUg49QTAzGRmSk5O1j//+U9deeWVGjdunIoVK6bjx49rxowZ2r17t6pVq6YbbrhB1apV83H2yGvz58/XoEGDVLZsWQUFBWnevHm67LLL9Mknn6h3794KDw9XqVKl5Ha79fHHH+ubb77RNddc4+u04SNnO4E60/LsZUeOHNGvv/6qunXrys/PL0cnJD/Jzu2LL77QZ599pt9++01BQUGaPn26E/Pbb7/pjjvuUFhYmAYPHqy2bdv6MGPAe4pye8GxIXcKe5uBc6PPAVw4ilIoMtasWaPDhw+rZcuWzrJJkybp3//+tzZs2KDLL7/ch9nBW37++We1bt1agwcPVokSJfTBBx9ow4YN+uqrr1SvXj2tWLFCS5cu1apVq1SlShVFR0erXr16vk4bPnLqyeVHH32kffv2qVatWrruuutUqlSp8/rGLzMzM9/fwnvRokVq166dbr31Vn3zzTcKDAzUuHHjdO+99zoxv/32m1q1aqXGjRtr1qxZKlWqlA8zBi4+2guODReqqLQZODf6HMCFoSiFImHv3r166KGHNH/+fA0cOFCtWrVSly5dJEkRERGqXLmyXn/9dZUoUcK3ieKiOPUkcfv27Zo6dapGjx4tSUpISNCDDz6o1atXa+nSpapbt64Tz4lh0Xbq383QoUM1e/ZsFS9eXGXKlNENN9ygF154QWXKlCnwQ9G3bdummTNnKjQ0VA8++KB+++039evXT1lZWerdu7e6d+/uxP7+++/KyspS9erVfZgxcPHQXpzEseHCFJU2A+dGnwO4cBwRUSSEhITo7bff1oIFC7Ru3To9/fTTatOmjVasWKHGjRtr//79HnfSQeGRfZK4aNEiPfnkkxo0aJA2btyoI0eOSJIqV66sqVOn6vrrr1ebNm20adMm56SysHUwcP5O7Vxs2LBBGzdu1GeffaYNGzaod+/e+uGHH9SvXz8lJyerWLFiysrK8nHGubNp0yY98MADmjdvnmrUqCFJql69ul5++WX5+flp2rRpevfdd534qlWrFulOJwo32ouTODZcmKLSZuCv0ecALhxFKRRK2QMA169fr/fee0/ff/+9XC6XbrvtNs2bN09vv/22MjIy9PjjjysuLk4ff/yx3nnnHR9njYvB5XLp888/V8eOHfXtt99q+/bt+vLLL7VixQon5vLLL9fUqVN15ZVX6o477lBGRoYPM0Z+kN25iImJ0eOPP65y5crp6quvVrly5TRw4EBFRUVp+/btevjhh3XgwAEVK1asQN7C2c/PT5UqVVJSUpKWLl3qLK9Vq5ZefvllBQQEaNy4cZo/f74PswS8g/biJI4NF6aotBnIiT4HkAcu3o39AN967733rHz58nbZZZdZjRo17J///Kft2rXLI+b999+3J554wrktKwqP7Nso79+/34YOHWpvvPGGmZn98ccfduedd1pwcLB98803Hq/ZtWuX7dixw+u5In/KyMiwRx991KpWrWr169f3WHf8+HF77bXX7MYbb7T27dvbwYMHfZTlhcn+v1i7dq3zt/7777/bAw88YA0aNLBXX33VI37Lli1255132vbt272eK+AttBccG/JCYWwzcH7ocwB/D0UpFCrZJ1W7d++2Tp062dtvv2179uyxiRMn2k033WRdu3a1xMTEHK/j5KBwmDNnjm3atMnMTvwtrF271oKCgqxu3br2wQcfOHFpaWlOR2PZsmW+Shf5TGZmZo5lhw4dsueee86qVq1qjzzyiB05csRZd/z4cRs/frz17dv3jK/Nb7KPj/Pnz7ewsDB78sknLSUlxczMfvnlF3vggQesSZMmOTqf6enpXs8VuNhoL07i2JA7hb3NwLnR5wDyDkUpFDpr1qyxe++917p27Wr79u1zlr/99tt24403ejQSGRkZZnayYUHB9euvv9pVV11lv//+u8fye++911wul73wwgt27NgxZ3l6erp1797dXC6XrVixwtvpIp85tYPw448/2m+//Wa//PKLmZkdOXLEhg8fbk2aNLEhQ4Z4/B1lZmY6x4+C0Mn45JNPLCAgwKZNm2a7d+/2WPfrr7/aAw88YC1atLAJEyb4KEPg4qO9yIljw4UpKm0Gzo0+B5A3uPseCp1///vfmj59ujIzM7VlyxaPWxNPnz5ds2bNkp+fn2bPnq2KFSv6MFPklYULF+r6669XhQoVJJ24rj8zM1PXXnutJOm+++7T/Pnz9d///le33nqr/P39JUnp6enq06ePnnzySdWqVctn+cO37JQJap966im9//77Onz4sDIzM/XII4/o8ccf17FjxzRmzBjFxcXpxhtv1KhRo3TJJZec8T3yq2PHjum+++5TzZo19fzzz+vIkSNKSkrSvHnzVKdOHbVu3VoHDx7U4MGDlZKSonfeeUdlypTxddpAnqK9yIljw4UpKm0G/hp9DiCP+LIiBlwM6enpNn78eKtSpYr17t3bDhw44LH+1VdftVtvvdUSEhJ8lCHyUlJSklWpUsXuv/9+W79+vaWlpVlYWJh169bN1q5d68T16NHD3G63ffDBB5aWlua7hJFvjR071sqXL29xcXH2+eef23/+8x/z8/OzRx55xMxOXJbx7LPPWvXq1W3y5Mm+TTYXjhw5Ytddd50NGDDA9u/fbw8//LC1bNnSLr/8cgsJCbGRI0ea2YnLdU4fKQEUBrQXZ8axIXcKe5uBv0afA8gbFKVQoGUPgU1KSrL9+/c7k3Omp6fbmDFjrGnTpta/f39LTU31eN3pjQYKtvj4eLv++uvtn//8pyUnJ9uXX35p1atXt169etn333/vxPXo0cOCg4Nt3rx5RaKjgbPLvswiW0ZGhnXs2NHpfGX76KOPzOVy2YwZM8zsRCfjzTfftOPHj3st17w0c+ZMCwgIsKCgILvjjjts5syZZmY2aNAga9WqlXN5AVBY0V6cGceGcyuqbQZOos8BXDxcvocCy/5/6POHH36oUaNG6eDBgzIz9erVS08//bQyMzP1f+3de1DVdf7H8Recw03AdHKCVVGxdKBUQN22bMHZEXDFsoZ1K1YkA0fFG7obs0Wm7eqYMDGjbWrhKF6WylnTvKSsOV7ShdwBwRTQZQkhNQtXSxQV5Hx+f/jzJOlupnIOR56PGWbge32fM8z3/f58vp/v55uVlaVNmzbp0Ucf1dy5c9WxY0dnh41WUlJSouTkZA0cOFBvvvmmysvLlZCQoGHDhmn69OmKiIiQJI0cOVJlZWU6fPiw/Pz8nBw1nGH06NHy9vbWX//6V/uy+vp6DRo0SGPGjNGcOXNks9lks9lktVo1adIk1dTUaN26dfL19bXv09zcLIvF4oyPcEfKy8t14sQJxcTEyGazyd3dXVOnTlV9fb1ycnLsjysB9yryxc1xbbi59p4zQJsDaHXO7BED7tQnn3xivLy8zKJFi0xeXp5ZuHChsVqtJiUlxRhz9e7F/PnzTUhIiElPT2dywXvcgQMHTHh4uElOTjZnz541+/btM0FBQWbcuHEtHs04fvy484KE0507d84+8uHrr7+2XxfS09NNnz59zOHDh40x309C+9JLL5kRI0Y4J9hWVlFRYTIyMsx9991nDh065OxwAIchX/xvXBu+R86AMbQ5gNZEpxRc0rULfWpqqvnd737XYt2uXbuMu7u7yczMNMZcfZ1zdna2qa6udnSYcILrGxpnzpwx+/btM7179za/+c1vzMGDB40xvPmkPbv+MZy3337bPPTQQ/YGaGFhoYmNjTWjRo0y5eXlxpirc63ExMSY8ePHOyPcVlVUVGQSEhJMaGioKS0tdXY4gMORL26Oa8P3yBmgzQG0Ph7fg0sx/z989sKFC/L19VVcXJw6d+6svLw8GWPU1NQkT09PzZ8/X2vXrtX27dt520U7dP2jGdnZ2SotLdW0adP097//XV27dnV2eGgD6uvrZbPZ1L9/f3Xv3l3Lly9XaGioNmzYoHfffVcFBQUKCwvTuXPnZLPZdODAAXl4eNxTb0y6ePGiioqK1KtXLwUFBTk7HMApyBc34tpwI3JG+0ObA3AcOqXgMq4lhx07dmjbtm1KS0vTtm3b9Prrr2vz5s0aPHiwfZulS5fq3XffVWFhoXx8fJwdOpygpKREEyZMUO/evZWTkyNPT0/+F9qxbdu26dSpU3rxxRc1Y8YMNTY2asmSJfr2228VERGhLl26aM2aNQoJCVFtba12796tf/3rXwoICFBqaqqsVquuXLkiq9Xq7I8C4C4jX+CHyBntG20OwLHcnR0AcKvc3Ny0fv16jRo1Sp06dVJdXZ1++ctf6uc//7nmzJmj4uJi+92oqqoqde7cWVeuXHFy1HCWiIgILVmyRKdOnVJDQwOFQjv23XffaePGjZo3b56efPJJLVu2TKmpqZKkTp06qaSkRKdPn9bYsWNVVlamoKAgJSUlad68eZo2bZqsVquam5tpXAD3KPIFrkfOAG0OwLEYKQWXcfToUY0YMULp6en24kCSNm7cqOXLl6ugoEC/+MUv1NzcrMLCQu3Zs0fh4eHOCxhtwqVLl+Tt7e3sMOBkX375pUaOHKnDhw/rz3/+s2bNmiVJunz5sry8vPTtt99q0KBBCgwM1KJFizR48GAnRwzA0cgXuIac0b7R5gAci5FScBm1tbWyWq2Ki4uTJNlsNknS008/raysLC1atEhdunTRoEGDtH//fpIDJIkGRjt37b6Lu7u7wsLCNHr0aL3//vvKzc2VJHl5eenSpUvq1KmTiouLdfDgQb3zzjvODBmAk5AvQM6ARJsDcDTGlcJlXLhwQZcuXWqxrLm5WRaLRadOndITTzyhMWPGOCk6AG2JzWaTu7u7fXh9t27dtGbNGlVWVmrhwoXKzMyUMUbJycny9vZWc3OzOnTooLq6Onl6ejo5egCAI5EzcD3aHIBjMVIKLiMsLEynT59WTk6OpKt3sSwWiyTpo48+Um5urhobG50ZIoA24FrjQpIKCgq0detW7du3T5LUp08fTZo0SdHR0crOzrZfT0aNGqXZs2fLx8dHFotFzc3NTosfAOA45Az8EG0OwLGYUwouZcWKFZo0aZJmzJihpKQkWSwWrVy5Ujk5OSosLFRISIizQwTgRNe/fjsjI0Pr169XfX29evbsqeDgYOXl5UmSDh06pJUrV2rZsmXq1q2bjDE6dOiQPDw8nBk+AMCByBn4b2hzAI5DpxRcis1m04cffqiJEyfK19dX3t7eslgsev/99xUREeHs8AC0EQsWLNDChQv14Ycf2t+Wk5mZqV//+tfaunWrJOn48eOqrq7WkSNHlJycLIvFwiu8AaAdImfgh2hzAI5DpxRc0smTJ1VTUyM3NzcFBwcrICDA2SEBcKLrH7+oqqpSamqqZsyYobi4OOXn5+u3v/2tkpKStGXLFoWFhWnTpk03HOPafBEAgHsbOQO3ijYH0Pro2odL6tq1q7p27ersMAC0EdcaF999950efPBBJSYmauDAgSosLNT48eOVnZ2tCRMmyBijd955R4899pg+++yzFsegcQEA7QM5A7eKNgfQ+pjoHADgsnbs2KHFixdLkqZMmaL09HRJUlJSkgIDA5Wfn6/o6GglJSVJkh588EE988wz6tevHxPTAkA7Q84AgLaHkVIAAJdUX1+v1atX6+jRo9q8ebMKCgpUWFjYYpvKykpVVlbK29tbTU1NKigo0NChQ5WWliaJxy8AoL0gZwBA28ScUgAAl/XNN98oNjZWn3/+uV599VXNnTtXkuyTz27ZskW///3v5evrK6vVqoaGBh08eFBWq7XFW5cAAPc+cgYAtD10SgEAXMr1E9TW1dUpLS1NFy9e1OnTp5WQkKDJkyfbt62vr9euXbu0Y8cOdejQQfPmzZPVauVuNwC0E+QMAGjb6JQCALiM6xsXH330kR5//HEFBATo+PHjmj17tioqKjR27NgWjYzTp0+rS5cu9r95hTcAtA/kDABo+5joHADgEowx9sZFRkaGJk+erA8++EANDQ3q3r27MjIy9PDDD+u9997TokWLZIxRdHS0MjMzWxyHxgUA3PvIGQDgGhgpBQBwKXPnztVbb72lrVu3KjQ0VH5+fva5PmpqapSVlaWPP/5Y7u7u6tChg0pKSuTh4eHssAEATkDOAIC2jU4pAIDLOHPmjJ577jmNGzdOY8aM0YkTJ1RVVaXly5crKipK8fHxstlsKi8v1xdffKHExERZLBYevwCAdoicAQBtH1dbAIDLcHNzU3l5uSoqKvTpp59qyZIlqq6ulpubm7Zs2aJz585p5syZioyMVGRkpKSrr/CmcQEA7Q85AwDaPkZKAQBcyvLly5Wenq7m5mZNmjRJMTExio6OVlJSkiRp9erVTo4QANBWkDMAoG3jNgAAwKWkpKQoJiZGly9fVp8+fSRdfcPSyZMn9dhjjzk5OgBAW0LOAIC2jZFSAACXdf78eZWWliozM1M1NTU6cOAAj10AAG6KnAEAbQ9XYQCASzLGqKioSNnZ2WpqalJxcbGsVquam5tlsVicHR4AoA0hZwBA28RIKQCAy7p8+bLKy8sVFhYmd3d33pgEAPivyBkA0PbQKQUAuCfYbDa5u7s7OwwAgAsgZwBA20CnFAAAAAAAAByO2wMAAAAAAABwODqlAAAAAAAA4HB0SgEAAAAAAMDh6JQCAAAAAACAw9EpBQAAAAAAAIejUwoAAAAAAAAOR6cUAJfWq1cvLVy48L+uP3bsmNzc3FRaWnpLxxs3bpyeeeaZuxIbAACAK6K+AuAodEoBcIqnnnpK0dHRN11XWFgoNzc3HThw4I7PExQUpK+++kr9+vW742P9mGsF2v/6ef3111s9DgAA0D5RXwFwNVZnBwCgfUpJSVF8fLxqamrUs2fPFutWrFih8PBwDRw48I7PY7FYFBgYeMfHuRXXCrRr3nzzTeXn52vHjh32ZX5+fg6JBQAAtD/UVwBcDSOlADjFk08+qQceeEArV65ssbyhoUFr165VSkqKJKmgoEBRUVHy8fFRUFCQpk+frgsXLtywT3Jysvz9/dWjRw/l5OTY191seHlZWZlGjhypjh07yt/fX5GRkaqqqrppnMYYZWVlqXfv3vLx8VFYWJjWrVt3022vFWjXfvz8/GS1WhUYGCh/f3/17dtX+fn5LfbZvHmzfH19VV9fb4/1gw8+0JAhQ+Tt7a1HHnlEu3fvbrFPeXm54uLi5Ofnp4CAAI0dO1anT5+2r1+3bp369+8vHx8f3X///YqOjr7hOwMAAPce6qurWqO+AtA66JQC4BRWq1VJSUlauXKljDH25X/729/U2NioMWPG6NChQxo+fLji4+P1+eefa+3atdq3b5+mTp3a4ljZ2dkaPHiwSkpKNHnyZKWmpurIkSM3Pe+JEycUFRUlb29v7dy5U8XFxUpOTtaVK1duuv2sWbOUm5urpUuXqqysTDNnzlRiYqL27Nnzkz6vr6+vnn/+eeXm5rZYnpubq9GjR8vf39++LD09XX/4wx9UUlKiIUOGaNSoUfrPf/4jSfrqq680dOhQhYeHq6ioSPn5+fr666/17LPP2tcnJCQoOTlZFRUV2r17t+Lj41t8xwAA4N5EfXXV3a6vALQiAwBOUlFRYSSZnTt32pdFRUWZhIQEY4wxY8eONRMmTGixz969e427u7u5ePGiMcaYnj17msTERPt6m81mHnjgAbN06VJjjDHV1dVGkikpKTHGGPPKK6+Y4OBg09jYeNOYXnjhBfP0008bY4w5f/688fb2NgUFBS22SUlJscf4v8yZM8eEhYXZ/96/f7+xWCzmxIkTxhhj6urqjIeHh9m9e3eLWBcsWGDfp6mpyXTv3t1kZmYaY4x57bXXTGxsbIvzfPnll0aSOXr0qCkuLjaSzLFjx340PgAAcO+hvrr79RWA1sOcUgCcJiQkREOGDNGKFSv0q1/9SlVVVdq7d6+2b98uSSouLta///1v5eXl2fcxxshms6m6ulqhoaGSpAEDBtjXu7m5KTAwUN98881Nz1laWqrIyEh5eHj8aHzl5eW6dOmSYmJiWixvbGxURETET/68jz76qB555BGtXr1aL7/8stasWaMePXooKiqqxXaPP/64/Xer1arBgweroqJC0tXvZNeuXTedO6GqqkqxsbEaNmyY+vfvr+HDhys2NlajR49W586df3K8AADA9VBf3f36qm/fvj85LgC3hk4pAE6VkpKiqVOnavHixcrNzVXPnj01bNgwSZLNZtPEiRM1ffr0G/br0aOH/fcfFkBubm6y2Ww3PZ+Pj88tx3btGB9//LG6devWYp2Xl9ctH+d648eP19tvv62XX35Zubm5evHFF+Xm5vaj+13bxmaz6amnnlJmZuYN2/zsZz+TxWLRJ598ooKCAm3fvl1/+ctf9Oqrr2r//v0KDg6+rZgBAIBrob66u/UVgNbDnFIAnOrZZ5+VxWLRe++9p1WrVrUoIgYOHKiysjI99NBDN/x4enre1vkGDBigvXv3qqmp6Ue3ffjhh+Xl5aXa2tobzh8UFHRb509MTFRtba3eeustlZWV6YUXXrhhm88++8z++5UrV1RcXKyQkBBJ338nvXr1uiEmX19fSVcLrCeeeEJ/+tOfVFJSIk9PT23YsOG24gUAAK6H+uru11cAWgedUgCcys/PT88995wyMjJ08uRJjRs3zr7uj3/8owoLCzVlyhSVlpaqsrJSmzZt0rRp0277fFOnTtW5c+f0/PPPq6ioSJWVlVqzZo2OHj16w7b+/v566aWXNHPmTK1atUpVVVUqKSnR4sWLtWrVqts6f+fOnRUfH6/09HTFxsaqe/fuN2yzePFibdiwQUeOHNGUKVN09uxZJScnS5KmTJmiM2fOKCEhQf/85z/1xRdfaPv27UpOTlZzc7P279+v+fPnq6ioSLW1tVq/fr3q6ursQ/EBAMC9j/rq7tZXAFoPnVIAnC4lJUVnz55VdHR0i2HjAwYM0J49e1RZWanIyEhFRETotddeu6Nh1Pfff7927typ8+fPa+jQoRo0aJCWLVv2X+dAmDt3rmbPnq033nhDoaGhGj58uDZv3nxHj8KlpKSosbHRXgj90IIFC5SZmamwsDDt3btXGzduVJcuXSRJXbt21T/+8Q81Nzdr+PDh6tevn9LS0nTffffJ3d1dHTt21Keffqq4uDj17dtXs2bNUnZ2tkaMGHHb8QIAANdDfdXSndRXAFqPmzG8JxwAHCkvL09paWk6efJki2Hyx44dU3BwsEpKShQeHu68AAEAAFwM9RXgmpjoHAAcpKGhQdXV1XrjjTc0ceLE2563AQAAAFdRXwGujbGIAOAgWVlZCg8PV0BAgF555RVnhwMAAODyqK8A18bjewAAAAAAAHA4Rko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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Assuming df is your DataFrame\n", "\n", "# Filter out rows where vehicletype is 'None' or 'Other'\n", "filtered_df = df[~df['vehicletype'].isin(['None', 