From 3781959992e729bb259d76c04f336757bded01f6 Mon Sep 17 00:00:00 2001 From: Simran Shaikh Date: Sat, 9 Nov 2024 00:07:09 +0530 Subject: [PATCH] final commit --- .../data/Cleaned_Car_data.csv | 817 ++++++++++++++++ .../data/raw_car_data.csv | 893 ++++++++++++++++++ models/PreOwnedCarPrediction/model.py | 31 + .../PreOwnedCarPrediction/modelEvalution.py | 21 + .../notebooks/car_price_predictor.ipynb | 1 + models/PreOwnedCarPrediction/predict.py | 16 + 6 files changed, 1779 insertions(+) create mode 100644 models/PreOwnedCarPrediction/data/Cleaned_Car_data.csv create mode 100644 models/PreOwnedCarPrediction/data/raw_car_data.csv create mode 100644 models/PreOwnedCarPrediction/model.py create mode 100644 models/PreOwnedCarPrediction/modelEvalution.py create mode 100644 models/PreOwnedCarPrediction/notebooks/car_price_predictor.ipynb create mode 100644 models/PreOwnedCarPrediction/predict.py diff --git a/models/PreOwnedCarPrediction/data/Cleaned_Car_data.csv b/models/PreOwnedCarPrediction/data/Cleaned_Car_data.csv new file mode 100644 index 00000000..4eadd267 --- /dev/null +++ b/models/PreOwnedCarPrediction/data/Cleaned_Car_data.csv @@ -0,0 +1,817 @@ +,name,company,year,Price,kms_driven,fuel_type +0,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol +1,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel +2,Hyundai Grand i10,Hyundai,2014,325000,28000,Petrol +3,Ford EcoSport Titanium,Ford,2014,575000,36000,Diesel +4,Ford Figo,Ford,2012,175000,41000,Diesel +5,Hyundai Eon,Hyundai,2013,190000,25000,Petrol +6,Ford EcoSport Ambiente,Ford,2016,830000,24530,Diesel +7,Maruti Suzuki Alto,Maruti,2015,250000,60000,Petrol +8,Skoda Fabia Classic,Skoda,2010,182000,60000,Petrol +9,Maruti Suzuki Stingray,Maruti,2015,315000,30000,Petrol +10,Hyundai Elite i20,Hyundai,2014,415000,32000,Petrol +11,Mahindra Scorpio SLE,Mahindra,2015,320000,48660,Diesel +12,Hyundai Santro Xing,Hyundai,2007,80000,45000,Petrol +13,Mahindra Jeep CL550,Mahindra,2006,425000,40,Diesel 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S,Mini,2013,1891111,13000,Petrol +286,Mini Cooper S,Mini,2013,1891111,13000,Petrol +287,Tata Indica V2,Tata,2008,150000,11000,Petrol +288,Mini Cooper S,Mini,2013,1891111,13000,Petrol +289,Tata Indigo CS,Tata,2009,72500,46000,Diesel +290,Maruti Suzuki Swift,Maruti,2019,610000,73,Petrol +291,Mahindra Scorpio VLX,Mahindra,2004,230000,160000,Diesel +292,Honda Accord,Honda,2009,175000,58559,Petrol +293,Mahindra Scorpio S4,Mahindra,2015,855000,30000,Diesel +294,Chevrolet Tavera Neo,Chevrolet,2013,375000,55000,Diesel +295,Ford EcoSport Titanium,Ford,2014,520000,57000,Diesel +296,Maruti Suzuki Ertiga,Maruti,2015,524999,50000,Diesel +297,Honda Amaze,Honda,2014,299999,37000,Petrol +298,Maruti Suzuki Dzire,Maruti,2012,299999,40000,Petrol +299,Honda City,Honda,2011,284999,55000,Petrol +300,Mahindra Scorpio 2.6,Mahindra,2007,220000,170000,Diesel +301,Maruti Suzuki Dzire,Maruti,2014,424999,55000,Diesel +302,Honda City,Honda,2015,644999,39000,Petrol +303,Honda Mobilio,Honda,2014,399999,44000,Petrol +304,Toyota Corolla Altis,Toyota,2009,199999,65000,Petrol +305,Honda City,Honda,2014,584999,39000,Petrol +306,Skoda Laura,Skoda,2012,349999,44000,Diesel +307,Renault Duster,Renault,2015,449999,49000,Diesel +308,Maruti Suzuki Ertiga,Maruti,2018,799999,9000,Diesel +309,Maruti Suzuki Dzire,Maruti,2015,444999,45000,Diesel +310,Mahindra XUV500,Mahindra,2014,649999,47000,Diesel +311,Hyundai Verna Fluidic,Hyundai,2012,444999,40000,Diesel +312,Maruti Suzuki Vitara,Maruti,2016,689999,29000,Diesel +313,Maruti Suzuki Wagon,Maruti,2016,344999,15000,Petrol +314,Mahindra Scorpio,Mahindra,2015,944999,45000,Diesel +315,Honda Amaze,Honda,2014,274999,35000,Petrol +316,Mahindra XUV500,Mahindra,2013,689999,80000,Diesel +317,Mahindra Scorpio,Mahindra,2013,574999,68000,Diesel +318,Skoda Laura,Skoda,2013,374999,50000,Diesel +319,Volkswagen Polo,Volkswagen,2010,199999,60000,Diesel +320,Hyundai Elite i20,Hyundai,2016,549999,9000,Petrol +321,Tata Manza Aura,Tata,2012,130000,72000,Diesel +322,Chevrolet Sail UVA,Chevrolet,2013,210000,60000,Petrol +323,Renault Duster 110,Renault,2012,501000,38000,Diesel +324,Hyundai Verna Fluidic,Hyundai,2013,401000,45000,Diesel +325,Audi A4 2.0,Audi,2012,1350000,40000,Diesel +326,Hyundai Elantra SX,Hyundai,2013,600000,20000,Petrol +327,Mahindra Scorpio VLX,Mahindra,2013,610000,35000,Diesel +328,Mahindra KUV100 K8,Mahindra,2016,400000,20000,Diesel +329,Renault Scala RxL,Renault,2015,375000,25000,Diesel +330,Mahindra Quanto C8,Mahindra,2013,375000,20000,Diesel +331,Hyundai Grand i10,Hyundai,2014,365000,20000,Petrol +332,Hyundai i20 Active,Hyundai,2015,500000,18000,Petrol +333,Mahindra Xylo E4,Mahindra,2012,400000,35000,Diesel +334,Hyundai Grand i10,Hyundai,2017,524999,6821,Petrol +335,Hyundai i20,Hyundai,2014,449999,23000,Petrol +336,Hyundai Eon,Hyundai,2014,174999,14000,Petrol +337,Hyundai i10,Hyundai,2012,244999,38000,Petrol +338,Hyundai i20 Active,Hyundai,2015,574999,35000,Diesel +339,Datsun Redi GO,Datsun,2017,244999,22000,Petrol +340,Toyota Etios Liva,Toyota,2011,239999,41000,Petrol +341,Hyundai Accent,Hyundai,2010,99999,45000,Petrol +342,Hyundai Verna,Hyundai,2014,489999,44000,Diesel +343,Maruti Suzuki Swift,Maruti,2013,324999,45000,Diesel +344,Toyota Fortuner,Toyota,2011,1074999,52000,Diesel +345,Hyundai i10 Sportz,Hyundai,2012,230000,34000,Petrol +346,Mahindra Bolero Power,Mahindra,2018,699000,1800,Diesel +347,Mahindra XUV500,Mahindra,2015,1000000,15000,Diesel +348,Honda City 1.5,Honda,2010,240000,400000,Petrol +349,Chevrolet Spark LT,Chevrolet,2009,110000,44000,Petrol +350,Mahindra Jeep MM,Mahindra,2019,390000,60,Diesel +351,Renault Duster 110PS,Renault,2012,501000,35000,Diesel +352,Mahindra XUV500,Mahindra,2016,1130000,72000,Diesel +353,Tata Indigo eCS,Tata,2014,250000,40000,Diesel +354,Mahindra Bolero SLE,Mahindra,2013,330000,80200,Diesel +355,Force Motors Force,Force,2015,580000,3200,Diesel +356,Skoda Rapid Elegance,Skoda,2013,340000,48000,Diesel +357,Tata Vista Quadrajet,Tata,2011,120000,90000,Diesel +358,Maruti Suzuki Alto,Maruti,2015,265000,12000,Petrol +359,Maruti Suzuki SX4,Maruti,2012,265000,46000,Diesel +360,Maruti Suzuki Zen,Maruti,2003,85000,69900,Petrol +361,Mahindra Jeep CL550,Mahindra,2019,379000,0,Diesel +362,Hyundai i10 Magna,Hyundai,2011,175000,45000,Petrol +363,Maruti Suzuki Alto,Maruti,2015,219000,5000,Petrol +364,Maruti Suzuki Swift,Maruti,2016,350000,166000,Diesel +365,Honda City ZX,Honda,2008,149000,42000,Petrol +366,Mahindra Jeep CL550,Mahindra,2018,385000,588,Diesel +367,Mahindra Jeep MM,Mahindra,2006,425000,122,Diesel +368,Chevrolet Beat Diesel,Chevrolet,2017,150000,62000,Diesel +369,Honda City 1.5,Honda,2010,225000,70000,Petrol +370,Hyundai Verna 1.4,Hyundai,2014,375000,36000,Petrol +371,Toyota Innova 2.5,Toyota,2012,770000,0,Diesel +372,Maruti Suzuki Maruti,Maruti,1995,30000,55000,Petrol +373,Toyota Etios,Toyota,2011,275000,36000,Diesel +374,Volkswagen Polo,Volkswagen,2015,330000,38000,Diesel +375,Maruti Suzuki Swift,Maruti,2014,335000,55000,Diesel +376,Hyundai Elite i20,Hyundai,2015,450000,20000,Diesel +377,Maruti Suzuki Swift,Maruti,2012,225000,40000,Petrol +378,Maruti Suzuki Versa,Maruti,2004,80000,50000,Petrol +379,Tata Indigo LX,Tata,2016,130000,104000,Diesel +380,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel +381,Mercedes Benz C,Mercedes,2002,399000,41000,Petrol +382,Maruti Suzuki Ertiga,Maruti,2013,450000,90000,Diesel +383,Honda City,Honda,2000,65000,80000,Petrol +384,Hyundai Santro Xing,Hyundai,2006,75000,46000,Petrol +385,Maruti Suzuki Omni,Maruti,2001,70000,70000,Petrol +386,Hyundai Sonata Transform,Hyundai,2017,190000,36469,Diesel +387,Hyundai Elite i20,Hyundai,2018,600000,7800,Petrol +388,Volkswagen Vento Konekt,Volkswagen,2011,245000,65000,Diesel +389,Maruti Suzuki Alto,Maruti,2017,240000,60000,Petrol +390,Maruti Suzuki Alto,Maruti,2011,155000,32000,Petrol +391,Honda Jazz S,Honda,2009,169999,24695,Petrol +392,Hyundai Grand i10,Hyundai,2017,450000,15141,Petrol +393,Maruti Suzuki Zen,Maruti,2001,40000,40000,Petrol +394,Mahindra Scorpio W,Mahindra,2012,165000,65000,Diesel +395,Maruti Suzuki Alto,Maruti,2014,270000,22000,Petrol +396,Hyundai Grand i10,Hyundai,2016,280000,59910,Diesel +397,Mahindra XUV500 W8,Mahindra,2012,560000,100000,Diesel +398,Hyundai Creta 1.6,Hyundai,2016,950000,25000,Petrol +399,Hyundai i20 Magna,Hyundai,2013,310000,35000,Petrol +400,Renault Duster 85,Renault,2015,715000,65000,Diesel +401,Hyundai Grand i10,Hyundai,2014,340000,35000,Petrol +402,Honda Brio V,Honda,2012,235000,33000,Petrol +403,Mahindra TUV300 T4,Mahindra,2017,610000,68000,Diesel +404,Chevrolet Spark LS,Chevrolet,2010,95000,23000,Petrol +405,Mahindra TUV300 T8,Mahindra,2018,1000000,4500,Diesel +406,Maruti Suzuki Swift,Maruti,2015,220000,129000,Diesel +407,Nissan X Trail,Nissan,2019,1200000,300,Diesel +408,Maruti Suzuki Alto,Maruti,2015,230000,5000,Petrol +409,Ford Ikon 1.3,Ford,2001,45000,65000,Petrol +410,Toyota Fortuner 3.0,Toyota,2010,940000,131000,Diesel +411,Tata Manza ELAN,Tata,2010,155555,111111,Petrol +412,Mercedes Benz A,Mercedes,2013,1500000,14000,Petrol +413,Chevrolet Beat LS,Chevrolet,2016,210000,22000,Diesel +414,Ford EcoSport Trend,Ford,2013,495000,38000,Diesel +415,Tata Indigo LS,Tata,2016,125000,70000,Diesel +416,Hyundai i20 Magna,Hyundai,2010,195000,36000,Petrol +417,Volkswagen Vento Highline,Volkswagen,2015,550000,34000,Diesel +418,Renault Kwid RXT,Renault,2015,270000,43000,Petrol +419,Ford EcoSport Titanium,Ford,2014,500000,40000,Diesel +420,Honda Amaze 1.5,Honda,2016,240000,160000,Diesel +421,Hyundai Verna 1.6,Hyundai,2017,800000,12000,Petrol +422,BMW 5 Series,BMW,2011,1299000,49000,Diesel +423,Skoda Superb 1.8,Skoda,2011,530000,68000,Petrol +424,Audi Q3 2.0,Audi,2013,1499000,37000,Diesel +425,Mahindra Bolero DI,Mahindra,2012,220000,59466,Diesel +426,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel +427,Ford Figo Duratorq,Ford,2012,250000,99000,Diesel +428,Maruti Suzuki Wagon,Maruti,2018,395000,25500,Petrol +429,Mahindra Logan Diesel,Mahindra,2009,130000,66000,Petrol +430,Tata Nano GenX,Tata,2010,32000,44005,Petrol +431,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel +432,Mahindra TUV300 T4,Mahindra,2016,540000,35000,Diesel +433,Hyundai Elite i20,Hyundai,2015,405000,28000,Petrol +434,Hyundai Elite i20,Hyundai,2015,400000,30000,Petrol +435,Honda City SV,Honda,2017,760000,4000,Petrol +436,Maruti Suzuki Baleno,Maruti,2016,500000,28000,Petrol +437,Ford Figo Petrol,Ford,2011,175000,75000,Petrol +438,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel +439,Honda City,Honda,2017,750000,3000,Petrol +440,Hyundai Elite i20,Hyundai,2015,419000,20000,Petrol +441,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol +442,Hyundai Eon Era,Hyundai,2018,140000,2110,Petrol +443,Mitsubishi Pajero Sport,Mitsubishi,2015,1540000,43222,Petrol +444,Hyundai i10 Magna,Hyundai,2008,275000,100200,Petrol +445,Toyota Corolla H2,Toyota,2003,150000,100000,Petrol +446,Maruti Suzuki Swift,Maruti,2011,230000,65,Petrol +447,Tata Indigo CS,Tata,2015,123000,100000,Diesel +448,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel +449,Mahindra Scorpio S10,Mahindra,2015,900000,97200,Diesel +450,Hyundai Xcent Base,Hyundai,2016,300000,140000,Diesel +451,Honda City,Honda,2015,499999,55000,Petrol +452,Hyundai Accent Executive,Hyundai,2009,165000,48000,Petrol +453,Maruti Suzuki Baleno,Maruti,2016,498000,22000,Petrol +454,Tata Zest XE,Tata,2018,480000,103553,Diesel +455,Maruti Suzuki Dzire,Maruti,2017,488000,80000,Diesel +456,Tata Sumo Gold,Tata,2014,250000,99000,Diesel +457,Toyota Corolla Altis,Toyota,2010,220000,58000,Petrol +458,Maruti Suzuki Eeco,Maruti,2013,290000,70000,LPG +459,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel +460,Mahindra XUV500 W6,Mahindra,2013,548900,49800,Diesel +461,Tata Tigor Revotron,Tata,2019,650000,100,Diesel +462,Maruti Suzuki 800,Maruti,2001,55000,81876,Petrol +463,Maruti Suzuki Ertiga,Maruti,2015,550000,75000,Petrol +464,Maruti Suzuki Versa,Maruti,2004,90000,50000,Petrol +465,Honda Mobilio S,Honda,2014,399000,44000,Diesel +466,Maruti Suzuki Ertiga,Maruti,2016,730000,55000,Diesel +467,Maruti Suzuki Vitara,Maruti,2017,725000,36000,Diesel +468,Hyundai Verna 1.6,Hyundai,2016,195000,56000,Diesel +469,Maruti Suzuki Swift,Maruti,2007,130000,62000,Petrol +470,Toyota Fortuner 3.0,Toyota,2015,1525000,120000,Diesel +471,Maruti Suzuki Omni,Maruti,2014,190000,6020,Petrol +472,Honda Amaze,Honda,2013,250000,55700,Diesel +473,Tata Indica,Tata,2005,80000,42000,Petrol +474,Hyundai Santro Xing,Hyundai,2003,120000,50000,Petrol +475,Maruti Suzuki Zen,Maruti,2010,149000,35000,Petrol +476,Maruti Suzuki Wagon,Maruti,2014,250000,18500,Petrol +477,Maruti Suzuki Wagon,Maruti,2007,120000,7000,Petrol +478,Honda Brio VX,Honda,2017,450000,11000,Petrol +479,Maruti Suzuki Zen,Maruti,2003,99999,53000,Petrol +480,Maruti Suzuki Zen,Maruti,2008,135000,23000,Petrol +481,Maruti Suzuki Wagon,Maruti,2016,225000,35500,Diesel +482,Maruti Suzuki Alto,Maruti,2010,99000,22134,Petrol +483,Renault Kwid RXT,Renault,2019,370000,1000,Petrol +484,Tata Nano Lx,Tata,2010,52000,9000,Petrol +485,Jaguar XE XE,Jaguar,2016,2800000,8500,Petrol +486,Hyundai Eon Magna,Hyundai,2014,190000,35000,Petrol +487,Honda City 1.5,Honda,2014,499000,22000,Petrol +488,Hindustan Motors Ambassador,Hindustan,2002,90000,25000,Diesel +489,Maruti Suzuki Ritz,Maruti,2010,149000,40000,Petrol +490,Hyundai Grand i10,Hyundai,2017,400000,20000,Petrol +491,Hyundai Eon D,Hyundai,2016,120000,87000,Petrol +492,Maruti Suzuki Swift,Maruti,2015,250000,55000,Petrol +493,Maruti Suzuki Wagon,Maruti,2017,375000,23000,Petrol +494,Honda Amaze 1.2,Honda,2014,381000,6000,Petrol +495,Maruti Suzuki Estilo,Maruti,2013,180000,65000,Petrol +496,Maruti Suzuki Vitara,Maruti,2016,580000,25000,Diesel +497,Maruti Suzuki Eeco,Maruti,2015,278000,39000,Petrol +498,Hyundai Creta 1.6,Hyundai,2016,1000000,8000,Petrol +499,Mahindra Scorpio Vlx,Mahindra,2013,690000,75000,Diesel +500,Maruti Suzuki Ertiga,Maruti,2012,480000,51000,Diesel +501,Mitsubishi Lancer 1.8,Mitsubishi,2006,85000,50000,Petrol +502,Maruti Suzuki Maruti,Maruti,2001,40000,75000,Petrol +503,Maruti Suzuki Alto,Maruti,2015,90000,55800,Petrol +504,Hyundai Grand i10,Hyundai,2015,340000,53000,Petrol +505,Hyundai Eon D,Hyundai,2018,260000,25000,Petrol +506,Ford Fiesta SXi,Ford,2009,250000,56400,Petrol +507,Maruti Suzuki Ritz,Maruti,2010,180000,72160,Diesel +508,Hyundai Verna Fluidic,Hyundai,2012,350000,10000,Diesel +509,Maruti Suzuki Wagon,Maruti,2006,90001,48000,Petrol +510,Maruti Suzuki Estilo,Maruti,2007,115000,36000,Petrol +511,Audi A6 2.0,Audi,2012,1599000,11500,Diesel +512,Maruti Suzuki Wagon,Maruti,2003,130000,133000,Petrol +513,Maruti Suzuki Wagon,Maruti,2009,159000,27000,Petrol +514,Maruti Suzuki Wagon,Maruti,2009,160000,35000,Petrol +515,Maruti Suzuki Alto,Maruti,2010,110000,55000,Petrol +516,Maruti Suzuki Baleno,Maruti,2016,425000,40000,Petrol +517,Hyundai Verna 1.6,Hyundai,2019,900000,2000,Petrol +518,Maruti Suzuki Swift,Maruti,2009,150000,45000,Petrol +519,Hyundai Getz Prime,Hyundai,2009,110000,20000,Petrol +520,Hyundai Santro,Hyundai,2000,51999,88000,Petrol +521,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol +522,Chevrolet Beat PS,Chevrolet,2012,215000,65422,Diesel +523,Ford EcoSport Trend,Ford,2017,580000,10000,Petrol +524,Maruti Suzuki Dzire,Maruti,2013,380000,35000,Petrol +525,Hyundai Fluidic Verna,Hyundai,2013,350000,117000,Diesel +526,Tata Indica V2,Tata,2005,35000,150000,Diesel +527,BMW X1 xDrive20d,BMW,2011,1150000,72000,Diesel +528,Hyundai i20 Asta,Hyundai,2010,300000,10750,Petrol +529,Honda City 1.5,Honda,2009,269000,55000,Petrol +530,Tata Nano,Tata,2013,60000,6800,Petrol +531,Chevrolet Cruze LTZ,Chevrolet,2014,400000,41000,Diesel +532,Hyundai Verna Fluidic,Hyundai,2015,430000,73000,Diesel +533,Maruti Suzuki Swift,Maruti,2011,140000,65000,Diesel +534,Mahindra XUV500 W6,Mahindra,2014,8500003,45000,Diesel +535,Mahindra XUV500 W10,Mahindra,2018,1299000,40000,Diesel +536,Maruti Suzuki Alto,Maruti,2014,199000,37000,Petrol +537,Hyundai Accent GLE,Hyundai,2006,90000,55000,Petrol +538,Force Motors One,Force,2013,550000,140000,Diesel +539,Maruti Suzuki Alto,Maruti,2019,265000,9800,Petrol +540,Chevrolet Spark 1.0,Chevrolet,2011,100000,27000,Petrol +541,Hyundai i10,Hyundai,2009,215000,27000,Petrol +542,Toyota Etios Liva,Toyota,2012,380000,20000,Diesel +543,Renault Duster 85PS,Renault,2013,401919,57923,Diesel +544,Chevrolet Enjoy,Chevrolet,2014,490000,30201,Diesel +545,Maruti Suzuki Alto,Maruti,2017,280000,6200,Petrol +546,BMW 5 Series,BMW,2009,650000,37518,Petrol +547,Toyota Etios Liva,Toyota,2014,160000,24652,Petrol +548,Mahindra Jeep MM,Mahindra,2004,424000,383,Diesel +549,Chevrolet Beat LS,Chevrolet,2016,225000,95000,Diesel +550,Chevrolet Cruze LTZ,Chevrolet,2011,350000,35000,Diesel +551,Jeep Wrangler Unlimited,Jeep,2015,950000,3528,Diesel +552,Maruti Suzuki Ertiga,Maruti,2013,485000,52500,Diesel +553,Hyundai Verna VGT,Hyundai,2010,205000,47900,Diesel +554,Maruti Suzuki Omni,Maruti,2012,160000,14000,Petrol +555,Maruti Suzuki Celerio,Maruti,2018,310000,37000,Petrol +556,Tata Zest Quadrajet,Tata,2017,180000,90000,Diesel +557,Mahindra XUV500 W6,Mahindra,2013,549900,52800,Diesel +558,Tata Indigo CS,Tata,2016,150000,104000,Diesel +559,Hyundai i10 Era,Hyundai,2011,175000,30000,Petrol +560,Tata Indigo eCS,Tata,2014,95000,195000,Diesel +561,Tata Indigo LX,Tata,2016,230000,104000,Diesel +562,Tata Indigo eCS,Tata,2016,230000,104000,Diesel +563,Tata Indigo Marina,Tata,2004,180000,70000,Diesel +564,Hyundai Xcent SX,Hyundai,2015,400000,43000,Diesel +565,Hyundai Eon Magna,Hyundai,2013,185000,23000,Petrol +566,Renault Duster 85,Renault,2015,385000,51000,Diesel +567,Maruti Suzuki Alto,Maruti,2009,90000,62000,Petrol +568,Tata Nano LX,Tata,2010,32000,48008,Petrol +569,Renault Duster 110,Renault,2013,435000,39000,Diesel +570,Maruti Suzuki Wagon,Maruti,2010,225000,40000,Petrol +571,Maruti Suzuki Swift,Maruti,2006,189700,48247,Petrol +572,Maruti Suzuki Ertiga,Maruti,2012,389700,39000,Diesel +573,Maruti Suzuki Swift,Maruti,2014,365000,23000,Petrol +574,Maruti Suzuki Alto,Maruti,2017,360000,9400,Petrol +575,Hyundai i20 Magna,Hyundai,2010,210000,50000,Petrol +576,Hyundai i10 Magna,Hyundai,2009,170000,75000,Petrol +577,Tata Zest XE,Tata,2017,380000,70000,Diesel +578,Mahindra Xylo E8,Mahindra,2009,295000,64000,Diesel +579,Toyota Corolla Altis,Toyota,2010,185000,55000,Petrol +580,Tata Manza Aqua,Tata,2014,160000,200000,Diesel +581,Renault Kwid 1.0,Renault,2018,290000,2137,Petrol +582,Tata Venture EX,Tata,2013,100000,30000,Diesel +583,Maruti Suzuki Swift,Maruti,2014,315000,44000,Petrol +584,Skoda Octavia Classic,Skoda,2006,114990,65000,Diesel +585,Maruti Suzuki Omni,Maruti,2012,120000,160000,LPG +586,Chevrolet Beat Diesel,Chevrolet,2011,125000,56000,Diesel +587,Tata Sumo Gold,Tata,2012,210000,75000,Diesel +588,Hyundai Verna 1.6,Hyundai,2018,855000,42000,Diesel +589,Tata Sumo Gold,Tata,2012,210000,75000,Diesel +590,Mahindra Scorpio 2.6,Mahindra,2007,260000,56000,Diesel +591,Maruti Suzuki Zen,Maruti,2002,95000,10544,Petrol +592,Maruti Suzuki Swift,Maruti,2011,255000,64000,Petrol +593,Mahindra Scorpio SLX,Mahindra,2008,300000,70000,Diesel +594,Hyundai Grand i10,Hyundai,2014,340000,25000,Petrol +595,Hyundai Elite i20,Hyundai,2017,550000,15000,Petrol +596,Ford Ikon 1.6,Ford,2003,60000,50000,Petrol +597,Toyota Innova 2.5,Toyota,2011,750000,147000,Diesel +598,Nissan Sunny XL,Nissan,2011,230000,52000,Petrol +599,Chevrolet Beat LT,Chevrolet,2012,130000,90001,Diesel +600,Maruti Suzuki Alto,Maruti,2017,270000,21000,Petrol +601,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel +602,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel +603,Maruti Suzuki Swift,Maruti,2012,280000,48006,Diesel +604,Toyota Innova 2.0,Toyota,2012,600000,80000,Diesel +605,Maruti Suzuki Swift,Maruti,2010,190000,74000,Diesel +606,Hyundai Elite i20,Hyundai,2015,500000,22000,Petrol +607,Mahindra XUV500 W10,Mahindra,2016,1065000,41000,Diesel +608,Volkswagen Polo Trendline,Volkswagen,2015,350000,25000,Diesel +609,Toyota Etios Liva,Toyota,2012,350000,85000,Diesel +610,Mahindra TUV300 T4,Mahindra,2016,540000,29500,Diesel +611,Hyundai Elite i20,Hyundai,2015,470000,30000,Petrol +612,Hyundai Santro Xing,Hyundai,2014,179000,57000,Petrol +613,Maruti Suzuki Zen,Maruti,2003,48000,60000,Petrol +614,Maruti Suzuki Ciaz,Maruti,2016,650000,50000,Petrol +615,Hyundai Eon Era,Hyundai,2013,190000,39700,Petrol +616,Hyundai Elantra 1.8,Hyundai,2012,500000,65000,Petrol +617,Maruti Suzuki Swift,Maruti,2010,270000,67000,Diesel +618,Maruti Suzuki Zen,Maruti,2008,125000,46000,Petrol +619,Hyundai Eon Era,Hyundai,2012,188000,38000,Petrol +620,Hyundai Grand i10,Hyundai,2016,380000,27000,Petrol +621,Hyundai Verna Fluidic,Hyundai,2011,365000,43000,Diesel +622,Ford EcoSport Trend,Ford,2014,465000,47000,Petrol +623,Hyundai i20 Magna,Hyundai,2011,240000,42000,Petrol +624,Chevrolet Beat Diesel,Chevrolet,2016,179999,19336,Diesel +625,Tata Indica eV2,Tata,2015,140000,60105,Diesel +626,Jaguar XF 2.2,Jaguar,2013,2190000,29000,Diesel +627,Audi Q5 2.0,Audi,2014,2390000,34000,Diesel +628,BMW 3 Series,BMW,2011,1075000,35000,Diesel +629,Maruti Suzuki Swift,Maruti,2015,475000,22000,Petrol +630,BMW X1 sDrive20d,BMW,2012,1025000,41000,Diesel +631,Maruti Suzuki S,Maruti,2016,615000,21000,Diesel +632,Maruti Suzuki Ertiga,Maruti,2013,475000,48000,Diesel +633,Maruti Suzuki Alto,Maruti,2016,270000,38000,Petrol +634,Honda City SV,Honda,2014,475000,34000,Diesel +635,Volkswagen Vento Comfortline,Volkswagen,2011,240000,45933,Petrol +636,Honda City 1.5,Honda,2005,120000,68000,Petrol +637,Audi A4 2.0,Audi,2016,1900000,44000,Diesel +638,Mahindra KUV100,Mahindra,2017,360000,35000,Diesel +639,Tata Zest XE,Tata,2018,450000,102563,Diesel +640,Mahindra XUV500 W8,Mahindra,2015,900000,28600,Diesel +641,Maruti Suzuki Swift,Maruti,2017,650000,41800,Diesel +642,Tata Sumo Gold,Tata,2014,275000,116000,Diesel +643,Maruti Suzuki Swift,Maruti,2009,210000,59000,Petrol +644,Mahindra Scorpio 2.6,Mahindra,2004,175000,58000,Diesel +645,Maruti Suzuki Omni,Maruti,2009,85000,45000,Petrol +646,Mitsubishi Pajero Sport,Mitsubishi,2015,1490000,42590,Diesel +647,Renault Duster,Renault,2014,800000,7400,Diesel +648,Volkswagen Jetta Comfortline,Volkswagen,2009,450000,54500,Diesel +649,Maruti Suzuki Ertiga,Maruti,2012,1000000,200000,Diesel +650,Audi A4 2.0,Audi,2013,1510000,27000,Diesel +651,Volvo S80 Summum,Volvo,2015,1850000,42000,Diesel +652,Toyota Corolla Altis,Toyota,2014,790000,29000,Petrol +653,Mitsubishi Pajero Sport,Mitsubishi,2015,1725000,37000,Diesel +654,Chevrolet Beat LT,Chevrolet,2012,135000,36000,Petrol +655,BMW X1,BMW,2011,1000000,34000,Diesel +656,Datsun Redi GO,Datsun,2018,299999,7000,Petrol +657,Mercedes Benz C,Mercedes,2009,1225000,76000,Diesel +658,Mahindra Scorpio SLX,Mahindra,2004,175000,60000,Diesel +659,Volkswagen Vento Comfortline,Volkswagen,2011,200000,95000,Diesel +660,Tata Indigo CS,Tata,2017,270000,50000,Diesel +661,Ford Figo Petrol,Ford,2019,525000,0,Petrol +662,Honda City ZX,Honda,2006,180000,50000,Petrol +663,Maruti Suzuki Wagon,Maruti,2008,140000,68000,Petrol +664,Ford EcoSport Trend,Ford,2014,400000,16000,Petrol +665,Maruti Suzuki Swift,Maruti,2016,499000,51000,Diesel +666,Maruti Suzuki Omni,Maruti,2009,85000,56000,Petrol +667,Maruti Suzuki Zen,Maruti,2004,70000,100000,Petrol +668,Renault Duster RxL,Renault,2015,550000,36000,Petrol +669,Maruti Suzuki Swift,Maruti,2014,370000,11523,Petrol +670,Maruti Suzuki Baleno,Maruti,2018,690000,1000,Petrol +671,Honda WR V,Honda,2009,250000,60000,Petrol +672,Tata Indigo CS,Tata,2016,110000,85000,Diesel +673,Renault Duster 110,Renault,2013,490000,38600,Diesel +674,Mahindra Scorpio LX,Mahindra,2009,320000,95500,Diesel +675,Maruti Suzuki Zen,Maruti,2004,68000,56000,Petrol +676,Maruti Suzuki Wagon,Maruti,2014,130000,37458,Petrol +677,Maruti Suzuki SX4,Maruti,2016,970000,85960,Diesel +678,Audi A3 Cabriolet,Audi,2015,3100000,12516,Petrol +679,Hyundai Eon D,Hyundai,2018,280000,35000,Petrol +680,Maruti Suzuki Zen,Maruti,2009,125000,0,Petrol +681,Mahindra Scorpio SLX,Mahindra,2008,285000,80000,Diesel +682,Hyundai Santro AE,Hyundai,2011,165000,45000,Petrol +683,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel +684,Mahindra Scorpio S4,Mahindra,2015,865000,30000,Diesel +685,Mahindra Xylo D2,Mahindra,2011,390000,48000,Diesel +686,Hyundai Santro,Hyundai,2003,60000,51000,Petrol +687,Chevrolet Beat LT,Chevrolet,2015,215000,90000,Diesel +688,Maruti Suzuki Swift,Maruti,2015,475000,43000,Diesel +689,Mahindra XUV500 W8,Mahindra,2015,899000,53000,Diesel +690,Toyota Fortuner 3.0,Toyota,2013,1499000,97000,Diesel +691,Maruti Suzuki Alto,Maruti,2013,240000,20000,Petrol +692,Hyundai Getz GLE,Hyundai,2007,99000,55000,Petrol +693,Maruti Suzuki Swift,Maruti,2014,260000,120000,Diesel +694,Hyundai Creta 1.6,Hyundai,2019,1200000,0,Petrol +695,Hyundai Santro Xing,Hyundai,2007,115000,46000,Petrol +696,Hyundai Santro Xing,Hyundai,2009,88000,43200,Petrol +697,Mahindra Xylo D2,Mahindra,2011,390000,56000,Diesel +698,Hyundai Santro Xing,Hyundai,2007,135000,42000,Petrol +699,Tata Indica V2,Tata,2009,90000,30600,Diesel +700,Hyundai i10 Sportz,Hyundai,2011,220000,38000,Petrol +701,Hyundai Grand i10,Hyundai,2017,424999,2550,Petrol +702,Hyundai Santro Xing,Hyundai,2007,135000,47000,Petrol +703,Honda City 1.5,Honda,2005,95000,41000,Petrol +704,Nissan Micra XL,Nissan,2017,430000,62500,Diesel +705,Honda City 1.5,Honda,2005,115000,68000,Petrol +706,Maruti Suzuki Alto,Maruti,2015,215000,50000,Petrol +707,Maruti Suzuki Wagon,Maruti,2004,53000,69000,Petrol +708,Maruti Suzuki Ertiga,Maruti,2012,500000,48000,Diesel +709,Tata Indica eV2,Tata,2012,85000,55000,Diesel +710,Maruti Suzuki Omni,Maruti,2013,165000,25000,Petrol +711,Hyundai Eon Era,Hyundai,2014,200000,28400,Petrol +712,Hyundai Eon,Hyundai,2014,200000,28000,Petrol +713,Maruti Suzuki Swift,Maruti,2015,425000,42000,Diesel +714,Hyundai Verna 1.6,Hyundai,2012,600000,29000,Diesel +715,Chevrolet Tavera LS,Chevrolet,2005,130000,68485,Diesel +716,Tata Tiago Revotron,Tata,2018,430000,3500,Petrol +717,Tata Tiago Revotorq,Tata,2019,568500,0,Petrol +718,Maruti Suzuki Zen,Maruti,2006,71000,32000,Petrol +719,Mahindra KUV100 K8,Mahindra,2018,560000,8000,Diesel +720,Ford EcoSport Titanium,Ford,2014,590000,34000,Diesel +721,Hindustan Motors Ambassador,Hindustan,1995,750000,37000,Petrol +722,Ford Fusion 1.4,Ford,2007,125000,85455,Diesel +723,Hyundai Santro Xing,Hyundai,2007,135000,46000,Petrol +724,Hyundai Santro,Hyundai,2002,60000,47000,Petrol +725,Fiat Linea Emotion,Fiat,2009,120000,64000,Petrol +726,Ford Ikon 1.3,Ford,2008,95000,46000,Petrol +727,Maruti Suzuki Omni,Maruti,2017,240000,8000,Petrol +728,Tata Indica V2,Tata,2012,115000,64000,Diesel +729,Mahindra Scorpio S4,Mahindra,2015,795000,63000,Diesel +730,Hyundai Santro Xing,Hyundai,2007,55000,65000,Petrol +731,Mahindra Xylo D2,Mahindra,2009,300000,62000,Diesel +732,Hyundai Grand i10,Hyundai,2014,320000,41000,Petrol +733,Maruti Suzuki Alto,Maruti,2015,265000,14000,Petrol +734,Toyota Corolla,Toyota,2006,160000,40000,Petrol +735,Hyundai Eon Magna,Hyundai,2017,300000,1600,Petrol +736,Tata Sumo Grande,Tata,2010,130000,90000,Diesel +737,Maruti Suzuki Swift,Maruti,2011,250000,58000,Diesel +738,Volkswagen Polo Highline1.2L,Volkswagen,2013,380000,27000,Petrol +739,Maruti Suzuki Alto,Maruti,2003,42000,60000,Petrol +740,Tata Tiago Revotron,Tata,2017,400000,31000,Petrol +741,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel +742,Maruti Suzuki Swift,Maruti,2009,120000,90000,Diesel +743,Tata Indigo eCS,Tata,2016,130000,150000,Diesel +744,Chevrolet Beat LS,Chevrolet,2014,189000,31000,Diesel +745,Mahindra Xylo E8,Mahindra,2011,365000,43000,Diesel +746,Hyundai Eon D,Hyundai,2013,170000,20000,Petrol +747,Tata Sumo Gold,Tata,2013,215000,100000,Petrol +748,Tata Nano,Tata,2013,60000,7000,Petrol +749,Hyundai Elite i20,Hyundai,2017,599999,31000,Petrol +750,Hyundai i10 Magna,Hyundai,2009,400000,33000,Petrol +751,Hyundai Creta,Hyundai,2016,900000,60000,Diesel +752,Volkswagen Polo,Volkswagen,2013,299999,48000,Diesel +753,Maruti Suzuki Dzire,Maruti,2014,374999,33000,Petrol +754,Tata Bolt XM,Tata,2015,600000,15000,Petrol +755,Maruti Suzuki Alto,Maruti,2005,70000,47000,Petrol +756,Maruti Suzuki Alto,Maruti,2005,100000,40000,Petrol +757,Maruti Suzuki Ritz,Maruti,2010,150000,38000,Diesel +758,Maruti Suzuki Alto,Maruti,2017,225000,12500,Petrol +759,Maruti Suzuki Dzire,Maruti,2009,210000,42000,Petrol +760,Hyundai i20 Asta,Hyundai,2014,425000,31000,Petrol +761,Maruti Suzuki Swift,Maruti,2008,162000,60000,Diesel +762,Tata Indica V2,Tata,2005,60000,80000,Diesel +763,Mahindra Scorpio VLX,Mahindra,2014,650000,77000,Diesel +764,Toyota Innova 2.5,Toyota,2012,750000,75000,Diesel +765,Mahindra Xylo E8,Mahindra,2010,375000,40000,Diesel +766,Hyundai i20 Magna,Hyundai,2011,230000,47000,Petrol +767,Maruti Suzuki Omni,Maruti,2000,35999,60000,Petrol +768,Mahindra KUV100,Mahindra,2016,380000,26500,Petrol +769,Mahindra KUV100 K8,Mahindra,2019,560000,2875,Petrol +770,Datsun Go Plus,Datsun,2016,285000,13900,Petrol +771,Ford Endeavor 4x4,Ford,2019,2900000,9000,Diesel +772,Tata Indica V2,Tata,2005,39999,80000,Diesel +773,Hyundai Santro Xing,Hyundai,2006,85000,60000,Petrol +774,Maruti Suzuki Wagon,Maruti,2016,395000,20000,Petrol +775,Maruti Suzuki Swift,Maruti,2008,175000,58000,Diesel +776,Maruti Suzuki Alto,Maruti,2019,400000,1500,Petrol +777,Toyota Innova 2.5,Toyota,2011,750000,75000,Diesel +778,Maruti Suzuki Alto,Maruti,2016,250000,2450,Petrol +779,Maruti Suzuki Alto,Maruti,2019,425000,1625,Petrol +780,Volkswagen Polo Highline1.2L,Volkswagen,2017,525000,45000,Petrol +781,Mahindra Logan,Mahindra,2009,130000,65000,Diesel +782,Maruti Suzuki 800,Maruti,2000,30000,33400,Petrol +783,Mahindra Scorpio,Mahindra,2011,475000,60123,Diesel +784,Chevrolet Sail 1.2,Chevrolet,2013,300000,28000,Petrol +785,Hyundai Santro AE,Hyundai,2003,60000,70000,Petrol +786,Maruti Suzuki Wagon,Maruti,2006,100000,7000,Petrol +787,Hyundai Eon,Hyundai,2018,260000,25000,Petrol +788,Tata Manza,Tata,2015,100000,100000,Diesel +789,Toyota Etios G,Toyota,2013,265000,42000,Petrol +790,Hyundai Getz Prime,Hyundai,2009,115000,20000,Petrol +791,Toyota Qualis,Toyota,2003,180000,100000,Diesel +792,Hyundai Santro Xing,Hyundai,2004,45000,137495,Petrol +793,Tata Indica eV2,Tata,2016,50500,91200,Diesel +794,Honda City 1.5,Honda,2009,270000,55000,Petrol +795,Tata Zest XE,Tata,2017,290000,120000,Diesel +796,Mahindra Quanto C4,Mahindra,2013,325000,63000,Diesel +797,Tata Indigo eCS,Tata,2016,160000,104000,Diesel +798,Maruti Suzuki Swift,Maruti,2016,350000,146000,Diesel +799,Hyundai Elite i20,Hyundai,2011,290000,40000,Petrol +800,Hyundai i20 Select,Hyundai,2011,290000,40000,Petrol +801,Chevrolet Tavera Neo,Chevrolet,2007,465000,100800,Diesel +802,Maruti Suzuki Dzire,Maruti,2016,325000,150000,Diesel +803,Hyundai Elite i20,Hyundai,2018,510000,2100,Petrol +804,Honda City VX,Honda,2016,860000,95000,Petrol +805,Maruti Suzuki Dzire,Maruti,2016,450000,2500,Diesel +806,Hyundai Getz,Hyundai,2006,125000,80000,Petrol +807,Mercedes Benz C,Mercedes,2006,500001,15000,Petrol +808,Maruti Suzuki Alto,Maruti,2005,95000,65000,Petrol +809,Maruti Suzuki Swift,Maruti,2009,250000,51000,Diesel +810,Skoda Fabia,Skoda,2009,110000,45000,Petrol +811,Maruti Suzuki Ritz,Maruti,2011,270000,50000,Petrol +812,Tata Indica V2,Tata,2009,110000,30000,Diesel +813,Toyota Corolla Altis,Toyota,2009,300000,132000,Petrol +814,Tata Zest XM,Tata,2018,260000,27000,Diesel +815,Mahindra Quanto C8,Mahindra,2013,390000,40000,Diesel diff --git a/models/PreOwnedCarPrediction/data/raw_car_data.csv b/models/PreOwnedCarPrediction/data/raw_car_data.csv new file mode 100644 index 00000000..76ad6211 --- /dev/null +++ b/models/PreOwnedCarPrediction/data/raw_car_data.csv @@ -0,0 +1,893 @@ +name,company,year,Price,kms_driven,fuel_type +Hyundai Santro Xing XO eRLX Euro III,Hyundai,2007,"80,000","45,000 kms",Petrol +Mahindra Jeep CL550 MDI,Mahindra,2006,"4,25,000",40 kms,Diesel +Maruti Suzuki Alto 800 Vxi,Maruti,2018,Ask For Price,"22,000 kms",Petrol +Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2014,"3,25,000","28,000 kms",Petrol +Ford EcoSport Titanium 1.5L TDCi,Ford,2014,"5,75,000","36,000 kms",Diesel +Ford EcoSport Titanium 1.5L TDCi,Ford,2015,Ask For Price,"59,000 kms",Diesel +Ford Figo,Ford,2012,"1,75,000","41,000 kms",Diesel +Hyundai Eon,Hyundai,2013,"1,90,000","25,000 kms",Petrol +Ford EcoSport Ambiente 1.5L TDCi,Ford,2016,"8,30,000","24,530 kms",Diesel +Maruti Suzuki Alto K10 VXi AMT,Maruti,2015,"2,50,000","60,000 kms",Petrol +Skoda Fabia Classic 1.2 MPI,Skoda,2010,"1,82,000","60,000 kms",Petrol +Maruti Suzuki Stingray VXi,Maruti,2015,"3,15,000","30,000 kms",Petrol +Hyundai Elite i20 Magna 1.2,Hyundai,2014,"4,15,000","32,000 kms",Petrol +Mahindra Scorpio SLE BS IV,Mahindra,2015,"3,20,000","48,660 kms",Diesel +Hyundai Santro Xing XO eRLX Euro III,Hyundai,2007,"80,000","45,000 kms",Petrol +Mahindra Jeep CL550 MDI,Mahindra,2006,"4,25,000",40 kms,Diesel +Audi A8,Audi,2017,"10,00,000","4,000 kms",Petrol +Audi Q7,Audi,2014,"5,00,000","16,934 kms",Diesel +Mahindra Scorpio S10,Mahindra,2016,"3,50,000","43,000 kms",Diesel +Maruti Suzuki Alto 800,Maruti,2014,"1,60,000","35,550 kms",Petrol +Mahindra Scorpio S10,Mahindra,2016,"3,50,000","43,000 kms",Diesel +Mahindra Scorpio S10,Mahindra,2016,"3,10,000","39,522 kms",Diesel +Maruti Suzuki Alto 800 Vxi,Maruti,2015,"75,000","39,000 kms",Petrol +Hyundai i20 Sportz 1.2,Hyundai,2012,"1,00,000","55,000 kms",Petrol +Hyundai i20 Sportz 1.2,Hyundai,2012,"1,00,000","55,000 kms",Petrol +Hyundai i20 Sportz 1.2,Hyundai,2012,"1,00,000","55,000 kms",Petrol +Maruti Suzuki Alto 800 Lx,Maruti,2017,"1,90,000","72,000 kms",Petrol +Maruti Suzuki Vitara Brezza ZDi,Maruti,2016,"2,90,000","15,975 kms",Diesel +Maruti Suzuki Alto LX,Maruti,2008,"95,000","70,000 kms",Petrol +Mahindra Bolero DI,Mahindra,2017,"1,80,000","23,452 kms",Diesel +Maruti Suzuki Swift Dzire ZDi,Maruti,2014,"3,85,000","35,522 kms",Diesel +Mahindra Scorpio S10 4WD,Mahindra,2015,"2,50,000","48,508 kms",Diesel +Maruti Suzuki Swift Vdi BSIII,Maruti,2017,"1,80,000","15,487 kms",Petrol +Maruti Suzuki Wagon R VXi BS III,Maruti,2013,"1,05,000","39,000 kms",Petrol +Maruti Suzuki Wagon R VXi Minor,Maruti,2013,"1,05,000","39,000 kms",Petrol +Toyota Innova 2.0 G 8 STR BS IV,Toyota,2012,"6,50,000","82,000 kms",Diesel +Renault Lodgy 85 PS RXL,Renault,2018,"6,89,999","20,000 kms",Diesel +Skoda Yeti Ambition 2.0 TDI CR 4x2,Skoda,2012,"4,48,000","68,000 kms",Diesel +Maruti Suzuki Baleno Delta 1.2,Maruti,2017,"5,49,000","32,000 kms",Diesel +Renault Duster 110 PS RxZ Diesel Plus,Renault,2012,"5,01,000","38,000 kms",Diesel +Renault Duster 85 PS RxE Diesel,Renault,2013,"4,89,999","27,000 kms",Diesel +Honda City 1.5 S MT,Honda,2011,"2,80,000","33,000 kms",Petrol +Maruti Suzuki Alto K10 VXi AMT,Maruti,2015,"2,50,000","60,000 kms",Petrol +Maruti Suzuki Dzire,Maruti,2013,"3,49,999","46,000 kms",Diesel +Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel +Honda Amaze 1.5 SX i DTEC,Honda,2015,"3,45,000","36,000 kms",Diesel +Honda City,Honda,2015,"4,99,999","55,000 kms",Petrol +Datsun Redi GO S,Datsun,2017,"2,35,000","16,000 kms",Petrol +Maruti Suzuki SX4 ZXI MT,Maruti,2010,"2,49,999","36,000 kms",Petrol +Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"14,75,000","47,000 kms",Diesel +Mahindra Bolero DI,Mahindra,2017,"1,80,000","23,452 kms",Diesel +Maruti Suzuki Swift Dzire ZDi,Maruti,2014,"3,85,000","35,522 kms",Diesel +Mahindra Scorpio S10 4WD,Mahindra,2015,"2,50,000","48,508 kms",Diesel +Maruti Suzuki Swift Vdi BSIII,Maruti,2017,"1,80,000","15,487 kms",Petrol +Maruti Suzuki Wagon R VXi BS III,Maruti,2013,"1,05,000","39,000 kms",Petrol +Maruti Suzuki Wagon R VXi Minor,Maruti,2013,"1,05,000","39,000 kms",Petrol +Mahindra Scorpio S10,Mahindra,2015,"3,95,000","35,000 kms",Diesel +Maruti Suzuki Swift VXi 1.2 ABS BS IV,Maruti,2017,"2,20,000","30,874 kms",Petrol +Honda City ZX CVT,Honda,2017,"1,70,000","15,000 kms",Diesel +Maruti Suzuki Wagon R LX BS IV,Maruti,2013,"85,000","29,685 kms",Petrol +Ford Figo,Ford,2012,"1,75,000","41,000 kms",Diesel +Hyundai Eon,Hyundai,2013,"1,90,000","25,000 kms",Petrol +Tata Indigo eCS LS CR4 BS IV,Tata,2017,"2,00,000","1,30,000 kms",Diesel +Ford EcoSport Ambiente 1.5L TDCi,Ford,2016,"8,30,000","24,530 kms",Diesel +Tata Indigo eCS LS CR4 BS IV,Tata,2017,"2,00,000","1,30,000 kms",Diesel +Mahindra Scorpio SLE BS IV,Mahindra,2012,"5,70,000","19,000 kms",Diesel +Volkswagen Polo Highline Exquisite P,Volkswagen,2014,"3,15,000","60,000 kms",Petrol +Skoda Fabia Classic 1.2 MPI,Skoda,2010,"1,82,000","60,000 kms",Petrol +Maruti Suzuki Stingray VXi,Maruti,2015,"3,15,000","30,000 kms",Petrol +I want to sell my car Tata Zest,I,2017,Ask For Price,, +Chevrolet Spark LS 1.0,Chevrolet,2010,"1,10,000","41,000 kms",Petrol +Renault Duster 110PS Diesel RxZ,Renault,2012,"5,01,000","35,000 kms",Diesel +Honda City,Honda,2015,"4,48,999","54,000 kms",Petrol +Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol +Datsun Redi GO S,Datsun,2017,"2,35,000","16,000 kms",Petrol +Skoda Fabia 1.2L Diesel Ambiente,Skoda,2011,"1,59,500","38,200 kms",Diesel +Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol +Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol +Renault Duster,Renault,2014,"4,49,999","50,000 kms",Diesel +Mini Cooper S 