From 666ed1f7f7ebce0d2bbf8a8b1362ab9f28cf5550 Mon Sep 17 00:00:00 2001
From: YaraLis <147532921+YaraLis@users.noreply.github.com>
Date: Thu, 14 Dec 2023 18:11:33 +0000
Subject: [PATCH] Add files via upload

---
 auto-mpg.csv |  798 +++++++++++++++----------------
 main.ipynb   | 1275 ++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 1674 insertions(+), 399 deletions(-)
 create mode 100644 main.ipynb

diff --git a/auto-mpg.csv b/auto-mpg.csv
index b10c25b..ca36bfd 100644
--- a/auto-mpg.csv
+++ b/auto-mpg.csv
@@ -1,399 +1,399 @@
-mpg,cylinders,displacement,horse_power,weight,acceleration,model_year,car_name
-18,8,307,130,3504,12,70,"	""chevrolet chevelle malibu"""
-15,8,350,165,3693,11.5,70,"	""buick skylark 320"""
-18,8,318,150,3436,11,70,"	""plymouth satellite"""
-16,8,304,150,3433,12,70,"	""amc rebel sst"""
-17,8,302,140,3449,10.5,70,"	""ford torino"""
-15,8,429,198,4341,10,70,"	""ford galaxie 500"""
-14,8,454,220,4354,9,70,"	""chevrolet impala"""
-14,8,440,215,4312,8.5,70,"	""plymouth fury iii"""
-14,8,455,225,4425,10,70,"	""pontiac catalina"""
-15,8,390,190,3850,8.5,70,"	""amc ambassador dpl"""
-15,8,383,170,3563,10,70,"	""dodge challenger se"""
-14,8,340,160,3609,8,70,"	""plymouth 'cuda 340"""
-15,8,400,150,3761,9.5,70,"	""chevrolet monte carlo"""
-14,8,455,225,3086,10,70,"	""buick estate wagon (sw)"""
-24,4,113,95,2372,15,70,"	""toyota corona mark ii"""
-22,6,198,95,2833,15.5,70,"	""plymouth duster"""
-18,6,199,97,2774,15.5,70,"	""amc hornet"""
-21,6,200,85,2587,16,70,"	""ford maverick"""
-27,4,97,88,2130,14.5,70,"	""datsun pl510"""
-26,4,97,46,1835,20.5,70,"	""volkswagen 1131 deluxe sedan"""
-25,4,110,87,2672,17.5,70,"	""peugeot 504"""
-24,4,107,90,2430,14.5,70,"	""audi 100 ls"""
-25,4,104,95,2375,17.5,70,"	""saab 99e"""
-26,4,121,113,2234,12.5,70,"	""bmw 2002"""
-21,6,199,90,2648,15,70,"	""amc gremlin"""
-10,8,360,215,4615,14,70,"	""ford f250"""
-10,8,307,200,4376,15,70,"	""chevy c20"""
-11,8,318,210,4382,13.5,70,"	""dodge d200"""
-9,8,304,193,4732,18.5,70,"	""hi 1200d"""
-27,4,97,88,2130,14.5,71,"	""datsun pl510"""
-28,4,140,90,2264,15.5,71,"	""chevrolet vega 2300"""
-25,4,113,95,2228,14,71,"	""toyota corona"""
-25,4,98,,2046,19,71,"	""ford pinto"""
-19,6,232,100,2634,13,71,"	""amc gremlin"""
-16,6,225,105,3439,15.5,71,"	""plymouth satellite custom"""
-17,6,250,100,3329,15.5,71,"	""chevrolet chevelle malibu"""
-19,6,250,88,3302,15.5,71,"	""ford torino 500"""
-18,6,232,100,3288,15.5,71,"	""amc matador"""
-14,8,350,165,4209,12,71,"	""chevrolet impala"""
-14,8,400,175,4464,11.5,71,"	""pontiac catalina brougham"""
-14,8,351,153,4154,13.5,71,"	""ford galaxie 500"""
-14,8,318,150,4096,13,71,"	""plymouth fury iii"""
-12,8,383,180,4955,11.5,71,"	""dodge monaco (sw)"""
-13,8,400,170,4746,12,71,"	""ford country squire (sw)"""
-13,8,400,175,5140,12,71,"	""pontiac safari (sw)"""
-18,6,258,110,2962,13.5,71,"	""amc hornet sportabout (sw)"""
-22,4,140,72,2408,19,71,"	""chevrolet vega (sw)"""
-19,6,250,100,3282,15,71,"	""pontiac firebird"""
-18,6,250,88,3139,14.5,71,"	""ford mustang"""
-23,4,122,86,2220,14,71,"	""mercury capri 2000"""
-28,4,116,90,2123,14,71,"	""opel 1900"""
-30,4,79,70,2074,19.5,71,"	""peugeot 304"""
-30,4,88,76,2065,14.5,71,"	""fiat 124b"""
-31,4,71,65,1773,19,71,"	""toyota corolla 1200"""
-35,4,72,69,1613,18,71,"	""datsun 1200"""
-27,4,97,60,1834,19,71,"	""volkswagen model 111"""
-26,4,91,70,1955,20.5,71,"	""plymouth cricket"""
-24,4,113,95,2278,15.5,72,"	""toyota corona hardtop"""
-25,4,97.5,80,2126,17,72,"	""dodge colt hardtop"""
-23,4,97,54,2254,23.5,72,"	""volkswagen type 3"""
-20,4,140,90,2408,19.5,72,"	""chevrolet vega"""
-21,4,122,86,2226,16.5,72,"	""ford pinto runabout"""
-13,8,350,165,4274,12,72,"	""chevrolet impala"""
-14,8,400,175,4385,12,72,"	""pontiac catalina"""
-15,8,318,150,4135,13.5,72,"	""plymouth fury iii"""
-14,8,351,153,4129,13,72,"	""ford galaxie 500"""
-17,8,304,150,3672,11.5,72,"	""amc ambassador sst"""
-11,8,429,208,4633,11,72,"	""mercury marquis"""
-13,8,350,155,4502,13.5,72,"	""buick lesabre custom"""
-12,8,350,160,4456,13.5,72,"	""oldsmobile delta 88 royale"""
-13,8,400,190,4422,12.5,72,"	""chrysler newport royal"""
-19,3,70,97,2330,13.5,72,"	""mazda rx2 coupe"""
-15,8,304,150,3892,12.5,72,"	""amc matador (sw)"""
-13,8,307,130,4098,14,72,"	""chevrolet chevelle concours (sw)"""
-13,8,302,140,4294,16,72,"	""ford gran torino (sw)"""
-14,8,318,150,4077,14,72,"	""plymouth satellite custom (sw)"""
-18,4,121,112,2933,14.5,72,"	""volvo 145e (sw)"""
-22,4,121,76,2511,18,72,"	""volkswagen 411 (sw)"""
-21,4,120,87,2979,19.5,72,"	""peugeot 504 (sw)"""
-26,4,96,69,2189,18,72,"	""renault 12 (sw)"""
-22,4,122,86,2395,16,72,"	""ford pinto (sw)"""
-28,4,97,92,2288,17,72,"	""datsun 510 (sw)"""
-23,4,120,97,2506,14.5,72,"	""toyouta corona mark ii (sw)"""
-28,4,98,80,2164,15,72,"	""dodge colt (sw)"""
-27,4,97,88,2100,16.5,72,"	""toyota corolla 1600 (sw)"""
-13,8,350,175,4100,13,73,"	""buick century 350"""
-14,8,304,150,3672,11.5,73,"	""amc matador"""
-13,8,350,145,3988,13,73,"	""chevrolet malibu"""
-14,8,302,137,4042,14.5,73,"	""ford gran torino"""
-15,8,318,150,3777,12.5,73,"	""dodge coronet custom"""
-12,8,429,198,4952,11.5,73,"	""mercury marquis brougham"""
-13,8,400,150,4464,12,73,"	""chevrolet caprice classic"""
-13,8,351,158,4363,13,73,"	""ford ltd"""
-14,8,318,150,4237,14.5,73,"	""plymouth fury gran sedan"""
-13,8,440,215,4735,11,73,"	""chrysler new yorker brougham"""
-12,8,455,225,4951,11,73,"	""buick electra 225 custom"""
-13,8,360,175,3821,11,73,"	""amc ambassador brougham"""
-18,6,225,105,3121,16.5,73,"	""plymouth valiant"""
-16,6,250,100,3278,18,73,"	""chevrolet nova custom"""
-18,6,232,100,2945,16,73,"	""amc hornet"""
-18,6,250,88,3021,16.5,73,"	""ford maverick"""
-23,6,198,95,2904,16,73,"	""plymouth duster"""
-26,4,97,46,1950,21,73,"	""volkswagen super beetle"""
-11,8,400,150,4997,14,73,"	""chevrolet impala"""
-12,8,400,167,4906,12.5,73,"	""ford country"""
-13,8,360,170,4654,13,73,"	""plymouth custom suburb"""
-12,8,350,180,4499,12.5,73,"	""oldsmobile vista cruiser"""
-18,6,232,100,2789,15,73,"	""amc gremlin"""
-20,4,97,88,2279,19,73,"	""toyota carina"""
-21,4,140,72,2401,19.5,73,"	""chevrolet vega"""
-22,4,108,94,2379,16.5,73,"	""datsun 610"""
-18,3,70,90,2124,13.5,73,"	""maxda rx3"""
-19,4,122,85,2310,18.5,73,"	""ford pinto"""
-21,6,155,107,2472,14,73,"	""mercury capri v6"""
-26,4,98,90,2265,15.5,73,"	""fiat 124 sport coupe"""
-15,8,350,145,4082,13,73,"	""chevrolet monte carlo s"""
-16,8,400,230,4278,9.5,73,"	""pontiac grand prix"""
-29,4,68,49,1867,19.5,73,"	""fiat 128"""
-24,4,116,75,2158,15.5,73,"	""opel manta"""
-20,4,114,91,2582,14,73,"	""audi 100ls"""
-19,4,121,112,2868,15.5,73,"	""volvo 144ea"""
-15,8,318,150,3399,11,73,"	""dodge dart custom"""
-24,4,121,110,2660,14,73,"	""saab 99le"""
-20,6,156,122,2807,13.5,73,"	""toyota mark ii"""
-11,8,350,180,3664,11,73,"	""oldsmobile omega"""
-20,6,198,95,3102,16.5,74,"	""plymouth duster"""
-21,6,200,,2875,17,74,"	""ford maverick"""
-19,6,232,100,2901,16,74,"	""amc hornet"""
-15,6,250,100,3336,17,74,"	""chevrolet nova"""
-31,4,79,67,1950,19,74,"	""datsun b210"""
-26,4,122,80,2451,16.5,74,"	""ford pinto"""
-32,4,71,65,1836,21,74,"	""toyota corolla 1200"""
-25,4,140,75,2542,17,74,"	""chevrolet vega"""
-16,6,250,100,3781,17,74,"	""chevrolet chevelle malibu classic"""
-16,6,258,110,3632,18,74,"	""amc matador"""
-18,6,225,105,3613,16.5,74,"	""plymouth satellite sebring"""
-16,8,302,140,4141,14,74,"	""ford gran torino"""
-13,8,350,150,4699,14.5,74,"	""buick century luxus (sw)"""
-14,8,318,150,4457,13.5,74,"	""dodge coronet custom (sw)"""
-14,8,302,140,4638,16,74,"	""ford gran torino (sw)"""
-14,8,304,150,4257,15.5,74,"	""amc matador (sw)"""
-29,4,98,83,2219,16.5,74,"	""audi fox"""
-26,4,79,67,1963,15.5,74,"	""volkswagen dasher"""
-26,4,97,78,2300,14.5,74,"	""opel manta"""
-31,4,76,52,1649,16.5,74,"	""toyota corona"""
-32,4,83,61,2003,19,74,"	""datsun 710"""
-28,4,90,75,2125,14.5,74,"	""dodge colt"""
-24,4,90,75,2108,15.5,74,"	""fiat 128"""
-26,4,116,75,2246,14,74,"	""fiat 124 tc"""
-24,4,120,97,2489,15,74,"	""honda civic"""
-26,4,108,93,2391,15.5,74,"	""subaru"""
-31,4,79,67,2000,16,74,"	""fiat x1.9"""
-19,6,225,95,3264,16,75,"	""plymouth valiant custom"""
-18,6,250,105,3459,16,75,"	""chevrolet nova"""
-15,6,250,72,3432,21,75,"	""mercury monarch"""
-15,6,250,72,3158,19.5,75,"	""ford maverick"""
-16,8,400,170,4668,11.5,75,"	""pontiac catalina"""
-15,8,350,145,4440,14,75,"	""chevrolet bel air"""
-16,8,318,150,4498,14.5,75,"	""plymouth grand fury"""
-14,8,351,148,4657,13.5,75,"	""ford ltd"""
-17,6,231,110,3907,21,75,"	""buick century"""
-16,6,250,105,3897,18.5,75,"	""chevroelt chevelle malibu"""
-15,6,258,110,3730,19,75,"	""amc matador"""
-18,6,225,95,3785,19,75,"	""plymouth fury"""
-21,6,231,110,3039,15,75,"	""buick skyhawk"""