'other'])]\n", "\n", "# Updated vehicle type and price data after filtering\n", "vehicletype = filtered_df['vehicletype'].value_counts()\n", "price_type = filtered_df.groupby('vehicletype')['price'].mean().sort_values(ascending=False)\n", "\n", "# Create a figure and two subplots\n", "fig, (axes1, axes2) = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", "# First subplot - Average prices based on vehicle types\n", "axes1.bar(price_type.index, price_type, color='purple')\n", "axes1.set_xlabel('Vehicle Types')\n", "axes1.set_ylabel('Prices')\n", "axes1.set_title('Top 7 Vehices per Price', fontweight='bold')\n", "axes1.tick_params(axis='x', rotation=45, labelsize=10) # Rotate x-axis labels for better reada6bility\n", "\n", "# Second subplot - Count of vehicles based on types\n", "axes2.bar(vehicletype.index, vehicletype, color='blue')\n", "axes2.set_xlabel('Vehicle Type')\n", "axes2.set_ylabel('Count')\n", "axes2.set_title('Top 7 Vehicles', fontweight='bold')\n", "axes2.tick_params(axis='x', rotation=45, labelsize=10) # Rotate x-axis labels for better readability\n", "\n", "plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "913e310f", "metadata": {}, "source": [ "### Vehicle Type and Brand have the same trend; inverse relationship between the price and the vehicle type\n", "\n", "- As the brands, the trend is the same. The high-pricey vehicles are low in quantity, while the less-pricey vehicles are high in quantity.\n", "- The data reveals that the expensive *vehicle types* do not meet the top 7 most sold or advertised *vehicle types* on Ebay. Invariably, the less pricey *vehicle types* are the most sold/advertised *vehicle types*\n", "- In the automobile industry, either the **limited quantity** of the expensive *vehicle types* influenced the increased prices or because they're made with **more quality-sourced resources**, they're more expensive. Brands would also play a pivotal role in the pricing. The demand and supply power is in play. The less pricey the *vehicle types* , the more they are in quantity, and vice-versa. This would nt be restricted to the platfomr alone, but the automobile industry in general. \n", "- Most of the pricey *vehicle types* are probably not sold on eBay (probaly there's a different market for those types of cars), and ebay is mostly popular for selling the less-pricey *vehicle types* .\n", "- eBay doesn't have as much car sellers of whose who own/deal in luxury cars in comparism with those who own the less pricey cars (*data sufficiency wouldn't allow us deterine this*).\n", "- This gives an insight to the type of Private Dealers on the platfomr. Middle to low-inclome earners." ] }, { "cell_type": "markdown", "id": "29a399e8", "metadata": {}, "source": [ "#### Trend Analysis over Time " ] }, { "cell_type": "code", "execution_count": 35, "id": "b1c1c67b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNone1993Manual0golf1500000PetrolvolkswagenNone2016-03-24T00:00:00
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190None1250005DieselaudiYes2016-03-24T00:00:00
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNone2016-03-14T00:00:00
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006PetrolvolkswagenNo2016-03-17T00:00:00
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007DieselskodaNo2016-03-31T00:00:00
......................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNone2005None0None200001Nonesonstige_autosNone2016-03-14T00:00:00
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003PetrolsmartNo2016-03-05T00:00:00
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003DieselvolkswagenNo2016-03-19T00:00:00
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNone2016-03-20T00:00:00
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008PetrolbmwNo2016-03-07T00:00:00
\n", "

371528 rows × 17 columns

\n", "
" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test None 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test None 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 None 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 None 0 None 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen None 2016-03-24T00:00:00 \n", "1 Diesel audi Yes 2016-03-24T00:00:00 \n", "2 Diesel jeep None 2016-03-14T00:00:00 \n", "3 Petrol volkswagen No 2016-03-17T00:00:00 \n", "4 Diesel skoda No 2016-03-31T00:00:00 \n", "... ... ... ... ... \n", "371523 None sonstige_autos None 2016-03-14T00:00:00 \n", "371524 Petrol smart No 2016-03-05T00:00:00 \n", "371525 Diesel volkswagen No 2016-03-19T00:00:00 \n", "371526 Diesel volkswagen None 2016-03-20T00:00:00 \n", "371527 Petrol bmw No 2016-03-07T00:00:00 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 36, "id": "df5661f4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2016 371498\n", "2015 