1.6,Mini,2013,"18,91,111","13,500 kms",Petrol +Mahindra Scorpio S4,Mahindra,2015,"8,65,000","30,000 kms",Diesel +Mahindra Scorpio VLX 2WD BS IV,Mahindra,2014,"6,99,000","50,000 kms",Diesel +Mahindra Quanto C8,Mahindra,2013,"3,75,000","20,000 kms",Diesel +Ford EcoSport,Ford,2017,"4,89,999","39,000 kms",Petrol +Honda Brio,Honda,2012,"2,24,999","30,000 kms",Petrol +I want to sell my car Tata Zest,I,2017,Ask For Price,, +Volkswagen Vento Highline Plus 1.5 Diesel AT,Volkswagen,2019,"12,00,000","3,600 kms",Diesel +Hyundai i20 Magna,Hyundai,2009,"1,95,000","32,000 kms",Petrol +Toyota Corolla Altis Diesel D4DG,Toyota,2010,"3,51,000","38,000 kms",Diesel +Hyundai Verna Transform SX VTVT,Hyundai,2008,"1,60,000","45,000 kms",Petrol +Toyota Corolla Altis Petrol Ltd,Toyota,2009,"2,40,000","35,000 kms",Petrol +Honda City 1.5 EXi New,Honda,2005,"90,000","50,000 kms",Petrol +Hyundai Elite i20 Magna 1.2,Hyundai,2014,"4,15,000","32,000 kms",Petrol +Skoda Fabia 1.2L Diesel Elegance,Skoda,2011,"1,55,000","45,863 kms",Diesel +BMW 3 Series 320i,BMW,2011,"6,00,000","60,500 kms",Petrol +Maruti Suzuki A Star Lxi,Maruti,2011,"1,89,500","12,500 kms",Petrol +Toyota Etios GD,Toyota,2013,"3,50,000","60,000 kms",Diesel +Ford Figo Diesel EXI Option,Ford,2012,"2,10,000","35,000 kms",Diesel +Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2014,"3,90,000","35,000 kms",Petrol +Chevrolet Beat LT Diesel,Chevrolet,2012,"1,35,000","45,000 kms",Diesel +BMW 7 Series 740Li Sedan,BMW,2009,"16,00,000","35,000 kms",Petrol +Mahindra XUV500 W8 AWD 2013,Mahindra,2013,"7,01,000","38,000 kms",Diesel +Hyundai i10 Magna 1.2,Hyundai,2014,"2,65,000","18,000 kms",Petrol +Hyundai Verna Fluidic New,Hyundai,2015,"5,25,000","35,000 kms",Diesel +Maruti Suzuki Swift VXi 1.2 BS IV,Maruti,2013,"3,72,000","13,349 kms",Petrol +Maruti Suzuki Ertiga ZXI Plus,Maruti,2016,"6,35,000","29,000 kms",Petrol +Ford EcoSport Titanium 1.5L TDCi,Ford,2014,"5,50,000","44,000 kms",Diesel +Maruti Suzuki Ertiga Vxi,Maruti,2016,"5,75,000","29,000 kms",Petrol +Maruti Suzuki Ertiga VDi,Maruti,2013,"4,85,000","42,000 kms",Diesel +Maruti Suzuki Alto LXi BS III,Maruti,2012,"1,55,000","14,000 kms",Petrol +Hyundai Grand i10 Asta 1.1 CRDi,Hyundai,2014,"3,45,000","49,000 kms",Diesel +Honda Amaze 1.2 S i VTEC,Honda,2014,"3,25,000","42,000 kms",Petrol +Hyundai i20 Asta 1.4 CRDI 6 Speed,Hyundai,2012,"3,29,500","36,200 kms",Diesel +Ford Figo Diesel EXI,Ford,2014,"1,95,000","50,000 kms",Diesel +Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,51,111","55,000 kms",Petrol +Maruti Suzuki Ertiga ZXi,Maruti,2014,"5,69,999","45,000 kms",Petrol +Maruti Suzuki Esteem LXi BS III,Maruti,2007,"69,999","51,000 kms",Petrol +Maruti Suzuki Ritz VXI,Maruti,2014,"2,99,999","19,000 kms",Petrol +Maruti Suzuki Dzire,Maruti,2009,"2,20,000","46,000 kms",Petrol +Maruti Suzuki Ritz LDi,Maruti,2013,"3,99,999","33,000 kms",Diesel +Maruti Suzuki Swift VXi 1.2 BS IV,Maruti,2013,"3,72,000","13,349 kms",Petrol +Maruti Suzuki Dzire VDI,Maruti,2015,"4,50,000","1,04,000 kms",Diesel +Toyota Etios Liva G,Toyota,2014,"2,70,000","55,000 kms",Petrol +Hyundai i20 Sportz 1.4 CRDI,Hyundai,2011,"3,50,000","33,333 kms",Diesel +Chevrolet Spark,Chevrolet,2012,"1,58,400","33,600 kms",Petrol +Maruti Suzuki Alto K10 VXi AMT,Maruti,2017,"3,50,000","5,600 kms",Petrol +Nissan Micra XV,Nissan,2011,"1,79,000","41,000 kms",Petrol +Maruti Suzuki Swift,Maruti,2007,"1,25,000","70,000 kms",Petrol +Maruti Suzuki Alto 800,Maruti,2018,"2,00,000","7,500 kms",Petrol +Honda Amaze 1.5 S i DTEC,Honda,2013,"2,99,000","45,000 kms",Diesel +Maruti Suzuki Alto 800 Vxi,Maruti,2015,"2,20,000","38,000 kms",Petrol +Chevrolet Beat,Chevrolet,2015,"1,50,000","30,000 kms",Petrol +Toyota Corolla,Toyota,2009,"2,75,000","26,000 kms", +Honda City 1.5 V MT,Honda,2010,"2,85,000","35,000 kms",Petrol +Ford EcoSport Trend 1.5L TDCi,Ford,2016,"8,30,000","24,330 kms",Diesel +Hyundai i20 Asta 1.2,Hyundai,2009,"2,10,000","65,480 kms",Petrol +Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2013,"3,40,000","41,000 kms",Petrol +Tata Indica V2 eLS,Tata,2006,"90,000","20,000 kms",Petrol +Maruti Suzuki Alto 800 Lxi,Maruti,2018,Ask For Price,"28,028 kms",Petrol +Hindustan Motors Ambassador,Hindustan,2000,"70,000","2,00,000 kms",Diesel +Toyota Corolla Altis 1.8 GL,Toyota,2010,"2,89,999","70,000 kms",Petrol +Toyota Corolla Altis 1.8 J,Toyota,2012,"3,49,999","59,000 kms",Petrol +Toyota Innova 2.5 GX BS IV 7 STR,Toyota,2012,"8,49,999","99,000 kms",Diesel +Volkswagen Jetta Highline TDI AT,Volkswagen,2014,"7,49,999","46,000 kms",Diesel +Volkswagen Polo Comfortline 1.2L P,Volkswagen,2015,"3,99,999","2,800 kms",Petrol +Volkswagen Polo,Volkswagen,2014,"2,74,999","32,000 kms",Petrol +Mahindra Scorpio,Mahindra,2015,"9,84,999","22,000 kms",Diesel +Renault Duster,Renault,2014,"4,49,999","50,000 kms",Diesel +Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol +Nissan Sunny,Nissan,2012,"2,24,999","45,000 kms",Petrol +Hyundai Elite i20,Hyundai,2018,"5,99,999","21,000 kms",Petrol +Renault Kwid,Renault,2016,"2,44,999","11,000 kms",Petrol +Renault Duster,Renault,2013,"3,99,999","41,000 kms",Diesel +Ford EcoSport,Ford,2017,"4,89,999","39,000 kms",Petrol +Renault Duster,Renault,2014,"4,74,999","50,000 kms",Diesel +Mahindra Scorpio VLX Airbag,Mahindra,2011,"4,99,999","66,000 kms",Diesel +Maruti Suzuki Alto 800 Lxi,Maruti,2018,"3,10,000","3,000 kms",Petrol +Chevrolet Spark LT 1.0,Chevrolet,2010,"85,000","45,000 kms",Petrol +Datsun Redi GO T O,Datsun,2016,"2,45,000","7,000 kms",Petrol +Maruti Suzuki Swift RS VDI,Maruti,2010,"1,89,500","38,500 kms",Diesel +Fiat Punto Emotion 1.2,Fiat,2012,"1,69,500","37,200 kms",Diesel +Maruti Suzuki Swift RS VDI,Maruti,2010,"1,59,500","43,200 kms",Diesel +Toyota Etios GD,Toyota,2013,"2,75,000","24,800 kms",Petrol +Hyundai i20 Sportz 1.4 CRDI,Hyundai,2014,"3,70,000","60,000 kms",Diesel +Hyundai i10 Sportz 1.2,Hyundai,2010,"1,68,000","45,872 kms",Petrol +Chevrolet Beat LT Opt Diesel,Chevrolet,2011,"1,50,000","40,000 kms",Diesel +Chevrolet Beat LS Diesel,Chevrolet,2011,"1,45,000","45,000 kms",Diesel +Chevrolet Beat LT Diesel,Chevrolet,2012,"98,500","38,000 kms",Diesel +Mahindra Scorpio VLX 2WD BS IV,Mahindra,2014,"6,99,000","50,000 kms",Diesel +Tata Indigo CS,Tata,2011,"85,000","11,400 kms",Diesel +Toyota Corolla Altis 1.8 J,Toyota,2015,"5,75,000","42,000 kms",Petrol +Honda City 1.5 V MT,Honda,2014,"5,49,000","39,000 kms",Petrol +Maruti Suzuki Swift VDi,Maruti,2011,"2,09,000","47,000 kms",Diesel +Hyundai Eon Era Plus,Hyundai,2013,"1,85,000","27,000 kms",Petrol +Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel +Mahindra XUV500,Mahindra,2014,"6,99,999","52,000 kms",Diesel +Honda Brio,Honda,2012,"2,24,999","30,000 kms",Petrol +Ford Fiesta,Ford,2011,"2,74,999","55,000 kms",Diesel +Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel +Honda City,Honda,2015,"5,99,999","30,000 kms",Diesel +Maruti Suzuki Wagon R,Maruti,2012,"1,99,999","44,000 kms",Petrol +Honda City,Honda,2014,"5,44,999","45,000 kms",Diesel +Hyundai i20,Hyundai,2009,"1,99,000","31,000 kms",Petrol +Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,430 kms",Diesel +Hyundai Fluidic Verna 1.6 CRDi SX,Hyundai,2015,"5,40,000","38,000 kms",Diesel +"Commercial , DZire LDI, 2016, for sale",Commercial,...,Ask For Price,, +Mahindra Quanto C8,Mahindra,2013,"3,40,000","37,000 kms",Diesel +Fiat Petra ELX 1.2 PS,Fiat,2008,"75,000","65,000 kms",Petrol +Skoda Fabia 1.2L Diesel Ambiente,Skoda,2011,"1,59,500","38,200 kms",Diesel +Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol +Hyundai Santro Xing XS,Hyundai,2005,"49,000","7,500 kms",Petrol +Maruti Suzuki Ciaz VXi Plus,Maruti,2016,"7,00,000","3,350 kms",Petrol +Maruti Suzuki Zen VX,Maruti,2000,"55,000","60,000 kms",Petrol +Honda City,Honda,2015,"4,48,999","54,000 kms",Petrol +Hyundai Creta 1.6 SX Plus Petrol,Hyundai,2017,"8,95,000","32,000 kms",Petrol +"Tata indigo ecs LX, 201",Tata,150k,"1,50,000",, +Mahindra Scorpio SLX,Mahindra,2007,"3,55,000","75,000 kms",Diesel +Mahindra Scorpio SLE BS IV,Mahindra,2012,"5,65,000","62,000 kms",Diesel +Toyota Innova 2.5 G BS III 8 STR,Toyota,2006,"3,65,000","73,000 kms",Diesel +Maruti Suzuki Alto K10 VXi AMT,Maruti,2011,"1,45,000","41,000 kms",Petrol +Maruti Suzuki Wagon R LXI BS IV,Maruti,2011,"2,10,000","35,000 kms",Petrol +Tata Nano Cx BSIV,Tata,2013,"40,000","2,200 kms",Petrol +Maruti Suzuki Alto Std BS IV,Maruti,2013,"1,25,000","39,000 kms",Petrol +Maruti Suzuki Wagon R LXi BS III,Maruti,2009,"1,35,000","45,000 kms",Petrol +Maruti Suzuki Swift VXI BSIII,Maruti,2006,"1,35,000","45,000 kms",Petrol +Tata Sumo Victa EX 10 by 7 Str BSIII,Tata,2012,"2,85,000","65,000 kms",Diesel +MARUTI SUZUKI DESI,MARUTI,TOUR,"4,00,000",, +Maruti Suzuki Wagon R LXi BS III,Maruti,2010,"1,45,000","54,870 kms",Petrol +Maruti Suzuki Alto LXi BS III,Maruti,2010,"1,35,000","34,580 kms",Petrol +Volkswagen Passat Diesel Comfortline AT,Volkswagen,2009,"4,50,000","97,000 kms",Diesel +Renault Scala RxL Diesel Travelogue,Renault,2015,"3,75,000","25,000 kms",Diesel +Mahindra Quanto C8,Mahindra,2013,"3,75,000","20,000 kms",Diesel +Hyundai Grand i10 Sportz O 1.2 Kappa VTVT,Hyundai,2014,"3,65,000","20,000 kms",Petrol +Hyundai i20 Active 1.2 SX,Hyundai,2015,"5,00,000","18,000 kms",Petrol +Mahindra Xylo E4,Mahindra,2012,"4,00,000","35,000 kms",Diesel +Mahindra Jeep MM 550 XDB,Mahindra,2019,"3,90,000",60 kms,Diesel +Renault Duster 110PS Diesel RxZ,Renault,2012,"5,01,000","35,000 kms",Diesel +Mahindra Bolero SLE BS IV,Mahindra,2013,"3,30,000","80,200 kms",Diesel +Force Motors Force One LX ABS 7 STR,Force,2015,"5,80,000","3,200 kms",Diesel +Maruti Suzuki SX4,Maruti,2012,"2,65,000","46,000 kms",Diesel +Mahindra Jeep CL550 MDI,Mahindra,2019,"3,79,000","0,000 kms",Diesel +Maruti Suzuki Alto 800,Maruti,2015,"2,19,000","5,000 kms",Petrol +Mahindra Jeep CL550 MDI,Mahindra,2018,"3,85,000",588 kms,Diesel +Toyota Etios,Toyota,2011,"2,75,000","36,000 kms",Diesel +Volkswagen Polo,Volkswagen,2015,"3,30,000","38,000 kms",Diesel +Honda City ZX VTEC,Honda,2008,"1,10,000","45,000 kms",Petrol +Maruti Suzuki Wagon R LX BS III,Maruti,2006,"80,000","71,200 kms",Petrol +Honda City VX O MT Diesel,Honda,2016,"5,19,000","52,000 kms",Diesel +Mahindra Thar CRDe 4x4 AC,Mahindra,2016,"7,30,000","29,000 kms",Diesel +Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"14,75,000","47,000 kms",Diesel +Audi A4 1.8 TFSI Multitronic Premium Plus,Audi,2009,"6,99,000","47,000 kms",Petrol +Mercedes Benz GLA Class 200 CDI Sport,Mercedes,2015,"20,00,000","20,000 kms",Diesel +Land Rover Freelander 2 SE,Land,2015,"21,00,000","30,000 kms",Diesel +Renault Kwid RXT,Renault,2017,"3,40,000","5,000 kms",Petrol +Tata Aria Pleasure 4X2,Tata,2014,"3,90,000","35,000 kms",Diesel +Mercedes Benz B Class B180 Sports,Mercedes,2014,"14,00,000","31,000 kms",Petrol +Datsun GO T O,Datsun,2016,"2,45,000","7,000 kms",Petrol +Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,430 kms",Diesel +Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,400 kms",Diesel +Honda Jazz VX MT,Honda,2016,"4,50,000","41,000 kms",Petrol +Honda Amaze 1.2 S i VTEC,Honda,2014,"3,11,000","33,000 kms",Petrol +Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel +Honda City,Honda,2012,"3,99,999","45,000 kms",Petrol +Honda City,Honda,2015,"5,99,999","39,000 kms",Diesel +Honda Amaze,Honda,2015,"3,44,999","22,000 kms",Petrol +Audi A4 1.8 TFSI Multitronic Premium Plus,Audi,2009,"6,99,000","47,000 kms",Petrol +Force Motors Force One LX ABS 7 STR,Force,2015,"5,80,000","3,200 kms",Diesel +Mahindra Scorpio S4,Mahindra,2015,"8,55,000","30,000 kms",Diesel +Hyundai i20 Active 1.4L SX O,Hyundai,2015,"5,35,000","37,000 kms",Diesel +Mini Cooper S,Mini,2013,"18,91,111","13,000 kms",Petrol +Maruti Suzuki Ciaz ZXI Plus,Maruti,2017,"6,99,000","14,000 kms",Petrol +Chevrolet Tavera Neo,Chevrolet,2013,"3,75,000","55,000 kms",Diesel +Honda Amaze,Honda,2013,"2,84,999","46,000 kms",Diesel +Hyundai Eon Sportz,Hyundai,2012,"1,78,000","30,000 kms",Petrol +Tata Sumo Gold Select Variant,Tata,2013,"3,00,000","50,000 kms",Diesel +Maruti Suzuki Wagon R 1.0,Maruti,2003,"90,000","45,000 kms",Petrol +Maruti Suzuki Esteem VXi BS III,Maruti,2006,"95,000","45,000 kms",Petrol +Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,55,000","9,300 kms",Petrol +Chevrolet Enjoy 1.4 LS 8 STR,Chevrolet,2013,"2,45,000","55,000 kms",Diesel +Hyundai i20 Asta 1.4 CRDI 6 Speed,Hyundai,2012,"3,29,500","36,200 kms",Diesel +Ford Figo Diesel EXI,Ford,2014,"1,95,000","50,000 kms",Diesel +Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,51,111","55,000 kms",Petrol +Maruti Suzuki Ertiga ZXi,Maruti,2014,"5,69,999","45,000 kms",Petrol +Maruti Suzuki Esteem LXi BS III,Maruti,2007,"69,999","51,000 kms",Petrol +Maruti Suzuki Ritz VXI,Maruti,2014,"2,99,999","19,000 kms",Petrol +Maruti Suzuki Dzire,Maruti,2009,"2,20,000","46,000 kms",Petrol +Maruti Suzuki Ritz LDi,Maruti,2013,"3,99,999","33,000 kms",Diesel +Maruti Suzuki SX4 ZXI MT,Maruti,2010,"2,49,999","36,000 kms",Petrol +Maruti Suzuki Wagon R 1.0 VXi,Maruti,2015,"2,89,999","22,000 kms",Petrol +Mini Cooper S 1.6,Mini,2013,"18,91,111","13,500 kms",Petrol +Nissan Terrano XL D Plus,Nissan,2015,"4,99,999","60,000 kms",Diesel +Renault Duster 85 PS RxE Diesel,Renault,2013,"4,89,999","27,000 kms",Diesel +Renault Duster 85 PS RxE Diesel,Renault,2014,"4,89,999","59,000 kms",Diesel +Renault Duster 85 PS RxL Diesel,Renault,2015,"5,49,999","19,000 kms",Diesel +Maruti Suzuki Dzire ZXI,Maruti,2013,"3,80,000","30,000 kms",Petrol +Renault Kwid RXT Opt,Renault,2018,"3,25,000","15,000 kms",Petrol +Maruti Suzuki Maruti 800 Std,Maruti,2003,"57,000","56,758 kms",Petrol +Renault Kwid 1.0 RXT AMT,Renault,2018,"3,49,999","10,000 kms",Petrol +Renault Lodgy 85 PS RXL,Renault,2018,"6,89,999","20,000 kms",Diesel +Renault Scala RxL Diesel,Renault,2014,"3,49,999","49,000 kms",Diesel +Hyundai Grand i10 Asta 1.2 Kappa VTVT O,Hyundai,2014,"4,10,000","41,000 kms",Petrol +Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2011,"2,25,000","45,000 kms",Petrol +Chevrolet Beat LS Petrol,Chevrolet,2010,"1,20,000","43,000 kms",Petrol +Tata Indigo eCS LX TDI BS III,Tata,2016,"3,20,000","1,75,430 kms",Diesel +Hyundai Santro Xing XO eRLX Euro III,Hyundai,2000,"59,000","56,450 kms",Petrol +Hyundai Fluidic Verna 1.6 CRDi SX,Hyundai,2015,"5,40,000","38,000 kms",Diesel +"Commercial , DZire LDI, 2016, for sale",Commercial,...,Ask For Price,, +Chevrolet Beat LS Petrol,Chevrolet,2010,"80,000","56,000 kms",Petrol +Mahindra Quanto C8,Mahindra,2013,"3,40,000","37,000 kms",Diesel +Fiat Petra ELX 1.2 PS,Fiat,2008,"75,000","65,000 kms",Petrol +Chevrolet Beat LS Petrol,Chevrolet,2015,"2,20,000","32,700 kms",Petrol +Skoda Fabia 1.2L Diesel Ambiente,Skoda,2011,"1,59,500","38,200 kms",Diesel +Ford EcoSport Titanium 1.5L TDCi,Ford,2016,"5,99,000","30,000 kms",Diesel +Hyundai Accent GLX,Hyundai,2006,"80,000","56,000 kms",Petrol +Yama,Yamaha,r 15,"55,000",, +Maruti Suzuki Swift LDi,Maruti,2010,Ask For Price,"52,000 kms",Diesel +Mahindra TUV300 T4 Plus,Mahindra,2016,"6,75,000","9,000 kms",Diesel +Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol +Mini Cooper S 1.6,Mini,2013,"18,91,111","13,000 kms",Petrol +Tata Indica V2 Xeta e GLE,Tata,2008,"1,50,000","11,000 kms",Petrol +Mini Cooper S,Mini,2013,"18,91,111","13,000 kms",Petrol +Tata Indigo CS LS DiCOR,Tata,2009,"72,500","46,000 kms",Diesel +Maruti Suzuki Swift VXi 1.2 ABS BS IV,Maruti,2019,"6,10,000",73 kms,Petrol +Mahindra Scorpio VLX Special Edition BS III,Mahindra,2004,"2,30,000","1,60,000 kms",Diesel +Tata Indica eV2 LS,Tata,2017,Ask For Price,"84,000 kms",Diesel +Honda Accord,Honda,2009,"1,75,000","58,559 kms",Petrol +Mahindra Scorpio S4,Mahindra,2015,"8,55,000","30,000 kms",Diesel +Chevrolet Tavera Neo,Chevrolet,2013,"3,75,000","55,000 kms",Diesel +Ford EcoSport Titanium 1.5 TDCi,Ford,2014,"5,20,000","57,000 kms",Diesel +Maruti Suzuki Ertiga,Maruti,2015,"5,24,999","50,000 kms",Diesel +Honda Amaze,Honda,2014,"2,99,999","37,000 kms",Petrol +Maruti Suzuki Dzire,Maruti,2012,"2,99,999","40,000 kms",Petrol +Honda City,Honda,2011,"2,84,999","55,000 kms",Petrol +Mahindra Scorpio 2.6 CRDe,Mahindra,2007,"2,20,000","1,70,000 kms",Diesel +Maruti Suzuki Dzire,Maruti,2014,"4,24,999","55,000 kms",Diesel +Honda City,Honda,2015,"6,44,999","39,000 kms",Petrol +Honda Mobilio,Honda,2014,"3,99,999","44,000 kms",Petrol +Toyota Corolla Altis,Toyota,2009,"1,99,999","65,000 kms",Petrol +Honda City,Honda,2014,"5,84,999","39,000 kms",Petrol +Skoda Laura,Skoda,2012,"3,49,999","44,000 kms",Diesel +Renault Duster,Renault,2015,"4,49,999","49,000 kms",Diesel +Maruti Suzuki Ertiga,Maruti,2018,"7,99,999","9,000 kms",Diesel +Maruti Suzuki Dzire,Maruti,2015,"4,44,999","45,000 kms",Diesel +Mahindra XUV500,Mahindra,2014,"6,49,999","47,000 kms",Diesel +Hyundai Verna Fluidic,Hyundai,2012,"4,44,999","40,000 kms",Diesel +Maruti Suzuki Vitara Brezza,Maruti,2016,"6,89,999","29,000 kms",Diesel +Maruti Suzuki Wagon R,Maruti,2016,"3,44,999","15,000 kms",Petrol +Mahindra Scorpio,Mahindra,2015,"9,44,999","45,000 kms",Diesel +Honda Amaze,Honda,2014,"2,74,999","35,000 kms",Petrol +Mahindra XUV500,Mahindra,2013,"6,89,999","80,000 kms",Diesel +Mahindra Scorpio,Mahindra,2013,"5,74,999","68,000 kms",Diesel +Skoda Laura,Skoda,2013,"3,74,999","50,000 kms",Diesel +Volkswagen Polo,Volkswagen,2010,"1,99,999","60,000 kms",Diesel +Hyundai Elite i20,Hyundai,2016,"5,49,999","9,000 kms",Petrol +Tata Manza Aura Quadrajet,Tata,2012,"1,30,000","72,000 kms",Diesel +Chevrolet Sail UVA Petrol LT ABS,Chevrolet,2013,"2,10,000","60,000 kms",Petrol +Renault Duster 110 PS RxZ Diesel Plus,Renault,2012,"5,01,000","38,000 kms",Diesel +Hyundai Verna Fluidic 1.6 VTVT SX,Hyundai,2013,"4,01,000","45,000 kms",Diesel +Audi A4 2.0 TDI 177bhp Premium,Audi,2012,"13,50,000","40,000 kms",Diesel +Hyundai Elantra SX,Hyundai,2013,"6,00,000","20,000 kms",Petrol +Mahindra Scorpio VLX 4WD Airbag,Mahindra,2013,"6,10,000","35,000 kms",Diesel +Mahindra KUV100 K8 D 6 STR,Mahindra,2016,"4,00,000","20,000 kms",Diesel +Renault Scala RxL Diesel Travelogue,Renault,2015,"3,75,000","25,000 kms",Diesel +Mahindra Quanto C8,Mahindra,2013,"3,75,000","20,000 kms",Diesel +Hyundai Grand i10 Sportz O 1.2 Kappa VTVT,Hyundai,2014,"3,65,000","20,000 kms",Petrol +Hyundai i20 Active 1.2 SX,Hyundai,2015,"5,00,000","18,000 kms",Petrol +Mahindra Xylo E4,Mahindra,2012,"4,00,000","35,000 kms",Diesel +Hyundai Grand i10,Hyundai,2017,"5,24,999","6,821 kms",Petrol +Hyundai i20,Hyundai,2014,"4,49,999","23,000 kms",Petrol +Hyundai Eon,Hyundai,2014,"1,74,999","14,000 kms",Petrol +Hyundai i10,Hyundai,2012,"2,44,999","38,000 kms",Petrol +Hyundai i20 Active,Hyundai,2015,"5,74,999","35,000 kms",Diesel +Datsun Redi GO,Datsun,2017,"2,44,999","22,000 kms",Petrol +Toyota Etios Liva,Toyota,2011,"2,39,999","41,000 kms",Petrol +Hyundai Accent,Hyundai,2010,"99,999","45,000 kms",Petrol +Hyundai Verna,Hyundai,2014,"4,89,999","44,000 kms",Diesel +Maruti Suzuki Swift,Maruti,2013,"3,24,999","45,000 kms",Diesel +Toyota Fortuner,Toyota,2011,"10,74,999","52,000 kms",Diesel +Hyundai i10 Sportz,Hyundai,2012,"2,30,000","34,000 kms",Petrol +Mahindra Bolero Power Plus SLE,Mahindra,2018,"6,99,000","1,800 kms",Diesel +selling car Ta,selling,Zest,Ask For Price,, +Mahindra XUV500,Mahindra,2015,"10,00,000","15,000 kms",Diesel +Honda City 1.5 V MT Exclusive,Honda,2010,"2,40,000","4,00,000 kms",Petrol +Chevrolet Spark LT 1.0 Airbag,Chevrolet,2009,"1,10,000","44,000 kms",Petrol +Mahindra Jeep MM 550 XDB,Mahindra,2019,"3,90,000",60 kms,Diesel +Renault Duster 110PS Diesel RxZ,Renault,2012,"5,01,000","35,000 kms",Diesel +Mahindra XUV500,Mahindra,2016,"11,30,000","72,000 kms",Diesel +Tata Indigo eCS VX CR4 BS IV,Tata,2014,"2,50,000","40,000 kms",Diesel +Tata Zest 90,Tata,/-Rs,Ask For Price,, +Mahindra Bolero SLE BS IV,Mahindra,2013,"3,30,000","80,200 kms",Diesel +Force Motors Force One LX ABS 7 STR,Force,2015,"5,80,000","3,200 kms",Diesel +Skoda Rapid Elegance 1.6 TDI CR MT,Skoda,2013,"3,40,000","48,000 kms",Diesel +Tata Vista Quadrajet VX,Tata,2011,"1,20,000","90,000 kms",Diesel +Maruti Suzuki Alto K10 VXi AT,Maruti,2015,"2,65,000","12,000 kms",Petrol +Maruti Suzuki SX4,Maruti,2012,"2,65,000","46,000 kms",Diesel +Maruti Suzuki Zen LXi BS III,Maruti,2003,"85,000","69,900 kms",Petrol +Mahindra Jeep CL550 MDI,Mahindra,2019,"3,79,000","0,000 kms",Diesel +Hyundai i10 Magna 1.2,Hyundai,2011,"1,75,000","45,000 kms",Petrol +Maruti Suzuki Alto 800,Maruti,2015,"2,19,000","5,000 kms",Petrol +Maruti Suzuki Swift Dzire Tour LDi,Maruti,2016,"3,50,000","1,66,000 kms",Diesel +Honda City ZX EXi,Honda,2008,"1,49,000","42,000 kms",Petrol +Mahindra Jeep CL550 MDI,Mahindra,2018,"3,85,000",588 kms,Diesel +Mahindra Jeep MM 550 XDB,Mahindra,2006,"4,25,000",122 kms,Diesel +Chevrolet Beat Diesel,Chevrolet,2017,"1,50,000","62,000 kms",Diesel +Honda City 1.5 S MT,Honda,2010,"2,25,000","70,000 kms",Petrol +Maruti Suzuki Swift Dzire car,Maruti,sale,"3,00,000",, +Hyundai Verna 1.4 VTVT,Hyundai,2014,"3,75,000","36,000 kms",Petrol +Toyota Innova 2.5 E MS 7 STR BS IV,Toyota,2012,"7,70,000",0 kms,Diesel +Maruti Suzuki Alto 800 Lxi,Maruti,2018,Ask For Price,"24,000 kms",Petrol +Maruti Suzuki Maruti 800 Std – Befo,Maruti,1995,"30,000","55,000 kms",Petrol +Toyota Etios,Toyota,2011,"2,75,000","36,000 kms",Diesel +Volkswagen Polo,Volkswagen,2015,"3,30,000","38,000 kms",Diesel +Maruti Suzuki Swift,Maruti,2014,"3,35,000","55,000 kms",Diesel +Hyundai Elite i20 Asta 1.4 CRDI,Hyundai,2015,"4,50,000","20,000 kms",Diesel +Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2012,"2,25,000","40,000 kms",Petrol +Maruti Suzuki Swift Dzire Tour (Gat,Maruti,ara),"3,00,000",, +Maruti Suzuki Versa DX2 8 SEATER BSIII,Maruti,2004,"80,000","50,000 kms",Petrol +Tata Indigo LX TDI BS III,Tata,2016,"1,30,000","1,04,000 kms",Diesel +Volkswagen Vento Konekt Diesel Highline,Volkswagen,2011,"2,45,000","65,000 kms",Diesel +Mercedes Benz C Class 200 CDI Classic,Mercedes,2002,"3,99,000","41,000 kms",Petrol +Maruti Suzuki Ertiga VDi,Maruti,2013,"4,50,000","90,000 kms",Diesel +URJE,URJENT,SELL,"1,80,000",, +Honda City,Honda,2000,"65,000","80,000 kms",Petrol +Hyundai Santro Xing GLS,Hyundai,2006,"75,000","46,000 kms",Petrol +Maruti Suzuki Omni Limited Edition,Maruti,2001,"70,000","70,000 kms",Petrol +Hyundai Sonata Transform 2.4 GDi MT,Hyundai,2017,"1,90,000","36,469 kms",Diesel +Hyundai Elite i20 Sportz 1.2,Hyundai,2018,"6,00,000","7,800 kms",Petrol +Volkswagen Vento Konekt Diesel Highline,Volkswagen,2011,"2,45,000","65,000 kms",Diesel +Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,40,000","60,000 kms",Petrol +Maruti Suzuki Alto LXi BS III,Maruti,2011,"1,55,000","32,000 kms",Petrol +Honda Jazz S MT,Honda,2009,"1,69,999","24,695 kms",Petrol +Hyundai Grand i10 Sportz 1.2 Kappa VTVT,Hyundai,2017,"4,50,000","15,141 kms",Petrol +Maruti Suzuki Zen LXi BSII,Maruti,2001,"40,000","40,000 kms",Petrol +Mahindra Scorpio W Turbo 2.6DX 9 Seater,Mahindra,2012,"1,65,000","65,000 kms",Diesel +Swift Dzire Tour 27 Dec 2016 Regis,Swift,tion,"3,70,000",, +Maruti Suzuki Alto K10 VXi,Maruti,2014,"2,70,000","22,000 kms",Petrol +Hyundai Grand i10 Asta 1.2 Kappa VTVT,Hyundai,2016,"2,80,000","59,910 kms",Diesel +Mahindra XUV500 W8,Mahindra,2012,"5,60,000","1,00,000 kms",Diesel +Hyundai Creta 1.6 SX Plus Petrol,Hyundai,2016,"9,50,000","25,000 kms",Petrol +Hyundai i20 Magna O 1.2,Hyundai,2013,"3,10,000","35,000 kms",Petrol +Renault Duster 85 PS RxL Explore LE,Renault,2015,"7,15,000","65,000 kms",Diesel +Hyundai Grand i10 Sportz 1.2 Kappa VTVT,Hyundai,2014,"3,40,000","35,000 kms",Petrol +Honda Brio V MT,Honda,2012,"2,35,000","33,000 kms",Petrol +Mahindra TUV300 T4 Plus,Mahindra,2017,"6,10,000","68,000 kms",Diesel +Chevrolet Spark LS 1.0,Chevrolet,2010,"95,000","23,000 kms",Petrol +Mahindra TUV300 T8,Mahindra,2018,"10,00,000","4,500 kms",Diesel +Maruti Suzuki Swift Dzire Tour LDi,Maruti,2015,"2,20,000","1,29,000 kms",Diesel +Nissan X Trail Select Variant,Nissan,2019,"12,00,000",300 kms,Diesel +Maruti Suzuki Alto 800 Vxi,Maruti,2015,"2,30,000","5,000 kms",Petrol +Ford Ikon 1.3 CLXi NXt Finesse,Ford,2001,"45,000","65,000 kms",Petrol +Toyota Fortuner 3.0 4x4 MT,Toyota,2010,"9,40,000","1,31,000 kms",Diesel +Tata Manza ELAN Quadrajet,Tata,2010,"1,55,555","1,11,111 kms",Petrol +Tata zest x,Tata,odel,"3,20,000",, +Mahindra xyl,Mahindra,2 bs,"3,50,000",, +Mercedes Benz A Class A 180 Sport Petrol,Mercedes,2013,"15,00,000","14,000 kms",Petrol +Chevrolet Beat LS Diesel,Chevrolet,2016,"2,10,000","22,000 kms",Diesel +Ford EcoSport Trend 1.5L TDCi,Ford,2013,"4,95,000","38,000 kms",Diesel +Tata Indigo LS,Tata,2016,"1,25,000","70,000 kms",Diesel +Hyundai i20 Magna 1.2,Hyundai,2010,"1,95,000","36,000 kms",Petrol +Volkswagen Vento Highline Plus 1.5 Diesel AT,Volkswagen,2015,"5,50,000","34,000 kms",Diesel +Renault Kwid RXT,Renault,2015,"2,70,000","43,000 kms",Petrol +Used Commercial Maruti Omn,Used,arry,"1,50,000",, +Ford EcoSport Titanium 1.5L TDCi,Ford,2014,"5,00,000","40,000 kms",Diesel +Honda Amaze 1.5 E i DTEC,Honda,2016,"2,40,000","1,60,000 kms",Diesel +Hyundai Verna 1.6 EX VTVT,Hyundai,2017,"8,00,000","12,000 kms",Petrol +BMW 5 Series 520d Sedan,BMW,2011,"12,99,000","49,000 kms",Diesel +Skoda Superb 1.8 TFSI AT,Skoda,2011,"5,30,000","68,000 kms",Petrol +Audi Q3 2.0 TDI quattro Premium,Audi,2013,"14,99,000","37,000 kms",Diesel +Mahindra Bolero DI BSII,Mahindra,2012,"2,20,000","59,466 kms",Diesel +Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2011,Ask For Price,"16,000 kms",Petrol +Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel +Ford Figo Duratorq Diesel Titanium 1.4,Ford,2012,"2,50,000","99,000 kms",Diesel +Maruti Suzuki Wagon R VXI BS IV,Maruti,2018,"3,95,000","25,500 kms",Petrol +Mahindra Logan Diesel 1.5 DLS,Mahindra,2009,"1,30,000","66,000 kms",Petrol +Tata Nano GenX XMA,Tata,2010,"32,000","44,005 kms",Petrol +Mahindra TUV300 T4 Plus,Mahindra,2016,"5,40,000","35,000 kms",Diesel +Mahindra TUV300 T4 Plus,Mahindra,2016,"5,40,000","35,000 kms",Diesel +Hyundai Elite i20 Magna 1.2,Hyundai,2015,"4,05,000","28,000 kms",Petrol +Hyundai Elite i20 Magna 1.2,Hyundai,2015,"4,00,000","30,000 kms",Petrol +Honda City SV,Honda,2017,"7,60,000","4,000 kms",Petrol +Maruti Suzuki Baleno Delta 1.2,Maruti,2016,"5,00,000","28,000 kms",Petrol +Ford Figo Petrol LXI,Ford,2011,"1,75,000","75,000 kms",Petrol +Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel +Honda City,Honda,2017,"7,50,000","3,000 kms",Petrol +Hyundai Elite i20 Magna 1.2,Hyundai,2015,"4,19,000","20,000 kms",Petrol +Maruti Suzuki Versa DX2 8 SEATER BSIII,Maruti,2004,"90,000","50,000 kms",Petrol +Hyundai Eon Era Plus,Hyundai,2018,"1,40,000","2,110 kms",Petrol +Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"15,40,000","43,222 kms",Petrol +Hyundai i10 Magna 1.2 Kappa2,Hyundai,2008,"2,75,000","1,00,200 kms",Petrol +Toyota Corolla H2,Toyota,2003,"1,50,000","1,00,000 kms",Petrol +Maruti Suzuki Swift Dzire Tour VXi,Maruti,2011,"2,30,000",65 kms,Petrol +Tata Indigo CS eLS BS IV,Tata,2015,"1,23,000","1,00,000 kms",Diesel +Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel +Mahindra Scorpio S10,Mahindra,2015,"9,00,000","97,200 kms",Diesel +Hyundai Xcent Base 1.1 CRDi,Hyundai,2016,"3,00,000","1,40,000 kms",Diesel +Honda City,Honda,2015,"4,99,999","55,000 kms",Petrol +Hyundai Accent Executive Edition,Hyundai,2009,"1,65,000","48,000 kms",Petrol +Maruti Suzuki Baleno Delta 1.2,Maruti,2016,"4,98,000","22,000 kms",Petrol +Tata Zest XE 75 PS Diesel,Tata,2018,"4,80,000","1,03,553 kms",Diesel +Maruti Suzuki Dzire LDI,Maruti,2017,"4,88,000","80,000 kms",Diesel +Tata Sumo Gold LX BS IV,Tata,2014,"2,50,000","99,000 kms",Diesel +Toyota Corolla Altis GL Petrol,Toyota,2010,"2,20,000","58,000 kms",Petrol +Maruti Suzuki Eeco 7 STR,Maruti,2013,"2,90,000","70,000 kms",LPG +Toyota Fortuner 3.0 4x2 MT,Toyota,2015,"15,25,000","1,20,000 kms",Diesel +Mahindra XUV500 W6,Mahindra,2013,"5,48,900","49,800 kms",Diesel +Tata Tigor Revotron XZ,Tata,2019,"6,50,000",100 kms,Diesel +Maruti Suzuki 800,Maruti,2001,"55,000","81,876 kms",Petrol +Maruti Suzuki Ertiga Vxi,Maruti,2015,"5,50,000","75,000 kms",Petrol +Maruti Suzuki Versa DX2 8 SEATER BSIII,Maruti,2004,"90,000","50,000 kms",Petrol +Honda Mobilio S i DTEC,Honda,2014,"3,99,000","44,000 kms",Diesel +Maruti Suzuki Ertiga,Maruti,2016,"7,30,000","55,000 kms",Diesel +Maruti Suzuki Vitara Brezza,Maruti,2017,"7,25,000","36,000 kms",Diesel +Hyundai Verna 1.6 CRDI E,Hyundai,2016,"1,95,000","56,000 kms",Diesel +Maruti Suzuki Swift VXI BSIII,Maruti,2007,"1,30,000","62,000 kms",Petrol +Toyota Fortuner 3.0 4x2 MT,Toyota,2015,"15,25,000","1,20,000 kms",Diesel +Maruti Suzuki Omni Select Variant,Maruti,2014,"1,90,000","6,020 kms",Petrol +Honda Amaze,Honda,2013,"2,50,000","55,700 kms",Diesel +Tata Indica,Tata,2005,"80,000","42,000 kms",Petrol +Hyundai Santro Xing,Hyundai,2003,"1,20,000","50,000 kms",Petrol +Maruti Suzuki Zen Estilo,Maruti,2010,"1,49,000","35,000 kms",Petrol +Maruti Suzuki Wagon R LXI BS IV,Maruti,2014,"2,50,000","18,500 kms",Petrol +Maruti Suzuki Wagon R,Maruti,2007,"1,20,000","7,000 kms",Petrol +Honda Brio VX AT,Honda,2017,"4,50,000","11,000 kms",Petrol +Hyundai Xcent Base 1.1 CRDi,Hyundai,2015,Ask For Price,"1,80,000 kms",Diesel +Maruti Suzuki Zen LXi BSII,Maruti,2003,"99,999","53,000 kms",Petrol +Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2008,"1,35,000","23,000 kms",Petrol +Maruti Suzuki Wagon R Select Variant,Maruti,2016,"2,25,000","35,500 kms",Diesel +Maruti Suzuki Alto LXi BS III,Maruti,2010,"99,000","22,134 kms",Petrol +Renault Kwid RXT,Renault,2019,"3,70,000","1,000 kms",Petrol +Tata Nano Lx BSIV,Tata,2010,"52,000","9,000 kms",Petrol +Jaguar XE XE Portfolio,Jaguar,2016,"28,00,000","8,500 kms",Petrol +Hyundai Xcent S 1.2,Hyundai,2015,Ask For Price,"35,000 kms",Petrol +Hyundai Eon Magna Plus,Hyundai,2014,"1,90,000","35,000 kms",Petrol +Honda City 1.5 S MT,Honda,2014,"4,99,000","22,000 kms",Petrol +Hindustan Motors Ambassador,Hindustan,2002,"90,000","25,000 kms",Diesel +Maruti Suzuki Ritz GENUS VXI,Maruti,2010,"1,49,000","40,000 kms",Petrol +Hyundai Grand i10 Magna AT 1.2 Kappa VTVT,Hyundai,2017,"4,00,000","20,000 kms",Petrol +Hyundai Eon D Lite Plus,Hyundai,2016,"1,20,000","87,000 kms",Petrol +Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2015,"2,50,000","55,000 kms",Petrol +Maruti Suzuki Wagon R VXI BS IV,Maruti,2017,"3,75,000","23,000 kms",Petrol +Honda Amaze 1.2 VX i VTEC,Honda,2014,"3,81,000","6,000 kms",Petrol +Maruti Suzuki Estilo VXi ABS BS IV,Maruti,2013,"1,80,000","65,000 kms",Petrol +Maruti Suzuki Vitara Brezza LDi O,Maruti,2016,"5,80,000","25,000 kms",Diesel +Maruti Suzuki Eeco 5 STR WITH AC HTR,Maruti,2015,"2,78,000","39,000 kms",Petrol +Toyota Innova 2.0 V,Toyota,2009,Ask For Price,"15,574 kms",Diesel +Hyundai Creta 1.6 SX Plus Petrol AT,Hyundai,2016,"10,00,000","8,000 kms",Petrol +Mahindra Scorpio Vlx BSIV,Mahindra,2013,"6,90,000","75,000 kms",Diesel +Maruti Suzuki Ertiga VDi,Maruti,2012,"4,80,000","51,000 kms",Diesel +Mitsubishi Lancer 1.8 LXi,Mitsubishi,2006,"85,000","50,000 kms",Petrol +Maruti Suzuki Maruti 800 AC,Maruti,2001,"40,000","75,000 kms",Petrol +Maruti Suzuki Alto 800 LXI CNG O,Maruti,2015,"90,000","55,800 kms",Petrol +Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2015,"3,40,000","53,000 kms",Petrol +Hyundai Eon D Lite Plus,Hyundai,2018,"2,60,000","25,000 kms",Petrol +Ford Fiesta SXi 1.6 ABS,Ford,2009,"2,50,000","56,400 kms",Petrol +Maruti Suzuki Ritz VDi,Maruti,2010,"1,80,000","72,160 kms",Diesel +Hyundai Verna Fluidic New,Hyundai,2012,"3,50,000","10,000 kms",Diesel +Maruti Suzuki Wagon R LXi BS III,Maruti,2006,"90,001","48,000 kms",Petrol +Maruti Suzuki Estilo LX BS IV,Maruti,2007,"1,15,000","36,000 kms",Petrol +Audi A6 2.0 TDI Premium,Audi,2012,"15,99,000","11,500 kms",Diesel +Maruti Suzuki Wagon R LXi BS III,Maruti,2003,"1,30,000","1,33,000 kms",Petrol +Maruti Suzuki Wagon R,Maruti,2009,"1,59,000","27,000 kms",Petrol +Maruti Suzuki Wagon R,Maruti,2009,"1,60,000","35,000 kms",Petrol +Maruti Suzuki Alto,Maruti,2010,"1,10,000","55,000 kms",Petrol +Maruti Suzuki Baleno Sigma 1.2,Maruti,2016,"4,25,000","40,000 kms",Petrol +Hyundai Verna 1.6 SX VTVT AT,Hyundai,2019,"9,00,000","2,000 kms",Petrol +Maruti Suzuki Swift GLAM,Maruti,2009,"1,50,000","45,000 kms",Petrol +Hyundai Getz Prime 1.3 GVS,Hyundai,2009,"1,10,000","20,000 kms",Petrol +Hyundai Santro,Hyundai,2000,"51,999","88,000 kms",Petrol +Hyundai Getz Prime 1.3 GLX,Hyundai,2009,"1,15,000","20,000 kms",Petrol +Chevrolet Beat PS Diesel,Chevrolet,2012,"2,15,000","65,422 kms",Diesel +Ford EcoSport Trend 1.5 Ti VCT,Ford,2017,"5,80,000","10,000 kms",Petrol +Maruti Suzuki Dzire ZXI,Maruti,2013,"3,80,000","35,000 kms",Petrol +Hyundai Fluidic Verna 1.6 CRDi SX,Hyundai,2013,"3,50,000","1,17,000 kms",Diesel +Tata Indica V2 DLG,Tata,2005,"35,000","1,50,000 kms",Diesel +BMW X1 xDrive20d xLine,BMW,2011,"11,50,000","72,000 kms",Diesel +Hyundai i20 Asta 1.2,Hyundai,2010,"3,00,000","10,750 kms",Petrol +Honda City 1.5 V AT,Honda,2009,"2,69,000","55,000 kms",Petrol +Tata Nano,Tata,2013,"60,000","6,800 kms",Petrol +Chevrolet Cruze LTZ AT,Chevrolet,2014,"4,00,000","41,000 kms",Diesel +Hyundai Verna Fluidic New,Hyundai,2015,"4,30,000","73,000 kms",Diesel +Hyun,Hyundai,Eon,Ask For Price,, +Maruti Suzuki Swift Dzire VDi,Maruti,2011,"1,40,000","65,000 kms",Diesel +Mahindra XUV500 W6,Mahindra,2014,"85,00,003","45,000 kms",Diesel +Mahindra XUV500 W10,Mahindra,2018,"12,99,000","40,000 kms",Diesel +Maruti Suzuki Alto K10 LXi CNG,Maruti,2014,"1,99,000","37,000 kms",Petrol +Hyundai Accent GLE,Hyundai,2006,"90,000","55,000 kms",Petrol +Force Motors One SUV,Force,2013,"5,50,000","1,40,000 kms",Diesel +Datsun Go Plus T O,Datsun,2016,Ask For Price,5 kms,Petrol +Maruti Suzuki Alto,Maruti,2019,"2,65,000","9,800 kms",Petrol +Chevrolet Spark 1.0 LT,Chevrolet,2011,"1,00,000","27,000 kms",Petrol +Hyundai i10,Hyundai,2009,"2,15,000","27,000 kms",Petrol +Toyota Etios Liva GD,Toyota,2012,"3,80,000","20,000 kms",Diesel +Renault Duster 85PS Diesel RxL Optional with Nav,Renault,2013,"4,01,919","57,923 kms",Diesel +Chevrolet Enjoy,Chevrolet,2014,"4,90,000","30,201 kms",Diesel +Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,80,000","6,200 kms",Petrol +BMW 5 Series 530i,BMW,2009,"6,50,000","37,518 kms",Petrol +Toyota Etios Liva G,Toyota,2014,"1,60,000","24,652 kms",Petrol +Mahindra Jeep MM 550 XDB,Mahindra,2004,"4,24,000",383 kms,Diesel +Chevrolet Beat LS Diesel,Chevrolet,2016,"2,25,000","95,000 kms",Diesel +Chevrolet Cruze LTZ,Chevrolet,2011,"3,50,000","35,000 kms",Diesel +Jeep Wrangler Unlimited 4x4 Diesel,Jeep,2015,"9,50,000","3,528 kms",Diesel +Maruti Suzuki Ertiga VDi,Maruti,2013,"4,85,000","52,500 kms",Diesel +Hyundai Verna VGT CRDi SX ABS,Hyundai,2010,"2,05,000","47,900 kms",Diesel +Maruti Suzuki Omni,Maruti,2012,"1,60,000","14,000 kms",Petrol +Maruti Suzuki Celerio VDi,Maruti,2018,"3,10,000","37,000 kms",Petrol +Tata Zest Quadrajet 1.3,Tata,2017,"1,80,000","90,000 kms",Diesel +Mahindra XUV500 W6,Mahindra,2013,"5,49,900","52,800 kms",Diesel +Tata Indigo CS eLX BS IV,Tata,2016,"1,50,000","1,04,000 kms",Diesel +Hyundai i10 Era,Hyundai,2011,"1,75,000","30,000 kms",Petrol +Tata Indigo eCS LX TDI BS III,Tata,2014,"95,000","1,95,000 kms",Diesel +Tata Indigo LX TDI BS III,Tata,2016,"2,30,000","1,04,000 kms",Diesel +Tata Indigo eCS LX CR4 BS IV,Tata,2016,"2,30,000","1,04,000 kms",Diesel +Tata Indigo Marina LS,Tata,2004,"1,80,000","70,000 kms",Diesel +Commercial Chevrolet Sail Hatchback ca,Commercial,o...,"2,25,000",, +Hyundai Xcent SX 1.2,Hyundai,2015,"4,00,000","43,000 kms",Diesel +Hyundai Eon Magna Plus,Hyundai,2013,"1,85,000","23,000 kms",Petrol +Renault Duster 85 PS RxL Diesel,Renault,2015,"3,85,000","51,000 kms",Diesel +Maruti Suzuki Alto K10 LXi CNG,Maruti,2009,"90,000","62,000 kms",Petrol +Tata Nano LX Special Edition,Tata,2010,"32,000","48,008 kms",Petrol +Commercial Car Ta,Commercial,Zest,"3,71,500",, +Renault Duster 110 PS RxZ Diesel,Renault,2013,"4,35,000","39,000 kms",Diesel +Maruti Suzuki Wagon R AX BSIV,Maruti,2010,"2,25,000","40,000 kms",Petrol +Maruti Suzuki Swift,Maruti,2006,"1,89,700","48,247 kms",Petrol +Maruti Suzuki Ertiga,Maruti,2012,"3,89,700","39,000 kms",Diesel +Maruti Suzuki Swift VXi 1.2 ABS BS IV,Maruti,2014,"3,65,000","23,000 kms",Petrol +Maruti Suzuki Alto K10 New,Maruti,2017,"3,60,000","9,400 kms",Petrol +Hyundai i20 Magna,Hyundai,2010,"2,10,000","50,000 kms",Petrol +Hyundai i10 Magna 1.2,Hyundai,2009,"1,70,000","75,000 kms",Petrol +tata Indica,tata,sale,"1,30,000",, +Tata Zest XE 75 PS Diesel,Tata,2017,"3,80,000","70,000 kms",Diesel +Mahindra Xylo E8,Mahindra,2009,"2,95,000","64,000 kms",Diesel +Toyota Corolla Altis GL Petrol,Toyota,2010,"1,85,000","55,000 kms",Petrol +Tata Manza Aqua Quadrajet,Tata,2014,"1,60,000","2,00,000 kms",Diesel +Mahindra KUV100 K8 D 6 STR,Mahindra,2018,Ask For Price,"7,500 kms",Diesel +Used bt new conditions ta,Used,Zest,"2,55,000",, +Renault Kwid 1.0,Renault,2018,"2,90,000","2,137 kms",Petrol +Sale tata,Sale,ture,"1,00,000",, +Tata Venture EX 8 STR,Tata,2013,"1,00,000","30,000 kms",Diesel +Maruti Suzuki Swift Dzire Tour LXi,Maruti,2014,"3,15,000","44,000 kms",Petrol +Maruti Suzuki Alto LX BSII,Maruti,2002,Ask For Price,"56,000 kms",Petrol +Skoda Octavia Classic 1.9 TDI MT,Skoda,2006,"1,14,990","65,000 kms",Diesel +Maruti Suzuki Omni LPG BS