-20,8,262,110,3221,13.5,75,"	""chevrolet monza 2+2"""
-13,8,302,129,3169,12,75,"	""ford mustang ii"""
-29,4,97,75,2171,16,75,"	""toyota corolla"""
-23,4,140,83,2639,17,75,"	""ford pinto"""
-20,6,232,100,2914,16,75,"	""amc gremlin"""
-23,4,140,78,2592,18.5,75,"	""pontiac astro"""
-24,4,134,96,2702,13.5,75,"	""toyota corona"""
-25,4,90,71,2223,16.5,75,"	""volkswagen dasher"""
-24,4,119,97,2545,17,75,"	""datsun 710"""
-18,6,171,97,2984,14.5,75,"	""ford pinto"""
-29,4,90,70,1937,14,75,"	""volkswagen rabbit"""
-19,6,232,90,3211,17,75,"	""amc pacer"""
-23,4,115,95,2694,15,75,"	""audi 100ls"""
-23,4,120,88,2957,17,75,"	""peugeot 504"""
-22,4,121,98,2945,14.5,75,"	""volvo 244dl"""
-25,4,121,115,2671,13.5,75,"	""saab 99le"""
-33,4,91,53,1795,17.5,75,"	""honda civic cvcc"""
-28,4,107,86,2464,15.5,76,"	""fiat 131"""
-25,4,116,81,2220,16.9,76,"	""opel 1900"""
-25,4,140,92,2572,14.9,76,"	""capri ii"""
-26,4,98,79,2255,17.7,76,"	""dodge colt"""
-27,4,101,83,2202,15.3,76,"	""renault 12tl"""
-17.5,8,305,140,4215,13,76,"	""chevrolet chevelle malibu classic"""
-16,8,318,150,4190,13,76,"	""dodge coronet brougham"""
-15.5,8,304,120,3962,13.9,76,"	""amc matador"""
-14.5,8,351,152,4215,12.8,76,"	""ford gran torino"""
-22,6,225,100,3233,15.4,76,"	""plymouth valiant"""
-22,6,250,105,3353,14.5,76,"	""chevrolet nova"""
-24,6,200,81,3012,17.6,76,"	""ford maverick"""
-22.5,6,232,90,3085,17.6,76,"	""amc hornet"""
-29,4,85,52,2035,22.2,76,"	""chevrolet chevette"""
-24.5,4,98,60,2164,22.1,76,"	""chevrolet woody"""
-29,4,90,70,1937,14.2,76,"	""vw rabbit"""
-33,4,91,53,1795,17.4,76,"	""honda civic"""
-20,6,225,100,3651,17.7,76,"	""dodge aspen se"""
-18,6,250,78,3574,21,76,"	""ford granada ghia"""
-18.5,6,250,110,3645,16.2,76,"	""pontiac ventura sj"""
-17.5,6,258,95,3193,17.8,76,"	""amc pacer d/l"""
-29.5,4,97,71,1825,12.2,76,"	""volkswagen rabbit"""
-32,4,85,70,1990,17,76,"	""datsun b-210"""
-28,4,97,75,2155,16.4,76,"	""toyota corolla"""
-26.5,4,140,72,2565,13.6,76,"	""ford pinto"""
-20,4,130,102,3150,15.7,76,"	""volvo 245"""
-13,8,318,150,3940,13.2,76,"	""plymouth volare premier v8"""
-19,4,120,88,3270,21.9,76,"	""peugeot 504"""
-19,6,156,108,2930,15.5,76,"	""toyota mark ii"""
-16.5,6,168,120,3820,16.7,76,"	""mercedes-benz 280s"""
-16.5,8,350,180,4380,12.1,76,"	""cadillac seville"""
-13,8,350,145,4055,12,76,"	""chevy c10"""
-13,8,302,130,3870,15,76,"	""ford f108"""
-13,8,318,150,3755,14,76,"	""dodge d100"""
-31.5,4,98,68,2045,18.5,77,"	""honda accord cvcc"""
-30,4,111,80,2155,14.8,77,"	""buick opel isuzu deluxe"""
-36,4,79,58,1825,18.6,77,"	""renault 5 gtl"""
-25.5,4,122,96,2300,15.5,77,"	""plymouth arrow gs"""
-33.5,4,85,70,1945,16.8,77,"	""datsun f-10 hatchback"""
-17.5,8,305,145,3880,12.5,77,"	""chevrolet caprice classic"""
-17,8,260,110,4060,19,77,"	""oldsmobile cutlass supreme"""
-15.5,8,318,145,4140,13.7,77,"	""dodge monaco brougham"""
-15,8,302,130,4295,14.9,77,"	""mercury cougar brougham"""
-17.5,6,250,110,3520,16.4,77,"	""chevrolet concours"""
-20.5,6,231,105,3425,16.9,77,"	""buick skylark"""
-19,6,225,100,3630,17.7,77,"	""plymouth volare custom"""
-18.5,6,250,98,3525,19,77,"	""ford granada"""
-16,8,400,180,4220,11.1,77,"	""pontiac grand prix lj"""
-15.5,8,350,170,4165,11.4,77,"	""chevrolet monte carlo landau"""
-15.5,8,400,190,4325,12.2,77,"	""chrysler cordoba"""
-16,8,351,149,4335,14.5,77,"	""ford thunderbird"""
-29,4,97,78,1940,14.5,77,"	""volkswagen rabbit custom"""
-24.5,4,151,88,2740,16,77,"	""pontiac sunbird coupe"""
-26,4,97,75,2265,18.2,77,"	""toyota corolla liftback"""
-25.5,4,140,89,2755,15.8,77,"	""ford mustang ii 2+2"""
-30.5,4,98,63,2051,17,77,"	""chevrolet chevette"""
-33.5,4,98,83,2075,15.9,77,"	""dodge colt m/m"""
-30,4,97,67,1985,16.4,77,"	""subaru dl"""
-30.5,4,97,78,2190,14.1,77,"	""volkswagen dasher"""
-22,6,146,97,2815,14.5,77,"	""datsun 810"""
-21.5,4,121,110,2600,12.8,77,"	""bmw 320i"""
-21.5,3,80,110,2720,13.5,77,"	""mazda rx-4"""
-43.1,4,90,48,1985,21.5,78,"	""volkswagen rabbit custom diesel"""
-36.1,4,98,66,1800,14.4,78,"	""ford fiesta"""
-32.8,4,78,52,1985,19.4,78,"	""mazda glc deluxe"""
-39.4,4,85,70,2070,18.6,78,"	""datsun b210 gx"""
-36.1,4,91,60,1800,16.4,78,"	""honda civic cvcc"""
-19.9,8,260,110,3365,15.5,78,"	""oldsmobile cutlass salon brougham"""
-19.4,8,318,140,3735,13.2,78,"	""dodge diplomat"""
-20.2,8,302,139,3570,12.8,78,"	""mercury monarch ghia"""
-19.2,6,231,105,3535,19.2,78,"	""pontiac phoenix lj"""
-20.5,6,200,95,3155,18.2,78,"	""chevrolet malibu"""
-20.2,6,200,85,2965,15.8,78,"	""ford fairmont (auto)"""
-25.1,4,140,88,2720,15.4,78,"	""ford fairmont (man)"""
-20.5,6,225,100,3430,17.2,78,"	""plymouth volare"""
-19.4,6,232,90,3210,17.2,78,"	""amc concord"""
-20.6,6,231,105,3380,15.8,78,"	""buick century special"""
-20.8,6,200,85,3070,16.7,78,"	""mercury zephyr"""
-18.6,6,225,110,3620,18.7,78,"	""dodge aspen"""
-18.1,6,258,120,3410,15.1,78,"	""amc concord d/l"""
-19.2,8,305,145,3425,13.2,78,"	""chevrolet monte carlo landau"""
-17.7,6,231,165,3445,13.4,78,"	""buick regal sport coupe (turbo)"""
-18.1,8,302,139,3205,11.2,78,"	""ford futura"""
-17.5,8,318,140,4080,13.7,78,"	""dodge magnum xe"""
-30,4,98,68,2155,16.5,78,"	""chevrolet chevette"""
-27.5,4,134,95,2560,14.2,78,"	""toyota corona"""
-27.2,4,119,97,2300,14.7,78,"	""datsun 510"""
-30.9,4,105,75,2230,14.5,78,"	""dodge omni"""
-21.1,4,134,95,2515,14.8,78,"	""toyota celica gt liftback"""
-23.2,4,156,105,2745,16.7,78,"	""plymouth sapporo"""
-23.8,4,151,85,2855,17.6,78,"	""oldsmobile starfire sx"""
-23.9,4,119,97,2405,14.9,78,"	""datsun 200-sx"""
-20.3,5,131,103,2830,15.9,78,"	""audi 5000"""
-17,6,163,125,3140,13.6,78,"	""volvo 264gl"""
-21.6,4,121,115,2795,15.7,78,"	""saab 99gle"""
-16.2,6,163,133,3410,15.8,78,"	""peugeot 604sl"""
-31.5,4,89,71,1990,14.9,78,"	""volkswagen scirocco"""
-29.5,4,98,68,2135,16.6,78,"	""honda accord lx"""
-21.5,6,231,115,3245,15.4,79,"	""pontiac lemans v6"""
-19.8,6,200,85,2990,18.2,79,"	""mercury zephyr 6"""
-22.3,4,140,88,2890,17.3,79,"	""ford fairmont 4"""
-20.2,6,232,90,3265,18.2,79,"	""amc concord dl 6"""
-20.6,6,225,110,3360,16.6,79,"	""dodge aspen 6"""
-17,8,305,130,3840,15.4,79,"	""chevrolet caprice classic"""
-17.6,8,302,129,3725,13.4,79,"	""ford ltd landau"""
-16.5,8,351,138,3955,13.2,79,"	""mercury grand marquis"""
-18.2,8,318,135,3830,15.2,79,"	""dodge st. regis"""
-16.9,8,350,155,4360,14.9,79,"	""buick estate wagon (sw)"""
-15.5,8,351,142,4054,14.3,79,"	""ford country squire (sw)"""
-19.2,8,267,125,3605,15,79,"	""chevrolet malibu classic (sw)"""
-18.5,8,360,150,3940,13,79,"	""chrysler lebaron town @ country (sw)"""
-31.9,4,89,71,1925,14,79,"	""vw rabbit custom"""
-34.1,4,86,65,1975,15.2,79,"	""maxda glc deluxe"""
-35.7,4,98,80,1915,14.4,79,"	""dodge colt hatchback custom"""
-27.4,4,121,80,2670,15,79,"	""amc spirit dl"""
-25.4,5,183,77,3530,20.1,79,"	""mercedes benz 300d"""
-23,8,350,125,3900,17.4,79,"	""cadillac eldorado"""
-27.2,4,141,71,3190,24.8,79,"	""peugeot 504"""
-23.9,8,260,90,3420,22.2,79,"	""oldsmobile cutlass salon brougham"""
-34.2,4,105,70,2200,13.2,79,"	""plymouth horizon"""
-34.5,4,105,70,2150,14.9,79,"	""plymouth horizon tc3"""
-31.8,4,85,65,2020,19.2,79,"	""datsun 210"""
-37.3,4,91,69,2130,14.7,79,"	""fiat strada custom"""
-28.4,4,151,90,2670,16,79,"	""buick skylark limited"""
-28.8,6,173,115,2595,11.3,79,"	""chevrolet citation"""
-26.8,6,173,115,2700,12.9,79,"	""oldsmobile omega brougham"""
-33.5,4,151,90,2556,13.2,79,"	""pontiac phoenix"""
-41.5,4,98,76,2144,14.7,80,"	""vw rabbit"""
-38.1,4,89,60,1968,18.8,80,"	""toyota corolla tercel"""
-32.1,4,98,70,2120,15.5,80,"	""chevrolet chevette"""
-37.2,4,86,65,2019,16.4,80,"	""datsun 310"""
-28,4,151,90,2678,16.5,80,"	""chevrolet citation"""
-26.4,4,140,88,2870,18.1,80,"	""ford fairmont"""
-24.3,4,151,90,3003,20.1,80,"	""amc concord"""
-19.1,6,225,90,3381,18.7,80,"	""dodge aspen"""
-34.3,4,97,78,2188,15.8,80,"	""audi 4000"""
-29.8,4,134,90,2711,15.5,80,"	""toyota corona liftback"""
-31.3,4,120,75,2542,17.5,80,"	""mazda 626"""
-37,4,119,92,2434,15,80,"	""datsun 510 hatchback"""
-32.2,4,108,75,2265,15.2,80,"	""toyota corolla"""
-46.6,4,86,65,2110,17.9,80,"	""mazda glc"""
-27.9,4,156,105,2800,14.4,80,"	""dodge colt"""
-40.8,4,85,65,2110,19.2,80,"	""datsun 210"""
-44.3,4,90,48,2085,21.7,80,"	""vw rabbit c (diesel)"""
-43.4,4,90,48,2335,23.7,80,"	""vw dasher (diesel)"""
-36.4,5,121,67,2950,19.9,80,"	""audi 5000s (diesel)"""
-30,4,146,67,3250,21.8,80,"	""mercedes-benz 240d"""
-44.6,4,91,67,1850,13.8,80,"	""honda civic 1500 gl"""
-40.9,4,85,,1835,17.3,80,"	""renault lecar deluxe"""
-33.8,4,97,67,2145,18,80,"	""subaru dl"""
-29.8,4,89,62,1845,15.3,80,"	""vokswagen rabbit"""
-32.7,6,168,132,2910,11.4,80,"	""datsun 280-zx"""
-23.7,3,70,100,2420,12.5,80,"	""mazda rx-7 gs"""
-35,4,122,88,2500,15.1,80,"	""triumph tr7 coupe"""