29\n", "2014 1\n", "Name: datecreated, dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['datecreated'] = pd.to_datetime(df['datecreated'])\n", "\n", "year = df['datecreated'].dt.year\n", "year_count = year.value_counts()\n", "\n", "year_count" ] }, { "cell_type": "code", "execution_count": 37, "id": "1bcf0725", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import matplotlib.ticker as ticker\n", "\n", "df['datecreated'] = pd.to_datetime(df['datecreated'])\n", "year_created = df['datecreated'].dt.year\n", "year_count = year_created.value_counts()\n", "\n", "fig = plt.figure()\n", "\n", "axes1 = fig.add_axes([0, 0, 1, 1])\n", "axes1.bar(year_count.index, year_count, color='blue')\n", "axes1.set_xlabel('Year')\n", "axes1.set_ylabel('Count')\n", "axes1.set_title('Sales Year Trend', fontweight='bold')\n", "axes1.xaxis.set_major_locator(ticker.MaxNLocator(integer=True)) # Ensure integer values on x-axis\n", "\n", "\n", "\n", "axes2 = fig.add_axes([0.25, 0.25, 0.25, 0.25])\n", "axes2.bar(year_count.index,year_count, color='purple')\n", "axes2.set_xlim(2014, 2015)\n", "axes2.set_ylim(0, 35)\n", "axes2.set_xlabel('Year', fontsize=8)\n", "axes2.set_ylabel('Count', fontsize=8)\n", "axes2.set_title('Zoomed_In View for years 2014 and 2015', fontsize=10)\n", "axes2.xaxis.set_major_locator(ticker.MaxNLocator(integer=True)) # Ensure integer values on x-axis\n", "\n", "\n", "plt.show()\n", "\n", "plt.tight_layout()\n" ] }, { "cell_type": "markdown", "id": "4f6964e5", "metadata": {}, "source": [ "### Analyzing the reasons for the spike in Car Advertisements in 2016 \n", "More Ads were put out in 2016 than ever advertised. What was the cause for this?\n", "\n", "- Firstly, most of our top advertised brands released new models in 2016. As a matter of [fact](https://www.best-selling-cars.com/germany/2016-full-year-germany-30-best-selling-car-models/), they topped the list of top 25 bestmodels and car sales in year 2016\n", "- The most cars were sold in 2016, than in previous year. This was a general market trend.\n", "- A speculation could be due to the. From this [report](https://www.best-selling-cars.com/germany/2016-germany-developments-new-car-market/), 2016 increased by 4.51% to almost 3.4 million cars sold; the best since 2009.\n", "\n", "- This general rise were attributed to [environmental bonus](https://www.electrive.com/2023/09/29/germany-hits-2-million-approved-environmental-bonus-mark/#:~:text=Since%202016%2C%20the%20German%20government,bonus%20experienced%20a%20strong%20boost.) (which must have been promoted since 2016) but was introducted in 2020." ] }, { "cell_type": "code", "execution_count": 38, "id": "8b27eea6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Zoomed_In view')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "sellers = df['seller'].value_counts()\n", "\n", "fig = plt.figure()\n", "\n", "ax1 = fig.add_axes([0, 0, 1, 1])\n", "ax1.bar(sellers.index, sellers)\n", "ax1.set_title(\"Sellers' Index\", fontweight='bold')\n", "\n", "ax2=fig.add_axes([0.5, 0.5, 0.25, 0.25])\n", "ax2.bar(sellers.index, sellers)\n", "ax2.set_ylim(0, 30)\n", "ax2.set_title('Zoomed_In view', size=10)\n" ] }, { "cell_type": "markdown", "id": "7bd8ad3d", "metadata": {}, "source": [ "### Why do we have more Privtate Sellers?\n", "\n", "- Firstly, the introduction of the [environmental bonus](https://www.electrive.com/2023/09/29/germany-hits-2-million-approved-environmental-bonus-mark/#:~:text=Since%202016%2C%20the%20German%20government,bonus%20experienced%20a%20strong%20boost.) tilted to favour individual ownership of vehicles. It's safe to assume most of the private sellers took the most of this bonus by plunging fulltime in car businesses.\n", "\n", "- Other questions revealed from thsi question is; \n", " - is our marketing content selling our plaform more to private sellers car dealers?\n", " - Could this mean, dealers have other marketing platforms or means they use in selling or advertising, that we're not aware of?\n", " " ] }, { "cell_type": "markdown", "id": "85c43c9e", "metadata": {}, "source": [ "#### Brand Comparism " ] }, { "cell_type": "code", "execution_count": 39, "id": "15b2b9e8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNone1993Manual0golf1500000PetrolvolkswagenNone2016-03-24
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190None1250005DieselaudiYes2016-03-24
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNone2016-03-14
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006PetrolvolkswagenNo2016-03-17
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007DieselskodaNo2016-03-31
......................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNone2005None0None200001Nonesonstige_autosNone2016-03-14
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003PetrolsmartNo2016-03-05