IV,Maruti,2012,"1,20,000","1,60,000 kms",LPG +Chevrolet Beat Diesel,Chevrolet,2011,"1,25,000","56,000 kms",Diesel +Tata Sumo Gold EX BS IV,Tata,2012,"2,10,000","75,000 kms",Diesel +Tata indigo 2017 top model..,Tata,emi,"1,70,000",, +Hyundai Verna 1.6 CRDI SX,Hyundai,2018,"8,55,000","42,000 kms",Diesel +Tata Sumo Gold EX BS IV,Tata,2012,"2,10,000","75,000 kms",Diesel +Mahindra Scorpio 2.6 CRDe,Mahindra,2007,"2,60,000","56,000 kms",Diesel +Maruti Suzuki Zen LXi BS III,Maruti,2002,"95,000","10,544 kms",Petrol +Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2011,"2,55,000","64,000 kms",Petrol +Mahindra Scorpio SLX 2.6 Turbo 8 Str,Mahindra,2008,"3,00,000","70,000 kms",Diesel +Hyundai Grand i10 Sportz 1.2 Kappa VTVT,Hyundai,2014,"3,40,000","25,000 kms",Petrol +Hyundai Elite i20 Sportz 1.2,Hyundai,2017,"5,50,000","15,000 kms",Petrol +Ford Ikon 1.6 Nxt,Ford,2003,"60,000","50,000 kms",Petrol +Hyundai Elite i20 Sportz 1.2,Hyundai,2015,Ask For Price,"49,500 kms",Petrol +Tata indigo,Tata,car,"1,50,000",, +Toyota Innova 2.5 V 7 STR,Toyota,2011,"7,50,000","1,47,000 kms",Diesel +Nissan Sunny XL,Nissan,2011,"2,30,000","52,000 kms",Petrol +Chevrolet Beat LT Diesel,Chevrolet,2012,"1,30,000","90,001 kms",Diesel +Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,70,000","21,000 kms",Petrol +Maruti Suzuki Swift VDi BS IV,Maruti,2012,"2,80,000","48,006 kms",Diesel +Maruti Suzuki Swift VDi BS IV,Maruti,2012,"2,80,000","48,006 kms",Diesel +Maruti Suzuki Swift,Maruti,2012,"2,80,000","48,006 kms",Diesel +very good condition tata bolts are av,very,able,"2,00,000",, +Toyota Innova 2.0 G4,Toyota,2012,"6,00,000","80,000 kms",Diesel +Sale Hyundai xcent commerc,Sale,no.,Ask For Price,, +Maruti Suzuki Swift VDi ABS,Maruti,2010,"1,90,000","74,000 kms",Diesel +Hyundai Elite i20 Asta 1.2,Hyundai,2015,"5,00,000","22,000 kms",Petrol +Mahindra XUV500 W10,Mahindra,2016,"10,65,000","41,000 kms",Diesel +Volkswagen Polo Trendline 1.5L D,Volkswagen,2015,"3,50,000","25,000 kms",Diesel +Toyota Etios Liva Diesel,Toyota,2012,"3,50,000","85,000 kms",Diesel +Mahindra TUV300 T4 Plus,Mahindra,2016,"5,40,000","29,500 kms",Diesel +Hyundai Elite i20 Asta 1.2,Hyundai,2015,"4,70,000","30,000 kms",Petrol +Hyundai Santro Xing GLS,Hyundai,2014,"1,79,000","57,000 kms",Petrol +Maruti Suzuki Zen LXi BS III,Maruti,2003,"48,000","60,000 kms",Petrol +Maruti Suzuki Ciaz ZXi Plus RS,Maruti,2016,"6,50,000","50,000 kms",Petrol +Hyundai Eon Era Plus,Hyundai,2013,"1,90,000","39,700 kms",Petrol +Hyundai Elantra 1.8 S,Hyundai,2012,"5,00,000","65,000 kms",Petrol +Maruti Suzuki Swift VDi,Maruti,2010,"2,70,000","67,000 kms",Diesel +Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2008,"1,25,000","46,000 kms",Petrol +Hyundai Eon Era Plus,Hyundai,2012,"1,88,000","38,000 kms",Petrol +Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2016,"3,80,000","27,000 kms",Petrol +Hyundai Verna Fluidic New,Hyundai,2011,"3,65,000","43,000 kms",Diesel +Ford EcoSport Trend 1.5L Ti VCT,Ford,2014,"4,65,000","47,000 kms",Petrol +Hyundai i20 Magna,Hyundai,2011,"2,40,000","42,000 kms",Petrol +Chevrolet Beat Diesel,Chevrolet,2016,"1,79,999","19,336 kms",Diesel +Tata Indica eV2 LS,Tata,2015,"1,40,000","60,105 kms",Diesel +Jaguar XF 2.2 Diesel Luxury,Jaguar,2013,"21,90,000","29,000 kms",Diesel +Audi Q5 2.0 TDI quattro Premium Plus,Audi,2014,"23,90,000","34,000 kms",Diesel +BMW 3 Series 320d Sedan,BMW,2011,"10,75,000","35,000 kms",Diesel +Maruti Suzuki Swift ZXi 1.2 BS IV,Maruti,2015,"4,75,000","22,000 kms",Petrol +BMW X1 sDrive20d,BMW,2012,"10,25,000","41,000 kms",Diesel +Maruti Suzuki S Cross Sigma 1.3,Maruti,2016,"6,15,000","21,000 kms",Diesel +Maruti Suzuki Ertiga LDi,Maruti,2013,"4,75,000","48,000 kms",Diesel +Maruti Suzuki Alto K10 VXi AMT,Maruti,2016,"2,70,000","38,000 kms",Petrol +Honda City SV,Honda,2014,"4,75,000","34,000 kms",Diesel +Volkswagen Vento Comfortline Petrol,Volkswagen,2011,"2,40,000","45,933 kms",Petrol +Honda City 1.5 EXi New,Honda,2005,"1,20,000","68,000 kms",Petrol +Audi A4 2.0 TDI 177bhp Premium,Audi,2016,"19,00,000","44,000 kms",Diesel +Mahindra KUV100,Mahindra,2017,"3,60,000","35,000 kms",Diesel +Tata Zest XE 75 PS Diesel,Tata,2018,"4,50,000","1,02,563 kms",Diesel +Mahindra XUV500 W8,Mahindra,2015,"9,00,000","28,600 kms",Diesel +Maruti Suzuki Swift Dzire Tour VDi,Maruti,2017,"6,50,000","41,800 kms",Diesel +Tata Sumo Gold LX BS IV,Tata,2014,"2,75,000","1,16,000 kms",Diesel +Maruti Suzuki Swift Dzire VXi 1.2 BS IV,Maruti,2009,"2,10,000","59,000 kms",Petrol +Mahindra Scorpio 2.6 SLX,Mahindra,2004,"1,75,000","58,000 kms",Diesel +Maruti Suzuki Omni 8 STR BS III,Maruti,2009,"85,000","45,000 kms",Petrol +Mitsubishi Pajero Sport Limited Edition,Mitsubishi,2015,"14,90,000","42,590 kms",Diesel +Renault Duster,Renault,2014,"8,00,000","7,400 kms",Diesel +Volkswagen Jetta Comfortline 1.9 TDI AT,Volkswagen,2009,"4,50,000","54,500 kms",Diesel +Maruti Suzuki Ertiga Vxi,Maruti,2012,"10,00,000","2,00,000 kms",Diesel +Audi A4 2.0 TDI 177bhp Premium,Audi,2013,"15,10,000","27,000 kms",Diesel +Volvo S80 Summum D4,Volvo,2015,"18,50,000","42,000 kms",Diesel +Toyota Corolla Altis VL AT Petrol,Toyota,2014,"7,90,000","29,000 kms",Petrol +Mitsubishi Pajero Sport 2.5 AT,Mitsubishi,2015,"17,25,000","37,000 kms",Diesel +Chevrolet Beat LT Petrol,Chevrolet,2012,"1,35,000","36,000 kms",Petrol +BMW X1,BMW,2011,"10,00,000","34,000 kms",Diesel +Datsun Redi GO S,Datsun,2018,"2,99,999","7,000 kms",Petrol +Mercedes Benz C Class C 220 CDI Avantgarde,Mercedes,2009,"12,25,000","76,000 kms",Diesel +Mahindra Scorpio SLX,Mahindra,2004,"1,75,000","60,000 kms",Diesel +Volkswagen Vento Comfortline Diesel,Volkswagen,2011,"2,00,000","95,000 kms",Diesel +Tata Indigo CS GLS,Tata,2017,"2,70,000","50,000 kms",Diesel +Ford Figo Petrol Titanium,Ford,2019,"5,25,000",00 kms,Petrol +Honda City ZX GXi,Honda,2006,"1,80,000","50,000 kms",Petrol +Maruti Suzuki Wagon R Duo Lxi,Maruti,2008,"1,40,000","68,000 kms",Petrol +Ford EcoSport Trend 1.5L TDCi,Ford,2014,"4,00,000","16,000 kms",Petrol +Maruti Suzuki Swift Dzire VDi,Maruti,2016,"4,99,000","51,000 kms",Diesel +Maruti Suzuki Omni 8 STR BS III,Maruti,2009,"85,000","56,000 kms",Petrol +Maruti Suzuki Zen LX BSII,Maruti,2004,"70,000","1,00,000 kms",Petrol +Renault Duster RxL Petrol,Renault,2015,"5,50,000","36,000 kms",Petrol +Maruti Suzuki Swift VXi 1.2 BS IV,Maruti,2014,"3,70,000","11,523 kms",Petrol +Maruti Suzuki Baleno Zeta 1.2,Maruti,2018,"6,90,000","1,000 kms",Petrol +Honda WR V S MT Petrol,Honda,2009,"2,50,000","60,000 kms",Petrol +Tata Indigo CS eLX BS IV,Tata,2016,"1,10,000","85,000 kms",Diesel +Renault Duster 110 PS RxL Diesel,Renault,2013,"4,90,000","38,600 kms",Diesel +Mahindra Scorpio LX BS III,Mahindra,2009,"3,20,000","95,500 kms",Diesel +Maruti Suzuki Zen LXi BS III,Maruti,2004,"68,000","56,000 kms",Petrol +Maruti Suzuki Wagon R LXi BS III,Maruti,2014,"1,30,000","37,458 kms",Petrol +Maruti Suzuki SX4 Celebration Diesel,Maruti,2016,"9,70,000","85,960 kms",Diesel +Audi A3 Cabriolet 40 TFSI,Audi,2015,"31,00,000","12,516 kms",Petrol +Hyundai Eon D Lite Plus,Hyundai,2018,"2,80,000","35,000 kms",Petrol +Maruti Suzuki Zen Estilo LXI Green CNG,Maruti,2009,"1,25,000",00 kms,Petrol +Mahindra Scorpio SLX,Mahindra,2008,"2,85,000","80,000 kms",Diesel +I want to sell my commercial car due t,I,o...,"4,75,000",, +Hyundai Santro AE GLS Audio,Hyundai,2011,"1,65,000","45,000 kms",Petrol +i want sale my car.no emi....uber atta,i,d...,"3,20,000",, +Maruti Suzuki Swift Dzire Tour VDi,Maruti,2009,"2,50,000","51,000 kms",Diesel +Mahindra Scorpio S4,Mahindra,2015,"8,65,000","30,000 kms",Diesel +Tata ZEST 6 month old,Tata,car,"3,70,000",, +Mahindra Xylo D2 BS IV,Mahindra,2011,"3,90,000","48,000 kms",Diesel +Hyundai Santro,Hyundai,2003,"60,000","51,000 kms",Petrol +Chevrolet Beat LT Diesel,Chevrolet,2015,"2,15,000","90,000 kms",Diesel +Maruti Suzuki Swift Dzire VDi,Maruti,2015,"4,75,000","43,000 kms",Diesel +Mahindra XUV500 W8,Mahindra,2015,"8,99,000","53,000 kms",Diesel +Toyota Fortuner 3.0 4x4 MT,Toyota,2013,"14,99,000","97,000 kms",Diesel +Maruti Suzuki Alto K10 VXi,Maruti,2013,"2,40,000","20,000 kms",Petrol +Hyundai Getz GLE,Hyundai,2007,"99,000","55,000 kms",Petrol +Maruti Suzuki Swift Dzire Tour LDi,Maruti,2014,"2,60,000","1,20,000 kms",Diesel +Hyundai Creta 1.6 SX,Hyundai,2019,"12,00,000",0 kms,Petrol +Hyundai Santro Xing XL AT eRLX Euro III,Hyundai,2007,"1,15,000","46,000 kms",Petrol +Hyundai Santro Xing XL eRLX Euro III,Hyundai,2009,"88,000","43,200 kms",Petrol +Mahindra Xylo D2 BS IV,Mahindra,2011,"3,90,000","56,000 kms",Diesel +Hyundai Santro Xing XL eRLX Euro III,Hyundai,2007,"1,35,000","42,000 kms",Petrol +Tata Indica V2 DLS BS III,Tata,2009,"90,000","30,600 kms",Diesel +Hyundai i10 Sportz 1.2,Hyundai,2011,"2,20,000","38,000 kms",Petrol +Hyundai Grand i10 Magna 1.2 Kappa VTVT,Hyundai,2017,"4,24,999","2,550 kms",Petrol +Hyundai Santro Xing XL AT eRLX Euro III,Hyundai,2007,"1,35,000","47,000 kms",Petrol +Honda City 1.5 E MT,Honda,2005,"95,000","41,000 kms",Petrol +Nissan Micra XL,Nissan,2017,"4,30,000","62,500 kms",Diesel +Honda City 1.5 S Inspire,Honda,2005,"1,15,000","68,000 kms",Petrol +Maruti Suzuki Alto 800 Lxi,Maruti,2015,"2,15,000","50,000 kms",Petrol +Maruti Suzuki Wagon R LX BS III,Maruti,2004,"53,000","69,000 kms",Petrol +Maruti Suzuki Ertiga VDi,Maruti,2012,"5,00,000","48,000 kms",Diesel +Tata Indica eV2 eXeta eGLX,Tata,2012,"85,000","55,000 kms",Diesel +Maruti Suzuki Omni E 8 STR BS IV,Maruti,2013,"1,65,000","25,000 kms",Petrol +Hyundai Eon Era Plus,Hyundai,2014,"2,00,000","28,400 kms",Petrol +Hyundai Eon,Hyundai,2014,"2,00,000","28,000 kms",Petrol +Maruti Suzuki Swift LDi,Maruti,2015,"4,25,000","42,000 kms",Diesel +MARUTI SUZUKI ERTIGA F,MARUTI,SALE,"6,50,000",, +Hyundai Verna 1.6 CRDI SX Plus AT,Hyundai,2012,"6,00,000","29,000 kms",Diesel +Chevrolet Tavera LS B3 10 Seats BSII,Chevrolet,2005,"1,30,000","68,485 kms",Diesel +Tata Tiago Revotron XM,Tata,2018,"4,30,000","3,500 kms",Petrol +Tata Tiago Revotorq XZ,Tata,2019,"5,68,500",0 kms,Petrol +Tata Nexon,Tata,2019,Ask For Price,0 kms,Petrol +Tata,Tata,digo,Ask For Price,, +Maruti Suzuki Zen LXi BS III,Maruti,2006,"71,000","32,000 kms",Petrol +Mahindra KUV100 K8 D 6 STR,Mahindra,2018,"5,60,000","8,000 kms",Diesel +Ford EcoSport Titanium 1.5 TDCi,Ford,2014,"5,90,000","34,000 kms",Diesel +Hindustan Motors Ambassador Classic Mark 4 – Befo,Hindustan,1995,"7,50,000","37,000 kms",Petrol +Ford Fusion 1.4 TDCi Diesel,Ford,2007,"1,25,000","85,455 kms",Diesel +Hyundai Santro Xing XL AT eRLX Euro III,Hyundai,2007,"1,35,000","46,000 kms",Petrol +Hyundai Santro,Hyundai,2002,"60,000","47,000 kms",Petrol +Fiat Linea Emotion 1.4 L T Jet Petrol,Fiat,2009,"1,20,000","64,000 kms",Petrol +Ford Ikon 1.3 Flair Josh 100,Ford,2008,"95,000","46,000 kms",Petrol +Maruti Suzuki Omni E 8 STR BS IV,Maruti,2017,"2,40,000","8,000 kms",Petrol +Tata Indica V2 LS,Tata,2012,"1,15,000","64,000 kms",Diesel +Mahindra Scorpio S4,Mahindra,2015,"7,95,000","63,000 kms",Diesel +Hyundai Santro Xing XL eRLX Euro III,Hyundai,2007,"55,000","65,000 kms",Petrol +Mahindra Xylo D2,Mahindra,2009,"3,00,000","62,000 kms",Diesel +Hyundai Grand i10 Asta 1.2 Kappa VTVT,Hyundai,2014,"3,20,000","41,000 kms",Petrol +Maruti Suzuki Alto 800 Lxi,Maruti,2015,"2,65,000","14,000 kms",Petrol +Toyota Corolla,Toyota,2006,"1,60,000","40,000 kms",Petrol +Hyundai Eon Magna,Hyundai,2017,"3,00,000","1,600 kms",Petrol +Tata Sumo Grande MKII GX,Tata,2010,"1,30,000","90,000 kms",Diesel +Maruti Suzuki Swift VDi,Maruti,2011,"2,50,000","58,000 kms",Diesel +Volkswagen Polo Highline1.2L P,Volkswagen,2013,"3,80,000","27,000 kms",Petrol +Maruti Suzuki Alto 800 Lx,Maruti,2003,"42,000","60,000 kms",Petrol +Tata Tiago Revotron XZ,Tata,2017,"4,00,000","31,000 kms",Petrol +Maruti Suzuki Swift LDi,Maruti,2009,"1,20,000","90,000 kms",Diesel +Maruti Suzuki Swift VDi,Maruti,2009,"1,20,000","90,000 kms",Diesel +Tata Indigo eCS,Tata,2016,"1,30,000","1,50,000 kms",Diesel +Chevrolet Beat LS Diesel,Chevrolet,2014,"1,89,000","31,000 kms",Diesel +2012 Tata Sumo Gold f,2012,sell,"2,50,000",, +Mahindra Xylo E8 BS IV,Mahindra,2011,"3,65,000","43,000 kms",Diesel +Hyundai Eon D Lite Plus,Hyundai,2013,"1,70,000","20,000 kms",Petrol +Well mentained Tata Sumo,Well,d Ex,"3,80,000",, +all paper updated tata indica v2 and u,all,n...,"1,45,000",, +Maruti Ertiga showroom condition with,Maruti,e...,"4,80,000",, +7 SEATER MAHINDRA BOLERO IN VERY GOOD,7,D...,Ask For Price,, +9 SEATER MAHINDRA BOL,9,", Ac",Ask For Price,, +scratch less Tata I,scratch,go .,"1,40,000",, +Maruti Suzuki swift dzire for sale in,Maruti,d...,"3,60,000",, +Commercial Chevrolet beat for sale in,Commercial,k...,"1,80,000",, +urgent sell my Mahindra qu,urgent,o c4,"3,50,000",, +Tata Sumo Gold FX BSIII,Tata,2013,"2,15,000","1,00,000 kms",Petrol +sell my car Maruti Suzuki Swif,sell,zire,"3,00,000",, +Maruti Suzuki Swift Dzire good car fo,Maruti,o...,"3,10,000",, +Hyunda,Hyundai,cent,Ask For Price,, +Commercial Maruti Suzuki Alto Lxi 800,Commercial,...,Ask For Price,, +urgent sale Ta,urgent,Sumo,"2,20,000",, +Maruti Suzuki Alto vxi t,Maruti,cab,"95,000",, +tata,tata,t xe,Ask For Price,, +TATA INDI,TATA,EV2,"1,10,000",, +Tata Nano,Tata,2013,"60,000","7,000 kms",Petrol +Hyundai Elite i20,Hyundai,2017,"5,99,999","31,000 kms",Petrol +Hyundai i10 Magna 1.2 Kappa2,Hyundai,2009,"4,00,000","33,000 kms",Petrol +Hyundai Creta,Hyundai,2016,"9,00,000","60,000 kms",Diesel +Volkswagen Polo,Volkswagen,2013,"2,99,999","48,000 kms",Diesel +Maruti Suzuki Dzire,Maruti,2014,"3,74,999","33,000 kms",Petrol +Tata Bolt XM Petrol,Tata,2015,"6,00,000","15,000 kms",Petrol +Maruti Suzuki Alto 800 Lx,Maruti,2005,"70,000","47,000 kms",Petrol +Maruti Suzuki Alto,Maruti,2005,"1,00,000","40,000 kms",Petrol +Hyundai Venue,Hyundai,2019,Ask For Price,"7,000 kms",Diesel +Maruti Suzuki Ritz,Maruti,2010,"1,50,000","38,000 kms",Diesel +Maruti Suzuki Alto 800 Lxi,Maruti,2017,"2,25,000","12,500 kms",Petrol +Maruti Suzuki Dzire,Maruti,2009,"2,10,000","42,000 kms",Petrol +Renault Lodgy,Renault,2016,Ask For Price,"20,000 kms",Diesel +Hyundai i20 Asta,Hyundai,2014,"4,25,000","31,000 kms",Petrol +Maruti Suzuki Swift Select Variant,Maruti,2008,"1,62,000","60,000 kms",Diesel +Tata Indica V2 DLX BS III,Tata,2005,"60,000","80,000 kms",Diesel +Mahindra Scorpio VLX 2.2 mHawk Airbag BSIV,Mahindra,2014,"6,50,000","77,000 kms",Diesel +Toyota Innova 2.5 E 8 STR,Toyota,2012,"7,50,000","75,000 kms",Diesel +Mahindra Xylo E8,Mahindra,2010,"3,75,000","40,000 kms",Diesel +Hyundai i20 Magna 1.2,Hyundai,2011,"2,30,000","47,000 kms",Petrol +Maruti Suzuki Omni,Maruti,2000,"35,999","60,000 kms",Petrol +Mahindra KUV100,Mahindra,2016,"3,80,000","26,500 kms",Petrol +Mahindra KUV100 K8 6 STR,Mahindra,2019,"5,60,000","2,875 kms",Petrol +Datsun Go Plus,Datsun,2016,"2,85,000","13,900 kms",Petrol +Ford Endeavor 4x4 Thunder Plus,Ford,2019,"29,00,000","9,000 kms",Diesel +Tata Indica V2,Tata,2005,"39,999","80,000 kms",Diesel +Hyundai Santro Xing GL,Hyundai,2006,"85,000","60,000 kms",Petrol +Maruti Suzuki Wagon R 1.0 VXi,Maruti,2016,"3,95,000","20,000 kms",Petrol +Maruti Suzuki Swift Select Variant,Maruti,2008,"1,75,000","58,000 kms",Diesel +Maruti Suzuki Alto 800 Lx,Maruti,2019,"4,00,000","1,500 kms",Petrol +Toyota Innova 2.5 Z Diesel 7 Seater,Toyota,2011,"7,50,000","75,000 kms",Diesel +Any type car avaiabel hare...comercica,Any,r...,"1,70,000",, +Maruti Suzuki Alto 800,Maruti,2016,"2,50,000","2,450 kms",Petrol +Maruti Suzuki Alto AX,Maruti,2019,"4,25,000","1,625 kms",Petrol +Maruti Suzuki Alto 800 Lx,Maruti,2019,Ask For Price,"1,500 kms",Petrol +Volkswagen Polo Highline1.2L P,Volkswagen,2017,"5,25,000","45,000 kms",Petrol +Mahindra Logan,Mahindra,2009,"1,30,000","65,000 kms",Diesel +Maruti Suzuki 800 Std BS III,Maruti,2000,"30,000","33,400 kms",Petrol +Mahindra Scorpio,Mahindra,2011,"4,75,000","60,123 kms",Diesel +Chevrolet Sail 1.2 LS,Chevrolet,2013,"3,00,000","28,000 kms",Petrol +Volkswagen Vento Highline Plus 1.5 Diesel,Volkswagen,2015,Ask For Price,"38,900 kms",Diesel +Hyundai Santro AE GLS Audio,Hyundai,2003,"60,000","70,000 kms",Petrol +Maruti Suzuki Wagon R VXi Minor,Maruti,2006,"1,00,000","7,000 kms",Petrol +Hyundai Eon,Hyundai,2018,"2,60,000","25,000 kms",Petrol +Tata Manza,Tata,2015,"1,00,000","1,00,000 kms",Diesel +Toyota Innova 2.0 G1 Petrol 8seater,Toyota,2019,Ask For Price,"4,000 kms",Petrol +Toyota Etios G,Toyota,2013,"2,65,000","42,000 kms",Petrol +Hyundai Getz Prime 1.3 GLX,Hyundai,2009,"1,15,000","20,000 kms",Petrol +Toyota Qualis,Toyota,2003,"1,80,000","1,00,000 kms",Diesel +Hyundai Santro Xing,Hyundai,2004,"45,000","1,37,495 kms",Petrol +Tata Indica eV2 LS,Tata,2016,"50,500","91,200 kms",Diesel +Honda City 1.5 S MT,Honda,2009,"2,70,000","55,000 kms",Petrol +Tata Zest XE 75 PS Diesel,Tata,2017,"2,90,000","1,20,000 kms",Diesel +Mahindra Quanto C4,Mahindra,2013,"3,25,000","63,000 kms",Diesel +Tata Indigo eCS LX CR4 BS IV,Tata,2016,"1,60,000","1,04,000 kms",Diesel +Maruti Suzuki Swift Dzire,Maruti,2016,"3,50,000","1,46,000 kms",Diesel +Hyundai Elite i20,Hyundai,2011,"2,90,000","40,000 kms",Petrol +Hyundai i20 Select Variant,Hyundai,2011,"2,90,000","40,000 kms",Petrol +Chevrolet Tavera Neo,Chevrolet,2007,"4,65,000","1,00,800 kms",Diesel +Maruti Suzuki Dzire,Maruti,2016,"3,25,000","1,50,000 kms",Diesel +Hyundai Elite i20,Hyundai,2018,"5,10,000","2,100 kms",Petrol +Honda City VX Petrol,Honda,2016,"8,60,000","95,000 kms",Petrol +Maruti Suzuki Dzire,Maruti,2016,"4,50,000","2,500 kms",Diesel +Hyundai Getz,Hyundai,2006,"1,25,000","80,000 kms",Petrol +Mercedes Benz C Class 200 K MT,Mercedes,2006,"5,00,001","15,000 kms",Petrol +Maruti Suzuki Alto LXi BS III,Maruti,2005,"95,000","65,000 kms",Petrol +Maruti Suzuki Swift Dzire Tour VDi,Maruti,2009,"2,50,000","51,000 kms",Diesel +Skoda Fabia,Skoda,2009,"1,10,000","45,000 kms",Petrol +Maruti Suzuki Alto 800 Select Variant,Maruti,2015,Ask For Price,"70,000 kms",Petrol +Maruti Suzuki Ritz VXI ABS,Maruti,2011,"2,70,000","50,000 kms",Petrol +tata zest 2017 f,tata,sale,"4,50,000",, +Tata Indica V2 DLE BS III,Tata,2009,"1,10,000","30,000 kms",Diesel +Toyota Corolla Altis,Toyota,2009,"3,00,000","1,32,000 kms",Petrol +Ta,Tara,zest,"3,10,000",, +Tata Zest XM Diesel,Tata,2018,"2,60,000","27,000 kms",Diesel +Mahindra Quanto C8,Mahindra,2013,"3,90,000","40,000 kms",Diesel +Honda Amaze 1.2 E i VTEC,Honda,2014,"1,80,000",Petrol, +Chevrolet Sail 1.2 LT ABS,Chevrolet,2014,"1,60,000",Petrol, \ No newline at end of file diff --git a/models/PreOwnedCarPrediction/model.py b/models/PreOwnedCarPrediction/model.py new file mode 100644 index 00000000..06eb417c --- /dev/null +++ b/models/PreOwnedCarPrediction/model.py @@ -0,0 +1,31 @@ +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LinearRegression +import joblib + +class CarPriceModel: + def __init__(self): + self.model = LinearRegression() + + def load_data(self, filepath): + data = pd.read_csv(filepath) + return data + + def preprocess_data(self, data): + # Perform preprocessing steps here + X = data.drop('price', axis=1) # Assuming 'price' is the target column + y = data['price'] + return train_test_split(X, y, test_size=0.2, random_state=42) + + def train(self, X_train, y_train): + self.model.fit(X_train, y_train) + + def save_model(self, model_path): + joblib.dump(self.model, model_path) + +if __name__ == "__main__": + car_model = CarPriceModel() + data = car_model.load_data('data/car_data.csv') # Example path + X_train, X_test, y_train, y_test = car_model.preprocess_data(data) + car_model.train(X_train, y_train) + car_model.save_model('saved_models/car_price_model.pkl') \ No newline at end of file diff --git a/models/PreOwnedCarPrediction/modelEvalution.py b/models/PreOwnedCarPrediction/modelEvalution.py new file mode 100644 index 00000000..aa55e970 --- /dev/null +++ b/models/PreOwnedCarPrediction/modelEvalution.py @@ -0,0 +1,21 @@ +import joblib +import pandas as pd +from sklearn.metrics import mean_squared_error, r2_score + +class ModelEvaluator: + def __init__(self, model_path): + self.model = joblib.load(model_path) + + def evaluate(self, X_test, y_test): + predictions = self.model.predict(X_test) + mse = mean_squared_error(y_test, predictions) + r2 = r2_score(y_test, predictions) + print("Mean Squared Error:", mse) + print("R^2 Score:", r2) + +if __name__ == "__main__": + data = pd.read_csv('data/car_data.csv') # Load your test data + X_test = data.drop('price', axis=1) # Adjust based on your dataset + y_test = data['price'] + evaluator = ModelEvaluator('saved_models/car_price_model.pkl') + evaluator.evaluate(X_test, y_test) \ No newline at end of file diff --git a/models/PreOwnedCarPrediction/notebooks/car_price_predictor.ipynb b/models/PreOwnedCarPrediction/notebooks/car_price_predictor.ipynb new file mode 100644 index 00000000..42ef73c4 --- /dev/null +++ b/models/PreOwnedCarPrediction/notebooks/car_price_predictor.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"l_ga3NT4ATXk"},"source":["# Car Price Predictor"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":1318,"status":"ok","timestamp":1708073867329,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"7YLJBi5zASXp"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import matplotlib as mpl\n","%matplotlib inline\n","mpl.style.use('ggplot')"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":694,"status":"ok","timestamp":1708073869573,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"njjlg8EwASXt"},"outputs":[],"source":["car=pd.read_csv('/content/drive/MyDrive/Data Sets/car.csv')"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1708073870976,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fp6N0MyRASXu","outputId":"d306fe5d-158d-4ec7-c4f3-bf635ef99c4d"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"car\",\n \"rows\": 892,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"samples\": [\n \"Maruti Suzuki Ritz GENUS VXI\",\n \"Toyota Innova 2.0 G4\",\n \"Hyundai Eon\"\n ],\n \"num_unique_values\": 525,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"URJENT\",\n \"7\",\n \"selling\"\n ],\n \"num_unique_values\": 48,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"2007\",\n \"2012\",\n \"n...\"\n ],\n \"num_unique_values\": 61,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Price\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"4,89,999\",\n \"2,39,999\",\n \"1,40,000\"\n ],\n \"num_unique_values\": 274,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"kms_driven\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"24,330 kms\",\n \"50,000 kms\",\n \"60,000 kms\"\n ],\n \"num_unique_values\": 258,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fuel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"Petrol\",\n \"Diesel\",\n \"LPG\"\n ],\n \"num_unique_values\": 3,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe","variable_name":"car"},"text/html":["\n","
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\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
namecompanyyearPricekms_drivenfuel_type
0Hyundai Santro Xing XO eRLX Euro IIIHyundai200780,00045,000 kmsPetrol
1Mahindra Jeep CL550 MDIMahindra20064,25,00040 kmsDiesel
2Maruti Suzuki Alto 800 VxiMaruti2018Ask For Price22,000 kmsPetrol
3Hyundai Grand i10 Magna 1.2 Kappa VTVTHyundai20143,25,00028,000 kmsPetrol
4Ford EcoSport Titanium 1.5L TDCiFord20145,75,00036,000 kmsDiesel
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\n"],"text/plain":[" name company year Price \\\n","0 Hyundai Santro Xing XO eRLX Euro III Hyundai 2007 80,000 \n","1 Mahindra Jeep CL550 MDI Mahindra 2006 4,25,000 \n","2 Maruti Suzuki Alto 800 Vxi Maruti 2018 Ask For Price \n","3 Hyundai Grand i10 Magna 1.2 Kappa VTVT Hyundai 2014 3,25,000 \n","4 Ford EcoSport Titanium 1.5L TDCi Ford 2014 5,75,000 \n","\n"," kms_driven fuel_type \n","0 45,000 kms Petrol \n","1 40 kms Diesel \n","2 22,000 kms Petrol \n","3 28,000 kms Petrol \n","4 36,000 kms Diesel "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["car.head()"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":583,"status":"ok","timestamp":1708073874288,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fHrl8CLxASXv","outputId":"5fd7196b-27f2-4d94-85de-36cbbc23d3dd"},"outputs":[{"data":{"text/plain":["(892, 6)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["car.shape"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1708073875018,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"-JgokO5SASXv","outputId":"c7e171f9-fdff-4e79-a3ae-e335a9507609"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 892 entries, 0 to 891\n","Data columns (total 6 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 name 892 non-null object\n"," 1 company 892 non-null object\n"," 2 year 892 non-null object\n"," 3 Price 892 non-null object\n"," 4 kms_driven 840 non-null object\n"," 5 fuel_type 837 non-null object\n","dtypes: object(6)\n","memory usage: 41.9+ KB\n"]}],"source":["car.info()"]},{"cell_type":"markdown","metadata":{"id":"Fm8hcXzjASXv"},"source":["##### Creating backup copy"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":461,"status":"ok","timestamp":1708073877771,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"GBBqPchyASXx"},"outputs":[],"source":["backup=car.copy()"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":502,"status":"ok","timestamp":1708073879568,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"QeJ2TzR4CG0v","outputId":"ed1f6f1b-0c71-4737-cc95-a7edd60c18ff"},"outputs":[{"data":{"text/plain":["array(['2007', '2006', '2018', '2014', '2015', '2012', '2013', '2016',\n"," '2010', '2017', '2008', '2011', '2019', '2009', '2005', '2000',\n"," '...', '150k', 'TOUR', '2003', 'r 15', '2004', 'Zest', '/-Rs',\n"," 'sale', '1995', 'ara)', '2002', 'SELL', '2001', 'tion', 'odel',\n"," '2 bs', 'arry', 'Eon', 'o...', 'ture', 'emi', 'car', 'able', 'no.',\n"," 'd...', 'SALE', 'digo', 'sell', 'd Ex', 'n...', 'e...', 'D...',\n"," ', Ac', 'go .', 'k...', 'o c4', 'zire', 'cent', 'Sumo', 'cab',\n"," 't xe', 'EV2', 'r...', 'zest'], dtype=object)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["car['year'].unique()"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1708073880921,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"DxXGLpdYCWxz","outputId":"94f1b6d0-bd9c-4119-cadd-f3f893996ce3"},"outputs":[{"data":{"text/plain":["array(['80,000', '4,25,000', 'Ask For Price', '3,25,000', '5,75,000',\n"," '1,75,000', '1,90,000', '8,30,000', '2,50,000', '1,82,000',\n"," '3,15,000', '4,15,000', '3,20,000', '10,00,000', '5,00,000',\n"," '3,50,000', '1,60,000', '3,10,000', '75,000', '1,00,000',\n"," '2,90,000', '95,000', '1,80,000', '3,85,000', '1,05,000',\n"," '6,50,000', '6,89,999', '4,48,000', '5,49,000', '5,01,000',\n"," '4,89,999', '2,80,000', '3,49,999', '2,84,999', '3,45,000',\n"," '4,99,999', '2,35,000', '2,49,999', '14,75,000', '3,95,000',\n"," '2,20,000', '1,70,000', '85,000', '2,00,000', '5,70,000',\n"," '1,10,000', '4,48,999', '18,91,111', '1,59,500', '3,44,999',\n"," '4,49,999', '8,65,000', '6,99,000', '3,75,000', '2,24,999',\n"," '12,00,000', '1,95,000', '3,51,000', '2,40,000', '90,000',\n"," '1,55,000', '6,00,000', '1,89,500', '2,10,000', '3,90,000',\n"," '1,35,000', '16,00,000', '7,01,000', '2,65,000', '5,25,000',\n"," '3,72,000', '6,35,000', '5,50,000', '4,85,000', '3,29,500',\n"," '2,51,111', '5,69,999', '69,999', '2,99,999', '3,99,999',\n"," '4,50,000', '2,70,000', '1,58,400', '1,79,000', '1,25,000',\n"," '2,99,000', '1,50,000', '2,75,000', '2,85,000', '3,40,000',\n"," '70,000', '2,89,999', '8,49,999', '7,49,999', '2,74,999',\n"," '9,84,999', '5,99,999', '2,44,999', '4,74,999', '2,45,000',\n"," '1,69,500', '3,70,000', '1,68,000', '1,45,000', '98,500',\n"," '2,09,000', '1,85,000', '9,00,000', '6,99,999', '1,99,999',\n"," '5,44,999', '1,99,000', '5,40,000', '49,000', '7,00,000', '55,000',\n"," '8,95,000', '3,55,000', '5,65,000', '3,65,000', '40,000',\n"," '4,00,000', '3,30,000', '5,80,000', '3,79,000', '2,19,000',\n"," '5,19,000', '7,30,000', '20,00,000', '21,00,000', '14,00,000',\n"," '3,11,000', '8,55,000', '5,35,000', '1,78,000', '3,00,000',\n"," '2,55,000', '5,49,999', '3,80,000', '57,000', '4,10,000',\n"," '2,25,000', '1,20,000', '59,000', '5,99,000', '6,75,000', '72,500',\n"," '6,10,000', '2,30,000', '5,20,000', '5,24,999', '4,24,999',\n"," '6,44,999', '5,84,999', '7,99,999', '4,44,999', '6,49,999',\n"," '9,44,999', '5,74,999', '3,74,999', '1,30,000', '4,01,000',\n"," '13,50,000', '1,74,999', '2,39,999', '99,999', '3,24,999',\n"," '10,74,999', '11,30,000', '1,49,000', '7,70,000', '30,000',\n"," '3,35,000', '3,99,000', '65,000', '1,69,999', '1,65,000',\n"," '5,60,000', '9,50,000', '7,15,000', '45,000', '9,40,000',\n"," '1,55,555', '15,00,000', '4,95,000', '8,00,000', '12,99,000',\n"," '5,30,000', '14,99,000', '32,000', '4,05,000', '7,60,000',\n"," '7,50,000', '4,19,000', '1,40,000', '15,40,000', '1,23,000',\n"," '4,98,000', '4,80,000', '4,88,000', '15,25,000', '5,48,900',\n"," '7,25,000', '99,000', '52,000', '28,00,000', '4,99,000',\n"," '3,81,000', '2,78,000', '6,90,000', '2,60,000', '90,001',\n"," '1,15,000', '15,99,000', '1,59,000', '51,999', '2,15,000',\n"," '35,000', '11,50,000', '2,69,000', '60,000', '4,30,000',\n"," '85,00,003', '4,01,919', '4,90,000', '4,24,000', '2,05,000',\n"," '5,49,900', '3,71,500', '4,35,000', '1,89,700', '3,89,700',\n"," '3,60,000', '2,95,000', '1,14,990', '10,65,000', '4,70,000',\n"," '48,000', '1,88,000', '4,65,000', '1,79,999', '21,90,000',\n"," '23,90,000', '10,75,000', '4,75,000', '10,25,000', '6,15,000',\n"," '19,00,000', '14,90,000', '15,10,000', '18,50,000', '7,90,000',\n"," '17,25,000', '12,25,000', '68,000', '9,70,000', '31,00,000',\n"," '8,99,000', '88,000', '53,000', '5,68,500', '71,000', '5,90,000',\n"," '7,95,000', '42,000', '1,89,000', '1,62,000', '35,999',\n"," '29,00,000', '39,999', '50,500', '5,10,000', '8,60,000',\n"," '5,00,001'], dtype=object)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["car['Price'].unique()"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1708073884075,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"F5LwVG2zCe2V","outputId":"b2ffe9fa-09ff-4789-c7c3-554ecf72cee7"},"outputs":[{"data":{"text/plain":["array(['45,000 kms', '40 kms', '22,000 kms', '28,000 kms', '36,000 kms',\n"," '59,000 kms', '41,000 kms', '25,000 kms', '24,530 kms',\n"," '60,000 kms', '30,000 kms', '32,000 kms', '48,660 kms',\n"," '4,000 kms', '16,934 kms', '43,000 kms', '35,550 kms',\n"," '39,522 kms', '39,000 kms', '55,000 kms', '72,000 kms',\n"," '15,975 kms', '70,000 kms', '23,452 kms', '35,522 kms',\n"," '48,508 kms', '15,487 kms', '82,000 kms', '20,000 kms',\n"," '68,000 kms', '38,000 kms', '27,000 kms', '33,000 kms',\n"," '46,000 kms', '16,000 kms', '47,000 kms', '35,000 kms',\n"," '30,874 kms', '15,000 kms', '29,685 kms', '1,30,000 kms',\n"," '19,000 kms', nan, '54,000 kms', '13,000 kms', '38,200 kms',\n"," '50,000 kms', '13,500 kms', '3,600 kms', '45,863 kms',\n"," '60,500 kms', '12,500 kms', '18,000 kms', '13,349 kms',\n"," '29,000 kms', '44,000 kms', '42,000 kms', '14,000 kms',\n"," '49,000 kms', '36,200 kms', '51,000 kms', '1,04,000 kms',\n"," '33,333 kms', '33,600 kms', '5,600 kms', '7,500 kms', '26,000 kms',\n"," '24,330 kms', '65,480 kms', '28,028 kms', '2,00,000 kms',\n"," '99,000 kms', '2,800 kms', '21,000 kms', '11,000 kms',\n"," '66,000 kms', '3,000 kms', '7,000 kms', '38,500 kms', '37,200 kms',\n"," '43,200 kms', '24,800 kms', '45,872 kms', '40,000 kms',\n"," '11,400 kms', '97,200 kms', '52,000 kms', '31,000 kms',\n"," '1,75,430 kms', '37,000 kms', '65,000 kms', '3,350 kms',\n"," '75,000 kms', '62,000 kms', '73,000 kms', '2,200 kms',\n"," '54,870 kms', '34,580 kms', '97,000 kms', '60 kms', '80,200 kms',\n"," '3,200 kms', '0,000 kms', '5,000 kms', '588 kms', '71,200 kms',\n"," '1,75,400 kms', '9,300 kms', '56,758 kms', '10,000 kms',\n"," '56,450 kms', '56,000 kms', '32,700 kms', '9,000 kms', '73 kms',\n"," '1,60,000 kms', '84,000 kms', '58,559 kms', '57,000 kms',\n"," '1,70,000 kms', '80,000 kms', '6,821 kms', '23,000 kms',\n"," '34,000 kms', '1,800 kms', '4,00,000 kms', '48,000 kms',\n"," '90,000 kms', '12,000 kms', '69,900 kms', '1,66,000 kms',\n"," '122 kms', '0 kms', '24,000 kms', '36,469 kms', '7,800 kms',\n"," '24,695 kms', '15,141 kms', '59,910 kms', '1,00,000 kms',\n"," '4,500 kms', '1,29,000 kms', '300 kms', '1,31,000 kms',\n"," '1,11,111 kms', '59,466 kms', '25,500 kms', '44,005 kms',\n"," '2,110 kms', '43,222 kms', '1,00,200 kms', '65 kms',\n"," '1,40,000 kms', '1,03,553 kms', '58,000 kms', '1,20,000 kms',\n"," '49,800 kms', '100 kms', '81,876 kms', '6,020 kms', '55,700 kms',\n"," '18,500 kms', '1,80,000 kms', '53,000 kms', '35,500 kms',\n"," '22,134 kms', '1,000 kms', '8,500 kms', '87,000 kms', '6,000 kms',\n"," '15,574 kms', '8,000 kms', '55,800 kms', '56,400 kms',\n"," '72,160 kms', '11,500 kms', '1,33,000 kms', '2,000 kms',\n"," '88,000 kms', '65,422 kms', '1,17,000 kms', '1,50,000 kms',\n"," '10,750 kms', '6,800 kms', '5 kms', '9,800 kms', '57,923 kms',\n"," '30,201 kms', '6,200 kms', '37,518 kms', '24,652 kms', '383 kms',\n"," '95,000 kms', '3,528 kms', '52,500 kms', '47,900 kms',\n"," '52,800 kms', '1,95,000 kms', '48,008 kms', '48,247 kms',\n"," '9,400 kms', '64,000 kms', '2,137 kms', '10,544 kms', '49,500 kms',\n"," '1,47,000 kms', '90,001 kms', '48,006 kms', '74,000 kms',\n"," '85,000 kms', '29,500 kms', '39,700 kms', '67,000 kms',\n"," '19,336 kms', '60,105 kms', '45,933 kms', '1,02,563 kms',\n"," '28,600 kms', '41,800 kms', '1,16,000 kms', '42,590 kms',\n"," '7,400 kms', '54,500 kms', '76,000 kms', '00 kms', '11,523 kms',\n"," '38,600 kms', '95,500 kms', '37,458 kms', '85,960 kms',\n"," '12,516 kms', '30,600 kms', '2,550 kms', '62,500 kms',\n"," '69,000 kms', '28,400 kms', '68,485 kms', '3,500 kms',\n"," '85,455 kms', '63,000 kms', '1,600 kms', '77,000 kms',\n"," '26,500 kms', '2,875 kms', '13,900 kms', '1,500 kms', '2,450 kms',\n"," '1,625 kms', '33,400 kms', '60,123 kms', '38,900 kms',\n"," '1,37,495 kms', '91,200 kms', '1,46,000 kms', '1,00,800 kms',\n"," '2,100 kms', '2,500 kms', '1,32,000 kms', 'Petrol'], dtype=object)"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["car['kms_driven'].unique()"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":628,"status":"ok","timestamp":1708073887692,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"YL1wb4MbCva-","outputId":"b3d31841-e623-4833-dfdd-4519ed98ed93"},"outputs":[{"data":{"text/plain":["array(['Petrol', 'Diesel', nan, 'LPG'], dtype=object)"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["car['fuel_type'].unique()"]},{"cell_type":"markdown","metadata":{"id":"6hoLXf-qASXy"},"source":["## Quality\n","\n","- names are pretty inconsistent\n","- names have company names attached to it\n","- some names are spam like 'Maruti Ertiga showroom condition with' and 'Well mentained Tata Sumo'\n","- company: many of the names are not of any company like 'Used', 'URJENT', and so on.\n","- year has many non-year values\n","- year is in object. Change to integer\n","- Price has Ask for Price\n","- Price has commas in its prices and is in object\n","- kms_driven has object values with kms at last.\n","- It has nan values and two rows have 'Petrol' in them\n","- fuel_type has nan values"]},{"cell_type":"markdown","metadata":{"id":"2ZqnNqAEASXy"},"source":["## Cleaning Data"]},{"cell_type":"markdown","metadata":{"id":"YkDLdGwzASXz"},"source":["#### year has many non-year values"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708073890227,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Cr4ss20kASXz"},"outputs":[],"source":["car=car[car['year'].str.isnumeric()]"]},{"cell_type":"markdown","metadata":{"id":"mxZzHsevASX0"},"source":["#### year is in object. Change to integer"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073891497,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fW4hzWXvASX1"},"outputs":[],"source":["car['year']=car['year'].astype(int)"]},{"cell_type":"markdown","metadata":{"id":"1-Vurw_FASX1"},"source":["#### Price has Ask for Price"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073893318,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"BTQ0N36hASX1"},"outputs":[],"source":["car=car[car['Price']!='Ask For Price']"]},{"cell_type":"markdown","metadata":{"id":"24MOBFFMASX1"},"source":["#### Price has commas in its prices and is in object"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073893800,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"PVKKKJuAASX2"},"outputs":[],"source":["car['Price']=car['Price'].str.replace(',','').astype(int)"]},{"cell_type":"markdown","metadata":{"id":"3lq9k6N6ASX2"},"source":["#### kms_driven has object values with kms at last."]