-23.6,4,140,,2905,14.3,80,"	""ford mustang cobra"""
-32.4,4,107,72,2290,17,80,"	""honda accord"""
-27.2,4,135,84,2490,15.7,81,"	""plymouth reliant"""
-26.6,4,151,84,2635,16.4,81,"	""buick skylark"""
-25.8,4,156,92,2620,14.4,81,"	""dodge aries wagon (sw)"""
-23.5,6,173,110,2725,12.6,81,"	""chevrolet citation"""
-30,4,135,84,2385,12.9,81,"	""plymouth reliant"""
-39.1,4,79,58,1755,16.9,81,"	""toyota starlet"""
-39,4,86,64,1875,16.4,81,"	""plymouth champ"""
-35.1,4,81,60,1760,16.1,81,"	""honda civic 1300"""
-32.3,4,97,67,2065,17.8,81,"	""subaru"""
-37,4,85,65,1975,19.4,81,"	""datsun 210 mpg"""
-37.7,4,89,62,2050,17.3,81,"	""toyota tercel"""
-34.1,4,91,68,1985,16,81,"	""mazda glc 4"""
-34.7,4,105,63,2215,14.9,81,"	""plymouth horizon 4"""
-34.4,4,98,65,2045,16.2,81,"	""ford escort 4w"""
-29.9,4,98,65,2380,20.7,81,"	""ford escort 2h"""
-33,4,105,74,2190,14.2,81,"	""volkswagen jetta"""
-34.5,4,100,,2320,15.8,81,"	""renault 18i"""
-33.7,4,107,75,2210,14.4,81,"	""honda prelude"""
-32.4,4,108,75,2350,16.8,81,"	""toyota corolla"""
-32.9,4,119,100,2615,14.8,81,"	""datsun 200sx"""
-31.6,4,120,74,2635,18.3,81,"	""mazda 626"""
-28.1,4,141,80,3230,20.4,81,"	""peugeot 505s turbo diesel"""
-30.7,6,145,76,3160,19.6,81,"	""volvo diesel"""
-25.4,6,168,116,2900,12.6,81,"	""toyota cressida"""
-24.2,6,146,120,2930,13.8,81,"	""datsun 810 maxima"""
-22.4,6,231,110,3415,15.8,81,"	""buick century"""
-26.6,8,350,105,3725,19,81,"	""oldsmobile cutlass ls"""
-20.2,6,200,88,3060,17.1,81,"	""ford granada gl"""
-17.6,6,225,85,3465,16.6,81,"	""chrysler lebaron salon"""
-28,4,112,88,2605,19.6,82,"	""chevrolet cavalier"""
-27,4,112,88,2640,18.6,82,"	""chevrolet cavalier wagon"""
-34,4,112,88,2395,18,82,"	""chevrolet cavalier 2-door"""
-31,4,112,85,2575,16.2,82,"	""pontiac j2000 se hatchback"""
-29,4,135,84,2525,16,82,"	""dodge aries se"""
-27,4,151,90,2735,18,82,"	""pontiac phoenix"""
-24,4,140,92,2865,16.4,82,"	""ford fairmont futura"""
-23,4,151,,3035,20.5,82,"	""amc concord dl"""
-36,4,105,74,1980,15.3,82,"	""volkswagen rabbit l"""
-37,4,91,68,2025,18.2,82,"	""mazda glc custom l"""
-31,4,91,68,1970,17.6,82,"	""mazda glc custom"""
-38,4,105,63,2125,14.7,82,"	""plymouth horizon miser"""
-36,4,98,70,2125,17.3,82,"	""mercury lynx l"""
-36,4,120,88,2160,14.5,82,"	""nissan stanza xe"""
-36,4,107,75,2205,14.5,82,"	""honda accord"""
-34,4,108,70,2245,16.9,82,"	""toyota corolla"""
-38,4,91,67,1965,15,82,"	""honda civic"""
-32,4,91,67,1965,15.7,82,"	""honda civic (auto)"""
-38,4,91,67,1995,16.2,82,"	""datsun 310 gx"""
-25,6,181,110,2945,16.4,82,"	""buick century limited"""
-38,6,262,85,3015,17,82,"	""oldsmobile cutlass ciera (diesel)"""
-26,4,156,92,2585,14.5,82,"	""chrysler lebaron medallion"""
-22,6,232,112,2835,14.7,82,"	""ford granada l"""
-32,4,144,96,2665,13.9,82,"	""toyota celica gt"""
-36,4,135,84,2370,13,82,"	""dodge charger 2.2"""
-27,4,151,90,2950,17.3,82,"	""chevrolet camaro"""
-27,4,140,86,2790,15.6,82,"	""ford mustang gl"""
-44,4,97,52,2130,24.6,82,"	""vw pickup"""
-32,4,135,84,2295,11.6,82,"	""dodge rampage"""
-28,4,120,79,2625,18.6,82,"	""ford ranger"""
-31,4,119,82,2720,19.4,82,"	""chevy s-10"""
+mpg,cylinders,displacement,horse_power,weight,acceleration,model_year,car_name
+18,8,307,130,3504,12,70,"	""chevrolet chevelle malibu"""
+15,8,350,165,3693,11.5,70,"	""buick skylark 320"""
+18,8,318,150,3436,11,70,"	""plymouth satellite"""
+16,8,304,150,3433,12,70,"	""amc rebel sst"""
+17,8,302,140,3449,10.5,70,"	""ford torino"""
+15,8,429,198,4341,10,70,"	""ford galaxie 500"""
+14,8,454,220,4354,9,70,"	""chevrolet impala"""
+14,8,440,215,4312,8.5,70,"	""plymouth fury iii"""
+14,8,455,225,4425,10,70,"	""pontiac catalina"""
+15,8,390,190,3850,8.5,70,"	""amc ambassador dpl"""
+15,8,383,170,3563,10,70,"	""dodge challenger se"""
+14,8,340,160,3609,8,70,"	""plymouth 'cuda 340"""
+15,8,400,150,3761,9.5,70,"	""chevrolet monte carlo"""
+14,8,455,225,3086,10,70,"	""buick estate wagon (sw)"""
+24,4,113,95,2372,15,70,"	""toyota corona mark ii"""
+22,6,198,95,2833,15.5,70,"	""plymouth duster"""
+18,6,199,97,2774,15.5,70,"	""amc hornet"""
+21,6,200,85,2587,16,70,"	""ford maverick"""
+27,4,97,88,2130,14.5,70,"	""datsun pl510"""
+26,4,97,46,1835,20.5,70,"	""volkswagen 1131 deluxe sedan"""
+25,4,110,87,2672,17.5,70,"	""peugeot 504"""
+24,4,107,90,2430,14.5,70,"	""audi 100 ls"""
+25,4,104,95,2375,17.5,70,"	""saab 99e"""
+26,4,121,113,2234,12.5,70,"	""bmw 2002"""
+21,6,199,90,2648,15,70,"	""amc gremlin"""
+10,8,360,215,4615,14,70,"	""ford f250"""
+10,8,307,200,4376,15,70,"	""chevy c20"""
+11,8,318,210,4382,13.5,70,"	""dodge d200"""
+9,8,304,193,4732,18.5,70,"	""hi 1200d"""
+27,4,97,88,2130,14.5,71,"	""datsun pl510"""
+28,4,140,90,2264,15.5,71,"	""chevrolet vega 2300"""
+25,4,113,95,2228,14,71,"	""toyota corona"""
+25,4,98,,2046,19,71,"	""ford pinto"""
+19,6,232,100,2634,13,71,"	""amc gremlin"""
+16,6,225,105,3439,15.5,71,"	""plymouth satellite custom"""
+17,6,250,100,3329,15.5,71,"	""chevrolet chevelle malibu"""
+19,6,250,88,3302,15.5,71,"	""ford torino 500"""
+18,6,232,100,3288,15.5,71,"	""amc matador"""
+14,8,350,165,4209,12,71,"	""chevrolet impala"""
+14,8,400,175,4464,11.5,71,"	""pontiac catalina brougham"""
+14,8,351,153,4154,13.5,71,"	""ford galaxie 500"""
+14,8,318,150,4096,13,71,"	""plymouth fury iii"""
+12,8,383,180,4955,11.5,71,"	""dodge monaco (sw)"""
+13,8,400,170,4746,12,71,"	""ford country squire (sw)"""
+13,8,400,175,5140,12,71,"	""pontiac safari (sw)"""
+18,6,258,110,2962,13.5,71,"	""amc hornet sportabout (sw)"""
+22,4,140,72,2408,19,71,"	""chevrolet vega (sw)"""
+19,6,250,100,3282,15,71,"	""pontiac firebird"""
+18,6,250,88,3139,14.5,71,"	""ford mustang"""
+23,4,122,86,2220,14,71,"	""mercury capri 2000"""
+28,4,116,90,2123,14,71,"	""opel 1900"""
+30,4,79,70,2074,19.5,71,"	""peugeot 304"""
+30,4,88,76,2065,14.5,71,"	""fiat 124b"""
+31,4,71,65,1773,19,71,"	""toyota corolla 1200"""
+35,4,72,69,1613,18,71,"	""datsun 1200"""
+27,4,97,60,1834,19,71,"	""volkswagen model 111"""
+26,4,91,70,1955,20.5,71,"	""plymouth cricket"""
+24,4,113,95,2278,15.5,72,"	""toyota corona hardtop"""
+25,4,97.5,80,2126,17,72,"	""dodge colt hardtop"""
+23,4,97,54,2254,23.5,72,"	""volkswagen type 3"""
+20,4,140,90,2408,19.5,72,"	""chevrolet vega"""
+21,4,122,86,2226,16.5,72,"	""ford pinto runabout"""
+13,8,350,165,4274,12,72,"	""chevrolet impala"""
+14,8,400,175,4385,12,72,"	""pontiac catalina"""
+15,8,318,150,4135,13.5,72,"	""plymouth fury iii"""
+14,8,351,153,4129,13,72,"	""ford galaxie 500"""
+17,8,304,150,3672,11.5,72,"	""amc ambassador sst"""
+11,8,429,208,4633,11,72,"	""mercury marquis"""
+13,8,350,155,4502,13.5,72,"	""buick lesabre custom"""
+12,8,350,160,4456,13.5,72,"	""oldsmobile delta 88 royale"""
+13,8,400,190,4422,12.5,72,"	""chrysler newport royal"""
+19,3,70,97,2330,13.5,72,"	""mazda rx2 coupe"""
+15,8,304,150,3892,12.5,72,"	""amc matador (sw)"""
+13,8,307,130,4098,14,72,"	""chevrolet chevelle concours (sw)"""
+13,8,302,140,4294,16,72,"	""ford gran torino (sw)"""
+14,8,318,150,4077,14,72,"	""plymouth satellite custom (sw)"""
+18,4,121,112,2933,14.5,72,"	""volvo 145e (sw)"""
+22,4,121,76,2511,18,72,"	""volkswagen 411 (sw)"""
+21,4,120,87,2979,19.5,72,"	""peugeot 504 (sw)"""
+26,4,96,69,2189,18,72,"	""renault 12 (sw)"""
+22,4,122,86,2395,16,72,"	""ford pinto (sw)"""
+28,4,97,92,2288,17,72,"	""datsun 510 (sw)"""
+23,4,120,97,2506,14.5,72,"	""toyouta corona mark ii (sw)"""
+28,4,98,80,2164,15,72,"	""dodge colt (sw)"""
+27,4,97,88,2100,16.5,72,"	""toyota corolla 1600 (sw)"""
+13,8,350,175,4100,13,73,"	""buick century 350"""
+14,8,304,150,3672,11.5,73,"	""amc matador"""
+13,8,350,145,3988,13,73,"	""chevrolet malibu"""
+14,8,302,137,4042,14.5,73,"	""ford gran torino"""
+15,8,318,150,3777,12.5,73,"	""dodge coronet custom"""
+12,8,429,198,4952,11.5,73,"	""mercury marquis brougham"""
+13,8,400,150,4464,12,73,"	""chevrolet caprice classic"""
+13,8,351,158,4363,13,73,"	""ford ltd"""
+14,8,318,150,4237,14.5,73,"	""plymouth fury gran sedan"""
+13,8,440,215,4735,11,73,"	""chrysler new yorker brougham"""
+12,8,455,225,4951,11,73,"	""buick electra 225 custom"""
+13,8,360,175,3821,11,73,"	""amc ambassador brougham"""
+18,6,225,105,3121,16.5,73,"	""plymouth valiant"""
+16,6,250,100,3278,18,73,"	""chevrolet nova custom"""
+18,6,232,100,2945,16,73,"	""amc hornet"""
+18,6,250,88,3021,16.5,73,"	""ford maverick"""
+23,6,198,95,2904,16,73,"	""plymouth duster"""
+26,4,97,46,1950,21,73,"	""volkswagen super beetle"""
+11,8,400,150,4997,14,73,"	""chevrolet impala"""
+12,8,400,167,4906,12.5,73,"	""ford country"""
+13,8,360,170,4654,13,73,"	""plymouth custom suburb"""
+12,8,350,180,4499,12.5,73,"	""oldsmobile vista cruiser"""
+18,6,232,100,2789,15,73,"	""amc gremlin"""
+20,4,97,88,2279,19,73,"	""toyota carina"""
+21,4,140,72,2401,19.5,73,"	""chevrolet vega"""
+22,4,108,94,2379,16.5,73,"	""datsun 610"""
+18,3,70,90,2124,13.5,73,"	""maxda rx3"""
+19,4,122,85,2310,18.5,73,"	""ford pinto"""
+21,6,155,107,2472,14,73,"	""mercury capri v6"""
+26,4,98,90,2265,15.5,73,"	""fiat 124 sport coupe"""
+15,8,350,145,4082,13,73,"	""chevrolet monte carlo s"""
+16,8,400,230,4278,9.5,73,"	""pontiac grand prix"""
+29,4,68,49,1867,19.5,73,"	""fiat 128"""
+24,4,116,75,2158,15.5,73,"	""opel manta"""
+20,4,114,91,2582,14,73,"	""audi 100ls"""
+19,4,121,112,2868,15.5,73,"	""volvo 144ea"""
+15,8,318,150,3399,11,73,"	""dodge dart custom"""
+24,4,121,110,2660,14,73,"	""saab 99le"""
+20,6,156,122,2807,13.5,73,"	""toyota mark ii"""
+11,8,350,180,3664,11,73,"	""oldsmobile omega"""
+20,6,198,95,3102,16.5,74,"	""plymouth duster"""
+21,6,200,,2875,17,74,"	""ford maverick"""
+19,6,232,100,2901,16,74,"	""amc hornet"""
+15,6,250,100,3336,17,74,"	""chevrolet nova"""
+31,4,79,67,1950,19,74,"	""datsun b210"""
+26,4,122,80,2451,16.5,74,"	""ford pinto"""
+32,4,71,65,1836,21,74,"	""toyota corolla 1200"""
+25,4,140,75,2542,17,74,"	""chevrolet vega"""
+16,6,250,100,3781,17,74,"	""chevrolet chevelle malibu classic"""
+16,6,258,110,3632,18,74,"	""amc matador"""
+18,6,225,105,3613,16.5,74,"	""plymouth satellite sebring"""
+16,8,302,140,4141,14,74,"	""ford gran torino"""
+13,8,350,150,4699,14.5,74,"	""buick century luxus (sw)"""
+14,8,318,150,4457,13.5,74,"	""dodge coronet custom (sw)"""
+14,8,302,140,4638,16,74,"	""ford gran torino (sw)"""
+14,8,304,150,4257,15.5,74,"	""amc matador (sw)"""
+29,4,98,83,2219,16.5,74,"	""audi fox"""
+26,4,79,67,1963,15.5,74,"	""volkswagen dasher"""
+26,4,97,78,2300,14.5,74,"	""opel manta"""
+31,4,76,52,1649,16.5,74,"	""toyota corona"""
+32,4,83,61,2003,19,74,"	""datsun 710"""
+28,4,90,75,2125,14.5,74,"	""dodge colt"""
+24,4,90,75,2108,15.5,74,"	""fiat 128"""
+26,4,116,75,2246,14,74,"	""fiat 124 tc"""
+24,4,120,97,2489,15,74,"	""honda civic"""
+26,4,108,93,2391,15.5,74,"	""subaru"""
+31,4,79,67,2000,16,74,"	""fiat x1.9"""
+19,6,225,95,3264,16,75,"	""plymouth valiant custom"""
+18,6,250,105,3459,16,75,"	""chevrolet nova"""
+15,6,250,72,3432,21,75,"	""mercury monarch"""
+15,6,250,72,3158,19.5,75,"	""ford maverick"""
+16,8,400,170,4668,11.5,75,"	""pontiac catalina"""
+15,8,350,145,4440,14,75,"	""chevrolet bel air"""
+16,8,318,150,4498,14.5,75,"	""plymouth grand fury"""
+14,8,351,148,4657,13.5,75,"	""ford ltd"""
+17,6,231,110,3907,21,75,"	""buick century"""
+16,6,250,105,3897,18.5,75,"	""chevroelt chevelle malibu"""
+15,6,258,110,3730,19,75,"	""amc matador"""
+18,6,225,95,3785,19,75,"	""plymouth fury"""
+21,6,231,110,3039,15,75,"	""buick skyhawk"""
+20,8,262,110,3221,13.5,75,"	""chevrolet monza 2+2"""
+13,8,302,129,3169,12,75,"	""ford mustang ii"""
+29,4,97,75,2171,16,75,"	""toyota corolla"""
+23,4,140,83,2639,17,75,"	""ford pinto"""
+20,6,232,100,2914,16,75,"	""amc gremlin"""
+23,4,140,78,2592,18.5,75,"	""pontiac astro"""
+24,4,134,96,2702,13.5,75,"	""toyota corona"""
+25,4,90,71,2223,16.5,75,"	""volkswagen dasher"""
+24,4,119,97,2545,17,75,"	""datsun 710"""
+18,6,171,97,2984,14.5,75,"	""ford pinto"""
+29,4,90,70,1937,14,75,"	""volkswagen rabbit"""
+19,6,232,90,3211,17,75,"	""amc pacer"""
+23,4,115,95,2694,15,75,"	""audi 100ls"""
+23,4,120,88,2957,17,75,"	""peugeot 504"""
+22,4,121,98,2945,14.5,75,"	""volvo 244dl"""
+25,4,121,115,2671,13.5,75,"	""saab 99le"""
+33,4,91,53,1795,17.5,75,"	""honda civic cvcc"""
+28,4,107,86,2464,15.5,76,"	""fiat 131"""
+25,4,116,81,2220,16.9,76,"	""opel 1900"""
+25,4,140,92,2572,14.9,76,"	""capri ii"""
+26,4,98,79,2255,17.7,76,"	""dodge colt"""
+27,4,101,83,2202,15.3,76,"	""renault 12tl"""
+17.5,8,305,140,4215,13,76,"	""chevrolet chevelle malibu classic"""
+16,8,318,150,4190,13,76,"	""dodge coronet brougham"""
+15.5,8,304,120,3962,13.9,76,"	""amc matador"""
+14.5,8,351,152,4215,12.8,76,"	""ford gran torino"""
+22,6,225,100,3233,15.4,76,"	""plymouth valiant"""
+22,6,250,105,3353,14.5,76,"	""chevrolet nova"""
+24,6,200,81,3012,17.6,76,"	""ford maverick"""
+22.5,6,232,90,3085,17.6,76,"	""amc hornet"""
+29,4,85,52,2035,22.2,76,"	""chevrolet chevette"""
+24.5,4,98,60,2164,22.1,76,"	""chevrolet woody"""
+29,4,90,70,1937,14.2,76,"	""vw rabbit"""
+33,4,91,53,1795,17.4,76,"	""honda civic"""
+20,6,225,100,3651,17.7,76,"	""dodge aspen se"""
+18,6,250,78,3574,21,76,"	""ford granada ghia"""
+18.5,6,250,110,3645,16.2,76,"	""pontiac ventura sj"""
+17.5,6,258,95,3193,17.8,76,"	""amc pacer d/l"""
+29.5,4,97,71,1825,12.2,76,"	""volkswagen rabbit"""
+32,4,85,70,1990,17,76,"	""datsun b-210"""
+28,4,97,75,2155,16.4,76,"	""toyota corolla"""
+26.5,4,140,72,2565,13.6,76,"	""ford pinto"""
+20,4,130,102,3150,15.7,76,"	""volvo 245"""
+13,8,318,150,3940,13.2,76,"	""plymouth volare premier v8"""
+19,4,120,88,3270,21.9,76,"	""peugeot 504"""
+19,6,156,108,2930,15.5,76,"	""toyota mark ii"""
+16.5,6,168,120,3820,16.7,76,"	""mercedes-benz 280s"""
+16.5,8,350,180,4380,12.1,76,"	""cadillac seville"""
+13,8,350,145,4055,12,76,"	""chevy c10"""
+13,8,302,130,3870,15,76,"	""ford f108"""
+13,8,318,150,3755,14,76,"	""dodge d100"""
+31.5,4,98,68,2045,18.5,77,"	""honda accord cvcc"""
+30,4,111,80,2155,14.8,77,"	""buick opel isuzu deluxe"""
+36,4,79,58,1825,18.6,77,"	""renault 5 gtl"""
+25.5,4,122,96,2300,15.5,77,"	""plymouth arrow gs"""
+33.5,4,85,70,1945,16.8,77,"	""datsun f-10 hatchback"""
+17.5,8,305,145,3880,12.5,77,"	""chevrolet caprice classic"""
+17,8,260,110,4060,19,77,"	""oldsmobile cutlass supreme"""
+15.5,8,318,145,4140,13.7,77,"	""dodge monaco brougham"""
+15,8,302,130,4295,14.9,77,"	""mercury cougar brougham"""
+17.5,6,250,110,3520,16.4,77,"	""chevrolet concours"""
+20.5,6,231,105,3425,16.9,77,"	""buick skylark"""
+19,6,225,100,3630,17.7,77,"	""plymouth volare custom"""
+18.5,6,250,98,3525,19,77,"	""ford granada"""
+16,8,400,180,4220,11.1,77,"	""pontiac grand prix lj"""
+15.5,8,350,170,4165,11.4,77,"	""chevrolet monte carlo landau"""
+15.5,8,400,190,4325,12.2,77,"	""chrysler cordoba"""
+16,8,351,149,4335,14.5,77,"	""ford thunderbird"""
+29,4,97,78,1940,14.5,77,"	""volkswagen rabbit custom"""
+24.5,4,151,88,2740,16,77,"	""pontiac sunbird coupe"""
+26,4,97,75,2265,18.2,77,"	""toyota corolla liftback"""
+25.5,4,140,89,2755,15.8,77,"	""ford mustang ii 2+2"""
+30.5,4,98,63,2051,17,77,"	""chevrolet chevette"""
+33.5,4,98,83,2075,15.9,77,"	""dodge colt m/m"""
+30,4,97,67,1985,16.4,77,"	""subaru dl"""
+30.5,4,97,78,2190,14.1,77,"	""volkswagen dasher"""
+22,6,146,97,2815,14.5,77,"	""datsun 810"""
+21.5,4,121,110,2600,12.8,77,"	""bmw 320i"""
+21.5,3,80,110,2720,13.5,77,"	""mazda rx-4"""
+43.1,4,90,48,1985,21.5,78,"	""volkswagen rabbit custom diesel"""
+36.1,4,98,66,1800,14.4,78,"	""ford fiesta"""
+32.8,4,78,52,1985,19.4,78,"	""mazda glc deluxe"""
+39.4,4,85,70,2070,18.6,78,"	""datsun b210 gx"""
+36.1,4,91,60,1800,16.4,78,"	""honda civic cvcc"""
+19.9,8,260,110,3365,15.5,78,"	""oldsmobile cutlass salon brougham"""
+19.4,8,318,140,3735,13.2,78,"	""dodge diplomat"""
+20.2,8,302,139,3570,12.8,78,"	""mercury monarch ghia"""
+19.2,6,231,105,3535,19.2,78,"	""pontiac phoenix lj"""
+20.5,6,200,95,3155,18.2,78,"	""chevrolet malibu"""
+20.2,6,200,85,2965,15.8,78,"	""ford fairmont (auto)"""
+25.1,4,140,88,2720,15.4,78,"	""ford fairmont (man)"""
+20.5,6,225,100,3430,17.2,78,"	""plymouth volare"""
+19.4,6,232,90,3210,17.2,78,"	""amc concord"""
+20.6,6,231,105,3380,15.8,78,"	""buick century special"""
+20.8,6,200,85,3070,16.7,78,"	""mercury zephyr"""
+18.6,6,225,110,3620,18.7,78,"	""dodge aspen"""
+18.1,6,258,120,3410,15.1,78,"	""amc concord d/l"""
+19.2,8,305,145,3425,13.2,78,"	""chevrolet monte carlo landau"""
+17.7,6,231,165,3445,13.4,78,"	""buick regal sport coupe (turbo)"""
+18.1,8,302,139,3205,11.2,78,"	""ford futura"""
+17.5,8,318,140,4080,13.7,78,"	""dodge magnum xe"""
+30,4,98,68,2155,16.5,78,"	""chevrolet chevette"""
+27.5,4,134,95,2560,14.2,78,"	""toyota corona"""
+27.2,4,119,97,2300,14.7,78,"	""datsun 510"""
+30.9,4,105,75,2230,14.5,78,"	""dodge omni"""
+21.1,4,134,95,2515,14.8,78,"	""toyota celica gt liftback"""
+23.2,4,156,105,2745,16.7,78,"	""plymouth sapporo"""
+23.8,4,151,85,2855,17.6,78,"	""oldsmobile starfire sx"""
+23.9,4,119,97,2405,14.9,78,"	""datsun 200-sx"""
+20.3,5,131,103,2830,15.9,78,"	""audi 5000"""
+17,6,163,125,3140,13.6,78,"	""volvo 264gl"""
+21.6,4,121,115,2795,15.7,78,"	""saab 99gle"""
+16.2,6,163,133,3410,15.8,78,"	""peugeot 604sl"""
+31.5,4,89,71,1990,14.9,78,"	""volkswagen scirocco"""
+29.5,4,98,68,2135,16.6,78,"	""honda accord lx"""
+21.5,6,231,115,3245,15.4,79,"	""pontiac lemans v6"""
+19.8,6,200,85,2990,18.2,79,"	""mercury zephyr 6"""
+22.3,4,140,88,2890,17.3,79,"	""ford fairmont 4"""
+20.2,6,232,90,3265,18.2,79,"	""amc concord dl 6"""
+20.6,6,225,110,3360,16.6,79,"	""dodge aspen 6"""
+17,8,305,130,3840,15.4,79,"	""chevrolet caprice classic"""
+17.6,8,302,129,3725,13.4,79,"	""ford ltd landau"""
+16.5,8,351,138,3955,13.2,79,"	""mercury grand marquis"""
+18.2,8,318,135,3830,15.2,79,"	""dodge st. regis"""
+16.9,8,350,155,4360,14.9,79,"	""buick estate wagon (sw)"""
+15.5,8,351,142,4054,14.3,79,"	""ford country squire (sw)"""
+19.2,8,267,125,3605,15,79,"	""chevrolet malibu classic (sw)"""
+18.5,8,360,150,3940,13,79,"	""chrysler lebaron town @ country (sw)"""
+31.9,4,89,71,1925,14,79,"	""vw rabbit custom"""
+34.1,4,86,65,1975,15.2,79,"	""maxda glc deluxe"""
+35.7,4,98,80,1915,14.4,79,"	""dodge colt hatchback custom"""
+27.4,4,121,80,2670,15,79,"	""amc spirit dl"""
+25.4,5,183,77,3530,20.1,79,"	""mercedes benz 300d"""
+23,8,350,125,3900,17.4,79,"	""cadillac eldorado"""
+27.2,4,141,71,3190,24.8,79,"	""peugeot 504"""
+23.9,8,260,90,3420,22.2,79,"	""oldsmobile cutlass salon brougham"""
+34.2,4,105,70,2200,13.2,79,"	""plymouth horizon"""
+34.5,4,105,70,2150,14.9,79,"	""plymouth horizon tc3"""
+31.8,4,85,65,2020,19.2,79,"	""datsun 210"""
+37.3,4,91,69,2130,14.7,79,"	""fiat strada custom"""
+28.4,4,151,90,2670,16,79,"	""buick skylark limited"""
+28.8,6,173,115,2595,11.3,79,"	""chevrolet citation"""
+26.8,6,173,115,2700,12.9,79,"	""oldsmobile omega brougham"""
+33.5,4,151,90,2556,13.2,79,"	""pontiac phoenix"""
+41.5,4,98,76,2144,14.7,80,"	""vw rabbit"""
+38.1,4,89,60,1968,18.8,80,"	""toyota corolla tercel"""
+32.1,4,98,70,2120,15.5,80,"	""chevrolet chevette"""
+37.2,4,86,65,2019,16.4,80,"	""datsun 310"""
+28,4,151,90,2678,16.5,80,"	""chevrolet citation"""
+26.4,4,140,88,2870,18.1,80,"	""ford fairmont"""
+24.3,4,151,90,3003,20.1,80,"	""amc concord"""
+19.1,6,225,90,3381,18.7,80,"	""dodge aspen"""
+34.3,4,97,78,2188,15.8,80,"	""audi 4000"""
+29.8,4,134,90,2711,15.5,80,"	""toyota corona liftback"""
+31.3,4,120,75,2542,17.5,80,"	""mazda 626"""
+37,4,119,92,2434,15,80,"	""datsun 510 hatchback"""
+32.2,4,108,75,2265,15.2,80,"	""toyota corolla"""
+46.6,4,86,65,2110,17.9,80,"	""mazda glc"""
+27.9,4,156,105,2800,14.4,80,"	""dodge colt"""
+40.8,4,85,65,2110,19.2,80,"	""datsun 210"""
+44.3,4,90,48,2085,21.7,80,"	""vw rabbit c (diesel)"""
+43.4,4,90,48,2335,23.7,80,"	""vw dasher (diesel)"""
+36.4,5,121,67,2950,19.9,80,"	""audi 5000s (diesel)"""
+30,4,146,67,3250,21.8,80,"	""mercedes-benz 240d"""
+44.6,4,91,67,1850,13.8,80,"	""honda civic 1500 gl"""
+40.9,4,85,,1835,17.3,80,"	""renault lecar deluxe"""
+33.8,4,97,67,2145,18,80,"	""subaru dl"""
+29.8,4,89,62,1845,15.3,80,"	""vokswagen rabbit"""
+32.7,6,168,132,2910,11.4,80,"	""datsun 280-zx"""
+23.7,3,70,100,2420,12.5,80,"	""mazda rx-7 gs"""
+35,4,122,88,2500,15.1,80,"	""triumph tr7 coupe"""
+23.6,4,140,,2905,14.3,80,"	""ford mustang cobra"""
+32.4,4,107,72,2290,17,80,"	""honda accord"""
+27.2,4,135,84,2490,15.7,81,"	""plymouth reliant"""
+26.6,4,151,84,2635,16.4,81,"	""buick skylark"""
+25.8,4,156,92,2620,14.4,81,"	""dodge aries wagon (sw)"""
+23.5,6,173,110,2725,12.6,81,"	""chevrolet citation"""
+30,4,135,84,2385,12.9,81,"	""plymouth reliant"""
+39.1,4,79,58,1755,16.9,81,"	""toyota starlet"""
+39,4,86,64,1875,16.4,81,"	""plymouth champ"""
+35.1,4,81,60,1760,16.1,81,"	""honda civic 1300"""
+32.3,4,97,67,2065,17.8,81,"	""subaru"""
+37,4,85,65,1975,19.4,81,"	""datsun 210 mpg"""
+37.7,4,89,62,2050,17.3,81,"	""toyota tercel"""
+34.1,4,91,68,1985,16,81,"	""mazda glc 4"""
+34.7,4,105,63,2215,14.9,81,"	""plymouth horizon 4"""
+34.4,4,98,65,2045,16.2,81,"	""ford escort 4w"""
+29.9,4,98,65,2380,20.7,81,"	""ford escort 2h"""
+33,4,105,74,2190,14.2,81,"	""volkswagen jetta"""
+34.5,4,100,,2320,15.8,81,"	""renault 18i"""
+33.7,4,107,75,2210,14.4,81,"	""honda prelude"""
+32.4,4,108,75,2350,16.8,81,"	""toyota corolla"""
+32.9,4,119,100,2615,14.8,81,"	""datsun 200sx"""
+31.6,4,120,74,2635,18.3,81,"	""mazda 626"""
+28.1,4,141,80,3230,20.4,81,"	""peugeot 505s turbo diesel"""
+30.7,6,145,76,3160,19.6,81,"	""volvo diesel"""
+25.4,6,168,116,2900,12.6,81,"	""toyota cressida"""
+24.2,6,146,120,2930,13.8,81,"	""datsun 810 maxima"""
+22.4,6,231,110,3415,15.8,81,"	""buick century"""
+26.6,8,350,105,3725,19,81,"	""oldsmobile cutlass ls"""
+20.2,6,200,88,3060,17.1,81,"	""ford granada gl"""
+17.6,6,225,85,3465,16.6,81,"	""chrysler lebaron salon"""
+28,4,112,88,2605,19.6,82,"	""chevrolet cavalier"""
+27,4,112,88,2640,18.6,82,"	""chevrolet cavalier wagon"""
+34,4,112,88,2395,18,82,"	""chevrolet cavalier 2-door"""
+31,4,112,85,2575,16.2,82,"	""pontiac j2000 se hatchback"""
+29,4,135,84,2525,16,82,"	""dodge aries se"""
+27,4,151,90,2735,18,82,"	""pontiac phoenix"""
+24,4,140,92,2865,16.4,82,"	""ford fairmont futura"""
+23,4,151,,3035,20.5,82,"	""amc concord dl"""
+36,4,105,74,1980,15.3,82,"	""volkswagen rabbit l"""
+37,4,91,68,2025,18.2,82,"	""mazda glc custom l"""
+31,4,91,68,1970,17.6,82,"	""mazda glc custom"""
+38,4,105,63,2125,14.7,82,"	""plymouth horizon miser"""
+36,4,98,70,2125,17.3,82,"	""mercury lynx l"""
+36,4,120,88,2160,14.5,82,"	""nissan stanza xe"""
+36,4,107,75,2205,14.5,82,"	""honda accord"""
+34,4,108,70,2245,16.9,82,"	""toyota corolla"""
+38,4,91,67,1965,15,82,"	""honda civic"""
+32,4,91,67,1965,15.7,82,"	""honda civic (auto)"""
+38,4,91,67,1995,16.2,82,"	""datsun 310 gx"""
+25,6,181,110,2945,16.4,82,"	""buick century limited"""
+38,6,262,85,3015,17,82,"	""oldsmobile cutlass ciera (diesel)"""
+26,4,156,92,2585,14.5,82,"	""chrysler lebaron medallion"""
+22,6,232,112,2835,14.7,82,"	""ford granada l"""
+32,4,144,96,2665,13.9,82,"	""toyota celica gt"""
+36,4,135,84,2370,13,82,"	""dodge charger 2.2"""
+27,4,151,90,2950,17.3,82,"	""chevrolet camaro"""
+27,4,140,86,2790,15.6,82,"	""ford mustang gl"""
+44,4,97,52,2130,24.6,82,"	""vw pickup"""
+32,4,135,84,2295,11.6,82,"	""dodge rampage"""
+28,4,120,79,2625,18.6,82,"	""ford ranger"""
+31,4,119,82,2720,19.4,82,"	""chevy s-10"""
diff --git a/main.ipynb b/main.ipynb
new file mode 100644
index 0000000..7a0a210
--- /dev/null
+++ b/main.ipynb
@@ -0,0 +1,1275 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Before your start:\n",
+    "- Read the README.md file\n",
+    "- Comment as much as you can and use the resources in the README.md file\n",
+    "- Happy learning!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn.datasets\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 1 - Explore the Scikit-Learn Datasets\n",
+    "\n",
+    "Before starting to work on our own datasets, let's first explore the datasets that are included in this Python library. These datasets have been cleaned and formatted for use in ML algorithms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will load the diabetes dataset. Do this in the cell below by importing the datasets and then loading the dataset  to the `diabetes` variable using the `load_diabetes()` function ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diabetes = sklearn.datasets.load_diabetes()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's explore this variable by looking at the different attributes (keys) of `diabetes`. Note that the `load_diabetes` function does not return dataframes. It returns you a Python dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename', 'data_module'])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes.keys()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The next step is to read the description of the dataset. \n",
+    "\n",
+    "Print the description in the cell below using the `DESCR` attribute of the `diabetes` variable. Read the data description carefully to fully understand what each column represents.\n",
+    "\n",
+    "*Hint: If your output is ill-formatted by displaying linebreaks as `\\n`, it means you are not using the `print` function.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _diabetes_dataset:\n",
+      "\n",
+      "Diabetes dataset\n",
+      "----------------\n",
+      "\n",
+      "Ten baseline variables, age, sex, body mass index, average blood\n",
+      "pressure, and six blood serum measurements were obtained for each of n =\n",
+      "442 diabetes patients, as well as the response of interest, a\n",
+      "quantitative measure of disease progression one year after baseline.\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "  :Number of Instances: 442\n",
+      "\n",
+      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
+      "\n",
+      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
+      "\n",
+      "  :Attribute Information:\n",
+      "      - age     age in years\n",
+      "      - sex\n",
+      "      - bmi     body mass index\n",
+      "      - bp      average blood pressure\n",
+      "      - s1      tc, total serum cholesterol\n",
+      "      - s2      ldl, low-density lipoproteins\n",
+      "      - s3      hdl, high-density lipoproteins\n",
+      "      - s4      tch, total cholesterol / HDL\n",
+      "      - s5      ltg, possibly log of serum triglycerides level\n",
+      "      - s6      glu, blood sugar level\n",
+      "\n",
+      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1).\n",
+      "\n",
+      "Source URL:\n",
+      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
+      "\n",
+      "For more information see:\n",
+      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
+      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes[\"DESCR\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Based on the data description, answer the following questions:\n",
+    "\n",
+    "1. How many attributes are there in the data? What do they mean?\n",
+    "\n",
+    "1. What is the relation between `diabetes['data']` and `diabetes['target']`?\n",
+    "\n",
+    "1. How many records are there in the data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"1. **How many attributes are there in the data? What do they mean?**\n",
+    "   - There are 10 attributes in the data, and they represent various health-related measurements for diabetes patients.\n",
+    "\n",
+    "2. **What is the relation between `diabetes['data']` and `diabetes['target']`?**\n",
+    "   - `diabetes['data']` contains the input features (attributes) for each record, representing the health measurements of diabetes patients.\n",
+    "   - `diabetes['target']` contains the corresponding target values, which are quantitative measures of disease progression one year after baseline for each patient.\n",
+    "\n",
+    "3. **How many records are there in the data?**\n",
+    "   - There are 442 records (instances) in the data.\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now explore what are contained in the *data* portion as well as the *target* portion of `diabetes`. \n",
+    "\n",
+    "Scikit-learn typically takes in 2D numpy arrays as input (though pandas dataframes are also accepted). Inspect the shape of `data` and `target`. Confirm they are consistent with the data description."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442, 10)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "diabetes[\"data\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes[\"target\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
+       "        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
+       "        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
+       "        87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
+       "       259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
+       "       128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
+       "       150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
+       "       200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
+       "        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
+       "        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
+       "       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
+       "       173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
+       "       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
+       "        60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
+       "       197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
+       "        59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
+       "       237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
+       "       143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
+       "       142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
+       "        77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
+       "        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
+       "       154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
+       "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
+       "       150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
+       "       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
+       "        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
+       "        60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
+       "        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
+       "       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
+       "       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
+       "       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
+       "       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
+       "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
+       "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
+       "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
+       "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
+       "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
+       "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
+       "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
+       "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
+       "       220.,  57.])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes[\"target\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 2 - Perform Supervised Learning on the Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data have already been split to predictor (*data*) and response (*target*) variables. Given this information, we'll apply what we have previously learned about linear regression and apply the algorithm to the diabetes dataset.\n",
+    "\n",
+    "#### Let's briefly revisit the linear regression formula:\n",
+    "\n",
+    "```\n",
+    "y = β0 + β1X1 + β2X2 + ... + βnXn + ϵ\n",
+    "```\n",
+    "\n",
+    "...where:\n",
+    "\n",
+    "- X1-Xn: data \n",
+    "- β0: intercept \n",
+    "- β1-βn: coefficients \n",
+    "- ϵ: error (cannot explained by model)\n",
+    "- y: target\n",
+    "\n",
+    "Also take a look at the `sklearn.linear_model.LinearRegression` [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html).\n",
+    "\n",
+    "#### In the cell below, import the `linear_model` class from `sklearn`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n",
+    "from sklearn import linear_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a new instance of the linear regression model and assign the new instance to the variable `diabetes_model`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diabetes_model = linear_model.LinearRegression()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Next, let's split the training and test data.\n",
+    "\n",
+    "Define `diabetes_data_train`, `diabetes_target_train`, `diabetes_data_test`, and `diabetes_target_test`. Use the last 20 records for the test data and the rest for the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "diabetes_data = diabetes['data']\n",
+    "diabetes_target = diabetes['target']\n",
+    "\n",
+    "diabetes_data_train, diabetes_data_test, diabetes_target_train, diabetes_target_test =train_test_split(diabetes_data, diabetes_target, test_size=20, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the training data and target to `diabetes_model`. Print the *intercept* and *coefficients* of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes_model.fit(diabetes_data_train, diabetes_target_train)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Inspecting the results\n",
+    "\n",
+    "From the outputs you should have seen:\n",
+    "\n",
+    "- The intercept is a float number.\n",
+    "- The coefficients are an array containing 10 float numbers.\n",
+    "\n",
+    "This is the linear regression model fitted to your training dataset.\n",
+    "\n",
+    "#### Using your fitted linear regression model, predict the *y* of `diabetes_data_test`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "151.3820926783504\n",
+      "[   0.88135058 -260.26547413  536.46965804  335.9950993  -835.78537996\n",
+      "  465.75983085  136.20013901  245.06432031  712.32537762   64.48680278]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes_model.intercept_)\n",
+    "print(diabetes_model.coef_)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Print your `diabetes_target_test` and compare with the prediction. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[141.94619731 176.31674205 134.53159785 287.22526514 122.58042918\n",
+      "  96.46043898 256.8468282  184.79870459  92.90893919 111.3914492\n",
+      "  98.52709941 166.01405738  58.45723061 205.10018437 100.72748232\n",
+      " 130.33505833 218.45993591 245.87432278 193.00193698 214.38106404]\n"
+     ]
+    }
+   ],
+   "source": [
+    "diabetes_target_test = diabetes_model.predict(diabetes_data_test)\n",
+    "print(diabetes_target_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Is `diabetes_target_test` exactly the same as the model prediction? Explain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your explanation here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bonus Challenge 1 - Hypothesis Testing with `statsmodels`\n",
+    "\n",
+    "After generating the linear regression model from the dataset, you probably wonder: then what? What is the statistical way to know if my model is reliable or not?\n",
+    "\n",
+    "Good question. We'll discuss that using Scikit-Learn in Challenge 5. But for now, let's use a fool-proof way by using the ([Linear Regression class of StatsModels](https://www.statsmodels.org/dev/regression.html)) which can also conduct linear regression analysis plus much more such as calcuating the F-score of the linear model as well as the standard errors and t-scores for each coefficient. The F-score and t-scores will tell you whether you can trust your linear model.\n",
+    "\n",
+    "To understand the statistical meaning of conducting hypothesis testing (e.g. F-test, t-test) for slopes, read [this webpage](https://onlinecourses.science.psu.edu/stat501/node/297/) at your leisure time. We'll give you a brief overview next.\n",
+    "\n",
+    "* The F-test of your linear model is to verify whether at least one of your coefficients is significantly different from zero. Translating that into the *null hypothesis* and *alternative hypothesis*, that is:\n",
+    "\n",
+    "    ```\n",
+    "    H0 : β1 = β2 = ... = β10 = 0\n",
+    "    HA : At least one βj ≠ 0 (for j = 1, 2, ..., 10)\n",
+    "    ```\n",
+    "\n",
+    "* The t-tests on each coefficient is to check whether the confidence interval for the variable contains zero. If the confidence interval contains zero, it means the null hypothesis for that variable is not rejected. In other words, this particular vaiable is not contributing to your linear model and you can remove it from your formula.\n",
+    "\n",
+    "Read the documentations of [StatsModels Linear Regression](https://www.statsmodels.org/dev/regression.html) as well as its [`OLS` class](https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html) which stands for *ordinary least squares*.\n",
+    "\n",
+    "#### In the next cell, analyze `diabetes_data_train` and `diabetes_target_train` with the linear regression model of `statsmodels`. Print the fit summary.\n",
+    "\n",
+    "Your output should look like:\n",
+    "\n",
+    "![statsmodels regression](../statsmodels.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Interpreting hypothesis testing results\n",
+    "\n",
+    "Answer the following questions in the cell below:\n",
+    "\n",
+    "1. What is the F-score of your linear model and is the null hypothesis rejected?\n",
+    "\n",
+    "1. Does any of the t-tests of the coefficients produce a confidence interval containing zero? What are they?\n",
+    "\n",
+    "1. How will you modify your linear reguression model according to the test results above?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answers here:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 3 - Peform Supervised Learning on a Pandas Dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have dealt with data that has been formatted for scikit-learn, let's look at data that we will need to format ourselves.\n",
+    "\n",
+    "In the next cell, load the `auto-mpg.csv` file included in this folder and assign it to a variable called `auto`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "auto = pd.read_csv (\"auto-mpg.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the first 5 rows using the `head()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>car_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"chevrolet chevelle malibu\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"buick skylark 320\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"plymouth satellite\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"amc rebel sst\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"ford torino\"</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "0  18.0          8         307.0        130.0    3504          12.0   \n",
+       "1  15.0          8         350.0        165.0    3693          11.5   \n",
+       "2  18.0          8         318.0        150.0    3436          11.0   \n",
+       "3  16.0          8         304.0        150.0    3433          12.0   \n",
+       "4  17.0          8         302.0        140.0    3449          10.5   \n",
+       "\n",
+       "   model_year                       car_name  \n",
+       "0          70  \\t\"chevrolet chevelle malibu\"  \n",
+       "1          70          \\t\"buick skylark 320\"  \n",
+       "2          70         \\t\"plymouth satellite\"  \n",
+       "3          70              \\t\"amc rebel sst\"  \n",
+       "4          70                \\t\"ford torino\"  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the data to ensure that all numeric columns are correctly detected as such by pandas. If a column is misclassified as object, coerce it to numeric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             float64\n",
+       "cylinders         int64\n",
+       "displacement    float64\n",
+       "horse_power     float64\n",
+       "weight            int64\n",
+       "acceleration    float64\n",
+       "model_year        int64\n",
+       "car_name         object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.dtypes\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the newest model year and the oldest model year?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The newest model year is: 82\n",
+      "The oldest model year is: 70\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "newest_model_year = auto['model_year'].max()\n",
+    "oldest_model_year = auto['model_year'].min()\n",
+    "\n",
+    "print(f\"The newest model year is: {newest_model_year}\")\n",
+    "print(f\"The oldest model year is: {oldest_model_year}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the dataset for missing values and remove all rows containing at least one missing value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "auto.isnull().sum()\n",
+    "auto.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the frequency table for the `cylinders` column using the `value_counts()` function. How many possible values of cylinders are there?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cylinders\n",
+       "4    199\n",
+       "8    103\n",
+       "6     83\n",
+       "3      4\n",
+       "5      3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto['cylinders'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We would like to generate a linear regression model that will predict mpg. To do this, first drop the `car_name` column since it does not contain any quantitative data. Next separate the dataframe to predictor and response variables. Separate those into test and training data with 80% of the data in the training set and the remainder in the test set. \n",
+    "\n",
+    "Assign the predictor and response training data to `X_train` and `y_train` respectively. Similarly, assign the predictor and response test data to `X_test` and `y_test`.\n",
+    "\n",
+    "*Hint: To separate data for training and test, use the `train_test_split` method we used in previous labs.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "auto = auto.drop(\"car_name\", axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = auto.drop(\"mpg\", axis=1)\n",
+    "y = auto[\"mpg\"]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will processed and peform linear regression on this data to predict the mpg for each vehicle. \n",
+    "\n",
+    "#### In the next cell, create an instance of the linear regression model and call it `auto_model`. Fit `auto_model` with your training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "auto_model = LinearRegression()\n",
+    "\n",
+    "auto_model.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 4 - Evaluate the Model\n",
+    "\n",
+    "In addition to evaluating your model with F-test and t-test, you can also use the *Coefficient of Determination* (a.k.a. *r squared score*). This method does not simply tell *yes* or *no* about the model fit but instead indicates how much variation can be explained by the model. Based on the r squared score, you can decide whether to improve your model in order to obtain a better fit.\n",
+    "\n",
+    "You can learn about the r squared score [here](https://en.wikipedia.org/wiki/Coefficient_of_determination). In the end, we want the r-squared score to be as high as possible.\n",
+    "\n",
+    "#### In the next cell, compute the predicted *y* based on `X_train` and call it `y_pred`. Then calcualte the r squared score between `y_pred` and `y_train` which indicates how well the estimated regression model fits the training data.\n",
+    "\n",
+    "*Hint: r squared score can be calculated using `sklearn.metrics.r2_score` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html)).*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-squared Score: 0.8107227953093896\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "y_pred = auto_model.predict(X_train)\n",
+    "\n",
+    "r_squared = r2_score(y_train, y_pred)\n",
+    "\n",
+    "print(f\"R-squared Score: {r_squared}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Our next step is to evaluate the model using the test data. \n",
+    "\n",
+    "We would like to ensure that our model is not overfitting the data. This means that our model was made to fit too closely to the training data by being overly complex. If a model is overfitted, it is not generalizable to data outside the training data. In that case, we need to reduce the complexity of the model by removing certain features (variables).\n",
+    "\n",
+    "In the cell below, use the model to generate the predicted values for the test data and assign them to `y_test_pred`. Compute the r squared score of the predicted `y_test_pred` and the oberserved `y_test` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-squared Score for Test Data: 0.7942349075428591\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "# Generate predicted values for the test data\n",
+    "y_test_pred = auto_model.predict(X_test)\n",
+    "\n",
+    "# Calculate the r squared score for the test data\n",
+    "r_squared_test = r2_score(y_test, y_test_pred)\n",
+    "\n",
+    "# Print the r squared score for the test data\n",
+    "print(f\"R-squared Score for Test Data: {r_squared_test}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Explaining the results\n",
+    "\n",
+    "The r squared scores of the training data and the test data are pretty close (0.8146 vs 0.7818). This means our model is not overfitted. However, there is still room to improve the model fit. Move on to the next challenge."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Challenge 5 - Improve the Model Fit\n",
+    "\n",
+    "While the most common way to improve the fit of a model is by using [regularization](https://datanice.github.io/machine-learning-101-what-is-regularization-interactive.html), there are other simpler ways to improve model fit. The first is to create a simpler model. The second is to increase the train sample size.\n",
+    "\n",
+    "Let us start with the easier option and increase our train sample size to 90% of the data. Create a new test train split and name the new predictors and response variables `X_train09`, `X_test09`, `y_train09`, `y_test09`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 392 entries, 0 to 397\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   mpg           392 non-null    float64\n",
+      " 1   cylinders     392 non-null    int64  \n",
+      " 2   displacement  392 non-null    float64\n",
+      " 3   horse_power   392 non-null    float64\n",
+      " 4   weight        392 non-null    int64  \n",
+      " 5   acceleration  392 non-null    float64\n",
+      " 6   model_year    392 non-null    int64  \n",
+      "dtypes: float64(4), int64(3)\n",
+      "memory usage: 24.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "auto.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictors = auto[['cylinders', 'displacement', 'horse_power', 'weight', 'acceleration', 'model_year']]\n",
+    "\n",
+    "# 'response' is the target variable 'mpg'\n",
+    "response = auto['mpg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes after 90-10 split:\n",
+      "X_train09 shape: (352, 6)\n",
+      "X_test09 shape: (40, 6)\n",
+      "y_train09 shape: (352,)\n",
+      "y_test09 shape: (40,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "response = auto['mpg']\n",
+    "\n",
+    "# Now you can use train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train09, X_test09, y_train09, y_test09 = train_test_split(predictors, response, test_size=0.1, random_state=42)\n",
+    "\n",
+    "# Print the shapes of the new sets\n",
+    "print(\"Shapes after 90-10 split:\")\n",
+    "print(\"X_train09 shape:\", X_train09.shape)\n",
+    "print(\"X_test09 shape:\", X_test09.shape)\n",
+    "print(\"y_train09 shape:\", y_train09.shape)\n",
+    "print(\"y_test09 shape:\", y_test09.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize a new linear regression model. Name this model `auto_model09`. Fit the model to the new sample (training) data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Initialize the linear regression model\n",
+    "auto_model09 = LinearRegression()\n",
+    "\n",
+    "# Fit the model to the training data\n",
+    "auto_model09.fit(X_train09, y_train09)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the predicted values and r squared score for our new model and new sample data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-squared score for the new model and new sample data: 0.8468911998183241\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "y_test_pred09 = auto_model09.predict(X_test09)\n",
+    "\n",
+    "r2_score_09 = r2_score(y_test09, y_test_pred09)\n",
+    "\n",
+    "print(f\"R-squared score for the new model and new sample data: {r2_score_09}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the r squared score for the smaller test set. Is there an improvement in the test r squared?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-squared score for the original model and test data: 0.7942349075428591\n",
+      "R-squared score for the new model and new test data: 0.8468911998183241\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "y_test_pred = auto_model.predict(X_test)\n",
+    "r2_score_original = r2_score(y_test, y_test_pred)\n",
+    "\n",
+    "\n",
+    "print(f\"R-squared score for the original model and test data: {r2_score_original}\")\n",
+    "\n",
+    "\n",
+    "print(f\"R-squared score for the new model and new test data: {r2_score_09}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bonus Challenge 2 - Backward Elimination \n",
+    "\n",
+    "The main way to produce a simpler linear regression model is to reduce the number of variables used in the model. In scikit-learn, we can do this by using recursive feature elimination. You can read more about RFE [here](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html).\n",
+    "\n",
+    "In the next cell, we will import RFE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import RFE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Follow the documentation and initialize an RFE model using the `auto_model` linear regression model. Set `n_features_to_select=3`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the model and print the ranking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feature importance is ranked from most important (1) to least important (4). Generate a model with the three most important features. The features correspond to variable names. For example, feature 1 is `cylinders` and feature 2 is `displacement`.\n",
+    "\n",
+    "Perform a test-train split on this reduced column data and call the split data `X_train_reduced`, `X_test_reduced`, `y_test_reduced`, `y_train_reduced`. Use an 80% split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a new model called `auto_model_reduced` and fit this model. Then proceed to compute the r squared score for the model. Did this cause an improvement in the r squared score?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here: \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Conclusion\n",
+    "\n",
+    "You may obtain the impression from this lab that without knowing statistical methods in depth, it is difficult to make major progress in machine learning. That is correct. If you are motivated to become a data scientist, statistics is the subject you must be proficient in and there is no shortcut. \n",
+    "\n",
+    "Completing these labs is not likely to make you a data scientist. But you will have a good sense about what are there in machine learning and what are good for you. In your future career, you can choose one of the three tracks:\n",
+    "\n",
+    "* Data scientists who need to be proficient in statistical methods.\n",
+    "\n",
+    "* Data engineers who need to be good at programming.\n",
+    "\n",
+    "* Data integration specialists who are business or content experts but also understand data and programming. This cross-disciplinary track brings together data, technology, and business and will be in high demands in the next decade."
+   ]
+  }
+ ],
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