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003DieselvolkswagenNo2016-03-19
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNone2016-03-20
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008PetrolbmwNo2016-03-07
\n", "

371528 rows × 17 columns

\n", "
" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test None 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test None 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 None 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 None 0 None 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen None 2016-03-24 \n", "1 Diesel audi Yes 2016-03-24 \n", "2 Diesel jeep None 2016-03-14 \n", "3 Petrol volkswagen No 2016-03-17 \n", "4 Diesel skoda No 2016-03-31 \n", "... ... ... ... ... \n", "371523 None sonstige_autos None 2016-03-14 \n", "371524 Petrol smart No 2016-03-05 \n", "371525 Diesel volkswagen No 2016-03-19 \n", "371526 Diesel volkswagen None 2016-03-20 \n", "371527 Petrol bmw No 2016-03-07 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 40, "id": "4db71209", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 volkswagen\n", "1 audi\n", "2 jeep\n", "3 volkswagen\n", "4 skoda\n", " ... \n", "371523 sonstige_autos\n", "371524 smart\n", "371525 volkswagen\n", "371526 volkswagen\n", "371527 bmw\n", "Name: brand, Length: 371528, dtype: object" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['brand']" ] }, { "cell_type": "code", "execution_count": 41, "id": "cf560ac4", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "\n", "filtered_df = df[~df['vehicletype'].isin(['None', 'other'])]\n", "\n", "vehicle_type = filtered_df['vehicletype'].value_counts()\n", "brand = df['brand'].value_counts().nlargest(15)\n", "\n", "\n", "\n", "fig = plt.figure(figsize=(24, 16))\n", "\n", "\n", "axes1 = fig.add_axes([0, 0, 1, 1])\n", "\n", "axes1.bar(brand.index, brand, color='#50ABC7')\n", "axes1.set_title('Brand', fontweight='bold', fontsize=20)\n", "axes1.set_xlabel('brands')\n", "axes1.set_ylabel('brand count')\n", "axes1.tick_params(axis='x', rotation=45, labelsize=13)\n", "\n", "\n", "axes2 = fig.add_axes([0.5, 0.5, 0.25, 0.25])\n", "axes2.bar(vehicle_type.index,vehicle_type)\n", "axes2.set_title('Types of Vehicle', fontweight='bold')\n", "# axes2.set_xlabel('vehicle types')\n", "axes2.set_ylabel('count')\n", "axes2.tick_params(axis='x', rotation=45, labelsize=10)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2897f5b2", "metadata": {}, "source": [ "### Why do we have more Privtate Sellers?\n", "\n", "- Firstly, the introduction of the [environmental bonus](https://www.electrive.com/2023/09/29/germany-hits-2-million-approved-environmental-bonus-mark/#:~:text=Since%202016%2C%20the%20German%20government,bonus%20experienced%20a%20strong%20boost.) tilted to favour individual ownership of vehicles. It's safe to assume most of the private sellers took the most of this bonus by plunging fulltime in car businesses.\n", "\n", "- Other questions revealed from thsi question is; \n", " - is our marketing content selling our plaform more to private sellers car dealers?\n", " - Could this mean, dealers have other marketing platforms or means they use in selling or advertising, that we're not aware of?\n", " " ] }, { "cell_type": "markdown", "id": "ef821bf3", "metadata": {}, "source": [ "#### Vehicle Condition and Price Correlation " ] }, { "cell_type": "code", "execution_count": 42, "id": "81384195", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNone1993Manual0golf1500000PetrolvolkswagenNone2016-03-24
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190None1250005DieselaudiYes2016-03-24
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNone2016-03-14
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006PetrolvolkswagenNo2016-03-17
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007DieselskodaNo2016-03-31
......................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNone2005None0None200001Nonesonstige_autosNone2016-03-14
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003PetrolsmartNo2016-03-05
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003DieselvolkswagenNo2016-03-19
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNone2016-03-20
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008PetrolbmwNo2016-03-07
\n", "

371528 rows × 17 columns

\n", "
" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test None 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test None 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 None 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 None 0 None 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen None 2016-03-24 \n", "1 Diesel audi Yes 2016-03-24 \n", "2 Diesel jeep None 2016-03-14 \n", "3 Petrol volkswagen No 2016-03-17 \n", "4 Diesel skoda No 2016-03-31 \n", "... ... ... ... ... \n", "371523 None sonstige_autos None 2016-03-14 \n", "371524 Petrol smart No 2016-03-05 \n", "371525 Diesel volkswagen No 2016-03-19 \n", "371526 Diesel volkswagen None 2016-03-20 \n", "371527 Petrol bmw No 2016-03-07 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 43, "id": "f18262c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "never_repaired 263182\n", "repaired 36286\n", "Name: notrepaireddamage, dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['notrepaireddamage'] = df['notrepaireddamage'].replace({'Yes': 'repaired', 'No': 'never_repaired'})\n", "filtered_damages = df['notrepaireddamage'][~df['notrepaireddamage'].isin(['None'])].value_counts()\n", "\n", "filtered_damages" ] }, { "cell_type": "code", "execution_count": 44, "id": "6a29b94b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "gearbox\n", "Automatic 122592.96\n", "Manual 127043.68\n", "Name: kilometer, dtype: float64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gearbox_counts = df[df['gearbox'] !='None']['gearbox'].value_counts()\n", "filtered_df = df[df['gearbox'] != 'None']\n", "gear_box = round(filtered_df.groupby('gearbox')['kilometer'].mean(), 2)\n", "\n", "gear_box" ] }, { "cell_type": "code", "execution_count": null, "id": "bc8f55d4", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 45, "id": "e059719f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "filtered_damages = df['notrepaireddamage'][df['notrepaireddamage'].isin(['Yes', 'No'])].value_counts()\n", "\n", "filtered_damages = df[~df['notrepaireddamage'].isin(['None'])]['notrepaireddamage'].value_counts()\n", "prices = df['price'].value_counts()\n", "\n", "gearbox_counts = df[df['gearbox'] !='None']['gearbox'].value_counts()\n", "filtered_df = df[df['gearbox'] != 'None']\n", "gear_box = round(filtered_df.groupby('gearbox')['kilometer'].mean(), 2)\n", "\n", "\n", " \n", "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(38, 15))\n", "\n", "ax1.bar(gearbox_counts.index, gearbox_counts, label='Gearbox')\n", "ax1.set_xlabel('Gear_Box', size=22, weight='bold')\n", "ax1.set_ylabel('Count', size=22, weight='bold')\n", "ax1.tick_params(axis='x', labelsize=20) \n", "ax1.tick_params(axis='y', labelsize=20) \n", "\n", "ax2.bar(filtered_damages.index, filtered_damages, label='Repair Status', color='skyblue')\n", "ax2.set_xlabel('Repair Status', size=22, weight='bold')\n", "ax2.set_ylabel('Count', size=22, weight='bold')\n", "ax2.tick_params(axis='x', labelsize=20) \n", "ax2.tick_params(axis='y', labelsize=20) \n", "\n", "\n", "ax3.bar(gear_box.index, gear_box, label='Avg kilomenter per Gear_Type')\n", "ax3.set_xlabel('Gear_Type', size=22, weight='bold')\n", "ax3.set_ylabel('Kilometer', size=22, weight='bold')\n", "ax3.tick_params(axis='x', labelsize=20) \n", "ax3.tick_params(axis='y', labelsize=20) \n", "\n", "\n", "ax1.legend(fontsize=26)\n", "ax2.legend(loc='center right', fontsize=26)\n", "ax3.legend(loc='lower left', fontsize=26)\n", "\n", "# plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e6abd584", "metadata": {}, "source": [ "### Vehicle conditions sold on the platform.\n", "\n", "- The data reveals that manual cars are the stongest and they make up cars which haven't been repaired.\n", " - If we are able to analyse the sales data, we can determine if the vehicle conditions have imporved sales." ] }, { "cell_type": "markdown", "id": "9af85f2c", "metadata": {}, "source": [ "#### Fuel Type Preference " ] }, { "cell_type": "code", "execution_count": 46, "id": "fc175051", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNone1993Manual0golf1500000PetrolvolkswagenNone2016-03-24
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190None1250005Dieselaudirepaired2016-03-24
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNone2016-03-14
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006Petrolvolkswagennever_repaired2016-03-17
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007Dieselskodanever_repaired2016-03-31
......................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNone2005None0None200001Nonesonstige_autosNone2016-03-14
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003Petrolsmartnever_repaired2016-03-05
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003Dieselvolkswagennever_repaired2016-03-19
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNone2016-03-20
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008Petrolbmwnever_repaired2016-03-07
\n", "

371528 rows × 17 columns

\n", "
" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test None 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test None 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 None 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 None 0 None 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen None 2016-03-24 \n", "1 Diesel audi repaired 2016-03-24 \n", "2 Diesel jeep None 2016-03-14 \n", "3 Petrol volkswagen never_repaired 2016-03-17 \n", "4 Diesel skoda never_repaired 2016-03-31 \n", "... ... ... ... ... \n", "371523 None sonstige_autos None 2016-03-14 \n", "371524 Petrol smart never_repaired 2016-03-05 \n", "371525 Diesel volkswagen never_repaired 2016-03-19 \n", "371526 Diesel volkswagen None 2016-03-20 \n", "371527 Petrol bmw never_repaired 2016-03-07 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 47, "id": "0206a79c", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "\n", "fuel_type = df['fueltype'][~df['fueltype'].isin(['None', 'Others'])].value_counts()\n", "\n", "plt.figure(figsize=(12, 8))\n", "plt.bar(fuel_type.index, fuel_type, label='Fuel Type Preference', color='#C79F50')\n", "plt.tick_params(axis='x', rotation = 45, labelsize=8)\n", "plt.tick_params(axis='y', labelsize=8)\n", "\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "84c79c32", "metadata": {}, "source": [ "### Vehicle Conditions\n", "\n", "- From research, this is a [general trend](https://www.acea.auto/figure/trends-in-fuel-type-of-new-cars-between-2015-and-2016-by-country/) across the country" ] }, { "cell_type": "markdown", "id": "6c92c5c9", "metadata": {}, "source": [ "#### Feature Importances " ] }, { "cell_type": "code", "execution_count": 48, "id": "573da951", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecrawlednameselleroffertypepriceabtestvehicletypeyearofregistrationgearboxpowerpsmodelkilometermonthofregistrationfueltypebrandnotrepaireddamagedatecreated
02016-03-24T11:52:17Golf_3_1.6privateOffer480testNone1993Manual0golf1500000PetrolvolkswagenNone2016-03-24
12016-03-24T10:58:45A5_Sportback_2.7_TdiprivateOffer18300testcoupe2011Manual190None1250005Dieselaudirepaired2016-03-24
22016-03-14T12:52:21Jeep_Grand_Cherokee_\"Overland\"privateOffer9800testsuv2004Automatic163grand1250008DieseljeepNone2016-03-14
32016-03-17T16:54:04GOLF_4_1_4__3TÜRERprivateOffer1500testsmall car2001Manual75golf1500006Petrolvolkswagennever_repaired2016-03-17
42016-03-31T17:25:20Skoda_Fabia_1.4_TDI_PD_ClassicprivateOffer3600testsmall car2008Manual69fabia900007Dieselskodanever_repaired2016-03-31
......................................................
3715232016-03-14T17:48:27Suche_t4___vito_ab_6_sitzeprivateOffer2200testNone2005None0None200001Nonesonstige_autosNone2016-03-14
3715242016-03-05T19:56:21Smart_smart_leistungssteigerung_100psprivateOffer1199testconvertible2000Automatic101fortwo1250003Petrolsmartnever_repaired2016-03-05
3715252016-03-19T18:57:12Volkswagen_Multivan_T4_TDI_7DC_UY2privateOffer9200testbus1996Manual102transporter1500003Dieselvolkswagennever_repaired2016-03-19
3715262016-03-20T19:41:08VW_Golf_Kombi_1_9l_TDIprivateOffer3400teststation wagon2002Manual100golf1500006DieselvolkswagenNone2016-03-20
3715272016-03-07T19:39:19BMW_M135i_vollausgestattet_NP_52.720____EuroprivateOffer28990controllimousine2013Manual320m_reihe500008Petrolbmwnever_repaired2016-03-07
\n", "

371528 rows × 17 columns

\n", "
" ], "text/plain": [ " datecrawled name \\\n", "0 2016-03-24T11:52:17 Golf_3_1.6 \n", "1 2016-03-24T10:58:45 A5_Sportback_2.7_Tdi \n", "2 2016-03-14T12:52:21 Jeep_Grand_Cherokee_\"Overland\" \n", "3 2016-03-17T16:54:04 GOLF_4_1_4__3TÜRER \n", "4 2016-03-31T17:25:20 Skoda_Fabia_1.4_TDI_PD_Classic \n", "... ... ... \n", "371523 2016-03-14T17:48:27 Suche_t4___vito_ab_6_sitze \n", "371524 2016-03-05T19:56:21 Smart_smart_leistungssteigerung_100ps \n", "371525 2016-03-19T18:57:12 Volkswagen_Multivan_T4_TDI_7DC_UY2 \n", "371526 2016-03-20T19:41:08 VW_Golf_Kombi_1_9l_TDI \n", "371527 2016-03-07T19:39:19 BMW_M135i_vollausgestattet_NP_52.720____Euro \n", "\n", " seller offertype price abtest vehicletype yearofregistration \\\n", "0 private Offer 480 test None 1993 \n", "1 private Offer 18300 test coupe 2011 \n", "2 private Offer 9800 test suv 2004 \n", "3 private Offer 1500 test small car 2001 \n", "4 private Offer 3600 test small car 2008 \n", "... ... ... ... ... ... ... \n", "371523 private Offer 2200 test None 2005 \n", "371524 private Offer 1199 test convertible 2000 \n", "371525 private Offer 9200 test bus 1996 \n", "371526 private Offer 3400 test station wagon 2002 \n", "371527 private Offer 28990 control limousine 2013 \n", "\n", " gearbox powerps model kilometer monthofregistration \\\n", "0 Manual 0 golf 150000 0 \n", "1 Manual 190 None 125000 5 \n", "2 Automatic 163 grand 125000 8 \n", "3 Manual 75 golf 150000 6 \n", "4 Manual 69 fabia 90000 7 \n", "... ... ... ... ... ... \n", "371523 None 0 None 20000 1 \n", "371524 Automatic 101 fortwo 125000 3 \n", "371525 Manual 102 transporter 150000 3 \n", "371526 Manual 100 golf 150000 6 \n", "371527 Manual 320 m_reihe 50000 8 \n", "\n", " fueltype