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073895228,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"9VbO2ntuASX2"},"outputs":[],"source":["car['kms_driven']=car['kms_driven'].str.split().str.get(0).str.replace(',','')"]},{"cell_type":"markdown","metadata":{"id":"Bl9AvzVxASX2"},"source":["#### It has nan values and two rows have 'Petrol' in them"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708073896782,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"gjlIcL7hASX2"},"outputs":[],"source":["car=car[car['kms_driven'].str.isnumeric()]"]},{"cell_type":"code","execution_count":17,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073898249,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"e29DzkWYASX2"},"outputs":[],"source":["car['kms_driven']=car['kms_driven'].astype(int)"]},{"cell_type":"markdown","metadata":{"id":"4CnnSYJxASX2"},"source":["#### fuel_type has nan values"]},{"cell_type":"code","execution_count":18,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1708073900698,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"r71rsddhASX2"},"outputs":[],"source":["car=car[~car['fuel_type'].isna()]"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1708073902297,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"J8-TLlOpASX2","outputId":"0194971a-87d0-4461-c45f-2a243d9fc7a5"},"outputs":[{"data":{"text/plain":["(816, 6)"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["car.shape"]},{"cell_type":"markdown","metadata":{"id":"1uOE4esxASX2"},"source":["### name and company had spammed data...but with the previous cleaning, those rows got removed."]},{"cell_type":"markdown","metadata":{"id":"z2tmQj0fASX3"},"source":["#### Company does not need any cleaning now. Changing car names. Keeping only the first three words"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":511,"status":"ok","timestamp":1708073908962,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"4hnWM22_ASX3"},"outputs":[],"source":["car['name']=car['name'].str.split().str.slice(start=0,stop=3).str.join(' ')"]},{"cell_type":"markdown","metadata":{"id":"7NGZVZLDASX3"},"source":["#### Resetting the index of the final cleaned data"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708073910818,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Ds2lZi52ASX3"},"outputs":[],"source":["car=car.reset_index(drop=True)"]},{"cell_type":"markdown","metadata":{"id":"iZVf6J-AASX3"},"source":["## Cleaned Data"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1708073913653,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"TVS_0yNVASX3","outputId":"452afd40-1710-4925-a93f-d597e9170694"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"car\",\n \"rows\": 816,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"Tata Nano\",\n \"Ford EcoSport Ambiente\",\n \"Renault Kwid\"\n ],\n \"num_unique_values\": 254,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"Honda\",\n \"Nissan\",\n \"Hyundai\"\n ],\n \"num_unique_values\": 25,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 1995,\n \"max\": 2019,\n \"samples\": [\n 2007,\n 2004,\n 2000\n ],\n \"num_unique_values\": 21,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 475184,\n \"min\": 30000,\n \"max\": 8500003,\n \"samples\": [\n 280000,\n 355000,\n 450000\n ],\n \"num_unique_values\": 272,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"kms_driven\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 34297,\n \"min\": 0,\n \"max\": 400000,\n \"samples\": [\n 47000,\n 24530,\n 24652\n ],\n \"num_unique_values\": 246,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fuel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"Petrol\",\n \"Diesel\",\n \"LPG\"\n ],\n \"num_unique_values\": 3,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe","variable_name":"car"},"text/html":["\n","
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\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
namecompanyyearPricekms_drivenfuel_type
0Hyundai Santro XingHyundai20078000045000Petrol
1Mahindra Jeep CL550Mahindra200642500040Diesel
2Hyundai Grand i10Hyundai201432500028000Petrol
3Ford EcoSport TitaniumFord201457500036000Diesel
4Ford FigoFord201217500041000Diesel
.....................
811Maruti Suzuki RitzMaruti201127000050000Petrol
812Tata Indica V2Tata200911000030000Diesel
813Toyota Corolla AltisToyota2009300000132000Petrol
814Tata Zest XMTata201826000027000Diesel
815Mahindra Quanto C8Mahindra201339000040000Diesel
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816 rows × 6 columns

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\n"],"text/plain":[" name company year Price kms_driven fuel_type\n","0 Hyundai Santro Xing Hyundai 2007 80000 45000 Petrol\n","1 Mahindra Jeep CL550 Mahindra 2006 425000 40 Diesel\n","2 Hyundai Grand i10 Hyundai 2014 325000 28000 Petrol\n","3 Ford EcoSport Titanium Ford 2014 575000 36000 Diesel\n","4 Ford Figo Ford 2012 175000 41000 Diesel\n",".. ... ... ... ... ... ...\n","811 Maruti Suzuki Ritz Maruti 2011 270000 50000 Petrol\n","812 Tata Indica V2 Tata 2009 110000 30000 Diesel\n","813 Toyota Corolla Altis Toyota 2009 300000 132000 Petrol\n","814 Tata Zest XM Tata 2018 260000 27000 Diesel\n","815 Mahindra Quanto C8 Mahindra 2013 390000 40000 Diesel\n","\n","[816 rows x 6 columns]"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["car"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":462,"status":"ok","timestamp":1708073917810,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"j9BagzInASX3"},"outputs":[],"source":["car.to_csv('Cleaned_Car_data.csv')"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":574,"status":"ok","timestamp":1708073920952,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"pBQp4NZCASX3","outputId":"67e98916-40a9-4a76-d398-3c8905ad0e02"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 816 entries, 0 to 815\n","Data columns (total 6 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 name 816 non-null object\n"," 1 company 816 non-null object\n"," 2 year 816 non-null int64 \n"," 3 Price 816 non-null int64 \n"," 4 kms_driven 816 non-null int64 \n"," 5 fuel_type 816 non-null object\n","dtypes: int64(3), object(3)\n","memory usage: 38.4+ KB\n"]}],"source":["car.info()"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"executionInfo":{"elapsed":789,"status":"ok","timestamp":1708073923700,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"9LelCjfMASX3","outputId":"eb4ca677-c8d9-4173-c81d-ad51a5fedcdd"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"car\",\n \"rows\": 11,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n 254,\n \"51\",\n \"816\"\n ],\n \"num_unique_values\": 4,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n 25,\n \"221\",\n \"816\"\n ],\n \"num_unique_values\": 4,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 772.1548489084256,\n \"min\": 4.002992497545103,\n \"max\": 2019.0,\n \"samples\": [\n 2012.4448529411766,\n 2013.0,\n 816.0\n ],\n \"num_unique_values\": 8,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2916207.4206268266,\n \"min\": 816.0,\n \"max\": 8500003.0,\n \"samples\": [\n 411717.61519607843,\n 299999.0,\n 816.0\n ],\n \"num_unique_values\": 8,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"kms_driven\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 132568.47861821018,\n \"min\": 0.0,\n \"max\": 400000.0,\n \"samples\": [\n 46275.5318627451,\n 41000.0,\n 816.0\n ],\n \"num_unique_values\": 8,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fuel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n 3,\n \"428\",\n \"816\"\n ],\n \"num_unique_values\": 4,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe"},"text/html":["\n","
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namecompanyyearPricekms_drivenfuel_type
count816816816.0000008.160000e+02816.000000816
unique25425NaNNaNNaN3
topMaruti Suzuki SwiftMarutiNaNNaNNaNPetrol
freq51221NaNNaNNaN428
meanNaNNaN2012.4448534.117176e+0546275.531863NaN
stdNaNNaN4.0029924.751844e+0534297.428044NaN
minNaNNaN1995.0000003.000000e+040.000000NaN
25%NaNNaN2010.0000001.750000e+0527000.000000NaN
50%NaNNaN2013.0000002.999990e+0541000.000000NaN
75%NaNNaN2015.0000004.912500e+0556818.500000NaN
maxNaNNaN2019.0000008.500003e+06400000.000000NaN
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\n"],"text/plain":[" name company year Price kms_driven \\\n","count 816 816 816.000000 8.160000e+02 816.000000 \n","unique 254 25 NaN NaN NaN \n","top Maruti Suzuki Swift Maruti NaN NaN NaN \n","freq 51 221 NaN NaN NaN \n","mean NaN NaN 2012.444853 4.117176e+05 46275.531863 \n","std NaN NaN 4.002992 4.751844e+05 34297.428044 \n","min NaN NaN 1995.000000 3.000000e+04 0.000000 \n","25% NaN NaN 2010.000000 1.750000e+05 27000.000000 \n","50% NaN NaN 2013.000000 2.999990e+05 41000.000000 \n","75% NaN NaN 2015.000000 4.912500e+05 56818.500000 \n","max NaN NaN 2019.000000 8.500003e+06 400000.000000 \n","\n"," fuel_type \n","count 816 \n","unique 3 \n","top Petrol \n","freq 428 \n","mean NaN \n","std NaN \n","min NaN \n","25% NaN \n","50% NaN \n","75% NaN \n","max NaN "]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["car.describe(include='all')"]},{"cell_type":"markdown","metadata":{"id":"QioNtToOASX4"},"source":["### Checking relationship of Company with Price"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":908,"status":"ok","timestamp":1708073930252,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"B80WbvwDASX4","outputId":"90c67240-51f8-44ee-ff44-766abba4b3e0"},"outputs":[{"data":{"text/plain":["array(['Hyundai', 'Mahindra', 'Ford', 'Maruti', 'Skoda', 'Audi', 'Toyota',\n"," 'Renault', 'Honda', 'Datsun', 'Mitsubishi', 'Tata', 'Volkswagen',\n"," 'Chevrolet', 'Mini', 'BMW', 'Nissan', 'Hindustan', 'Fiat', 'Force',\n"," 'Mercedes', 'Land', 'Jaguar', 'Jeep', 'Volvo'], dtype=object)"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["car['company'].unique()"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":871,"status":"ok","timestamp":1708073943708,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"R8Ux3EYdASX-"},"outputs":[],"source":["import seaborn as sns"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":722},"executionInfo":{"elapsed":1190,"status":"ok","timestamp":1708073946413,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"S-sSXIWKASX-","outputId":"ef77542f-2f00-4432-c844-a849adaa72d6"},"outputs":[{"name":"stderr","output_type":"stream","text":[":3: UserWarning: FixedFormatter should only be used together with FixedLocator\n"," 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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.subplots(figsize=(15,7))\n","ax=sns.boxplot(x='company',y='Price',data=car)\n","ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"hm7zPqDPASX-"},"source":["### Checking relationship of Year with Price"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":4837,"status":"ok","timestamp":1708073970789,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"HEjWfxs5ASX-","outputId":"18ea26dd-5feb-4446-cde1-0603f5795bfc"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 15.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 30.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 31.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 36.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 42.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 32.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 42.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 39.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 16.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 20.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n",":3: UserWarning: FixedFormatter should only be used together with FixedLocator\n"," ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 22.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 26.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 12.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 38.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 34.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 35.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 29.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 35.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 37.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 13.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 17.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n"," warnings.warn(msg, UserWarning)\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABkwAAANnCAYAAABzoXQAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACkf0lEQVR4nOz9e7Al5XkY/D699mwNICwE0SCYg8aMZI/GVokwtmPPZyexwucTiRFlWzBj5ySKZJsoNkzqOAFXHMZOlf1VBkpfwInLBHwOko0qOrbMAFJkA1YUX1K2nC3F9kZIti4gGEZmQGyEwBKgYc9eff5Ys/fsS9/XWntd+verUonpXrvX2+/q7tXrffp9niRN0zQAAAAAAABarDPqBgAAAAAAAIyagAkAAAAAANB6AiYAAAAAAEDrCZgAAAAAAACtJ2ACAAAAAAC0noAJAAAAAADQegImAAAAAABA6wmYAAAAAAAArSdgAgAAAAAAtJ6ACQAAAAAA0HpbRt2AYfnrv/7r+OhHPxqPPfZYfO1rX4uf+7mfi+/93u+ttY00TeN3f/d34w/+4A9iYWEhvuVbviXe+ta3xpVXXjmkVgMAAAAAAKMwtQGTEydOxMUXXxyXXXZZ3HzzzY228Zu/+Zvx0EMPxT//5/88duzYEd/4xjfiG9/4xoBbCgAAAAAAjNrUBkz27NkTe/bsyV2/uLgYv/3bvx2f+MQn4sUXX4zXve518c/+2T+LN73pTRER8Td/8zfx8Y9/PG655ZbYvn17REScf/75m9J2AAAAAABgc01twKTM+9///njiiSfiX//rfx3nnntufOpTn4obb7wxbr755rjwwgvjL/7iL+L888+Pv/iLv4jDhw9HRMSb3/zmeOc73xlnn332iFsPAAAAAAAMUiuLvj/zzDPxx3/8x/Fv/s2/ie/4ju+ICy64IH74h384du/eHX/0R38UERFf+cpX4plnnom5ubn4V//qX8W1114bjz76aNxyyy0jbj0AAAAAADBorZxhcuzYseh2u/GzP/uza5afPHlyZfZImqaxuLgYBw8eXEnJ9TM/8zPx7/7dv4vjx4+vLAMAAAAAACZfKwMm3/zmN6PT6cR73/ve6HTWTrI544wzIiLi3HPPjZmZmTWBkYsuuigiejNUBEwAAAAAAGB6tDJgcvHFF0e3243nn38+vuM7viPzNW984xtjaWkpnnrqqbjgggsiIuL48eMREfGa17xm09oKAAAAAAAM39TWMPnmN78ZR48ejaNHj0ZExNNPPx1Hjx5dmR3y9//+349bb701PvnJT8bTTz8djzzySHz4wx+Ov/zLv4yIXoH3nTt3xu233x6PPfZYPProo3HHHXfEJZdcYnYJAAAAAABMmSRN03TUjRiGv/qrv4pf/uVf3rD8B3/wB+PgwYNx8uTJuPfee+N//s//Gc8++2y86lWvim//9m+PH/uxH4sdO3ZERMSzzz4bv/EbvxEPPfRQbN26Nfbs2RPvete7VuqcAAAAAAAA02FqAyYAAAAAAABVTW1KLgAAAAAAgKoETAAAAAAAgNYTMAEAAAAAAFpvy6gbMAxf+9rX4uTJk6NuBgAAAAAAMEJbtmyJc889t9prh9yWkTh58mQsLi6OuhkAAAAAAMCEkJILAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaL0to24AAAAAAEREpPNz0b3/SMTxYxHbd0Rn34FI9uwddbMAaAkzTAAAAAAYuXR+Lrq33Rhx9OGIl09EHH04urffFOn83KibBkBLCJgAAAAAMHLd+49sXJim0X3g7s1vDACtJGACAAAAwOgdP1ZvOQAMmIAJAAAAAKO3fUe95QAwYAImAAAAAIxcZ9+BiCRZuzBJorNv/2gaBEDrJGmapqNuxKAtLCzE4uLiqJsBAAAAQA3p/FyvZsnxYxHbd0Rn3/5ILt076mYBMMFmZ2dj27ZtlV4rYAIAAAAAAEylOgETKbkAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNbbMuoGrNbtduOuu+6KP/mTP4nnnnsuzjvvvPjBH/zBuOqqqyJJklE3DwAAAAAAmFJjFTD5yEc+Eh//+Mfj4MGDcdFFF8Wjjz4at912W5x11lmxb9++UTcPAAAAAACYUmMVMPniF78Y3/M93xPf9V3fFRER559/fvzpn/5pPPLIIyNuGQAAAAAAMM3GKmCya9eu+IM/+IM4fvx4bN++PY4ePRpf+MIX4l3velfm6xcXF2NxcXHl30mSxJlnnrny3wAAAAAAAFUkaZqmo27Esm63G7/9278dH/3oR6PT6US3241/8k/+SbzjHe/IfP1dd90Vd99998q/d+7cGe9973s3q7kAAAAAAMCUGKuAySc+8Yn44Ac/GO985zvjda97XRw9ejTuvPPOeNe73hVvectbNrw+b4bJwsJCnDx5chNbDgAAAAAAjJstW7bEtm3bqr12yG2p5YMf/GD8yI/8SPzAD/xARETs2LEjFhYW4iMf+UhmwGR2djZmZ2cztzVGcSAAAAAAAGDMdUbdgNVOnDgRnc7aJnU6HcEPAAAAAABgqMZqhsl3f/d3x7333huvec1r4qKLLoqjR4/G7/3e78U/+kf/aNRNAwAAAAAApthY1TB56aWX4nd+53fiU5/6VDz//PNx3nnnxQ/8wA/E/v37Y8uW6rGdhYWFNbVNAAAAAACA9pmdna1cw2SsAiaDImACAAAAAADUCZiMVQ0TAAAAAACAURAwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAAgNYTMAEAAAAAAFpPwAQAAAAAAGg9ARMAAAAAAKD1BEwAAAAAAIDWEzABAAAAAABaT8AEAAAAAABoPQETAAAAAACg9QRMAAAAAACA1hMwAQAAAAAAWk/ABAAAAAAAaD0BEwAAAAAAoPUETAAAAAAA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"]},"metadata":{},"output_type":"display_data"}],"source":["plt.subplots(figsize=(20,10))\n","ax=sns.swarmplot(x='year',y='Price',data=car)\n","ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Y_2jYadGASX_"},"source":["### Checking relationship of kms_driven with Price"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":724},"executionInfo":{"elapsed":820,"status":"ok","timestamp":1708073981338,"user":{"displayName":"Alok Kumar 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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.relplot(x='kms_driven',y='Price',data=car,height=7,aspect=1.5)"]},{"cell_type":"markdown","metadata":{"id":"w-c5nrmhASX_"},"source":["### Checking relationship of Fuel Type with Price"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":655},"executionInfo":{"elapsed":580,"status":"ok","timestamp":1708073985715,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Re3TgViuASX_","outputId":"8d1c5f0d-1e62-4275-9b55-f6442f5c79f4"},"outputs":[{"data":{"text/plain":[""]},"execution_count":32,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["plt.subplots(figsize=(14,7))\n","sns.boxplot(x='fuel_type',y='Price',data=car)"]},{"cell_type":"markdown","metadata":{"id":"Qd_F4KkMASX_"},"source":["### Relationship of Price with FuelType, Year and Company mixed"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":727},"executionInfo":{"elapsed":1978,"status":"ok","timestamp":1708073991202,"user":{"displayName":"Alok Kumar 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"]},"metadata":{},"output_type":"display_data"}],"source":["ax=sns.relplot(x='company',y='Price',data=car,hue='fuel_type',size='year',height=7,aspect=2)\n","ax.set_xticklabels(rotation=40,ha='right')"]},{"cell_type":"markdown","metadata":{"id":"8oZSIWeBASX_"},"source":["### Extracting Training Data"]},{"cell_type":"code","execution_count":34,"metadata":{"executionInfo":{"elapsed":489,"status":"ok","timestamp":1708074007636,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"HHPXWP7_ASX_"},"outputs":[],"source":["X=car[['name','company','year','kms_driven','fuel_type']]\n","y=car['Price']"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":655,"status":"ok","timestamp":1708074015394,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"lU6Pi6sHASX_","outputId":"b6766d19-b232-4888-ddd7-251097c8b17c"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"X\",\n \"rows\": 816,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"Tata Nano\",\n \"Ford EcoSport Ambiente\",\n \"Renault Kwid\"\n ],\n \"num_unique_values\": 254,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"company\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"Honda\",\n \"Nissan\",\n \"Hyundai\"\n ],\n \"num_unique_values\": 25,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 1995,\n \"max\": 2019,\n \"samples\": [\n 2007,\n 2004,\n 2000\n ],\n \"num_unique_values\": 21,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"kms_driven\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 34297,\n \"min\": 0,\n \"max\": 400000,\n \"samples\": [\n 47000,\n 24530,\n 24652\n ],\n \"num_unique_values\": 246,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fuel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"samples\": [\n \"Petrol\",\n \"Diesel\",\n \"LPG\"\n ],\n \"num_unique_values\": 3,\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe","variable_name":"X"},"text/html":["\n","
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namecompanyyearkms_drivenfuel_type
0Hyundai Santro XingHyundai200745000Petrol
1Mahindra Jeep CL550Mahindra200640Diesel
2Hyundai Grand i10Hyundai201428000Petrol
3Ford EcoSport TitaniumFord201436000Diesel
4Ford FigoFord201241000Diesel
..................
811Maruti Suzuki RitzMaruti201150000Petrol
812Tata Indica V2Tata200930000Diesel
813Toyota Corolla AltisToyota2009132000Petrol
814Tata Zest XMTata201827000Diesel
815Mahindra Quanto C8Mahindra201340000Diesel
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816 rows × 5 columns

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\n","\n","
\n","
\n"],"text/plain":[" name company year kms_driven fuel_type\n","0 Hyundai Santro Xing Hyundai 2007 45000 Petrol\n","1 Mahindra Jeep CL550 Mahindra 2006 40 Diesel\n","2 Hyundai Grand i10 Hyundai 2014 28000 Petrol\n","3 Ford EcoSport Titanium Ford 2014 36000 Diesel\n","4 Ford Figo Ford 2012 41000 Diesel\n",".. ... ... ... ... ...\n","811 Maruti Suzuki Ritz Maruti 2011 50000 Petrol\n","812 Tata Indica V2 Tata 2009 30000 Diesel\n","813 Toyota Corolla Altis Toyota 2009 132000 Petrol\n","814 Tata Zest XM Tata 2018 27000 Diesel\n","815 Mahindra Quanto C8 Mahindra 2013 40000 Diesel\n","\n","[816 rows x 5 columns]"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["X"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1708074020081,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"UzuQCTGUASYA","outputId":"7c6258c1-a847-43c8-dce6-b229431a0779"},"outputs":[{"data":{"text/plain":["(816,)"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["y.shape"]},{"cell_type":"markdown","metadata":{"id":"4K_H0rJlASYA"},"source":["### Applying Train Test Split"]},{"cell_type":"code","execution_count":37,"metadata":{"executionInfo":{"elapsed":493,"status":"ok","timestamp":1708074030497,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"TZs0yQQ-ASYA"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"]},{"cell_type":"code","execution_count":38,"metadata":{"executionInfo":{"elapsed":463,"status":"ok","timestamp":1708074041194,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"pDNZaOEEASYA"},"outputs":[],"source":["from sklearn.linear_model import LinearRegression"]},{"cell_type":"code","execution_count":39,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1708074042724,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"LsGjTL94ASYA"},"outputs":[],"source":["from sklearn.preprocessing import OneHotEncoder\n","from sklearn.compose import make_column_transformer\n","from sklearn.pipeline import make_pipeline\n","from sklearn.metrics import r2_score"]},{"cell_type":"markdown","metadata":{"id":"diicWbHHASYA"},"source":["#### Creating an OneHotEncoder object to contain all the possible categories"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"executionInfo":{"elapsed":535,"status":"ok","timestamp":1708074050282,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"QXeFmYSfASYA","outputId":"39d81469-9754-49ea-9da3-1039e5b8c3c6"},"outputs":[{"data":{"text/html":["
OneHotEncoder()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"],"text/plain":["OneHotEncoder()"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["ohe=OneHotEncoder()\n","ohe.fit(X[['name','company','fuel_type']])"]},{"cell_type":"markdown","metadata":{"id":"XEaQLySCASYB"},"source":["#### Creating a column transformer to transform categorical columns"]},{"cell_type":"code","execution_count":41,"metadata":{"executionInfo":{"elapsed":710,"status":"ok","timestamp":1708074142733,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"LmRY2gStASYB"},"outputs":[],"source":["column_trans=make_column_transformer((OneHotEncoder(categories=ohe.categories_),['name','company','fuel_type']),\n"," remainder='passthrough')"]},{"cell_type":"markdown","metadata":{"id":"VpSa2b10ASYB"},"source":["#### Linear Regression Model"]},{"cell_type":"code","execution_count":42,"metadata":{"executionInfo":{"elapsed":491,"status":"ok","timestamp":1708074146127,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"rvgPblK7ASYB"},"outputs":[],"source":["lr=LinearRegression()"]},{"cell_type":"markdown","metadata":{"id":"jPvhGhNiASYB"},"source":["#### Making a pipeline"]},{"cell_type":"code","execution_count":43,"metadata":{"executionInfo":{"elapsed":891,"status":"ok","timestamp":1708074151905,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"QubEsgltASYB"},"outputs":[],"source":["pipe=make_pipeline(column_trans,lr)"]},{"cell_type":"markdown","metadata":{"id":"HacmTQ7DASYB"},"source":["#### Fitting the model"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":636},"executionInfo":{"elapsed":818,"status":"ok","timestamp":1708074189375,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"Oo-g6rY5ASYB","outputId":"43ec7301-3e74-48fb-cf2d-90cee57bf41d","scrolled":true},"outputs":[{"data":{"text/html":["
Pipeline(steps=[('columntransformer',\n","                 ColumnTransformer(remainder='passthrough',\n","                                   transformers=[('onehotencoder',\n","                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n","       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n","       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n","       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n","                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n","       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n","       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n","       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n","      dtype=object),\n","                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n","                                                  ['name', 'company',\n","                                                   'fuel_type'])])),\n","                ('linearregression', LinearRegression())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"],"text/plain":["Pipeline(steps=[('columntransformer',\n"," ColumnTransformer(remainder='passthrough',\n"," transformers=[('onehotencoder',\n"," OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n"," 'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n"," 'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n"," 'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n"," array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n"," 'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n"," 'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n"," 'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n"," dtype=object),\n"," array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n"," ['name', 'company',\n"," 'fuel_type'])])),\n"," ('linearregression', LinearRegression())])"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["pipe.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":45,"metadata":{"executionInfo":{"elapsed":507,"status":"ok","timestamp":1708074258512,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"qimpYCLQASYB"},"outputs":[],"source":["y_pred=pipe.predict(X_test)"]},{"cell_type":"markdown","metadata":{"id":"IEssz0HwASYB"},"source":["#### Checking R2 Score"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":599,"status":"ok","timestamp":1708074278028,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"RhlIxudNASYC","outputId":"55bf9355-e8d6-4947-8273-dde8c81a72a1"},"outputs":[{"data":{"text/plain":["-0.15149763480164546"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["r2_score(y_test,y_pred)"]},{"cell_type":"code","execution_count":47,"metadata":{"executionInfo":{"elapsed":23523,"status":"ok","timestamp":1708074338093,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"LtwzxwYdASYC"},"outputs":[],"source":["scores=[]\n","for i in range(1000):\n"," X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=i)\n"," lr=LinearRegression()\n"," pipe=make_pipeline(column_trans,lr)\n"," pipe.fit(X_train,y_train)\n"," y_pred=pipe.predict(X_test)\n"," scores.append(r2_score(y_test,y_pred))"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":485,"status":"ok","timestamp":1708074346361,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"n_fehP7hASYC","outputId":"56782bf1-7b53-4f5c-cde0-f2fccdfffe7f"},"outputs":[{"data":{"text/plain":["247"]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["np.argmax(scores)"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":636,"status":"ok","timestamp":1708074350322,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"2QGHXKrkASYC","outputId":"1423ff3c-a707-4e5e-cc25-d9d40321dbdd"},"outputs":[{"data":{"text/plain":["0.8604602644312209"]},"execution_count":49,"metadata":{},"output_type":"execute_result"}],"source":["scores[np.argmax(scores)]"]},{"cell_type":"code","execution_count":50,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":643,"status":"ok","timestamp":1708074446306,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"fpKTj-BXASYC","outputId":"ce88e413-3a96-4f85-be6c-14c0e049c604"},"outputs":[{"data":{"text/plain":["array([453164.82282671])"]},"execution_count":50,"metadata":{},"output_type":"execute_result"}],"source":["pipe.predict(pd.DataFrame(columns=X_test.columns,data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"]},{"cell_type":"markdown","metadata":{"id":"DRnBh9hnASYC"},"source":["#### The best model is found at a certain random state"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":486,"status":"ok","timestamp":1708074449831,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"8UE1OEdyASYC","outputId":"abb46b6c-91de-4ec5-85d1-f4644a450784"},"outputs":[{"data":{"text/plain":["0.8604602644312209"]},"execution_count":51,"metadata":{},"output_type":"execute_result"}],"source":["X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=np.argmax(scores))\n","lr=LinearRegression()\n","pipe=make_pipeline(column_trans,lr)\n","pipe.fit(X_train,y_train)\n","y_pred=pipe.predict(X_test)\n","r2_score(y_test,y_pred)"]},{"cell_type":"code","execution_count":52,"metadata":{"executionInfo":{"elapsed":467,"status":"ok","timestamp":1708074455968,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"q0BV9fJxASYC"},"outputs":[],"source":["import pickle"]},{"cell_type":"code","execution_count":53,"metadata":{"executionInfo":{"elapsed":573,"status":"ok","timestamp":1708074478602,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"AYew5982ASYD"},"outputs":[],"source":["pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))"]},{"cell_type":"code","execution_count":54,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":485,"status":"ok","timestamp":1708074501651,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"o3OFBISaASYD","outputId":"ada68292-051d-4a68-f9ab-46584517b52a"},"outputs":[{"data":{"text/plain":["array([455413.55294819])"]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["pipe.predict(pd.DataFrame(columns=['name','company','year','kms_driven','fuel_type'],data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"]},{"cell_type":"code","execution_count":55,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":490,"status":"ok","timestamp":1708074568157,"user":{"displayName":"Alok Kumar Choudhary","userId":"07053937684293052645"},"user_tz":-330},"id":"YwEs-8nEASYD","outputId":"3ece31cd-2afa-40a0-e623-e2c01c81309d"},"outputs":[{"data":{"text/plain":["array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n"," 'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n"," 'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n"," 'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n"," 'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n"," 'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet Enjoy 1.4',\n"," 'Chevrolet Sail 1.2', 'Chevrolet Sail UVA', 'Chevrolet Spark',\n"," 'Chevrolet Spark 1.0', 'Chevrolet Spark LS', 'Chevrolet Spark LT',\n"," 'Chevrolet Tavera LS', 'Chevrolet Tavera Neo', 'Datsun GO T',\n"," 'Datsun Go Plus', 'Datsun Redi GO', 'Fiat Linea Emotion',\n"," 'Fiat Petra ELX', 'Fiat Punto Emotion', 'Force Motors Force',\n"," 'Force Motors One', 'Ford EcoSport', 'Ford EcoSport Ambiente',\n"," 'Ford EcoSport Titanium', 'Ford EcoSport Trend',\n"," 'Ford Endeavor 4x4', 'Ford Fiesta', 'Ford Fiesta SXi', 'Ford Figo',\n"," 'Ford Figo Diesel', 'Ford Figo Duratorq', 'Ford Figo Petrol',\n"," 'Ford Fusion 1.4', 'Ford Ikon 1.3', 'Ford Ikon 1.6',\n"," 'Hindustan Motors Ambassador', 'Honda Accord', 'Honda Amaze',\n"," 'Honda Amaze 1.2', 'Honda Amaze 1.5', 'Honda Brio', 'Honda Brio V',\n"," 'Honda Brio VX', 'Honda City', 'Honda City 1.5', 'Honda City SV',\n"," 'Honda City VX', 'Honda City ZX', 'Honda Jazz S', 'Honda Jazz VX',\n"," 'Honda Mobilio', 'Honda Mobilio S', 'Honda WR V', 'Hyundai Accent',\n"," 'Hyundai Accent Executive', 'Hyundai Accent GLE',\n"," 'Hyundai Accent GLX', 'Hyundai Creta', 'Hyundai Creta 1.6',\n"," 'Hyundai Elantra 1.8', 'Hyundai Elantra SX', 'Hyundai Elite i20',\n"," 'Hyundai Eon', 'Hyundai Eon D', 'Hyundai Eon Era',\n"," 'Hyundai Eon Magna', 'Hyundai Eon Sportz', 'Hyundai Fluidic Verna',\n"," 'Hyundai Getz', 'Hyundai Getz GLE', 'Hyundai Getz Prime',\n"," 'Hyundai Grand i10', 'Hyundai Santro', 'Hyundai Santro AE',\n"," 'Hyundai Santro Xing', 'Hyundai Sonata Transform', 'Hyundai Verna',\n"," 'Hyundai Verna 1.4', 'Hyundai Verna 1.6', 'Hyundai Verna Fluidic',\n"," 'Hyundai Verna Transform', 'Hyundai Verna VGT',\n"," 'Hyundai Xcent Base', 'Hyundai Xcent SX', 'Hyundai i10',\n"," 'Hyundai i10 Era', 'Hyundai i10 Magna', 'Hyundai i10 Sportz',\n"," 'Hyundai i20', 'Hyundai i20 Active', 'Hyundai i20 Asta',\n"," 'Hyundai i20 Magna', 'Hyundai i20 Select', 'Hyundai i20 Sportz',\n"," 'Jaguar XE XE', 'Jaguar XF 2.2', 'Jeep Wrangler Unlimited',\n"," 'Land Rover Freelander', 'Mahindra Bolero DI',\n"," 'Mahindra Bolero Power', 'Mahindra Bolero SLE',\n"," 'Mahindra Jeep CL550', 'Mahindra Jeep MM', 'Mahindra KUV100',\n"," 'Mahindra KUV100 K8', 'Mahindra Logan', 'Mahindra Logan Diesel',\n"," 'Mahindra Quanto C4', 'Mahindra Quanto C8', 'Mahindra Scorpio',\n"," 'Mahindra Scorpio 2.6', 'Mahindra Scorpio LX',\n"," 'Mahindra Scorpio S10', 'Mahindra Scorpio S4',\n"," 'Mahindra Scorpio SLE', 'Mahindra Scorpio SLX',\n"," 'Mahindra Scorpio VLX', 'Mahindra Scorpio Vlx',\n"," 'Mahindra Scorpio W', 'Mahindra TUV300 T4', 'Mahindra TUV300 T8',\n"," 'Mahindra Thar CRDe', 'Mahindra XUV500', 'Mahindra XUV500 W10',\n"," 'Mahindra XUV500 W6', 'Mahindra XUV500 W8', 'Mahindra Xylo D2',\n"," 'Mahindra Xylo E4', 'Mahindra Xylo E8', 'Maruti Suzuki 800',\n"," 'Maruti Suzuki A', 'Maruti Suzuki Alto', 'Maruti Suzuki Baleno',\n"," 'Maruti Suzuki Celerio', 'Maruti Suzuki Ciaz',\n"," 'Maruti Suzuki Dzire', 'Maruti Suzuki Eeco',\n"," 'Maruti Suzuki Ertiga', 'Maruti Suzuki Esteem',\n"," 'Maruti Suzuki Estilo', 'Maruti Suzuki Maruti',\n"," 'Maruti Suzuki Omni', 'Maruti Suzuki Ritz', 'Maruti Suzuki S',\n"," 'Maruti Suzuki SX4', 'Maruti Suzuki Stingray',\n"," 'Maruti Suzuki Swift', 'Maruti Suzuki Versa',\n"," 'Maruti Suzuki Vitara', 'Maruti Suzuki Wagon', 'Maruti Suzuki Zen',\n"," 'Mercedes Benz A', 'Mercedes Benz B', 'Mercedes Benz C',\n"," 'Mercedes Benz GLA', 'Mini Cooper S', 'Mitsubishi Lancer 1.8',\n"," 'Mitsubishi Pajero Sport', 'Nissan Micra XL', 'Nissan Micra XV',\n"," 'Nissan Sunny', 'Nissan Sunny XL', 'Nissan Terrano XL',\n"," 'Nissan X Trail', 'Renault Duster', 'Renault Duster 110',\n"," 'Renault Duster 110PS', 'Renault Duster 85', 'Renault Duster 85PS',\n"," 'Renault Duster RxL', 'Renault Kwid', 'Renault Kwid 1.0',\n"," 'Renault Kwid RXT', 'Renault Lodgy 85', 'Renault Scala RxL',\n"," 'Skoda Fabia', 'Skoda Fabia 1.2L', 'Skoda Fabia Classic',\n"," 'Skoda Laura', 'Skoda Octavia Classic', 'Skoda Rapid Elegance',\n"," 'Skoda Superb 1.8', 'Skoda Yeti Ambition', 'Tata Aria Pleasure',\n"," 'Tata Bolt XM', 'Tata Indica', 'Tata Indica V2', 'Tata Indica eV2',\n"," 'Tata Indigo CS', 'Tata Indigo LS', 'Tata Indigo LX',\n"," 'Tata Indigo Marina', 'Tata Indigo eCS', 'Tata Manza',\n"," 'Tata Manza Aqua', 'Tata Manza Aura', 'Tata Manza ELAN',\n"," 'Tata Nano', 'Tata Nano Cx', 'Tata Nano GenX', 'Tata Nano LX',\n"," 'Tata Nano Lx', 'Tata Sumo Gold', 'Tata Sumo Grande',\n"," 'Tata Sumo Victa', 'Tata Tiago Revotorq', 'Tata Tiago Revotron',\n"," 'Tata Tigor Revotron', 'Tata Venture EX', 'Tata Vista Quadrajet',\n"," 'Tata Zest Quadrajet', 'Tata Zest XE', 'Tata Zest XM',\n"," 'Toyota Corolla', 'Toyota Corolla Altis', 'Toyota Corolla H2',\n"," 'Toyota Etios', 'Toyota Etios G', 'Toyota Etios GD',\n"," 'Toyota Etios Liva', 'Toyota Fortuner', 'Toyota Fortuner 3.0',\n"," 'Toyota Innova 2.0', 'Toyota Innova 2.5', 'Toyota Qualis',\n"," 'Volkswagen Jetta Comfortline', 'Volkswagen Jetta Highline',\n"," 'Volkswagen Passat Diesel', 'Volkswagen Polo',\n"," 'Volkswagen Polo Comfortline', 'Volkswagen Polo Highline',\n"," 'Volkswagen Polo Highline1.2L', 'Volkswagen Polo Trendline',\n"," 'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n"," 'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object)"]},"execution_count":55,"metadata":{},"output_type":"execute_result"}],"source":["pipe.steps[0][1].transformers[0][1].categories[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qGp9xSicASYD"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.3"}},"nbformat":4,"nbformat_minor":0} diff --git a/models/PreOwnedCarPrediction/predict.py b/models/PreOwnedCarPrediction/predict.py new file mode 100644 index 00000000..449d0b14 --- /dev/null +++ b/models/PreOwnedCarPrediction/predict.py @@ -0,0 +1,16 @@ +import joblib +import pandas as pd + +class CarPricePredictor: + def __init__(self, model_path): + self.model = joblib.load(model_path) + + def predict(self, input_data): + return self.model.predict(input_data) + +if __name__ == "__main__": + predictor = CarPricePredictor('saved_models/car_price_model.pkl') + # Example input data, replace with actual data + input_data = pd.DataFrame([[...]], columns=[...]) # Replace with actual column names + predictions = predictor.predict(input_data) + print(predictions) \ No newline at end of file