brand notrepaireddamage datecreated \n", "0 Petrol volkswagen None 2016-03-24 \n", "1 Diesel audi repaired 2016-03-24 \n", "2 Diesel jeep None 2016-03-14 \n", "3 Petrol volkswagen never_repaired 2016-03-17 \n", "4 Diesel skoda never_repaired 2016-03-31 \n", "... ... ... ... ... \n", "371523 None sonstige_autos None 2016-03-14 \n", "371524 Petrol smart never_repaired 2016-03-05 \n", "371525 Diesel volkswagen never_repaired 2016-03-19 \n", "371526 Diesel volkswagen None 2016-03-20 \n", "371527 Petrol bmw never_repaired 2016-03-07 \n", "\n", "[371528 rows x 17 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 49, "id": "2f4a63a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 40820\n", "75 24035\n", "60 15907\n", "150 15442\n", "140 13585\n", " ... \n", "1339 1\n", "780 1\n", "6920 1\n", "1659 1\n", "564 1\n", "Name: powerps, Length: 794, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "powerps = df['powerps'].value_counts()\n", "\n", "# plt.bar(powerps.index, powerps)\n", "powerps \n", "\n", "\n", "# power, gearbox type, vehicle type," ] }, { "cell_type": "code", "execution_count": 50, "id": "2ca05f42", "metadata": {}, "outputs": [], "source": [ "filtered_gearbox = df[~df['gearbox'].isin(['None'])]\n", "\n", "avg_power_gear = round(filtered_gearbox.groupby('gearbox')['powerps'].mean(), 2)" ] }, { "cell_type": "code", "execution_count": 51, "id": "3904cccc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "vehicletype\n", "bus 114.0\n", "convertible 145.0\n", "coupe 173.0\n", "limousine 132.0\n", "other 102.0\n", "small car 69.0\n", "station wagon 136.0\n", "suv 166.0\n", "Name: powerps, dtype: float64" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_vehicles = df[df['vehicletype']!='None']\n", "\n", "avg_power_veh = round(filtered_vehicles.groupby('vehicletype')['powerps'].mean())\n", "\n", "avg_power_veh" ] }, { "cell_type": "code", "execution_count": 52, "id": "0b6f57e3", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "filtered_gearbox = df[~df['gearbox'].isin(['None'])]\n", "avg_power_gear = round(filtered_gearbox.groupby('gearbox')['powerps'].mean(), 2)\n", "filtered_vehicles = df[df['vehicletype']!='None']\n", "avg_power_veh = round(filtered_vehicles.groupby('vehicletype')['powerps'].mean())\n", "\n", "\n", "fig, (axis1, axis2) = plt.subplots(1, 2, figsize=(20, 14), gridspec_kw={'width_ratios': [1, 2]})\n", "\n", "\n", "axis1.bar(avg_power_gear.index, avg_power_gear)\n", "\n", "axis1.set_xlabel('Gearbox', fontweight='bold')\n", "axis1.set_ylabel('Powerps', fontweight='bold')\n", "\n", "axis1.set_title('Avg Powerups per GearBox', fontweight='bold', fontsize=10)\n", "\n", "# Axis2\n", "axis2.bar(avg_power_veh.index, avg_power_veh)\n", "\n", "axis2.set_title('Avg Powerups per Vehicle Type', fontweight='bold', fontsize=10)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3cd8118a", "metadata": {}, "source": [ "### Powerps of Advertised Vehicles\n", "\n", "- The data reveals that automatic cars have more Powerps than manual Cars; even though previous graph showed that manual cars are sttronger, with less repairs. This simply means Automatic cars focus on efficiency and speed, rather than the durability and effectiveness of namual cars.\n", " \n", "Since majority of the cars sold on the platform are manual cars, this would help us understand customer preference" ] }, { "cell_type": "markdown", "id": "73c5654a", "metadata": {}, "source": [ "### Conclusion:\n", "\n", "- Automatic cars have more horsepower than manual cars, showing a focus on efficiency and speed for automatics versus the durability of manuals. Despite this, most cars sold are manuals, reflecting customer preference.\n", "- Expensive cars aren't in the top-selling list on eBay; cheaper cars dominate. Likely, demand and supply dynamics favor lower-priced cars, possibly due to a different market for pricier vehicles.\n", "- Similar trends exist among brands and vehicle types: high-priced ones are less common, suggesting limited quantities or higher-quality resources contributing to their cost.\n", "- More ads were posted in 2016, likely due to top brands releasing new models, a general market trend of increased car sales, and factors like the [environmental bonus](https://www.electrive.com/2023/09/29/germany-hits-2-million-approved-environmental-bonus-mark/#:~:text=Since%202016%2C%20the%20German%20government,bonus%20experienced%20a%20strong%20boost.) introduced in 2020.\n", "- The environmental bonus likely encouraged individual ownership, possibly driving more private sellers into the car business.\n", "- Questions arise about the effectiveness of the platform's marketing to private sellers and whether dealers use alternative marketing channels we're not aware of.\n", " \n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }