├── Analyzing-Movie-Reviews ├── .ipynb_checkpoints │ └── Analyzing Movie Reviews-checkpoint.ipynb ├── Analyzing Movie Reviews.ipynb └── fandango_score_comparison.csv ├── Dhaka_Tribune_Code.ipynb ├── NERTagger example.ipynb ├── Police-Killings ├── Police_killings_fivethirtyeight_data.ipynb ├── To-do.txt ├── police_killings.csv └── state_population.csv ├── Predicting board game reviews ├── Board_games_basic_machine_learning-checkpoint.ipynb ├── Board_games_basic_machine_learning.ipynb └── board_games.csv ├── Predicting-Bike-Rentals ├── Predict Bike Rentals.ipynb ├── To-do.txt ├── day.csv └── hour.csv ├── README.md ├── Star-Wars-survey-from-Five-ThirtyEight-data ├── Star Wars survey .ipynb ├── To-do.txt └── star_wars.csv ├── Udacity-Descriptive-Statistics-Final-Project ├── Descriptive statistics final project.ipynb └── Readme.md └── Visualizing-Pixar-movie-data ├── Pixar Visualization.ipynb ├── PixarMovies.csv └── To-do.txt /Analyzing-Movie-Reviews/fandango_score_comparison.csv: -------------------------------------------------------------------------------- 1 | FILM,RottenTomatoes,RottenTomatoes_User,Metacritic,Metacritic_User,IMDB,Fandango_Stars,Fandango_Ratingvalue,RT_norm,RT_user_norm,Metacritic_norm,Metacritic_user_nom,IMDB_norm,RT_norm_round,RT_user_norm_round,Metacritic_norm_round,Metacritic_user_norm_round,IMDB_norm_round,Metacritic_user_vote_count,IMDB_user_vote_count,Fandango_votes,Fandango_Difference 2 | Avengers: Age of Ultron (2015),74,86,66,7.1,7.8,5,4.5,3.7,4.3,3.3,3.55,3.9,3.5,4.5,3.5,3.5,4,1330,271107,14846,0.5 3 | Cinderella (2015),85,80,67,7.5,7.1,5,4.5,4.25,4,3.35,3.75,3.55,4.5,4,3.5,4,3.5,249,65709,12640,0.5 4 | Ant-Man (2015),80,90,64,8.1,7.8,5,4.5,4,4.5,3.2,4.05,3.9,4,4.5,3,4,4,627,103660,12055,0.5 5 | Do You Believe? (2015),18,84,22,4.7,5.4,5,4.5,0.9,4.2,1.1,2.35,2.7,1,4,1,2.5,2.5,31,3136,1793,0.5 6 | Hot Tub Time Machine 2 (2015),14,28,29,3.4,5.1,3.5,3,0.7,1.4,1.45,1.7,2.55,0.5,1.5,1.5,1.5,2.5,88,19560,1021,0.5 7 | The Water Diviner (2015),63,62,50,6.8,7.2,4.5,4,3.15,3.1,2.5,3.4,3.6,3,3,2.5,3.5,3.5,34,39373,397,0.5 8 | Irrational Man (2015),42,53,53,7.6,6.9,4,3.5,2.1,2.65,2.65,3.8,3.45,2,2.5,2.5,4,3.5,17,2680,252,0.5 9 | Top Five (2014),86,64,81,6.8,6.5,4,3.5,4.3,3.2,4.05,3.4,3.25,4.5,3,4,3.5,3.5,124,16876,3223,0.5 10 | Shaun the Sheep Movie (2015),99,82,81,8.8,7.4,4.5,4,4.95,4.1,4.05,4.4,3.7,5,4,4,4.5,3.5,62,12227,896,0.5 11 | Love & Mercy (2015),89,87,80,8.5,7.8,4.5,4,4.45,4.35,4,4.25,3.9,4.5,4.5,4,4.5,4,54,5367,864,0.5 12 | Far From The Madding Crowd (2015),84,77,71,7.5,7.2,4.5,4,4.2,3.85,3.55,3.75,3.6,4,4,3.5,4,3.5,35,12129,804,0.5 13 | Black Sea (2015),82,60,62,6.6,6.4,4,3.5,4.1,3,3.1,3.3,3.2,4,3,3,3.5,3,37,16547,218,0.5 14 | Leviathan (2014),99,79,92,7.2,7.7,4,3.5,4.95,3.95,4.6,3.6,3.85,5,4,4.5,3.5,4,145,22521,64,0.5 15 | Unbroken (2014),51,70,59,6.5,7.2,4.5,4.1,2.55,3.5,2.95,3.25,3.6,2.5,3.5,3,3.5,3.5,218,77518,9443,0.4 16 | The Imitation Game (2014),90,92,73,8.2,8.1,5,4.6,4.5,4.6,3.65,4.1,4.05,4.5,4.5,3.5,4,4,566,334164,8055,0.4 17 | Taken 3 (2015),9,46,26,4.6,6.1,4.5,4.1,0.45,2.3,1.3,2.3,3.05,0.5,2.5,1.5,2.5,3,240,104235,6757,0.4 18 | Ted 2 (2015),46,58,48,6.5,6.6,4.5,4.1,2.3,2.9,2.4,3.25,3.3,2.5,3,2.5,3.5,3.5,197,49102,6437,0.4 19 | Southpaw (2015),59,80,57,8.2,7.8,5,4.6,2.95,4,2.85,4.1,3.9,3,4,3,4,4,128,23561,5597,0.4 20 | Night at the Museum: Secret of the Tomb (2014),50,58,47,5.8,6.3,4.5,4.1,2.5,2.9,2.35,2.9,3.15,2.5,3,2.5,3,3,103,50291,5445,0.4 21 | Pixels (2015),17,54,27,5.3,5.6,4.5,4.1,0.85,2.7,1.35,2.65,2.8,1,2.5,1.5,2.5,3,246,19521,3886,0.4 22 | "McFarland, USA (2015)",79,89,60,7.2,7.5,5,4.6,3.95,4.45,3,3.6,3.75,4,4.5,3,3.5,4,59,13769,3364,0.4 23 | Insidious: Chapter 3 (2015),59,56,52,6.9,6.3,4.5,4.1,2.95,2.8,2.6,3.45,3.15,3,3,2.5,3.5,3,115,25134,3276,0.4 24 | The Man From U.N.C.L.E. (2015),68,80,55,7.9,7.6,4.5,4.1,3.4,4,2.75,3.95,3.8,3.5,4,3,4,4,144,22104,2686,0.4 25 | Run All Night (2015),60,59,59,7.3,6.6,4.5,4.1,3,2.95,2.95,3.65,3.3,3,3,3,3.5,3.5,141,50438,2066,0.4 26 | Trainwreck (2015),85,74,75,6,6.7,4.5,4.1,4.25,3.7,3.75,3,3.35,4.5,3.5,4,3,3.5,169,27380,8381,0.4 27 | Selma (2014),99,86,89,7.1,7.5,5,4.6,4.95,4.3,4.45,3.55,3.75,5,4.5,4.5,3.5,4,316,45344,7025,0.4 28 | Ex Machina (2015),92,86,78,7.9,7.7,4.5,4.1,4.6,4.3,3.9,3.95,3.85,4.5,4.5,4,4,4,672,154499,3458,0.4 29 | Still Alice (2015),88,85,72,7.8,7.5,4.5,4.1,4.4,4.25,3.6,3.9,3.75,4.5,4.5,3.5,4,4,153,57123,1258,0.4 30 | Wild Tales (2014),96,92,77,8.8,8.2,4.5,4.1,4.8,4.6,3.85,4.4,4.1,5,4.5,4,4.5,4,107,50285,235,0.4 31 | The End of the Tour (2015),92,89,84,7.5,7.9,4.5,4.1,4.6,4.45,4.2,3.75,3.95,4.5,4.5,4,4,4,19,1320,121,0.4 32 | Red Army (2015),96,86,82,7.4,7.7,4.5,4.1,4.8,4.3,4.1,3.7,3.85,5,4.5,4,3.5,4,11,2275,54,0.4 33 | When Marnie Was There (2015),89,89,71,6.4,7.8,4.5,4.1,4.45,4.45,3.55,3.2,3.9,4.5,4.5,3.5,3,4,29,4160,46,0.4 34 | The Hunting Ground (2015),92,72,77,7.8,7.5,4.5,4.1,4.6,3.6,3.85,3.9,3.75,4.5,3.5,4,4,4,6,1196,42,0.4 35 | The Boy Next Door (2015),10,35,30,5.5,4.6,4,3.6,0.5,1.75,1.5,2.75,2.3,0.5,2,1.5,3,2.5,75,19658,2800,0.4 36 | Aloha (2015),19,31,40,4,5.5,3.5,3.1,0.95,1.55,2,2,2.75,1,1.5,2,2,3,67,12255,2284,0.4 37 | The Loft (2015),11,40,24,2.4,6.3,4,3.6,0.55,2,1.2,1.2,3.15,0.5,2,1,1,3,80,21319,811,0.4 38 | 5 Flights Up (2015),52,47,55,6.8,6.1,4,3.6,2.6,2.35,2.75,3.4,3.05,2.5,2.5,3,3.5,3,6,2174,79,0.4 39 | Welcome to Me (2015),71,47,67,6.9,5.9,4,3.6,3.55,2.35,3.35,3.45,2.95,3.5,2.5,3.5,3.5,3,33,8301,56,0.4 40 | Saint Laurent (2015),51,45,52,6.8,6.3,3.5,3.1,2.55,2.25,2.6,3.4,3.15,2.5,2.5,2.5,3.5,3,8,2196,43,0.4 41 | Maps to the Stars (2015),60,46,67,5.8,6.3,3.5,3.1,3,2.3,3.35,2.9,3.15,3,2.5,3.5,3,3,46,22440,35,0.4 42 | I'll See You In My Dreams (2015),94,70,75,6.9,6.9,4,3.6,4.7,3.5,3.75,3.45,3.45,4.5,3.5,4,3.5,3.5,14,1151,281,0.4 43 | Timbuktu (2015),99,78,91,6.9,7.2,4,3.6,4.95,3.9,4.55,3.45,3.6,5,4,4.5,3.5,3.5,37,6246,74,0.4 44 | About Elly (2015),97,86,87,9.6,8.2,4,3.6,4.85,4.3,4.35,4.8,4.1,5,4.5,4.5,5,4,23,20659,43,0.4 45 | The Diary of a Teenage Girl (2015),95,81,87,6.3,7,4,3.6,4.75,4.05,4.35,3.15,3.5,5,4,4.5,3,3.5,18,1107,38,0.4 46 | Kingsman: The Secret Service (2015),75,84,58,7.9,7.8,4.5,4.2,3.75,4.2,2.9,3.95,3.9,4,4,3,4,4,1054,272204,15205,0.3 47 | Tomorrowland (2015),50,53,60,6.4,6.6,4,3.7,2.5,2.65,3,3.2,3.3,2.5,2.5,3,3,3.5,262,42937,8077,0.3 48 | The Divergent Series: Insurgent (2015),30,61,42,5.4,6.4,4.5,4.2,1.5,3.05,2.1,2.7,3.2,1.5,3,2,2.5,3,206,89618,7123,0.3 49 | Annie (2014),27,61,33,4.8,5.2,4.5,4.2,1.35,3.05,1.65,2.4,2.6,1.5,3,1.5,2.5,2.5,108,19222,6835,0.3 50 | Fantastic Four (2015),9,20,27,2.5,4,3,2.7,0.45,1,1.35,1.25,2,0.5,1,1.5,1.5,2,421,39838,6288,0.3 51 | Terminator Genisys (2015),26,60,38,6.4,6.9,4.5,4.2,1.3,3,1.9,3.2,3.45,1.5,3,2,3,3.5,779,85585,6272,0.3 52 | Pitch Perfect 2 (2015),67,68,63,5.7,6.7,4.5,4.2,3.35,3.4,3.15,2.85,3.35,3.5,3.5,3,3,3.5,192,56333,4577,0.3 53 | Entourage (2015),32,68,38,5.2,7.1,4.5,4.2,1.6,3.4,1.9,2.6,3.55,1.5,3.5,2,2.5,3.5,96,21914,4279,0.3 54 | The Age of Adaline (2015),54,68,51,7.4,7.3,4.5,4.2,2.7,3.4,2.55,3.7,3.65,2.5,3.5,2.5,3.5,3.5,100,45510,3325,0.3 55 | Hot Pursuit (2015),8,37,31,3.7,4.9,4,3.7,0.4,1.85,1.55,1.85,2.45,0.5,2,1.5,2,2.5,78,17061,2618,0.3 56 | The DUFF (2015),71,68,56,6.4,6.6,4.5,4.2,3.55,3.4,2.8,3.2,3.3,3.5,3.5,3,3,3.5,69,33594,2273,0.3 57 | Black or White (2015),39,68,45,7.9,6.6,4.5,4.2,1.95,3.4,2.25,3.95,3.3,2,3.5,2.5,4,3.5,24,4857,1862,0.3 58 | Project Almanac (2015),34,46,47,5.4,6.4,4,3.7,1.7,2.3,2.35,2.7,3.2,1.5,2.5,2.5,2.5,3,95,40057,1834,0.3 59 | Ricki and the Flash (2015),64,53,54,7,6.2,4,3.7,3.2,2.65,2.7,3.5,3.1,3,2.5,2.5,3.5,3,37,1769,1462,0.3 60 | Seventh Son (2015),12,35,30,3.9,5.5,3.5,3.2,0.6,1.75,1.5,1.95,2.75,0.5,2,1.5,2,3,126,41177,1213,0.3 61 | Mortdecai (2015),12,30,27,3.2,5.5,3.5,3.2,0.6,1.5,1.35,1.6,2.75,0.5,1.5,1.5,1.5,3,144,31878,1196,0.3 62 | Unfinished Business (2015),11,27,32,3.8,5.4,3.5,3.2,0.55,1.35,1.6,1.9,2.7,0.5,1.5,1.5,2,2.5,39,14346,821,0.3 63 | American Ultra (2015),46,52,50,6.8,6.5,4,3.7,2.3,2.6,2.5,3.4,3.25,2.5,2.5,2.5,3.5,3.5,42,3017,638,0.3 64 | True Story (2015),45,41,50,5.7,6.3,3.5,3.2,2.25,2.05,2.5,2.85,3.15,2.5,2,2.5,3,3,37,16069,540,0.3 65 | Child 44 (2015),26,44,41,5.3,6.4,4,3.7,1.3,2.2,2.05,2.65,3.2,1.5,2,2,2.5,3,73,19220,308,0.3 66 | Dark Places (2015),26,33,39,7.9,6.3,4,3.7,1.3,1.65,1.95,3.95,3.15,1.5,1.5,2,4,3,18,9856,55,0.3 67 | Birdman (2014),92,78,88,8,7.9,4,3.7,4.6,3.9,4.4,4,3.95,4.5,4,4.5,4,4,1171,303505,4194,0.3 68 | The Gift (2015),93,79,77,8.3,7.6,4,3.7,4.65,3.95,3.85,4.15,3.8,4.5,4,4,4,4,121,10891,2680,0.3 69 | Unfriended (2015),60,39,59,5.8,5.9,3,2.7,3,1.95,2.95,2.9,2.95,3,2,3,3,3,130,22348,2507,0.3 70 | Monkey Kingdom (2015),94,77,72,7.5,7.3,4.5,4.2,4.7,3.85,3.6,3.75,3.65,4.5,4,3.5,4,3.5,15,883,701,0.3 71 | Mr. Turner (2014),98,56,94,6.6,6.9,3.5,3.2,4.9,2.8,4.7,3.3,3.45,5,3,4.5,3.5,3.5,98,13296,290,0.3 72 | Seymour: An Introduction (2015),100,87,83,6,7.7,4.5,4.2,5,4.35,4.15,3,3.85,5,4.5,4,3,4,4,243,41,0.3 73 | The Wrecking Crew (2015),93,84,67,7,7.8,4.5,4.2,4.65,4.2,3.35,3.5,3.9,4.5,4,3.5,3.5,4,4,732,38,0.3 74 | American Sniper (2015),72,85,72,6.6,7.4,5,4.8,3.6,4.25,3.6,3.3,3.7,3.5,4.5,3.5,3.5,3.5,850,251856,34085,0.2 75 | Furious 7 (2015),81,84,67,6.8,7.4,5,4.8,4.05,4.2,3.35,3.4,3.7,4,4,3.5,3.5,3.5,764,207211,33538,0.2 76 | The Hobbit: The Battle of the Five Armies (2014),61,75,59,7,7.5,4.5,4.3,3.05,3.75,2.95,3.5,3.75,3,4,3,3.5,4,903,289464,15337,0.2 77 | San Andreas (2015),50,58,43,5.5,6.5,4.5,4.3,2.5,2.9,2.15,2.75,3.25,2.5,3,2,3,3.5,199,45723,9749,0.2 78 | Straight Outta Compton (2015),90,94,72,7.3,8.4,5,4.8,4.5,4.7,3.6,3.65,4.2,4.5,4.5,3.5,3.5,4,90,15982,8096,0.2 79 | Vacation (2015),27,55,34,6.2,6.3,4,3.8,1.35,2.75,1.7,3.1,3.15,1.5,3,1.5,3,3,72,8179,3815,0.2 80 | Chappie (2015),30,57,41,7.4,7,4,3.8,1.5,2.85,2.05,3.7,3.5,1.5,3,2,3.5,3.5,637,125088,3642,0.2 81 | Poltergeist (2015),31,24,47,3.7,5,3,2.8,1.55,1.2,2.35,1.85,2.5,1.5,1,2.5,2,2.5,142,21372,2704,0.2 82 | Paper Towns (2015),55,57,56,6.2,6.9,4,3.8,2.75,2.85,2.8,3.1,3.45,3,3,3,3,3.5,51,14156,1750,0.2 83 | Big Eyes (2014),72,69,62,7.5,7,4,3.8,3.6,3.45,3.1,3.75,3.5,3.5,3.5,3,4,3.5,127,39152,1501,0.2 84 | Blackhat (2015),34,25,51,5.4,5.4,3,2.8,1.7,1.25,2.55,2.7,2.7,1.5,1.5,2.5,2.5,2.5,80,27328,1430,0.2 85 | Self/less (2015),20,51,34,8.4,6.6,4,3.8,1,2.55,1.7,4.2,3.3,1,2.5,1.5,4,3.5,77,5626,1415,0.2 86 | Sinister 2 (2015),13,34,31,5,5.5,3.5,3.3,0.65,1.7,1.55,2.5,2.75,0.5,1.5,1.5,2.5,3,37,3200,973,0.2 87 | Little Boy (2015),20,81,30,5.9,7.4,4.5,4.3,1,4.05,1.5,2.95,3.7,1,4,1.5,3,3.5,38,5927,811,0.2 88 | Me and Earl and The Dying Girl (2015),81,89,74,8.4,8.2,4.5,4.3,4.05,4.45,3.7,4.2,4.1,4,4.5,3.5,4,4,41,5269,624,0.2 89 | Maggie (2015),54,32,52,6.5,5.6,3.5,3.3,2.7,1.6,2.6,3.25,2.8,2.5,1.5,2.5,3.5,3,90,18986,95,0.2 90 | Mad Max: Fury Road (2015),97,88,89,8.7,8.3,4.5,4.3,4.85,4.4,4.45,4.35,4.15,5,4.5,4.5,4.5,4,2375,292023,10509,0.2 91 | Spy (2015),93,82,75,6.3,7.3,4.5,4.3,4.65,4.1,3.75,3.15,3.65,4.5,4,4,3,3.5,318,66636,9418,0.2 92 | The SpongeBob Movie: Sponge Out of Water (2015),78,55,62,6.5,6.1,3.5,3.3,3.9,2.75,3.1,3.25,3.05,4,3,3,3.5,3,196,26046,4493,0.2 93 | Paddington (2015),98,81,77,8.2,7.2,4.5,4.3,4.9,4.05,3.85,4.1,3.6,5,4,4,4,3.5,149,38593,4045,0.2 94 | Dope (2015),87,86,72,7.2,7.5,4.5,4.3,4.35,4.3,3.6,3.6,3.75,4.5,4.5,3.5,3.5,4,43,4911,2195,0.2 95 | What We Do in the Shadows (2015),96,86,75,8.3,7.6,4.5,4.3,4.8,4.3,3.75,4.15,3.8,5,4.5,4,4,4,69,39561,259,0.2 96 | The Overnight (2015),82,65,65,8.6,6.9,3.5,3.3,4.1,3.25,3.25,4.3,3.45,4,3.5,3.5,4.5,3.5,13,867,110,0.2 97 | The Salt of the Earth (2015),96,90,83,7.8,8.4,4.5,4.3,4.8,4.5,4.15,3.9,4.2,5,4.5,4,4,4,13,6605,83,0.2 98 | Song of the Sea (2014),99,92,86,8.2,8.2,4.5,4.3,4.95,4.6,4.3,4.1,4.1,5,4.5,4.5,4,4,62,14067,66,0.2 99 | Fifty Shades of Grey (2015),25,42,46,3.2,4.2,4,3.9,1.25,2.1,2.3,1.6,2.1,1.5,2,2.5,1.5,2,778,179506,34846,0.1 100 | Get Hard (2015),29,48,34,3.8,6.1,4,3.9,1.45,2.4,1.7,1.9,3.05,1.5,2.5,1.5,2,3,145,50022,5933,0.1 101 | Focus (2015),57,54,56,6.2,6.6,4,3.9,2.85,2.7,2.8,3.1,3.3,3,2.5,3,3,3.5,167,101264,4933,0.1 102 | Jupiter Ascending (2015),26,40,40,4.5,5.5,3.5,3.4,1.3,2,2,2.25,2.75,1.5,2,2,2.5,3,503,105412,4122,0.1 103 | The Gallows (2015),16,27,30,7,4.4,3,2.9,0.8,1.35,1.5,3.5,2.2,1,1.5,1.5,3.5,2,80,5511,1896,0.1 104 | The Second Best Exotic Marigold Hotel (2015),62,63,51,6.1,6.6,4,3.9,3.1,3.15,2.55,3.05,3.3,3,3,2.5,3,3.5,41,12940,1870,0.1 105 | Strange Magic (2015),17,50,25,5.3,5.7,3.5,3.4,0.85,2.5,1.25,2.65,2.85,1,2.5,1.5,2.5,3,41,3658,1117,0.1 106 | The Gunman (2015),17,34,39,4.3,5.8,3.5,3.4,0.85,1.7,1.95,2.15,2.9,1,1.5,2,2,3,49,16663,996,0.1 107 | Hitman: Agent 47 (2015),7,49,28,3.3,5.9,4,3.9,0.35,2.45,1.4,1.65,2.95,0.5,2.5,1.5,1.5,3,67,4260,917,0.1 108 | Cake (2015),49,47,49,6.4,6.5,3.5,3.4,2.45,2.35,2.45,3.2,3.25,2.5,2.5,2.5,3,3.5,44,19627,482,0.1 109 | The Vatican Tapes (2015),13,21,37,5.4,4.6,3,2.9,0.65,1.05,1.85,2.7,2.3,0.5,1,2,2.5,2.5,5,952,210,0.1 110 | A Little Chaos (2015),40,47,51,7,6.4,4,3.9,2,2.35,2.55,3.5,3.2,2,2.5,2.5,3.5,3,7,4778,83,0.1 111 | The 100-Year-Old Man Who Climbed Out the Window and Disappeared (2015),67,69,58,4.6,7.1,4,3.9,3.35,3.45,2.9,2.3,3.55,3.5,3.5,3,2.5,3.5,5,17237,63,0.1 112 | Escobar: Paradise Lost (2015),52,52,56,6.9,6.6,4,3.9,2.6,2.6,2.8,3.45,3.3,2.5,2.5,3,3.5,3.5,7,7819,48,0.1 113 | Into the Woods (2014),71,50,69,6.1,6,3.5,3.4,3.55,2.5,3.45,3.05,3,3.5,2.5,3.5,3,3,307,81679,13055,0.1 114 | It Follows (2015),96,65,83,7.5,6.9,3,2.9,4.8,3.25,4.15,3.75,3.45,5,3.5,4,4,3.5,551,64656,2097,0.1 115 | Inherent Vice (2014),73,52,81,7.4,6.7,3,2.9,3.65,2.6,4.05,3.7,3.35,3.5,2.5,4,3.5,3.5,286,44711,1078,0.1 116 | A Most Violent Year (2014),90,69,79,7,7.1,3.5,3.4,4.5,3.45,3.95,3.5,3.55,4.5,3.5,4,3.5,3.5,133,32166,675,0.1 117 | While We're Young (2015),83,52,76,6.7,6.4,3,2.9,4.15,2.6,3.8,3.35,3.2,4,2.5,4,3.5,3,65,17647,449,0.1 118 | Clouds of Sils Maria (2015),89,67,78,7.1,6.8,3.5,3.4,4.45,3.35,3.9,3.55,3.4,4.5,3.5,4,3.5,3.5,36,11392,162,0.1 119 | Testament of Youth (2015),81,79,77,7.9,7.3,4,3.9,4.05,3.95,3.85,3.95,3.65,4,4,4,4,3.5,15,5495,127,0.1 120 | Infinitely Polar Bear (2015),80,76,64,7.9,7.2,4,3.9,4,3.8,3.2,3.95,3.6,4,4,3,4,3.5,8,1062,124,0.1 121 | Phoenix (2015),99,81,91,8,7.2,3.5,3.4,4.95,4.05,4.55,4,3.6,5,4,4.5,4,3.5,21,3687,70,0.1 122 | The Wolfpack (2015),84,73,75,7,7.1,3.5,3.4,4.2,3.65,3.75,3.5,3.55,4,3.5,4,3.5,3.5,8,1488,66,0.1 123 | The Stanford Prison Experiment (2015),84,87,68,8.5,7.1,4,3.9,4.2,4.35,3.4,4.25,3.55,4,4.5,3.5,4.5,3.5,6,950,51,0.1 124 | Tangerine (2015),95,86,86,7.3,7.4,4,3.9,4.75,4.3,4.3,3.65,3.7,5,4.5,4.5,3.5,3.5,14,696,36,0.1 125 | Magic Mike XXL (2015),62,64,60,5.4,6.3,4.5,4.4,3.1,3.2,3,2.7,3.15,3,3,3,2.5,3,52,11937,9363,0.1 126 | Home (2015),45,65,55,7.3,6.7,4.5,4.4,2.25,3.25,2.75,3.65,3.35,2.5,3.5,3,3.5,3.5,177,41158,7705,0.1 127 | The Wedding Ringer (2015),27,66,35,3.3,6.7,4.5,4.4,1.35,3.3,1.75,1.65,3.35,1.5,3.5,2,1.5,3.5,126,37292,6506,0.1 128 | Woman in Gold (2015),52,81,51,7.2,7.4,4.5,4.4,2.6,4.05,2.55,3.6,3.7,2.5,4,2.5,3.5,3.5,72,17957,2435,0.1 129 | The Last Five Years (2015),60,60,60,6.9,6,4.5,4.4,3,3,3,3.45,3,3,3,3,3.5,3,20,4110,99,0.1 130 | Mission: Impossible – Rogue Nation (2015),92,90,75,8,7.8,4.5,4.4,4.6,4.5,3.75,4,3.9,4.5,4.5,4,4,4,362,82579,8357,0.1 131 | Amy (2015),97,91,85,8.8,8,4.5,4.4,4.85,4.55,4.25,4.4,4,5,4.5,4.5,4.5,4,60,5630,729,0.1 132 | Jurassic World (2015),71,81,59,7,7.3,4.5,4.5,3.55,4.05,2.95,3.5,3.65,3.5,4,3,3.5,3.5,1281,241807,34390,0 133 | Minions (2015),54,52,56,5.7,6.7,4,4,2.7,2.6,2.8,2.85,3.35,2.5,2.5,3,3,3.5,204,55895,14998,0 134 | Max (2015),35,73,47,5.9,7,4.5,4.5,1.75,3.65,2.35,2.95,3.5,2,3.5,2.5,3,3.5,15,5444,3412,0 135 | Paul Blart: Mall Cop 2 (2015),5,36,13,2.4,4.3,3.5,3.5,0.25,1.8,0.65,1.2,2.15,0.5,2,0.5,1,2,211,15004,3054,0 136 | The Longest Ride (2015),31,73,33,4.8,7.2,4.5,4.5,1.55,3.65,1.65,2.4,3.6,1.5,3.5,1.5,2.5,3.5,49,25214,2603,0 137 | The Lazarus Effect (2015),14,23,31,4.9,5.2,3,3,0.7,1.15,1.55,2.45,2.6,0.5,1,1.5,2.5,2.5,62,17691,1651,0 138 | The Woman In Black 2 Angel of Death (2015),22,25,42,4.4,4.9,3,3,1.1,1.25,2.1,2.2,2.45,1,1.5,2,2,2.5,55,14873,1333,0 139 | Danny Collins (2015),77,75,58,7.1,7.1,4,4,3.85,3.75,2.9,3.55,3.55,4,4,3,3.5,3.5,33,11206,531,0 140 | Spare Parts (2015),52,83,50,7.1,7.2,4.5,4.5,2.6,4.15,2.5,3.55,3.6,2.5,4,2.5,3.5,3.5,7,47377,450,0 141 | Serena (2015),18,25,36,5.3,5.4,3,3,0.9,1.25,1.8,2.65,2.7,1,1.5,2,2.5,2.5,19,12165,50,0 142 | Inside Out (2015),98,90,94,8.9,8.6,4.5,4.5,4.9,4.5,4.7,4.45,4.3,5,4.5,4.5,4.5,4.5,807,96252,15749,0 143 | Mr. Holmes (2015),87,78,67,7.9,7.4,4,4,4.35,3.9,3.35,3.95,3.7,4.5,4,3.5,4,3.5,33,7367,1348,0 144 | '71 (2015),97,82,83,7.5,7.2,3.5,3.5,4.85,4.1,4.15,3.75,3.6,5,4,4,4,3.5,60,24116,192,0 145 | "Two Days, One Night (2014)",97,78,89,8.8,7.4,3.5,3.5,4.85,3.9,4.45,4.4,3.7,5,4,4.5,4.5,3.5,123,24345,118,0 146 | Gett: The Trial of Viviane Amsalem (2015),100,81,90,7.3,7.8,3.5,3.5,5,4.05,4.5,3.65,3.9,5,4,4.5,3.5,4,19,1955,59,0 147 | "Kumiko, The Treasure Hunter (2015)",87,63,68,6.4,6.7,3.5,3.5,4.35,3.15,3.4,3.2,3.35,4.5,3,3.5,3,3.5,19,5289,41,0 148 | -------------------------------------------------------------------------------- /Dhaka_Tribune_Code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "name": "stdout", 12 | "output_type": "stream", 13 | "text": [ 14 | "\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "from pymongo import MongoClient\n", 20 | "\n", 21 | "#mongoimport --db bd_news --collection dhaka_tribune file_name\n", 22 | "client = MongoClient()\n", 23 | "db = client.bd_news\n", 24 | "coll = db.dhaka_tribune\n", 25 | "x = coll.find()\n", 26 | "print type(x)\n", 27 | "\n", 28 | "news = list()\n", 29 | "\n", 30 | "for i in x:\n", 31 | " news.append(i)\n", 32 | " \n", 33 | "\n", 34 | " \n", 35 | " " 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": { 42 | "collapsed": false 43 | }, 44 | "outputs": [ 45 | { 46 | "data": { 47 | "text/plain": [ 48 | "49055" 49 | ] 50 | }, 51 | "execution_count": 2, 52 | "metadata": {}, 53 | "output_type": "execute_result" 54 | } 55 | ], 56 | "source": [ 57 | "len(news)" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 3, 63 | "metadata": { 64 | "collapsed": true 65 | }, 66 | "outputs": [], 67 | "source": [ 68 | "specific_keyword = ['rape','raped','raping','sexual assult','gang raped','dowry','acid victim','eve teasing','felani','tonu',]" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 4, 74 | "metadata": { 75 | "collapsed": true 76 | }, 77 | "outputs": [], 78 | "source": [ 79 | "filtered_news = []\n", 80 | "\n", 81 | "for n in news:\n", 82 | " for k in specific_keyword:\n", 83 | " if k in n['news_keywords']:\n", 84 | " #print n['news_headline']\n", 85 | " #print n['news_keywords']\n", 86 | " #print n['news_ner_tags']['organizations_unique']\n", 87 | " #print n['news_ner_tags']['persons_unique']\n", 88 | " filtered_news.append(n)" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": { 95 | "collapsed": true 96 | }, 97 | "outputs": [], 98 | "source": [ 99 | "import networkx as nx\n", 100 | "from itertools import combinations\n", 101 | "import matplotlib.pyplot as plt\n", 102 | "%matplotlib inline\n", 103 | "\n", 104 | "\n", 105 | "N = nx.Graph()\n", 106 | "for n in filtered_news:\n", 107 | " nodes = (n['news_ner_tags']['organizations_unique'] + n['news_ner_tags']['persons_unique'] + n['news_ner_tags']['locations_unique'])\n", 108 | " edges = list(combinations(nodes,2))\n", 109 | " M = nx.Graph()\n", 110 | " M.add_nodes_from(nodes)\n", 111 | " M.add_edges_from(edges)\n", 112 | " N = nx.compose(M,N)\n", 113 | " " 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": null, 119 | "metadata": { 120 | "collapsed": true 121 | }, 122 | "outputs": [], 123 | "source": [ 124 | "# sorted(degrees.items(), key=lambda x: x[1],reverse = True)\n", 125 | "\n", 126 | "N.remove_nodes_from(['Bangladesh','Dhaka Tribune'])" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": null, 132 | "metadata": { 133 | "collapsed": true 134 | }, 135 | "outputs": [], 136 | "source": [ 137 | "nx.write_graphml(N,\"dhaka_tribune_women_2.graphml\")\n", 138 | "\n", 139 | "nx.write_gexf(N, \"harassment_women_2.gexf\")" 140 | ] 141 | } 142 | ], 143 | "metadata": { 144 | "kernelspec": { 145 | "display_name": "Python 2", 146 | "language": "python", 147 | "name": "python2" 148 | }, 149 | "language_info": { 150 | "codemirror_mode": { 151 | "name": "ipython", 152 | "version": 2 153 | }, 154 | "file_extension": ".py", 155 | "mimetype": "text/x-python", 156 | "name": "python", 157 | "nbconvert_exporter": "python", 158 | "pygments_lexer": "ipython2", 159 | "version": "2.7.12" 160 | } 161 | }, 162 | "nbformat": 4, 163 | "nbformat_minor": 0 164 | } 165 | -------------------------------------------------------------------------------- /NERTagger example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 14, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import nltk\n", 12 | "from nltk.tag import StanfordNERTagger" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "metadata": { 19 | "collapsed": true 20 | }, 21 | "outputs": [], 22 | "source": [ 23 | "from nltk.tokenize import word_tokenize" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 11, 29 | "metadata": { 30 | "collapsed": true 31 | }, 32 | "outputs": [ 33 | { 34 | "data": { 35 | "text/plain": [ 36 | "'G:\\\\stanford-ner-2015-12-09\\\\classifiers\\\\english.all.3class.distsim.crf.ser.gz'" 37 | ] 38 | }, 39 | "execution_count": 11, 40 | "metadata": {}, 41 | "output_type": "execute_result" 42 | } 43 | ], 44 | "source": [ 45 | "import os\n", 46 | "tagger_path = os.path.abspath(\"G:\\stanford-ner-2015-12-09\\stanford-ner.jar\")\n", 47 | "classifier_path = os.path.abspath(\"G:\\stanford-ner-2015-12-09\\classifiers\\english.all.3class.distsim.crf.ser.gz\")\n" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 29, 53 | "metadata": { 54 | "collapsed": false 55 | }, 56 | "outputs": [], 57 | "source": [ 58 | "\n", 59 | "java_path = \"C:\\Program Files (x86)\\Java\\jre1.8.0_77\\\\bin\\java.exe\"\n", 60 | "os.environ['JAVAHOME'] = java_path\n" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 40, 66 | "metadata": { 67 | "collapsed": false 68 | }, 69 | "outputs": [], 70 | "source": [ 71 | "st = StanfordNERTagger(\"G:\\stanford-ner-2015-12-09\\classifiers\\english.all.3class.distsim.crf.ser.gz\",\n", 72 | " \"G:\\stanford-ner-2015-12-09\\stanford-ner.jar\",encoding = \"UTF-8\")" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 41, 78 | "metadata": { 79 | "collapsed": false 80 | }, 81 | "outputs": [], 82 | "source": [ 83 | "#text = 'While in France, Christine Lagarde discussed short-term stimulus efforts in a recent interview with the Wall Street Journal.'\n", 84 | "\n", 85 | "#tokenized_text = word_tokenize(text)\n", 86 | "#classified_text = st.tag(tokenized_text)\n", 87 | "\n", 88 | "#print(classified_text)\n", 89 | "#print st.tag(\"Alice was assulted by John in Dhaka\")" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 44, 95 | "metadata": { 96 | "collapsed": false 97 | }, 98 | "outputs": [ 99 | { 100 | "name": "stdout", 101 | "output_type": "stream", 102 | "text": [ 103 | "[(u'Alice', u'PERSON'), (u'was', u'O'), (u'assulted', u'O'), (u'by', u'O'), (u'John', u'PERSON'), (u'in', u'O'), (u'Dhaka', u'LOCATION')]\n" 104 | ] 105 | } 106 | ], 107 | "source": [ 108 | "text_2 = \"Alice was assulted by John in Dhaka\"\n", 109 | "tokenized_text_2 = word_tokenize(text_2)\n", 110 | "classified_text_2 = st.tag(tokenized_text_2)\n", 111 | "print classified_text_2" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": null, 117 | "metadata": { 118 | "collapsed": true 119 | }, 120 | "outputs": [], 121 | "source": [] 122 | } 123 | ], 124 | "metadata": { 125 | "kernelspec": { 126 | "display_name": "Python 2", 127 | "language": "python", 128 | "name": "python2" 129 | }, 130 | "language_info": { 131 | "codemirror_mode": { 132 | "name": "ipython", 133 | "version": 2 134 | }, 135 | "file_extension": ".py", 136 | "mimetype": "text/x-python", 137 | "name": "python", 138 | "nbconvert_exporter": "python", 139 | "pygments_lexer": "ipython2", 140 | "version": "2.7.12" 141 | } 142 | }, 143 | "nbformat": 4, 144 | "nbformat_minor": 0 145 | } 146 | -------------------------------------------------------------------------------- /Police-Killings/To-do.txt: -------------------------------------------------------------------------------- 1 | Read 2 | -------------------------------------------------------------------------------- /Police-Killings/police_killings.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tahsin-Mayeesha/Exploratory-Data-Analysis-Projects/25fd053252dd4f696d61a282a8cef096995db33f/Police-Killings/police_killings.csv -------------------------------------------------------------------------------- /Police-Killings/state_population.csv: -------------------------------------------------------------------------------- 1 | SUMLEV,REGION,DIVISION,STATE,NAME,POPESTIMATE2015,POPEST18PLUS2015,PCNT_POPEST18PLUS 2 | 010,0,0,00,United States,321418820,247773709,77.1 3 | 040,3,6,01,Alabama,4858979,3755483,77.3 4 | 040,4,9,02,Alaska,738432,552166,74.8 5 | 040,4,8,04,Arizona,6828065,5205215,76.2 6 | 040,3,7,05,Arkansas,2978204,2272904,76.3 7 | 040,4,9,06,California,39144818,30023902,76.7 8 | 040,4,8,08,Colorado,5456574,4199509,77 9 | 040,1,1,09,Connecticut,3590886,2826827,78.7 10 | 040,3,5,10,Delaware,945934,741548,78.4 11 | 040,3,5,11,District of Columbia,672228,554121,82.4 12 | 040,3,5,12,Florida,20271272,16166143,79.7 13 | 040,3,5,13,Georgia,10214860,7710688,75.5 14 | 040,4,9,15,Hawaii,1431603,1120770,78.3 15 | 040,4,8,16,Idaho,1654930,1222093,73.8 16 | 040,2,3,17,Illinois,12859995,9901322,77 17 | 040,2,3,18,Indiana,6619680,5040224,76.1 18 | 040,2,4,19,Iowa,3123899,2395103,76.7 19 | 040,2,4,20,Kansas,2911641,2192084,75.3 20 | 040,3,6,21,Kentucky,4425092,3413425,77.1 21 | 040,3,7,22,Louisiana,4670724,3555911,76.1 22 | 040,1,1,23,Maine,1329328,1072948,80.7 23 | 040,3,5,24,Maryland,6006401,4658175,77.6 24 | 040,1,1,25,Massachusetts,6794422,5407335,79.6 25 | 040,2,3,26,Michigan,9922576,7715272,77.8 26 | 040,2,4,27,Minnesota,5489594,4205207,76.6 27 | 040,3,6,28,Mississippi,2992333,2265485,75.7 28 | 040,2,4,29,Missouri,6083672,4692196,77.1 29 | 040,4,8,30,Montana,1032949,806529,78.1 30 | 040,2,4,31,Nebraska,1896190,1425853,75.2 31 | 040,4,8,32,Nevada,2890845,2221681,76.9 32 | 040,1,1,33,New Hampshire,1330608,1066610,80.2 33 | 040,1,2,34,New Jersey,8958013,6959192,77.7 34 | 040,4,8,35,New Mexico,2085109,1588201,76.2 35 | 040,1,2,36,New York,19795791,15584974,78.7 36 | 040,3,5,37,North Carolina,10042802,7752234,77.2 37 | 040,2,4,38,North Dakota,756927,583001,77 38 | 040,2,3,39,Ohio,11613423,8984946,77.4 39 | 040,3,7,40,Oklahoma,3911338,2950017,75.4 40 | 040,4,9,41,Oregon,4028977,3166121,78.6 41 | 040,1,2,42,Pennsylvania,12802503,10112229,79 42 | 040,1,1,44,Rhode Island,1056298,845254,80 43 | 040,3,5,45,South Carolina,4896146,3804558,77.7 44 | 040,2,4,46,South Dakota,858469,647145,75.4 45 | 040,3,6,47,Tennessee,6600299,5102688,77.3 46 | 040,3,7,48,Texas,27469114,20257343,73.7 47 | 040,4,8,49,Utah,2995919,2083423,69.5 48 | 040,1,1,50,Vermont,626042,506119,80.8 49 | 040,3,5,51,Virginia,8382993,6512571,77.7 50 | 040,4,9,53,Washington,7170351,5558509,77.5 51 | 040,3,5,54,West Virginia,1844128,1464532,79.4 52 | 040,2,3,55,Wisconsin,5771337,4476711,77.6 53 | 040,4,8,56,Wyoming,586107,447212,76.3 54 | 040,X,X,72,Puerto Rico Commonwealth,3474182,2736791,78.8 55 | -------------------------------------------------------------------------------- /Predicting-Bike-Rentals/Predict Bike Rentals.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd\n", 12 | "import seaborn as sns\n", 13 | "import numpy as np\n", 14 | "import matplotlib.pyplot as plt\n", 15 | "%matplotlib inline" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 3, 21 | "metadata": { 22 | "collapsed": true 23 | }, 24 | "outputs": [], 25 | "source": [ 26 | "bike_rentals = pd.read_csv(\"hour.csv\")" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 5, 32 | "metadata": { 33 | "collapsed": false 34 | }, 35 | "outputs": [ 36 | { 37 | "data": { 38 | "text/html": [ 39 | "
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instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-01101006010.240.28790.810.031316
122011-01-01101106010.220.27270.800.083240
232011-01-01101206010.220.27270.800.052732
342011-01-01101306010.240.28790.750.031013
452011-01-01101406010.240.28790.750.0011
\n", 166 | "
" 167 | ], 168 | "text/plain": [ 169 | " instant dteday season yr mnth hr holiday weekday workingday \\\n", 170 | "0 1 2011-01-01 1 0 1 0 0 6 0 \n", 171 | "1 2 2011-01-01 1 0 1 1 0 6 0 \n", 172 | "2 3 2011-01-01 1 0 1 2 0 6 0 \n", 173 | "3 4 2011-01-01 1 0 1 3 0 6 0 \n", 174 | "4 5 2011-01-01 1 0 1 4 0 6 0 \n", 175 | "\n", 176 | " weathersit temp atemp hum windspeed casual registered cnt \n", 177 | "0 1 0.24 0.2879 0.81 0.0 3 13 16 \n", 178 | "1 1 0.22 0.2727 0.80 0.0 8 32 40 \n", 179 | "2 1 0.22 0.2727 0.80 0.0 5 27 32 \n", 180 | "3 1 0.24 0.2879 0.75 0.0 3 10 13 \n", 181 | "4 1 0.24 0.2879 0.75 0.0 0 1 1 " 182 | ] 183 | }, 184 | "execution_count": 5, 185 | "metadata": {}, 186 | "output_type": "execute_result" 187 | } 188 | ], 189 | "source": [ 190 | "bike_rentals.head()" 191 | ] 192 | }, 193 | { 194 | "cell_type": "markdown", 195 | "metadata": {}, 196 | "source": [ 197 | "In many American cities, there are communal bicycle sharing stations where you can rent bicycles by the hour or by the day. Washington, D.C. is one of these cities, and has detailed data available about how many bicycles were rented by hour and by day.\n", 198 | "This dataset comes from UCI Machine Learning Repository.\n", 199 | "\n", 200 | "Here are explanations of the relevant columns:\n", 201 | "\n", 202 | "\n" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 7, 226 | "metadata": { 227 | "collapsed": false 228 | }, 229 | "outputs": [ 230 | { 231 | "data": { 232 | "text/plain": [ 233 | "" 234 | ] 235 | }, 236 | "execution_count": 7, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | }, 240 | { 241 | "data": { 242 | "image/png": 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243 | "text/plain": [ 244 | "" 245 | ] 246 | }, 247 | "metadata": {}, 248 | "output_type": "display_data" 249 | } 250 | ], 251 | "source": [ 252 | "# Take a look at the distribution of the target variable\n", 253 | "\n", 254 | "plt.hist(bike_rentals[\"cnt\"])\n", 255 | "plt.xlabel(\"Distribution of bikes rented \")" 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "Seems like there was almost no bike rented in many days, around 7000, but at the same time there are days where 1000+ bikes were rented." 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "execution_count": 9, 268 | "metadata": { 269 | "collapsed": false 270 | }, 271 | "outputs": [ 272 | { 273 | "data": { 274 | "text/html": [ 275 | "
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instantseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
instant1.0000000.4040460.8660140.489164-0.0047750.0147230.001357-0.003416-0.0141980.1361780.1376150.009577-0.0745050.1582950.2820460.278379
season0.4040461.000000-0.0107420.830386-0.006117-0.009585-0.0023350.013743-0.0145240.3120250.3193800.150625-0.1497730.1202060.1742260.178056
yr0.866014-0.0107421.000000-0.010473-0.0038670.006692-0.004485-0.002196-0.0191570.0409130.039222-0.083546-0.0087400.1427790.2536840.250495
mnth0.4891640.830386-0.0104731.000000-0.0057720.0184300.010400-0.0034770.0054000.2016910.2080960.164411-0.1353860.0684570.1222730.120638
hr-0.004775-0.006117-0.003867-0.0057721.0000000.000479-0.0034980.002285-0.0202030.1376030.133750-0.2764980.1372520.3012020.3741410.394071
holiday0.014723-0.0095850.0066920.0184300.0004791.000000-0.102088-0.252471-0.017036-0.027340-0.030973-0.0105880.0039880.031564-0.047345-0.030927
weekday0.001357-0.002335-0.0044850.010400-0.003498-0.1020881.0000000.0359550.003311-0.001795-0.008821-0.0371580.0115020.0327210.0215780.026900
workingday-0.0034160.013743-0.002196-0.0034770.002285-0.2524710.0359551.0000000.0446720.0553900.0546670.015688-0.011830-0.3009420.1343260.030284
weathersit-0.014198-0.014524-0.0191570.005400-0.020203-0.0170360.0033110.0446721.000000-0.102640-0.1055630.4181300.026226-0.152628-0.120966-0.142426
temp0.1361780.3120250.0409130.2016910.137603-0.027340-0.0017950.055390-0.1026401.0000000.987672-0.069881-0.0231250.4596160.3353610.404772
atemp0.1376150.3193800.0392220.2080960.133750-0.030973-0.0088210.054667-0.1055630.9876721.000000-0.051918-0.0623360.4540800.3325590.400929
hum0.0095770.150625-0.0835460.164411-0.276498-0.010588-0.0371580.0156880.418130-0.069881-0.0519181.000000-0.290105-0.347028-0.273933-0.322911
windspeed-0.074505-0.149773-0.008740-0.1353860.1372520.0039880.011502-0.0118300.026226-0.023125-0.062336-0.2901051.0000000.0902870.0823210.093234
casual0.1582950.1202060.1427790.0684570.3012020.0315640.032721-0.300942-0.1526280.4596160.454080-0.3470280.0902871.0000000.5066180.694564
registered0.2820460.1742260.2536840.1222730.374141-0.0473450.0215780.134326-0.1209660.3353610.332559-0.2739330.0823210.5066181.0000000.972151
cnt0.2783790.1780560.2504950.1206380.394071-0.0309270.0269000.030284-0.1424260.4047720.400929-0.3229110.0932340.6945640.9721511.000000
\n", 605 | "
" 606 | ], 607 | "text/plain": [ 608 | " instant season yr mnth hr holiday \\\n", 609 | "instant 1.000000 0.404046 0.866014 0.489164 -0.004775 0.014723 \n", 610 | "season 0.404046 1.000000 -0.010742 0.830386 -0.006117 -0.009585 \n", 611 | "yr 0.866014 -0.010742 1.000000 -0.010473 -0.003867 0.006692 \n", 612 | "mnth 0.489164 0.830386 -0.010473 1.000000 -0.005772 0.018430 \n", 613 | "hr -0.004775 -0.006117 -0.003867 -0.005772 1.000000 0.000479 \n", 614 | "holiday 0.014723 -0.009585 0.006692 0.018430 0.000479 1.000000 \n", 615 | "weekday 0.001357 -0.002335 -0.004485 0.010400 -0.003498 -0.102088 \n", 616 | "workingday -0.003416 0.013743 -0.002196 -0.003477 0.002285 -0.252471 \n", 617 | "weathersit -0.014198 -0.014524 -0.019157 0.005400 -0.020203 -0.017036 \n", 618 | "temp 0.136178 0.312025 0.040913 0.201691 0.137603 -0.027340 \n", 619 | "atemp 0.137615 0.319380 0.039222 0.208096 0.133750 -0.030973 \n", 620 | "hum 0.009577 0.150625 -0.083546 0.164411 -0.276498 -0.010588 \n", 621 | "windspeed -0.074505 -0.149773 -0.008740 -0.135386 0.137252 0.003988 \n", 622 | "casual 0.158295 0.120206 0.142779 0.068457 0.301202 0.031564 \n", 623 | "registered 0.282046 0.174226 0.253684 0.122273 0.374141 -0.047345 \n", 624 | "cnt 0.278379 0.178056 0.250495 0.120638 0.394071 -0.030927 \n", 625 | "\n", 626 | " weekday workingday weathersit temp atemp hum \\\n", 627 | "instant 0.001357 -0.003416 -0.014198 0.136178 0.137615 0.009577 \n", 628 | "season -0.002335 0.013743 -0.014524 0.312025 0.319380 0.150625 \n", 629 | "yr -0.004485 -0.002196 -0.019157 0.040913 0.039222 -0.083546 \n", 630 | "mnth 0.010400 -0.003477 0.005400 0.201691 0.208096 0.164411 \n", 631 | "hr -0.003498 0.002285 -0.020203 0.137603 0.133750 -0.276498 \n", 632 | "holiday -0.102088 -0.252471 -0.017036 -0.027340 -0.030973 -0.010588 \n", 633 | "weekday 1.000000 0.035955 0.003311 -0.001795 -0.008821 -0.037158 \n", 634 | "workingday 0.035955 1.000000 0.044672 0.055390 0.054667 0.015688 \n", 635 | "weathersit 0.003311 0.044672 1.000000 -0.102640 -0.105563 0.418130 \n", 636 | "temp -0.001795 0.055390 -0.102640 1.000000 0.987672 -0.069881 \n", 637 | "atemp -0.008821 0.054667 -0.105563 0.987672 1.000000 -0.051918 \n", 638 | "hum -0.037158 0.015688 0.418130 -0.069881 -0.051918 1.000000 \n", 639 | "windspeed 0.011502 -0.011830 0.026226 -0.023125 -0.062336 -0.290105 \n", 640 | "casual 0.032721 -0.300942 -0.152628 0.459616 0.454080 -0.347028 \n", 641 | "registered 0.021578 0.134326 -0.120966 0.335361 0.332559 -0.273933 \n", 642 | "cnt 0.026900 0.030284 -0.142426 0.404772 0.400929 -0.322911 \n", 643 | "\n", 644 | " windspeed casual registered cnt \n", 645 | "instant -0.074505 0.158295 0.282046 0.278379 \n", 646 | "season -0.149773 0.120206 0.174226 0.178056 \n", 647 | "yr -0.008740 0.142779 0.253684 0.250495 \n", 648 | "mnth -0.135386 0.068457 0.122273 0.120638 \n", 649 | "hr 0.137252 0.301202 0.374141 0.394071 \n", 650 | "holiday 0.003988 0.031564 -0.047345 -0.030927 \n", 651 | "weekday 0.011502 0.032721 0.021578 0.026900 \n", 652 | "workingday -0.011830 -0.300942 0.134326 0.030284 \n", 653 | "weathersit 0.026226 -0.152628 -0.120966 -0.142426 \n", 654 | "temp -0.023125 0.459616 0.335361 0.404772 \n", 655 | "atemp -0.062336 0.454080 0.332559 0.400929 \n", 656 | "hum -0.290105 -0.347028 -0.273933 -0.322911 \n", 657 | "windspeed 1.000000 0.090287 0.082321 0.093234 \n", 658 | "casual 0.090287 1.000000 0.506618 0.694564 \n", 659 | "registered 0.082321 0.506618 1.000000 0.972151 \n", 660 | "cnt 0.093234 0.694564 0.972151 1.000000 " 661 | ] 662 | }, 663 | "execution_count": 9, 664 | "metadata": {}, 665 | "output_type": "execute_result" 666 | } 667 | ], 668 | "source": [ 669 | "# Check the pairwise correlation among different variables\n", 670 | "\n", 671 | "bike_rentals.corr()" 672 | ] 673 | }, 674 | { 675 | "cell_type": "code", 676 | "execution_count": 10, 677 | "metadata": { 678 | "collapsed": false 679 | }, 680 | "outputs": [ 681 | { 682 | "data": { 683 | "text/plain": [ 684 | "instant 0.278379\n", 685 | "season 0.178056\n", 686 | "yr 0.250495\n", 687 | "mnth 0.120638\n", 688 | "hr 0.394071\n", 689 | "holiday -0.030927\n", 690 | "weekday 0.026900\n", 691 | "workingday 0.030284\n", 692 | "weathersit -0.142426\n", 693 | "temp 0.404772\n", 694 | "atemp 0.400929\n", 695 | "hum -0.322911\n", 696 | "windspeed 0.093234\n", 697 | "casual 0.694564\n", 698 | "registered 0.972151\n", 699 | "cnt 1.000000\n", 700 | "Name: cnt, dtype: float64" 701 | ] 702 | }, 703 | "execution_count": 10, 704 | "metadata": {}, 705 | "output_type": "execute_result" 706 | } 707 | ], 708 | "source": [ 709 | "bike_rentals.corr()[\"cnt\"]" 710 | ] 711 | }, 712 | { 713 | "cell_type": "markdown", 714 | "metadata": {}, 715 | "source": [ 716 | "Holiday is negatively correlated with bike rentals, which might suggest most people were taking bikes for going to work, but working day and weekday seems to be losely correated. Hr seems to have good correlation because it's possible some hours are busier than others. Casual and registered are heavily correlated but it's meaningless as casual + registered = total cnt." 717 | ] 718 | }, 719 | { 720 | "cell_type": "markdown", 721 | "metadata": {}, 722 | "source": [ 723 | "# Feature engineering :" 724 | ] 725 | }, 726 | { 727 | "cell_type": "markdown", 728 | "metadata": {}, 729 | "source": [ 730 | "Even if the hours are related, scikit learn might decipher the hours as seperate and treat all 24 hours as different categories. So we create a new column time labels to label hours as morning = 1, afternoon = 2, evening = 3 and night = 4." 731 | ] 732 | }, 733 | { 734 | "cell_type": "code", 735 | "execution_count": 14, 736 | "metadata": { 737 | "collapsed": true 738 | }, 739 | "outputs": [], 740 | "source": [ 741 | "def apply_label(hour):\n", 742 | " if hour >=6 and hour<= 12:\n", 743 | " return 1 #morning\n", 744 | " elif hour >=12 and hour<=18:\n", 745 | " return 2 #afternoon\n", 746 | " elif hour >=18 and hour <=24:\n", 747 | " return 3 #evening\n", 748 | " else:\n", 749 | " return 4 #night" 750 | ] 751 | }, 752 | { 753 | "cell_type": "code", 754 | "execution_count": 15, 755 | "metadata": { 756 | "collapsed": true 757 | }, 758 | "outputs": [], 759 | "source": [ 760 | "bike_rentals[\"time_label\"] = bike_rentals[\"hr\"].apply(apply_label)" 761 | ] 762 | }, 763 | { 764 | "cell_type": "code", 765 | "execution_count": 16, 766 | "metadata": { 767 | "collapsed": false 768 | }, 769 | "outputs": [ 770 | { 771 | "data": { 772 | "text/html": [ 773 | "
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instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnttime_label
012011-01-01101006010.240.28790.810.0313164
122011-01-01101106010.220.27270.800.0832404
232011-01-01101206010.220.27270.800.0527324
342011-01-01101306010.240.28790.750.0310134
452011-01-01101406010.240.28790.750.00114
\n", 906 | "
" 907 | ], 908 | "text/plain": [ 909 | " instant dteday season yr mnth hr holiday weekday workingday \\\n", 910 | "0 1 2011-01-01 1 0 1 0 0 6 0 \n", 911 | "1 2 2011-01-01 1 0 1 1 0 6 0 \n", 912 | "2 3 2011-01-01 1 0 1 2 0 6 0 \n", 913 | "3 4 2011-01-01 1 0 1 3 0 6 0 \n", 914 | "4 5 2011-01-01 1 0 1 4 0 6 0 \n", 915 | "\n", 916 | " weathersit temp atemp hum windspeed casual registered cnt \\\n", 917 | "0 1 0.24 0.2879 0.81 0.0 3 13 16 \n", 918 | "1 1 0.22 0.2727 0.80 0.0 8 32 40 \n", 919 | "2 1 0.22 0.2727 0.80 0.0 5 27 32 \n", 920 | "3 1 0.24 0.2879 0.75 0.0 3 10 13 \n", 921 | "4 1 0.24 0.2879 0.75 0.0 0 1 1 \n", 922 | "\n", 923 | " time_label \n", 924 | "0 4 \n", 925 | "1 4 \n", 926 | "2 4 \n", 927 | "3 4 \n", 928 | "4 4 " 929 | ] 930 | }, 931 | "execution_count": 16, 932 | "metadata": {}, 933 | "output_type": "execute_result" 934 | } 935 | ], 936 | "source": [ 937 | "bike_rentals.head()" 938 | ] 939 | }, 940 | { 941 | "cell_type": "code", 942 | "execution_count": 21, 943 | "metadata": { 944 | "collapsed": false 945 | }, 946 | "outputs": [ 947 | { 948 | "data": { 949 | "text/plain": [ 950 | "" 951 | ] 952 | }, 953 | "execution_count": 21, 954 | "metadata": {}, 955 | "output_type": "execute_result" 956 | }, 957 | { 958 | "data": { 959 | "image/png": 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960 | "text/plain": [ 961 | "" 962 | ] 963 | }, 964 | "metadata": {}, 965 | "output_type": "display_data" 966 | } 967 | ], 968 | "source": [ 969 | "sns.barplot(x = \"time_label\", y = \"cnt\", data = bike_rentals)\n" 970 | ] 971 | }, 972 | { 973 | "cell_type": "markdown", 974 | "metadata": {}, 975 | "source": [ 976 | "It looks like most of the bikes were rented in afternoon, and really few in night which makes sense." 977 | ] 978 | }, 979 | { 980 | "cell_type": "markdown", 981 | "metadata": {}, 982 | "source": [ 983 | "# Train Test Split:" 984 | ] 985 | }, 986 | { 987 | "cell_type": "markdown", 988 | "metadata": {}, 989 | "source": [ 990 | "We use MSE(Mean squared error) as our error metric as it's a good fit for continuous numeric variables as count of bike rentals and it's going to punish the large errors more." 991 | ] 992 | }, 993 | { 994 | "cell_type": "code", 995 | "execution_count": 22, 996 | "metadata": { 997 | "collapsed": true 998 | }, 999 | "outputs": [], 1000 | "source": [ 1001 | "train = bike_rentals.sample(frac = 0.8)" 1002 | ] 1003 | }, 1004 | { 1005 | "cell_type": "code", 1006 | "execution_count": 23, 1007 | "metadata": { 1008 | "collapsed": false 1009 | }, 1010 | "outputs": [ 1011 | { 1012 | "data": { 1013 | "text/html": [ 1014 | "
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910491052012-01-20111605110.240.21210.600.2836068681
529452952011-08-133081206020.660.59090.890.16421752464211
426242632011-07-013071205110.800.69700.260.0000831802631
12236122372012-05-30215103130.640.57580.890.19400444
\n", 1147 | "
" 1148 | ], 1149 | "text/plain": [ 1150 | " instant dteday season yr mnth hr holiday weekday \\\n", 1151 | "346 347 2011-01-15 1 0 1 22 0 6 \n", 1152 | "9104 9105 2012-01-20 1 1 1 6 0 5 \n", 1153 | "5294 5295 2011-08-13 3 0 8 12 0 6 \n", 1154 | "4262 4263 2011-07-01 3 0 7 12 0 5 \n", 1155 | "12236 12237 2012-05-30 2 1 5 1 0 3 \n", 1156 | "\n", 1157 | " workingday weathersit temp atemp hum windspeed casual \\\n", 1158 | "346 0 2 0.30 0.3182 0.42 0.1045 0 \n", 1159 | "9104 1 1 0.24 0.2121 0.60 0.2836 0 \n", 1160 | "5294 0 2 0.66 0.5909 0.89 0.1642 175 \n", 1161 | "4262 1 1 0.80 0.6970 0.26 0.0000 83 \n", 1162 | "12236 1 3 0.64 0.5758 0.89 0.1940 0 \n", 1163 | "\n", 1164 | " registered cnt time_label \n", 1165 | "346 26 26 3 \n", 1166 | "9104 68 68 1 \n", 1167 | "5294 246 421 1 \n", 1168 | "4262 180 263 1 \n", 1169 | "12236 4 4 4 " 1170 | ] 1171 | }, 1172 | "execution_count": 23, 1173 | "metadata": {}, 1174 | "output_type": "execute_result" 1175 | } 1176 | ], 1177 | "source": [ 1178 | "train.head()" 1179 | ] 1180 | }, 1181 | { 1182 | "cell_type": "code", 1183 | "execution_count": 24, 1184 | "metadata": { 1185 | "collapsed": true 1186 | }, 1187 | "outputs": [], 1188 | "source": [ 1189 | "test = bike_rentals[~bike_rentals.index.isin(train.index)]" 1190 | ] 1191 | }, 1192 | { 1193 | "cell_type": "code", 1194 | "execution_count": 25, 1195 | "metadata": { 1196 | "collapsed": false 1197 | }, 1198 | "outputs": [ 1199 | { 1200 | "data": { 1201 | "text/html": [ 1202 | "
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instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnttime_label
122011-01-01101106010.220.27270.800.0000832404
232011-01-01101206010.220.27270.800.0000527324
452011-01-01101406010.240.28790.750.00000114
672011-01-01101606010.220.27270.800.00002021
19202011-01-011011906030.420.42420.880.2537631373
\n", 1335 | "
" 1336 | ], 1337 | "text/plain": [ 1338 | " instant dteday season yr mnth hr holiday weekday workingday \\\n", 1339 | "1 2 2011-01-01 1 0 1 1 0 6 0 \n", 1340 | "2 3 2011-01-01 1 0 1 2 0 6 0 \n", 1341 | "4 5 2011-01-01 1 0 1 4 0 6 0 \n", 1342 | "6 7 2011-01-01 1 0 1 6 0 6 0 \n", 1343 | "19 20 2011-01-01 1 0 1 19 0 6 0 \n", 1344 | "\n", 1345 | " weathersit temp atemp hum windspeed casual registered cnt \\\n", 1346 | "1 1 0.22 0.2727 0.80 0.0000 8 32 40 \n", 1347 | "2 1 0.22 0.2727 0.80 0.0000 5 27 32 \n", 1348 | "4 1 0.24 0.2879 0.75 0.0000 0 1 1 \n", 1349 | "6 1 0.22 0.2727 0.80 0.0000 2 0 2 \n", 1350 | "19 3 0.42 0.4242 0.88 0.2537 6 31 37 \n", 1351 | "\n", 1352 | " time_label \n", 1353 | "1 4 \n", 1354 | "2 4 \n", 1355 | "4 4 \n", 1356 | "6 1 \n", 1357 | "19 3 " 1358 | ] 1359 | }, 1360 | "execution_count": 25, 1361 | "metadata": {}, 1362 | "output_type": "execute_result" 1363 | } 1364 | ], 1365 | "source": [ 1366 | "test.head()" 1367 | ] 1368 | }, 1369 | { 1370 | "cell_type": "code", 1371 | "execution_count": 26, 1372 | "metadata": { 1373 | "collapsed": false 1374 | }, 1375 | "outputs": [ 1376 | { 1377 | "name": "stdout", 1378 | "output_type": "stream", 1379 | "text": [ 1380 | "13903\n", 1381 | "3476\n" 1382 | ] 1383 | } 1384 | ], 1385 | "source": [ 1386 | "print train.shape[0]\n", 1387 | "print test.shape[0]" 1388 | ] 1389 | }, 1390 | { 1391 | "cell_type": "markdown", 1392 | "metadata": {}, 1393 | "source": [ 1394 | "# Linear Regression:" 1395 | ] 1396 | }, 1397 | { 1398 | "cell_type": "markdown", 1399 | "metadata": {}, 1400 | "source": [ 1401 | "Considering Linear Regression is a simple model, it's kind of unlikely it will fit the data well given there's a huge difference between days with large numbers of rentals vs the days that have none." 1402 | ] 1403 | }, 1404 | { 1405 | "cell_type": "code", 1406 | "execution_count": 32, 1407 | "metadata": { 1408 | "collapsed": false 1409 | }, 1410 | "outputs": [ 1411 | { 1412 | "data": { 1413 | "text/plain": [ 1414 | "['instant',\n", 1415 | " 'dteday',\n", 1416 | " 'season',\n", 1417 | " 'yr',\n", 1418 | " 'mnth',\n", 1419 | " 'hr',\n", 1420 | " 'holiday',\n", 1421 | " 'weekday',\n", 1422 | " 'workingday',\n", 1423 | " 'weathersit',\n", 1424 | " 'temp',\n", 1425 | " 'atemp',\n", 1426 | " 'hum',\n", 1427 | " 'windspeed',\n", 1428 | " 'casual',\n", 1429 | " 'registered',\n", 1430 | " 'cnt',\n", 1431 | " 'time_label']" 1432 | ] 1433 | }, 1434 | "execution_count": 32, 1435 | "metadata": {}, 1436 | "output_type": "execute_result" 1437 | } 1438 | ], 1439 | "source": [ 1440 | "predictors = list(bike_rentals.columns)\n", 1441 | "predictors" 1442 | ] 1443 | }, 1444 | { 1445 | "cell_type": "code", 1446 | "execution_count": 33, 1447 | "metadata": { 1448 | "collapsed": false 1449 | }, 1450 | "outputs": [], 1451 | "source": [ 1452 | "predictors.remove('casual')\n", 1453 | "predictors.remove('registered')\n", 1454 | "predictors.remove('dteday')\n", 1455 | "predictors.remove('cnt')" 1456 | ] 1457 | }, 1458 | { 1459 | "cell_type": "code", 1460 | "execution_count": 37, 1461 | "metadata": { 1462 | "collapsed": false 1463 | }, 1464 | "outputs": [ 1465 | { 1466 | "data": { 1467 | "text/plain": [ 1468 | "['instant',\n", 1469 | " 'season',\n", 1470 | " 'yr',\n", 1471 | " 'mnth',\n", 1472 | " 'hr',\n", 1473 | " 'holiday',\n", 1474 | " 'weekday',\n", 1475 | " 'workingday',\n", 1476 | " 'weathersit',\n", 1477 | " 'temp',\n", 1478 | " 'atemp',\n", 1479 | " 'hum',\n", 1480 | " 'windspeed',\n", 1481 | " 'time_label']" 1482 | ] 1483 | }, 1484 | "execution_count": 37, 1485 | "metadata": {}, 1486 | "output_type": "execute_result" 1487 | } 1488 | ], 1489 | "source": [ 1490 | "predictors" 1491 | ] 1492 | }, 1493 | { 1494 | "cell_type": "code", 1495 | "execution_count": 39, 1496 | "metadata": { 1497 | "collapsed": false 1498 | }, 1499 | "outputs": [ 1500 | { 1501 | "data": { 1502 | "text/plain": [ 1503 | "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" 1504 | ] 1505 | }, 1506 | "execution_count": 39, 1507 | "metadata": {}, 1508 | "output_type": "execute_result" 1509 | } 1510 | ], 1511 | "source": [ 1512 | "from sklearn.linear_model import LinearRegression\n", 1513 | "\n", 1514 | "lin = LinearRegression()\n", 1515 | "\n", 1516 | "lin.fit(train[predictors],train[\"cnt\"])\n", 1517 | "\n" 1518 | ] 1519 | }, 1520 | { 1521 | "cell_type": "code", 1522 | "execution_count": 41, 1523 | "metadata": { 1524 | "collapsed": false 1525 | }, 1526 | "outputs": [], 1527 | "source": [ 1528 | "pred = lin.predict(test[predictors])" 1529 | ] 1530 | }, 1531 | { 1532 | "cell_type": "code", 1533 | "execution_count": 42, 1534 | "metadata": { 1535 | "collapsed": false 1536 | }, 1537 | "outputs": [ 1538 | { 1539 | "name": "stdout", 1540 | "output_type": "stream", 1541 | "text": [ 1542 | "16795.228163\n" 1543 | ] 1544 | } 1545 | ], 1546 | "source": [ 1547 | "from sklearn.metrics import mean_squared_error\n", 1548 | "\n", 1549 | "print mean_squared_error(test[\"cnt\"],pred)" 1550 | ] 1551 | }, 1552 | { 1553 | "cell_type": "markdown", 1554 | "metadata": {}, 1555 | "source": [ 1556 | "With this high error, this model is not going anywhere. Incredibly bad performance." 1557 | ] 1558 | }, 1559 | { 1560 | "cell_type": "markdown", 1561 | "metadata": {}, 1562 | "source": [ 1563 | "# Decision Tree : " 1564 | ] 1565 | }, 1566 | { 1567 | "cell_type": "code", 1568 | "execution_count": 44, 1569 | "metadata": { 1570 | "collapsed": false 1571 | }, 1572 | "outputs": [], 1573 | "source": [ 1574 | "from sklearn.tree import DecisionTreeRegressor" 1575 | ] 1576 | }, 1577 | { 1578 | "cell_type": "code", 1579 | "execution_count": 46, 1580 | "metadata": { 1581 | "collapsed": false 1582 | }, 1583 | "outputs": [ 1584 | { 1585 | "data": { 1586 | "text/plain": [ 1587 | "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n", 1588 | " max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n", 1589 | " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", 1590 | " splitter='best')" 1591 | ] 1592 | }, 1593 | "execution_count": 46, 1594 | "metadata": {}, 1595 | "output_type": "execute_result" 1596 | } 1597 | ], 1598 | "source": [ 1599 | "reg = DecisionTreeRegressor()\n", 1600 | "\n", 1601 | "reg.fit(train[predictors],train[\"cnt\"])" 1602 | ] 1603 | }, 1604 | { 1605 | "cell_type": "code", 1606 | "execution_count": 47, 1607 | "metadata": { 1608 | "collapsed": true 1609 | }, 1610 | "outputs": [], 1611 | "source": [ 1612 | "pred = reg.predict(test[predictors])" 1613 | ] 1614 | }, 1615 | { 1616 | "cell_type": "code", 1617 | "execution_count": 48, 1618 | "metadata": { 1619 | "collapsed": false 1620 | }, 1621 | "outputs": [ 1622 | { 1623 | "name": "stdout", 1624 | "output_type": "stream", 1625 | "text": [ 1626 | "3090.89298044\n" 1627 | ] 1628 | } 1629 | ], 1630 | "source": [ 1631 | "print mean_squared_error(test[\"cnt\"],pred)" 1632 | ] 1633 | }, 1634 | { 1635 | "cell_type": "markdown", 1636 | "metadata": {}, 1637 | "source": [ 1638 | "Decision trees have way better performance than Linear Regression had done. Wow." 1639 | ] 1640 | }, 1641 | { 1642 | "cell_type": "markdown", 1643 | "metadata": {}, 1644 | "source": [ 1645 | "# Random Forest :" 1646 | ] 1647 | }, 1648 | { 1649 | "cell_type": "code", 1650 | "execution_count": 57, 1651 | "metadata": { 1652 | "collapsed": false 1653 | }, 1654 | "outputs": [], 1655 | "source": [ 1656 | "from sklearn.ensemble import RandomForestRegressor" 1657 | ] 1658 | }, 1659 | { 1660 | "cell_type": "code", 1661 | "execution_count": 58, 1662 | "metadata": { 1663 | "collapsed": true 1664 | }, 1665 | "outputs": [], 1666 | "source": [ 1667 | "r_forest = RandomForestRegressor()" 1668 | ] 1669 | }, 1670 | { 1671 | "cell_type": "code", 1672 | "execution_count": 59, 1673 | "metadata": { 1674 | "collapsed": false 1675 | }, 1676 | "outputs": [ 1677 | { 1678 | "data": { 1679 | "text/plain": [ 1680 | "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", 1681 | " max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n", 1682 | " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", 1683 | " n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n", 1684 | " verbose=0, warm_start=False)" 1685 | ] 1686 | }, 1687 | "execution_count": 59, 1688 | "metadata": {}, 1689 | "output_type": "execute_result" 1690 | } 1691 | ], 1692 | "source": [ 1693 | "r_forest.fit(train[predictors],train[\"cnt\"])" 1694 | ] 1695 | }, 1696 | { 1697 | "cell_type": "code", 1698 | "execution_count": 60, 1699 | "metadata": { 1700 | "collapsed": true 1701 | }, 1702 | "outputs": [], 1703 | "source": [ 1704 | "pred = r_forest.predict(test[predictors])" 1705 | ] 1706 | }, 1707 | { 1708 | "cell_type": "code", 1709 | "execution_count": 61, 1710 | "metadata": { 1711 | "collapsed": false 1712 | }, 1713 | "outputs": [ 1714 | { 1715 | "name": "stdout", 1716 | "output_type": "stream", 1717 | "text": [ 1718 | "1595.55758631\n" 1719 | ] 1720 | } 1721 | ], 1722 | "source": [ 1723 | "print mean_squared_error(test[\"cnt\"],pred)" 1724 | ] 1725 | }, 1726 | { 1727 | "cell_type": "markdown", 1728 | "metadata": {}, 1729 | "source": [ 1730 | "And Random Forest even cut the error by half. Honestly I think this is good enough for performance." 1731 | ] 1732 | }, 1733 | { 1734 | "cell_type": "code", 1735 | "execution_count": null, 1736 | "metadata": { 1737 | "collapsed": true 1738 | }, 1739 | "outputs": [], 1740 | "source": [] 1741 | } 1742 | ], 1743 | "metadata": { 1744 | "kernelspec": { 1745 | "display_name": "Python 2", 1746 | "language": "python", 1747 | "name": "python2" 1748 | }, 1749 | "language_info": { 1750 | "codemirror_mode": { 1751 | "name": "ipython", 1752 | "version": 2 1753 | }, 1754 | "file_extension": ".py", 1755 | "mimetype": "text/x-python", 1756 | "name": "python", 1757 | "nbconvert_exporter": "python", 1758 | "pygments_lexer": "ipython2", 1759 | "version": "2.7.11" 1760 | } 1761 | }, 1762 | "nbformat": 4, 1763 | "nbformat_minor": 0 1764 | } 1765 | -------------------------------------------------------------------------------- /Predicting-Bike-Rentals/To-do.txt: -------------------------------------------------------------------------------- 1 | 1. Figure out how to get GraphViz and Pydot to visualize the decision tree. 2 | -------------------------------------------------------------------------------- /Predicting-Bike-Rentals/day.csv: -------------------------------------------------------------------------------- 1 | instant,dteday,season,yr,mnth,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed,casual,registered,cnt 2 | 1,2011-01-01,1,0,1,0,6,0,2,0.344167,0.363625,0.805833,0.160446,331,654,985 3 | 2,2011-01-02,1,0,1,0,0,0,2,0.363478,0.353739,0.696087,0.248539,131,670,801 4 | 3,2011-01-03,1,0,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,120,1229,1349 5 | 4,2011-01-04,1,0,1,0,2,1,1,0.2,0.212122,0.590435,0.160296,108,1454,1562 6 | 5,2011-01-05,1,0,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869,82,1518,1600 7 | 6,2011-01-06,1,0,1,0,4,1,1,0.204348,0.233209,0.518261,0.0895652,88,1518,1606 8 | 7,2011-01-07,1,0,1,0,5,1,2,0.196522,0.208839,0.498696,0.168726,148,1362,1510 9 | 8,2011-01-08,1,0,1,0,6,0,2,0.165,0.162254,0.535833,0.266804,68,891,959 10 | 9,2011-01-09,1,0,1,0,0,0,1,0.138333,0.116175,0.434167,0.36195,54,768,822 11 | 10,2011-01-10,1,0,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,41,1280,1321 12 | 11,2011-01-11,1,0,1,0,2,1,2,0.169091,0.191464,0.686364,0.122132,43,1220,1263 13 | 12,2011-01-12,1,0,1,0,3,1,1,0.172727,0.160473,0.599545,0.304627,25,1137,1162 14 | 13,2011-01-13,1,0,1,0,4,1,1,0.165,0.150883,0.470417,0.301,38,1368,1406 15 | 14,2011-01-14,1,0,1,0,5,1,1,0.16087,0.188413,0.537826,0.126548,54,1367,1421 16 | 15,2011-01-15,1,0,1,0,6,0,2,0.233333,0.248112,0.49875,0.157963,222,1026,1248 17 | 16,2011-01-16,1,0,1,0,0,0,1,0.231667,0.234217,0.48375,0.188433,251,953,1204 18 | 17,2011-01-17,1,0,1,1,1,0,2,0.175833,0.176771,0.5375,0.194017,117,883,1000 19 | 18,2011-01-18,1,0,1,0,2,1,2,0.216667,0.232333,0.861667,0.146775,9,674,683 20 | 19,2011-01-19,1,0,1,0,3,1,2,0.292174,0.298422,0.741739,0.208317,78,1572,1650 21 | 20,2011-01-20,1,0,1,0,4,1,2,0.261667,0.25505,0.538333,0.195904,83,1844,1927 22 | 21,2011-01-21,1,0,1,0,5,1,1,0.1775,0.157833,0.457083,0.353242,75,1468,1543 23 | 22,2011-01-22,1,0,1,0,6,0,1,0.0591304,0.0790696,0.4,0.17197,93,888,981 24 | 23,2011-01-23,1,0,1,0,0,0,1,0.0965217,0.0988391,0.436522,0.2466,150,836,986 25 | 24,2011-01-24,1,0,1,0,1,1,1,0.0973913,0.11793,0.491739,0.15833,86,1330,1416 26 | 25,2011-01-25,1,0,1,0,2,1,2,0.223478,0.234526,0.616957,0.129796,186,1799,1985 27 | 26,2011-01-26,1,0,1,0,3,1,3,0.2175,0.2036,0.8625,0.29385,34,472,506 28 | 27,2011-01-27,1,0,1,0,4,1,1,0.195,0.2197,0.6875,0.113837,15,416,431 29 | 28,2011-01-28,1,0,1,0,5,1,2,0.203478,0.223317,0.793043,0.1233,38,1129,1167 30 | 29,2011-01-29,1,0,1,0,6,0,1,0.196522,0.212126,0.651739,0.145365,123,975,1098 31 | 30,2011-01-30,1,0,1,0,0,0,1,0.216522,0.250322,0.722174,0.0739826,140,956,1096 32 | 31,2011-01-31,1,0,1,0,1,1,2,0.180833,0.18625,0.60375,0.187192,42,1459,1501 33 | 32,2011-02-01,1,0,2,0,2,1,2,0.192174,0.23453,0.829565,0.053213,47,1313,1360 34 | 33,2011-02-02,1,0,2,0,3,1,2,0.26,0.254417,0.775417,0.264308,72,1454,1526 35 | 34,2011-02-03,1,0,2,0,4,1,1,0.186957,0.177878,0.437826,0.277752,61,1489,1550 36 | 35,2011-02-04,1,0,2,0,5,1,2,0.211304,0.228587,0.585217,0.127839,88,1620,1708 37 | 36,2011-02-05,1,0,2,0,6,0,2,0.233333,0.243058,0.929167,0.161079,100,905,1005 38 | 37,2011-02-06,1,0,2,0,0,0,1,0.285833,0.291671,0.568333,0.1418,354,1269,1623 39 | 38,2011-02-07,1,0,2,0,1,1,1,0.271667,0.303658,0.738333,0.0454083,120,1592,1712 40 | 39,2011-02-08,1,0,2,0,2,1,1,0.220833,0.198246,0.537917,0.36195,64,1466,1530 41 | 40,2011-02-09,1,0,2,0,3,1,2,0.134783,0.144283,0.494783,0.188839,53,1552,1605 42 | 41,2011-02-10,1,0,2,0,4,1,1,0.144348,0.149548,0.437391,0.221935,47,1491,1538 43 | 42,2011-02-11,1,0,2,0,5,1,1,0.189091,0.213509,0.506364,0.10855,149,1597,1746 44 | 43,2011-02-12,1,0,2,0,6,0,1,0.2225,0.232954,0.544167,0.203367,288,1184,1472 45 | 44,2011-02-13,1,0,2,0,0,0,1,0.316522,0.324113,0.457391,0.260883,397,1192,1589 46 | 45,2011-02-14,1,0,2,0,1,1,1,0.415,0.39835,0.375833,0.417908,208,1705,1913 47 | 46,2011-02-15,1,0,2,0,2,1,1,0.266087,0.254274,0.314348,0.291374,140,1675,1815 48 | 47,2011-02-16,1,0,2,0,3,1,1,0.318261,0.3162,0.423478,0.251791,218,1897,2115 49 | 48,2011-02-17,1,0,2,0,4,1,1,0.435833,0.428658,0.505,0.230104,259,2216,2475 50 | 49,2011-02-18,1,0,2,0,5,1,1,0.521667,0.511983,0.516667,0.264925,579,2348,2927 51 | 50,2011-02-19,1,0,2,0,6,0,1,0.399167,0.391404,0.187917,0.507463,532,1103,1635 52 | 51,2011-02-20,1,0,2,0,0,0,1,0.285217,0.27733,0.407826,0.223235,639,1173,1812 53 | 52,2011-02-21,1,0,2,1,1,0,2,0.303333,0.284075,0.605,0.307846,195,912,1107 54 | 53,2011-02-22,1,0,2,0,2,1,1,0.182222,0.186033,0.577778,0.195683,74,1376,1450 55 | 54,2011-02-23,1,0,2,0,3,1,1,0.221739,0.245717,0.423043,0.094113,139,1778,1917 56 | 55,2011-02-24,1,0,2,0,4,1,2,0.295652,0.289191,0.697391,0.250496,100,1707,1807 57 | 56,2011-02-25,1,0,2,0,5,1,2,0.364348,0.350461,0.712174,0.346539,120,1341,1461 58 | 57,2011-02-26,1,0,2,0,6,0,1,0.2825,0.282192,0.537917,0.186571,424,1545,1969 59 | 58,2011-02-27,1,0,2,0,0,0,1,0.343478,0.351109,0.68,0.125248,694,1708,2402 60 | 59,2011-02-28,1,0,2,0,1,1,2,0.407273,0.400118,0.876364,0.289686,81,1365,1446 61 | 60,2011-03-01,1,0,3,0,2,1,1,0.266667,0.263879,0.535,0.216425,137,1714,1851 62 | 61,2011-03-02,1,0,3,0,3,1,1,0.335,0.320071,0.449583,0.307833,231,1903,2134 63 | 62,2011-03-03,1,0,3,0,4,1,1,0.198333,0.200133,0.318333,0.225754,123,1562,1685 64 | 63,2011-03-04,1,0,3,0,5,1,2,0.261667,0.255679,0.610417,0.203346,214,1730,1944 65 | 64,2011-03-05,1,0,3,0,6,0,2,0.384167,0.378779,0.789167,0.251871,640,1437,2077 66 | 65,2011-03-06,1,0,3,0,0,0,2,0.376522,0.366252,0.948261,0.343287,114,491,605 67 | 66,2011-03-07,1,0,3,0,1,1,1,0.261739,0.238461,0.551304,0.341352,244,1628,1872 68 | 67,2011-03-08,1,0,3,0,2,1,1,0.2925,0.3024,0.420833,0.12065,316,1817,2133 69 | 68,2011-03-09,1,0,3,0,3,1,2,0.295833,0.286608,0.775417,0.22015,191,1700,1891 70 | 69,2011-03-10,1,0,3,0,4,1,3,0.389091,0.385668,0,0.261877,46,577,623 71 | 70,2011-03-11,1,0,3,0,5,1,2,0.316522,0.305,0.649565,0.23297,247,1730,1977 72 | 71,2011-03-12,1,0,3,0,6,0,1,0.329167,0.32575,0.594583,0.220775,724,1408,2132 73 | 72,2011-03-13,1,0,3,0,0,0,1,0.384348,0.380091,0.527391,0.270604,982,1435,2417 74 | 73,2011-03-14,1,0,3,0,1,1,1,0.325217,0.332,0.496957,0.136926,359,1687,2046 75 | 74,2011-03-15,1,0,3,0,2,1,2,0.317391,0.318178,0.655652,0.184309,289,1767,2056 76 | 75,2011-03-16,1,0,3,0,3,1,2,0.365217,0.36693,0.776522,0.203117,321,1871,2192 77 | 76,2011-03-17,1,0,3,0,4,1,1,0.415,0.410333,0.602917,0.209579,424,2320,2744 78 | 77,2011-03-18,1,0,3,0,5,1,1,0.54,0.527009,0.525217,0.231017,884,2355,3239 79 | 78,2011-03-19,1,0,3,0,6,0,1,0.4725,0.466525,0.379167,0.368167,1424,1693,3117 80 | 79,2011-03-20,1,0,3,0,0,0,1,0.3325,0.32575,0.47375,0.207721,1047,1424,2471 81 | 80,2011-03-21,2,0,3,0,1,1,2,0.430435,0.409735,0.737391,0.288783,401,1676,2077 82 | 81,2011-03-22,2,0,3,0,2,1,1,0.441667,0.440642,0.624583,0.22575,460,2243,2703 83 | 82,2011-03-23,2,0,3,0,3,1,2,0.346957,0.337939,0.839565,0.234261,203,1918,2121 84 | 83,2011-03-24,2,0,3,0,4,1,2,0.285,0.270833,0.805833,0.243787,166,1699,1865 85 | 84,2011-03-25,2,0,3,0,5,1,1,0.264167,0.256312,0.495,0.230725,300,1910,2210 86 | 85,2011-03-26,2,0,3,0,6,0,1,0.265833,0.257571,0.394167,0.209571,981,1515,2496 87 | 86,2011-03-27,2,0,3,0,0,0,2,0.253043,0.250339,0.493913,0.1843,472,1221,1693 88 | 87,2011-03-28,2,0,3,0,1,1,1,0.264348,0.257574,0.302174,0.212204,222,1806,2028 89 | 88,2011-03-29,2,0,3,0,2,1,1,0.3025,0.292908,0.314167,0.226996,317,2108,2425 90 | 89,2011-03-30,2,0,3,0,3,1,2,0.3,0.29735,0.646667,0.172888,168,1368,1536 91 | 90,2011-03-31,2,0,3,0,4,1,3,0.268333,0.257575,0.918333,0.217646,179,1506,1685 92 | 91,2011-04-01,2,0,4,0,5,1,2,0.3,0.283454,0.68625,0.258708,307,1920,2227 93 | 92,2011-04-02,2,0,4,0,6,0,2,0.315,0.315637,0.65375,0.197146,898,1354,2252 94 | 93,2011-04-03,2,0,4,0,0,0,1,0.378333,0.378767,0.48,0.182213,1651,1598,3249 95 | 94,2011-04-04,2,0,4,0,1,1,1,0.573333,0.542929,0.42625,0.385571,734,2381,3115 96 | 95,2011-04-05,2,0,4,0,2,1,2,0.414167,0.39835,0.642083,0.388067,167,1628,1795 97 | 96,2011-04-06,2,0,4,0,3,1,1,0.390833,0.387608,0.470833,0.263063,413,2395,2808 98 | 97,2011-04-07,2,0,4,0,4,1,1,0.4375,0.433696,0.602917,0.162312,571,2570,3141 99 | 98,2011-04-08,2,0,4,0,5,1,2,0.335833,0.324479,0.83625,0.226992,172,1299,1471 100 | 99,2011-04-09,2,0,4,0,6,0,2,0.3425,0.341529,0.8775,0.133083,879,1576,2455 101 | 100,2011-04-10,2,0,4,0,0,0,2,0.426667,0.426737,0.8575,0.146767,1188,1707,2895 102 | 101,2011-04-11,2,0,4,0,1,1,2,0.595652,0.565217,0.716956,0.324474,855,2493,3348 103 | 102,2011-04-12,2,0,4,0,2,1,2,0.5025,0.493054,0.739167,0.274879,257,1777,2034 104 | 103,2011-04-13,2,0,4,0,3,1,2,0.4125,0.417283,0.819167,0.250617,209,1953,2162 105 | 104,2011-04-14,2,0,4,0,4,1,1,0.4675,0.462742,0.540417,0.1107,529,2738,3267 106 | 105,2011-04-15,2,0,4,1,5,0,1,0.446667,0.441913,0.67125,0.226375,642,2484,3126 107 | 106,2011-04-16,2,0,4,0,6,0,3,0.430833,0.425492,0.888333,0.340808,121,674,795 108 | 107,2011-04-17,2,0,4,0,0,0,1,0.456667,0.445696,0.479583,0.303496,1558,2186,3744 109 | 108,2011-04-18,2,0,4,0,1,1,1,0.5125,0.503146,0.5425,0.163567,669,2760,3429 110 | 109,2011-04-19,2,0,4,0,2,1,2,0.505833,0.489258,0.665833,0.157971,409,2795,3204 111 | 110,2011-04-20,2,0,4,0,3,1,1,0.595,0.564392,0.614167,0.241925,613,3331,3944 112 | 111,2011-04-21,2,0,4,0,4,1,1,0.459167,0.453892,0.407083,0.325258,745,3444,4189 113 | 112,2011-04-22,2,0,4,0,5,1,2,0.336667,0.321954,0.729583,0.219521,177,1506,1683 114 | 113,2011-04-23,2,0,4,0,6,0,2,0.46,0.450121,0.887917,0.230725,1462,2574,4036 115 | 114,2011-04-24,2,0,4,0,0,0,2,0.581667,0.551763,0.810833,0.192175,1710,2481,4191 116 | 115,2011-04-25,2,0,4,0,1,1,1,0.606667,0.5745,0.776667,0.185333,773,3300,4073 117 | 116,2011-04-26,2,0,4,0,2,1,1,0.631667,0.594083,0.729167,0.3265,678,3722,4400 118 | 117,2011-04-27,2,0,4,0,3,1,2,0.62,0.575142,0.835417,0.3122,547,3325,3872 119 | 118,2011-04-28,2,0,4,0,4,1,2,0.6175,0.578929,0.700833,0.320908,569,3489,4058 120 | 119,2011-04-29,2,0,4,0,5,1,1,0.51,0.497463,0.457083,0.240063,878,3717,4595 121 | 120,2011-04-30,2,0,4,0,6,0,1,0.4725,0.464021,0.503333,0.235075,1965,3347,5312 122 | 121,2011-05-01,2,0,5,0,0,0,2,0.451667,0.448204,0.762083,0.106354,1138,2213,3351 123 | 122,2011-05-02,2,0,5,0,1,1,2,0.549167,0.532833,0.73,0.183454,847,3554,4401 124 | 123,2011-05-03,2,0,5,0,2,1,2,0.616667,0.582079,0.697083,0.342667,603,3848,4451 125 | 124,2011-05-04,2,0,5,0,3,1,2,0.414167,0.40465,0.737083,0.328996,255,2378,2633 126 | 125,2011-05-05,2,0,5,0,4,1,1,0.459167,0.441917,0.444167,0.295392,614,3819,4433 127 | 126,2011-05-06,2,0,5,0,5,1,1,0.479167,0.474117,0.59,0.228246,894,3714,4608 128 | 127,2011-05-07,2,0,5,0,6,0,1,0.52,0.512621,0.54125,0.16045,1612,3102,4714 129 | 128,2011-05-08,2,0,5,0,0,0,1,0.528333,0.518933,0.631667,0.0746375,1401,2932,4333 130 | 129,2011-05-09,2,0,5,0,1,1,1,0.5325,0.525246,0.58875,0.176,664,3698,4362 131 | 130,2011-05-10,2,0,5,0,2,1,1,0.5325,0.522721,0.489167,0.115671,694,4109,4803 132 | 131,2011-05-11,2,0,5,0,3,1,1,0.5425,0.5284,0.632917,0.120642,550,3632,4182 133 | 132,2011-05-12,2,0,5,0,4,1,1,0.535,0.523363,0.7475,0.189667,695,4169,4864 134 | 133,2011-05-13,2,0,5,0,5,1,2,0.5125,0.4943,0.863333,0.179725,692,3413,4105 135 | 134,2011-05-14,2,0,5,0,6,0,2,0.520833,0.500629,0.9225,0.13495,902,2507,3409 136 | 135,2011-05-15,2,0,5,0,0,0,2,0.5625,0.536,0.867083,0.152979,1582,2971,4553 137 | 136,2011-05-16,2,0,5,0,1,1,1,0.5775,0.550512,0.787917,0.126871,773,3185,3958 138 | 137,2011-05-17,2,0,5,0,2,1,2,0.561667,0.538529,0.837917,0.277354,678,3445,4123 139 | 138,2011-05-18,2,0,5,0,3,1,2,0.55,0.527158,0.87,0.201492,536,3319,3855 140 | 139,2011-05-19,2,0,5,0,4,1,2,0.530833,0.510742,0.829583,0.108213,735,3840,4575 141 | 140,2011-05-20,2,0,5,0,5,1,1,0.536667,0.529042,0.719583,0.125013,909,4008,4917 142 | 141,2011-05-21,2,0,5,0,6,0,1,0.6025,0.571975,0.626667,0.12065,2258,3547,5805 143 | 142,2011-05-22,2,0,5,0,0,0,1,0.604167,0.5745,0.749583,0.148008,1576,3084,4660 144 | 143,2011-05-23,2,0,5,0,1,1,2,0.631667,0.590296,0.81,0.233842,836,3438,4274 145 | 144,2011-05-24,2,0,5,0,2,1,2,0.66,0.604813,0.740833,0.207092,659,3833,4492 146 | 145,2011-05-25,2,0,5,0,3,1,1,0.660833,0.615542,0.69625,0.154233,740,4238,4978 147 | 146,2011-05-26,2,0,5,0,4,1,1,0.708333,0.654688,0.6775,0.199642,758,3919,4677 148 | 147,2011-05-27,2,0,5,0,5,1,1,0.681667,0.637008,0.65375,0.240679,871,3808,4679 149 | 148,2011-05-28,2,0,5,0,6,0,1,0.655833,0.612379,0.729583,0.230092,2001,2757,4758 150 | 149,2011-05-29,2,0,5,0,0,0,1,0.6675,0.61555,0.81875,0.213938,2355,2433,4788 151 | 150,2011-05-30,2,0,5,1,1,0,1,0.733333,0.671092,0.685,0.131225,1549,2549,4098 152 | 151,2011-05-31,2,0,5,0,2,1,1,0.775,0.725383,0.636667,0.111329,673,3309,3982 153 | 152,2011-06-01,2,0,6,0,3,1,2,0.764167,0.720967,0.677083,0.207092,513,3461,3974 154 | 153,2011-06-02,2,0,6,0,4,1,1,0.715,0.643942,0.305,0.292287,736,4232,4968 155 | 154,2011-06-03,2,0,6,0,5,1,1,0.62,0.587133,0.354167,0.253121,898,4414,5312 156 | 155,2011-06-04,2,0,6,0,6,0,1,0.635,0.594696,0.45625,0.123142,1869,3473,5342 157 | 156,2011-06-05,2,0,6,0,0,0,2,0.648333,0.616804,0.6525,0.138692,1685,3221,4906 158 | 157,2011-06-06,2,0,6,0,1,1,1,0.678333,0.621858,0.6,0.121896,673,3875,4548 159 | 158,2011-06-07,2,0,6,0,2,1,1,0.7075,0.65595,0.597917,0.187808,763,4070,4833 160 | 159,2011-06-08,2,0,6,0,3,1,1,0.775833,0.727279,0.622083,0.136817,676,3725,4401 161 | 160,2011-06-09,2,0,6,0,4,1,2,0.808333,0.757579,0.568333,0.149883,563,3352,3915 162 | 161,2011-06-10,2,0,6,0,5,1,1,0.755,0.703292,0.605,0.140554,815,3771,4586 163 | 162,2011-06-11,2,0,6,0,6,0,1,0.725,0.678038,0.654583,0.15485,1729,3237,4966 164 | 163,2011-06-12,2,0,6,0,0,0,1,0.6925,0.643325,0.747917,0.163567,1467,2993,4460 165 | 164,2011-06-13,2,0,6,0,1,1,1,0.635,0.601654,0.494583,0.30535,863,4157,5020 166 | 165,2011-06-14,2,0,6,0,2,1,1,0.604167,0.591546,0.507083,0.269283,727,4164,4891 167 | 166,2011-06-15,2,0,6,0,3,1,1,0.626667,0.587754,0.471667,0.167912,769,4411,5180 168 | 167,2011-06-16,2,0,6,0,4,1,2,0.628333,0.595346,0.688333,0.206471,545,3222,3767 169 | 168,2011-06-17,2,0,6,0,5,1,1,0.649167,0.600383,0.735833,0.143029,863,3981,4844 170 | 169,2011-06-18,2,0,6,0,6,0,1,0.696667,0.643954,0.670417,0.119408,1807,3312,5119 171 | 170,2011-06-19,2,0,6,0,0,0,2,0.699167,0.645846,0.666667,0.102,1639,3105,4744 172 | 171,2011-06-20,2,0,6,0,1,1,2,0.635,0.595346,0.74625,0.155475,699,3311,4010 173 | 172,2011-06-21,3,0,6,0,2,1,2,0.680833,0.637646,0.770417,0.171025,774,4061,4835 174 | 173,2011-06-22,3,0,6,0,3,1,1,0.733333,0.693829,0.7075,0.172262,661,3846,4507 175 | 174,2011-06-23,3,0,6,0,4,1,2,0.728333,0.693833,0.703333,0.238804,746,4044,4790 176 | 175,2011-06-24,3,0,6,0,5,1,1,0.724167,0.656583,0.573333,0.222025,969,4022,4991 177 | 176,2011-06-25,3,0,6,0,6,0,1,0.695,0.643313,0.483333,0.209571,1782,3420,5202 178 | 177,2011-06-26,3,0,6,0,0,0,1,0.68,0.637629,0.513333,0.0945333,1920,3385,5305 179 | 178,2011-06-27,3,0,6,0,1,1,2,0.6825,0.637004,0.658333,0.107588,854,3854,4708 180 | 179,2011-06-28,3,0,6,0,2,1,1,0.744167,0.692558,0.634167,0.144283,732,3916,4648 181 | 180,2011-06-29,3,0,6,0,3,1,1,0.728333,0.654688,0.497917,0.261821,848,4377,5225 182 | 181,2011-06-30,3,0,6,0,4,1,1,0.696667,0.637008,0.434167,0.185312,1027,4488,5515 183 | 182,2011-07-01,3,0,7,0,5,1,1,0.7225,0.652162,0.39625,0.102608,1246,4116,5362 184 | 183,2011-07-02,3,0,7,0,6,0,1,0.738333,0.667308,0.444583,0.115062,2204,2915,5119 185 | 184,2011-07-03,3,0,7,0,0,0,2,0.716667,0.668575,0.6825,0.228858,2282,2367,4649 186 | 185,2011-07-04,3,0,7,1,1,0,2,0.726667,0.665417,0.637917,0.0814792,3065,2978,6043 187 | 186,2011-07-05,3,0,7,0,2,1,1,0.746667,0.696338,0.590417,0.126258,1031,3634,4665 188 | 187,2011-07-06,3,0,7,0,3,1,1,0.72,0.685633,0.743333,0.149883,784,3845,4629 189 | 188,2011-07-07,3,0,7,0,4,1,1,0.75,0.686871,0.65125,0.1592,754,3838,4592 190 | 189,2011-07-08,3,0,7,0,5,1,2,0.709167,0.670483,0.757917,0.225129,692,3348,4040 191 | 190,2011-07-09,3,0,7,0,6,0,1,0.733333,0.664158,0.609167,0.167912,1988,3348,5336 192 | 191,2011-07-10,3,0,7,0,0,0,1,0.7475,0.690025,0.578333,0.183471,1743,3138,4881 193 | 192,2011-07-11,3,0,7,0,1,1,1,0.7625,0.729804,0.635833,0.282337,723,3363,4086 194 | 193,2011-07-12,3,0,7,0,2,1,1,0.794167,0.739275,0.559167,0.200254,662,3596,4258 195 | 194,2011-07-13,3,0,7,0,3,1,1,0.746667,0.689404,0.631667,0.146133,748,3594,4342 196 | 195,2011-07-14,3,0,7,0,4,1,1,0.680833,0.635104,0.47625,0.240667,888,4196,5084 197 | 196,2011-07-15,3,0,7,0,5,1,1,0.663333,0.624371,0.59125,0.182833,1318,4220,5538 198 | 197,2011-07-16,3,0,7,0,6,0,1,0.686667,0.638263,0.585,0.208342,2418,3505,5923 199 | 198,2011-07-17,3,0,7,0,0,0,1,0.719167,0.669833,0.604167,0.245033,2006,3296,5302 200 | 199,2011-07-18,3,0,7,0,1,1,1,0.746667,0.703925,0.65125,0.215804,841,3617,4458 201 | 200,2011-07-19,3,0,7,0,2,1,1,0.776667,0.747479,0.650417,0.1306,752,3789,4541 202 | 201,2011-07-20,3,0,7,0,3,1,1,0.768333,0.74685,0.707083,0.113817,644,3688,4332 203 | 202,2011-07-21,3,0,7,0,4,1,2,0.815,0.826371,0.69125,0.222021,632,3152,3784 204 | 203,2011-07-22,3,0,7,0,5,1,1,0.848333,0.840896,0.580417,0.1331,562,2825,3387 205 | 204,2011-07-23,3,0,7,0,6,0,1,0.849167,0.804287,0.5,0.131221,987,2298,3285 206 | 205,2011-07-24,3,0,7,0,0,0,1,0.83,0.794829,0.550833,0.169171,1050,2556,3606 207 | 206,2011-07-25,3,0,7,0,1,1,1,0.743333,0.720958,0.757083,0.0908083,568,3272,3840 208 | 207,2011-07-26,3,0,7,0,2,1,1,0.771667,0.696979,0.540833,0.200258,750,3840,4590 209 | 208,2011-07-27,3,0,7,0,3,1,1,0.775,0.690667,0.402917,0.183463,755,3901,4656 210 | 209,2011-07-28,3,0,7,0,4,1,1,0.779167,0.7399,0.583333,0.178479,606,3784,4390 211 | 210,2011-07-29,3,0,7,0,5,1,1,0.838333,0.785967,0.5425,0.174138,670,3176,3846 212 | 211,2011-07-30,3,0,7,0,6,0,1,0.804167,0.728537,0.465833,0.168537,1559,2916,4475 213 | 212,2011-07-31,3,0,7,0,0,0,1,0.805833,0.729796,0.480833,0.164813,1524,2778,4302 214 | 213,2011-08-01,3,0,8,0,1,1,1,0.771667,0.703292,0.550833,0.156717,729,3537,4266 215 | 214,2011-08-02,3,0,8,0,2,1,1,0.783333,0.707071,0.49125,0.20585,801,4044,4845 216 | 215,2011-08-03,3,0,8,0,3,1,2,0.731667,0.679937,0.6575,0.135583,467,3107,3574 217 | 216,2011-08-04,3,0,8,0,4,1,2,0.71,0.664788,0.7575,0.19715,799,3777,4576 218 | 217,2011-08-05,3,0,8,0,5,1,1,0.710833,0.656567,0.630833,0.184696,1023,3843,4866 219 | 218,2011-08-06,3,0,8,0,6,0,2,0.716667,0.676154,0.755,0.22825,1521,2773,4294 220 | 219,2011-08-07,3,0,8,0,0,0,1,0.7425,0.715292,0.752917,0.201487,1298,2487,3785 221 | 220,2011-08-08,3,0,8,0,1,1,1,0.765,0.703283,0.592083,0.192175,846,3480,4326 222 | 221,2011-08-09,3,0,8,0,2,1,1,0.775,0.724121,0.570417,0.151121,907,3695,4602 223 | 222,2011-08-10,3,0,8,0,3,1,1,0.766667,0.684983,0.424167,0.200258,884,3896,4780 224 | 223,2011-08-11,3,0,8,0,4,1,1,0.7175,0.651521,0.42375,0.164796,812,3980,4792 225 | 224,2011-08-12,3,0,8,0,5,1,1,0.708333,0.654042,0.415,0.125621,1051,3854,4905 226 | 225,2011-08-13,3,0,8,0,6,0,2,0.685833,0.645858,0.729583,0.211454,1504,2646,4150 227 | 226,2011-08-14,3,0,8,0,0,0,2,0.676667,0.624388,0.8175,0.222633,1338,2482,3820 228 | 227,2011-08-15,3,0,8,0,1,1,1,0.665833,0.616167,0.712083,0.208954,775,3563,4338 229 | 228,2011-08-16,3,0,8,0,2,1,1,0.700833,0.645837,0.578333,0.236329,721,4004,4725 230 | 229,2011-08-17,3,0,8,0,3,1,1,0.723333,0.666671,0.575417,0.143667,668,4026,4694 231 | 230,2011-08-18,3,0,8,0,4,1,1,0.711667,0.662258,0.654583,0.233208,639,3166,3805 232 | 231,2011-08-19,3,0,8,0,5,1,2,0.685,0.633221,0.722917,0.139308,797,3356,4153 233 | 232,2011-08-20,3,0,8,0,6,0,1,0.6975,0.648996,0.674167,0.104467,1914,3277,5191 234 | 233,2011-08-21,3,0,8,0,0,0,1,0.710833,0.675525,0.77,0.248754,1249,2624,3873 235 | 234,2011-08-22,3,0,8,0,1,1,1,0.691667,0.638254,0.47,0.27675,833,3925,4758 236 | 235,2011-08-23,3,0,8,0,2,1,1,0.640833,0.606067,0.455417,0.146763,1281,4614,5895 237 | 236,2011-08-24,3,0,8,0,3,1,1,0.673333,0.630692,0.605,0.253108,949,4181,5130 238 | 237,2011-08-25,3,0,8,0,4,1,2,0.684167,0.645854,0.771667,0.210833,435,3107,3542 239 | 238,2011-08-26,3,0,8,0,5,1,1,0.7,0.659733,0.76125,0.0839625,768,3893,4661 240 | 239,2011-08-27,3,0,8,0,6,0,2,0.68,0.635556,0.85,0.375617,226,889,1115 241 | 240,2011-08-28,3,0,8,0,0,0,1,0.707059,0.647959,0.561765,0.304659,1415,2919,4334 242 | 241,2011-08-29,3,0,8,0,1,1,1,0.636667,0.607958,0.554583,0.159825,729,3905,4634 243 | 242,2011-08-30,3,0,8,0,2,1,1,0.639167,0.594704,0.548333,0.125008,775,4429,5204 244 | 243,2011-08-31,3,0,8,0,3,1,1,0.656667,0.611121,0.597917,0.0833333,688,4370,5058 245 | 244,2011-09-01,3,0,9,0,4,1,1,0.655,0.614921,0.639167,0.141796,783,4332,5115 246 | 245,2011-09-02,3,0,9,0,5,1,2,0.643333,0.604808,0.727083,0.139929,875,3852,4727 247 | 246,2011-09-03,3,0,9,0,6,0,1,0.669167,0.633213,0.716667,0.185325,1935,2549,4484 248 | 247,2011-09-04,3,0,9,0,0,0,1,0.709167,0.665429,0.742083,0.206467,2521,2419,4940 249 | 248,2011-09-05,3,0,9,1,1,0,2,0.673333,0.625646,0.790417,0.212696,1236,2115,3351 250 | 249,2011-09-06,3,0,9,0,2,1,3,0.54,0.5152,0.886957,0.343943,204,2506,2710 251 | 250,2011-09-07,3,0,9,0,3,1,3,0.599167,0.544229,0.917083,0.0970208,118,1878,1996 252 | 251,2011-09-08,3,0,9,0,4,1,3,0.633913,0.555361,0.939565,0.192748,153,1689,1842 253 | 252,2011-09-09,3,0,9,0,5,1,2,0.65,0.578946,0.897917,0.124379,417,3127,3544 254 | 253,2011-09-10,3,0,9,0,6,0,1,0.66,0.607962,0.75375,0.153608,1750,3595,5345 255 | 254,2011-09-11,3,0,9,0,0,0,1,0.653333,0.609229,0.71375,0.115054,1633,3413,5046 256 | 255,2011-09-12,3,0,9,0,1,1,1,0.644348,0.60213,0.692174,0.088913,690,4023,4713 257 | 256,2011-09-13,3,0,9,0,2,1,1,0.650833,0.603554,0.7125,0.141804,701,4062,4763 258 | 257,2011-09-14,3,0,9,0,3,1,1,0.673333,0.6269,0.697083,0.1673,647,4138,4785 259 | 258,2011-09-15,3,0,9,0,4,1,2,0.5775,0.553671,0.709167,0.271146,428,3231,3659 260 | 259,2011-09-16,3,0,9,0,5,1,2,0.469167,0.461475,0.590417,0.164183,742,4018,4760 261 | 260,2011-09-17,3,0,9,0,6,0,2,0.491667,0.478512,0.718333,0.189675,1434,3077,4511 262 | 261,2011-09-18,3,0,9,0,0,0,1,0.5075,0.490537,0.695,0.178483,1353,2921,4274 263 | 262,2011-09-19,3,0,9,0,1,1,2,0.549167,0.529675,0.69,0.151742,691,3848,4539 264 | 263,2011-09-20,3,0,9,0,2,1,2,0.561667,0.532217,0.88125,0.134954,438,3203,3641 265 | 264,2011-09-21,3,0,9,0,3,1,2,0.595,0.550533,0.9,0.0964042,539,3813,4352 266 | 265,2011-09-22,3,0,9,0,4,1,2,0.628333,0.554963,0.902083,0.128125,555,4240,4795 267 | 266,2011-09-23,4,0,9,0,5,1,2,0.609167,0.522125,0.9725,0.0783667,258,2137,2395 268 | 267,2011-09-24,4,0,9,0,6,0,2,0.606667,0.564412,0.8625,0.0783833,1776,3647,5423 269 | 268,2011-09-25,4,0,9,0,0,0,2,0.634167,0.572637,0.845,0.0503792,1544,3466,5010 270 | 269,2011-09-26,4,0,9,0,1,1,2,0.649167,0.589042,0.848333,0.1107,684,3946,4630 271 | 270,2011-09-27,4,0,9,0,2,1,2,0.636667,0.574525,0.885417,0.118171,477,3643,4120 272 | 271,2011-09-28,4,0,9,0,3,1,2,0.635,0.575158,0.84875,0.148629,480,3427,3907 273 | 272,2011-09-29,4,0,9,0,4,1,1,0.616667,0.574512,0.699167,0.172883,653,4186,4839 274 | 273,2011-09-30,4,0,9,0,5,1,1,0.564167,0.544829,0.6475,0.206475,830,4372,5202 275 | 274,2011-10-01,4,0,10,0,6,0,2,0.41,0.412863,0.75375,0.292296,480,1949,2429 276 | 275,2011-10-02,4,0,10,0,0,0,2,0.356667,0.345317,0.791667,0.222013,616,2302,2918 277 | 276,2011-10-03,4,0,10,0,1,1,2,0.384167,0.392046,0.760833,0.0833458,330,3240,3570 278 | 277,2011-10-04,4,0,10,0,2,1,1,0.484167,0.472858,0.71,0.205854,486,3970,4456 279 | 278,2011-10-05,4,0,10,0,3,1,1,0.538333,0.527138,0.647917,0.17725,559,4267,4826 280 | 279,2011-10-06,4,0,10,0,4,1,1,0.494167,0.480425,0.620833,0.134954,639,4126,4765 281 | 280,2011-10-07,4,0,10,0,5,1,1,0.510833,0.504404,0.684167,0.0223917,949,4036,4985 282 | 281,2011-10-08,4,0,10,0,6,0,1,0.521667,0.513242,0.70125,0.0454042,2235,3174,5409 283 | 282,2011-10-09,4,0,10,0,0,0,1,0.540833,0.523983,0.7275,0.06345,2397,3114,5511 284 | 283,2011-10-10,4,0,10,1,1,0,1,0.570833,0.542925,0.73375,0.0423042,1514,3603,5117 285 | 284,2011-10-11,4,0,10,0,2,1,2,0.566667,0.546096,0.80875,0.143042,667,3896,4563 286 | 285,2011-10-12,4,0,10,0,3,1,3,0.543333,0.517717,0.90625,0.24815,217,2199,2416 287 | 286,2011-10-13,4,0,10,0,4,1,2,0.589167,0.551804,0.896667,0.141787,290,2623,2913 288 | 287,2011-10-14,4,0,10,0,5,1,2,0.550833,0.529675,0.71625,0.223883,529,3115,3644 289 | 288,2011-10-15,4,0,10,0,6,0,1,0.506667,0.498725,0.483333,0.258083,1899,3318,5217 290 | 289,2011-10-16,4,0,10,0,0,0,1,0.511667,0.503154,0.486667,0.281717,1748,3293,5041 291 | 290,2011-10-17,4,0,10,0,1,1,1,0.534167,0.510725,0.579583,0.175379,713,3857,4570 292 | 291,2011-10-18,4,0,10,0,2,1,2,0.5325,0.522721,0.701667,0.110087,637,4111,4748 293 | 292,2011-10-19,4,0,10,0,3,1,3,0.541739,0.513848,0.895217,0.243339,254,2170,2424 294 | 293,2011-10-20,4,0,10,0,4,1,1,0.475833,0.466525,0.63625,0.422275,471,3724,4195 295 | 294,2011-10-21,4,0,10,0,5,1,1,0.4275,0.423596,0.574167,0.221396,676,3628,4304 296 | 295,2011-10-22,4,0,10,0,6,0,1,0.4225,0.425492,0.629167,0.0926667,1499,2809,4308 297 | 296,2011-10-23,4,0,10,0,0,0,1,0.421667,0.422333,0.74125,0.0995125,1619,2762,4381 298 | 297,2011-10-24,4,0,10,0,1,1,1,0.463333,0.457067,0.772083,0.118792,699,3488,4187 299 | 298,2011-10-25,4,0,10,0,2,1,1,0.471667,0.463375,0.622917,0.166658,695,3992,4687 300 | 299,2011-10-26,4,0,10,0,3,1,2,0.484167,0.472846,0.720417,0.148642,404,3490,3894 301 | 300,2011-10-27,4,0,10,0,4,1,2,0.47,0.457046,0.812917,0.197763,240,2419,2659 302 | 301,2011-10-28,4,0,10,0,5,1,2,0.330833,0.318812,0.585833,0.229479,456,3291,3747 303 | 302,2011-10-29,4,0,10,0,6,0,3,0.254167,0.227913,0.8825,0.351371,57,570,627 304 | 303,2011-10-30,4,0,10,0,0,0,1,0.319167,0.321329,0.62375,0.176617,885,2446,3331 305 | 304,2011-10-31,4,0,10,0,1,1,1,0.34,0.356063,0.703333,0.10635,362,3307,3669 306 | 305,2011-11-01,4,0,11,0,2,1,1,0.400833,0.397088,0.68375,0.135571,410,3658,4068 307 | 306,2011-11-02,4,0,11,0,3,1,1,0.3775,0.390133,0.71875,0.0820917,370,3816,4186 308 | 307,2011-11-03,4,0,11,0,4,1,1,0.408333,0.405921,0.702083,0.136817,318,3656,3974 309 | 308,2011-11-04,4,0,11,0,5,1,2,0.403333,0.403392,0.6225,0.271779,470,3576,4046 310 | 309,2011-11-05,4,0,11,0,6,0,1,0.326667,0.323854,0.519167,0.189062,1156,2770,3926 311 | 310,2011-11-06,4,0,11,0,0,0,1,0.348333,0.362358,0.734583,0.0920542,952,2697,3649 312 | 311,2011-11-07,4,0,11,0,1,1,1,0.395,0.400871,0.75875,0.057225,373,3662,4035 313 | 312,2011-11-08,4,0,11,0,2,1,1,0.408333,0.412246,0.721667,0.0690375,376,3829,4205 314 | 313,2011-11-09,4,0,11,0,3,1,1,0.4,0.409079,0.758333,0.0621958,305,3804,4109 315 | 314,2011-11-10,4,0,11,0,4,1,2,0.38,0.373721,0.813333,0.189067,190,2743,2933 316 | 315,2011-11-11,4,0,11,1,5,0,1,0.324167,0.306817,0.44625,0.314675,440,2928,3368 317 | 316,2011-11-12,4,0,11,0,6,0,1,0.356667,0.357942,0.552917,0.212062,1275,2792,4067 318 | 317,2011-11-13,4,0,11,0,0,0,1,0.440833,0.43055,0.458333,0.281721,1004,2713,3717 319 | 318,2011-11-14,4,0,11,0,1,1,1,0.53,0.524612,0.587083,0.306596,595,3891,4486 320 | 319,2011-11-15,4,0,11,0,2,1,2,0.53,0.507579,0.68875,0.199633,449,3746,4195 321 | 320,2011-11-16,4,0,11,0,3,1,3,0.456667,0.451988,0.93,0.136829,145,1672,1817 322 | 321,2011-11-17,4,0,11,0,4,1,2,0.341667,0.323221,0.575833,0.305362,139,2914,3053 323 | 322,2011-11-18,4,0,11,0,5,1,1,0.274167,0.272721,0.41,0.168533,245,3147,3392 324 | 323,2011-11-19,4,0,11,0,6,0,1,0.329167,0.324483,0.502083,0.224496,943,2720,3663 325 | 324,2011-11-20,4,0,11,0,0,0,2,0.463333,0.457058,0.684583,0.18595,787,2733,3520 326 | 325,2011-11-21,4,0,11,0,1,1,3,0.4475,0.445062,0.91,0.138054,220,2545,2765 327 | 326,2011-11-22,4,0,11,0,2,1,3,0.416667,0.421696,0.9625,0.118792,69,1538,1607 328 | 327,2011-11-23,4,0,11,0,3,1,2,0.440833,0.430537,0.757917,0.335825,112,2454,2566 329 | 328,2011-11-24,4,0,11,1,4,0,1,0.373333,0.372471,0.549167,0.167304,560,935,1495 330 | 329,2011-11-25,4,0,11,0,5,1,1,0.375,0.380671,0.64375,0.0988958,1095,1697,2792 331 | 330,2011-11-26,4,0,11,0,6,0,1,0.375833,0.385087,0.681667,0.0684208,1249,1819,3068 332 | 331,2011-11-27,4,0,11,0,0,0,1,0.459167,0.4558,0.698333,0.208954,810,2261,3071 333 | 332,2011-11-28,4,0,11,0,1,1,1,0.503478,0.490122,0.743043,0.142122,253,3614,3867 334 | 333,2011-11-29,4,0,11,0,2,1,2,0.458333,0.451375,0.830833,0.258092,96,2818,2914 335 | 334,2011-11-30,4,0,11,0,3,1,1,0.325,0.311221,0.613333,0.271158,188,3425,3613 336 | 335,2011-12-01,4,0,12,0,4,1,1,0.3125,0.305554,0.524583,0.220158,182,3545,3727 337 | 336,2011-12-02,4,0,12,0,5,1,1,0.314167,0.331433,0.625833,0.100754,268,3672,3940 338 | 337,2011-12-03,4,0,12,0,6,0,1,0.299167,0.310604,0.612917,0.0957833,706,2908,3614 339 | 338,2011-12-04,4,0,12,0,0,0,1,0.330833,0.3491,0.775833,0.0839583,634,2851,3485 340 | 339,2011-12-05,4,0,12,0,1,1,2,0.385833,0.393925,0.827083,0.0622083,233,3578,3811 341 | 340,2011-12-06,4,0,12,0,2,1,3,0.4625,0.4564,0.949583,0.232583,126,2468,2594 342 | 341,2011-12-07,4,0,12,0,3,1,3,0.41,0.400246,0.970417,0.266175,50,655,705 343 | 342,2011-12-08,4,0,12,0,4,1,1,0.265833,0.256938,0.58,0.240058,150,3172,3322 344 | 343,2011-12-09,4,0,12,0,5,1,1,0.290833,0.317542,0.695833,0.0827167,261,3359,3620 345 | 344,2011-12-10,4,0,12,0,6,0,1,0.275,0.266412,0.5075,0.233221,502,2688,3190 346 | 345,2011-12-11,4,0,12,0,0,0,1,0.220833,0.253154,0.49,0.0665417,377,2366,2743 347 | 346,2011-12-12,4,0,12,0,1,1,1,0.238333,0.270196,0.670833,0.06345,143,3167,3310 348 | 347,2011-12-13,4,0,12,0,2,1,1,0.2825,0.301138,0.59,0.14055,155,3368,3523 349 | 348,2011-12-14,4,0,12,0,3,1,2,0.3175,0.338362,0.66375,0.0609583,178,3562,3740 350 | 349,2011-12-15,4,0,12,0,4,1,2,0.4225,0.412237,0.634167,0.268042,181,3528,3709 351 | 350,2011-12-16,4,0,12,0,5,1,2,0.375,0.359825,0.500417,0.260575,178,3399,3577 352 | 351,2011-12-17,4,0,12,0,6,0,2,0.258333,0.249371,0.560833,0.243167,275,2464,2739 353 | 352,2011-12-18,4,0,12,0,0,0,1,0.238333,0.245579,0.58625,0.169779,220,2211,2431 354 | 353,2011-12-19,4,0,12,0,1,1,1,0.276667,0.280933,0.6375,0.172896,260,3143,3403 355 | 354,2011-12-20,4,0,12,0,2,1,2,0.385833,0.396454,0.595417,0.0615708,216,3534,3750 356 | 355,2011-12-21,1,0,12,0,3,1,2,0.428333,0.428017,0.858333,0.2214,107,2553,2660 357 | 356,2011-12-22,1,0,12,0,4,1,2,0.423333,0.426121,0.7575,0.047275,227,2841,3068 358 | 357,2011-12-23,1,0,12,0,5,1,1,0.373333,0.377513,0.68625,0.274246,163,2046,2209 359 | 358,2011-12-24,1,0,12,0,6,0,1,0.3025,0.299242,0.5425,0.190304,155,856,1011 360 | 359,2011-12-25,1,0,12,0,0,0,1,0.274783,0.279961,0.681304,0.155091,303,451,754 361 | 360,2011-12-26,1,0,12,1,1,0,1,0.321739,0.315535,0.506957,0.239465,430,887,1317 362 | 361,2011-12-27,1,0,12,0,2,1,2,0.325,0.327633,0.7625,0.18845,103,1059,1162 363 | 362,2011-12-28,1,0,12,0,3,1,1,0.29913,0.279974,0.503913,0.293961,255,2047,2302 364 | 363,2011-12-29,1,0,12,0,4,1,1,0.248333,0.263892,0.574167,0.119412,254,2169,2423 365 | 364,2011-12-30,1,0,12,0,5,1,1,0.311667,0.318812,0.636667,0.134337,491,2508,2999 366 | 365,2011-12-31,1,0,12,0,6,0,1,0.41,0.414121,0.615833,0.220154,665,1820,2485 367 | 366,2012-01-01,1,1,1,0,0,0,1,0.37,0.375621,0.6925,0.192167,686,1608,2294 368 | 367,2012-01-02,1,1,1,1,1,0,1,0.273043,0.252304,0.381304,0.329665,244,1707,1951 369 | 368,2012-01-03,1,1,1,0,2,1,1,0.15,0.126275,0.44125,0.365671,89,2147,2236 370 | 369,2012-01-04,1,1,1,0,3,1,2,0.1075,0.119337,0.414583,0.1847,95,2273,2368 371 | 370,2012-01-05,1,1,1,0,4,1,1,0.265833,0.278412,0.524167,0.129987,140,3132,3272 372 | 371,2012-01-06,1,1,1,0,5,1,1,0.334167,0.340267,0.542083,0.167908,307,3791,4098 373 | 372,2012-01-07,1,1,1,0,6,0,1,0.393333,0.390779,0.531667,0.174758,1070,3451,4521 374 | 373,2012-01-08,1,1,1,0,0,0,1,0.3375,0.340258,0.465,0.191542,599,2826,3425 375 | 374,2012-01-09,1,1,1,0,1,1,2,0.224167,0.247479,0.701667,0.0989,106,2270,2376 376 | 375,2012-01-10,1,1,1,0,2,1,1,0.308696,0.318826,0.646522,0.187552,173,3425,3598 377 | 376,2012-01-11,1,1,1,0,3,1,2,0.274167,0.282821,0.8475,0.131221,92,2085,2177 378 | 377,2012-01-12,1,1,1,0,4,1,2,0.3825,0.381938,0.802917,0.180967,269,3828,4097 379 | 378,2012-01-13,1,1,1,0,5,1,1,0.274167,0.249362,0.5075,0.378108,174,3040,3214 380 | 379,2012-01-14,1,1,1,0,6,0,1,0.18,0.183087,0.4575,0.187183,333,2160,2493 381 | 380,2012-01-15,1,1,1,0,0,0,1,0.166667,0.161625,0.419167,0.251258,284,2027,2311 382 | 381,2012-01-16,1,1,1,1,1,0,1,0.19,0.190663,0.5225,0.231358,217,2081,2298 383 | 382,2012-01-17,1,1,1,0,2,1,2,0.373043,0.364278,0.716087,0.34913,127,2808,2935 384 | 383,2012-01-18,1,1,1,0,3,1,1,0.303333,0.275254,0.443333,0.415429,109,3267,3376 385 | 384,2012-01-19,1,1,1,0,4,1,1,0.19,0.190038,0.4975,0.220158,130,3162,3292 386 | 385,2012-01-20,1,1,1,0,5,1,2,0.2175,0.220958,0.45,0.20275,115,3048,3163 387 | 386,2012-01-21,1,1,1,0,6,0,2,0.173333,0.174875,0.83125,0.222642,67,1234,1301 388 | 387,2012-01-22,1,1,1,0,0,0,2,0.1625,0.16225,0.79625,0.199638,196,1781,1977 389 | 388,2012-01-23,1,1,1,0,1,1,2,0.218333,0.243058,0.91125,0.110708,145,2287,2432 390 | 389,2012-01-24,1,1,1,0,2,1,1,0.3425,0.349108,0.835833,0.123767,439,3900,4339 391 | 390,2012-01-25,1,1,1,0,3,1,1,0.294167,0.294821,0.64375,0.161071,467,3803,4270 392 | 391,2012-01-26,1,1,1,0,4,1,2,0.341667,0.35605,0.769583,0.0733958,244,3831,4075 393 | 392,2012-01-27,1,1,1,0,5,1,2,0.425,0.415383,0.74125,0.342667,269,3187,3456 394 | 393,2012-01-28,1,1,1,0,6,0,1,0.315833,0.326379,0.543333,0.210829,775,3248,4023 395 | 394,2012-01-29,1,1,1,0,0,0,1,0.2825,0.272721,0.31125,0.24005,558,2685,3243 396 | 395,2012-01-30,1,1,1,0,1,1,1,0.269167,0.262625,0.400833,0.215792,126,3498,3624 397 | 396,2012-01-31,1,1,1,0,2,1,1,0.39,0.381317,0.416667,0.261817,324,4185,4509 398 | 397,2012-02-01,1,1,2,0,3,1,1,0.469167,0.466538,0.507917,0.189067,304,4275,4579 399 | 398,2012-02-02,1,1,2,0,4,1,2,0.399167,0.398971,0.672917,0.187187,190,3571,3761 400 | 399,2012-02-03,1,1,2,0,5,1,1,0.313333,0.309346,0.526667,0.178496,310,3841,4151 401 | 400,2012-02-04,1,1,2,0,6,0,2,0.264167,0.272725,0.779583,0.121896,384,2448,2832 402 | 401,2012-02-05,1,1,2,0,0,0,2,0.265833,0.264521,0.687917,0.175996,318,2629,2947 403 | 402,2012-02-06,1,1,2,0,1,1,1,0.282609,0.296426,0.622174,0.1538,206,3578,3784 404 | 403,2012-02-07,1,1,2,0,2,1,1,0.354167,0.361104,0.49625,0.147379,199,4176,4375 405 | 404,2012-02-08,1,1,2,0,3,1,2,0.256667,0.266421,0.722917,0.133721,109,2693,2802 406 | 405,2012-02-09,1,1,2,0,4,1,1,0.265,0.261988,0.562083,0.194037,163,3667,3830 407 | 406,2012-02-10,1,1,2,0,5,1,2,0.280833,0.293558,0.54,0.116929,227,3604,3831 408 | 407,2012-02-11,1,1,2,0,6,0,3,0.224167,0.210867,0.73125,0.289796,192,1977,2169 409 | 408,2012-02-12,1,1,2,0,0,0,1,0.1275,0.101658,0.464583,0.409212,73,1456,1529 410 | 409,2012-02-13,1,1,2,0,1,1,1,0.2225,0.227913,0.41125,0.167283,94,3328,3422 411 | 410,2012-02-14,1,1,2,0,2,1,2,0.319167,0.333946,0.50875,0.141179,135,3787,3922 412 | 411,2012-02-15,1,1,2,0,3,1,1,0.348333,0.351629,0.53125,0.1816,141,4028,4169 413 | 412,2012-02-16,1,1,2,0,4,1,2,0.316667,0.330162,0.752917,0.091425,74,2931,3005 414 | 413,2012-02-17,1,1,2,0,5,1,1,0.343333,0.351629,0.634583,0.205846,349,3805,4154 415 | 414,2012-02-18,1,1,2,0,6,0,1,0.346667,0.355425,0.534583,0.190929,1435,2883,4318 416 | 415,2012-02-19,1,1,2,0,0,0,2,0.28,0.265788,0.515833,0.253112,618,2071,2689 417 | 416,2012-02-20,1,1,2,1,1,0,1,0.28,0.273391,0.507826,0.229083,502,2627,3129 418 | 417,2012-02-21,1,1,2,0,2,1,1,0.287826,0.295113,0.594348,0.205717,163,3614,3777 419 | 418,2012-02-22,1,1,2,0,3,1,1,0.395833,0.392667,0.567917,0.234471,394,4379,4773 420 | 419,2012-02-23,1,1,2,0,4,1,1,0.454167,0.444446,0.554583,0.190913,516,4546,5062 421 | 420,2012-02-24,1,1,2,0,5,1,2,0.4075,0.410971,0.7375,0.237567,246,3241,3487 422 | 421,2012-02-25,1,1,2,0,6,0,1,0.290833,0.255675,0.395833,0.421642,317,2415,2732 423 | 422,2012-02-26,1,1,2,0,0,0,1,0.279167,0.268308,0.41,0.205229,515,2874,3389 424 | 423,2012-02-27,1,1,2,0,1,1,1,0.366667,0.357954,0.490833,0.268033,253,4069,4322 425 | 424,2012-02-28,1,1,2,0,2,1,1,0.359167,0.353525,0.395833,0.193417,229,4134,4363 426 | 425,2012-02-29,1,1,2,0,3,1,2,0.344348,0.34847,0.804783,0.179117,65,1769,1834 427 | 426,2012-03-01,1,1,3,0,4,1,1,0.485833,0.475371,0.615417,0.226987,325,4665,4990 428 | 427,2012-03-02,1,1,3,0,5,1,2,0.353333,0.359842,0.657083,0.144904,246,2948,3194 429 | 428,2012-03-03,1,1,3,0,6,0,2,0.414167,0.413492,0.62125,0.161079,956,3110,4066 430 | 429,2012-03-04,1,1,3,0,0,0,1,0.325833,0.303021,0.403333,0.334571,710,2713,3423 431 | 430,2012-03-05,1,1,3,0,1,1,1,0.243333,0.241171,0.50625,0.228858,203,3130,3333 432 | 431,2012-03-06,1,1,3,0,2,1,1,0.258333,0.255042,0.456667,0.200875,221,3735,3956 433 | 432,2012-03-07,1,1,3,0,3,1,1,0.404167,0.3851,0.513333,0.345779,432,4484,4916 434 | 433,2012-03-08,1,1,3,0,4,1,1,0.5275,0.524604,0.5675,0.441563,486,4896,5382 435 | 434,2012-03-09,1,1,3,0,5,1,2,0.410833,0.397083,0.407083,0.4148,447,4122,4569 436 | 435,2012-03-10,1,1,3,0,6,0,1,0.2875,0.277767,0.350417,0.22575,968,3150,4118 437 | 436,2012-03-11,1,1,3,0,0,0,1,0.361739,0.35967,0.476957,0.222587,1658,3253,4911 438 | 437,2012-03-12,1,1,3,0,1,1,1,0.466667,0.459592,0.489167,0.207713,838,4460,5298 439 | 438,2012-03-13,1,1,3,0,2,1,1,0.565,0.542929,0.6175,0.23695,762,5085,5847 440 | 439,2012-03-14,1,1,3,0,3,1,1,0.5725,0.548617,0.507083,0.115062,997,5315,6312 441 | 440,2012-03-15,1,1,3,0,4,1,1,0.5575,0.532825,0.579583,0.149883,1005,5187,6192 442 | 441,2012-03-16,1,1,3,0,5,1,2,0.435833,0.436229,0.842083,0.113192,548,3830,4378 443 | 442,2012-03-17,1,1,3,0,6,0,2,0.514167,0.505046,0.755833,0.110704,3155,4681,7836 444 | 443,2012-03-18,1,1,3,0,0,0,2,0.4725,0.464,0.81,0.126883,2207,3685,5892 445 | 444,2012-03-19,1,1,3,0,1,1,1,0.545,0.532821,0.72875,0.162317,982,5171,6153 446 | 445,2012-03-20,1,1,3,0,2,1,1,0.560833,0.538533,0.807917,0.121271,1051,5042,6093 447 | 446,2012-03-21,2,1,3,0,3,1,2,0.531667,0.513258,0.82125,0.0895583,1122,5108,6230 448 | 447,2012-03-22,2,1,3,0,4,1,1,0.554167,0.531567,0.83125,0.117562,1334,5537,6871 449 | 448,2012-03-23,2,1,3,0,5,1,2,0.601667,0.570067,0.694167,0.1163,2469,5893,8362 450 | 449,2012-03-24,2,1,3,0,6,0,2,0.5025,0.486733,0.885417,0.192783,1033,2339,3372 451 | 450,2012-03-25,2,1,3,0,0,0,2,0.4375,0.437488,0.880833,0.220775,1532,3464,4996 452 | 451,2012-03-26,2,1,3,0,1,1,1,0.445833,0.43875,0.477917,0.386821,795,4763,5558 453 | 452,2012-03-27,2,1,3,0,2,1,1,0.323333,0.315654,0.29,0.187192,531,4571,5102 454 | 453,2012-03-28,2,1,3,0,3,1,1,0.484167,0.47095,0.48125,0.291671,674,5024,5698 455 | 454,2012-03-29,2,1,3,0,4,1,1,0.494167,0.482304,0.439167,0.31965,834,5299,6133 456 | 455,2012-03-30,2,1,3,0,5,1,2,0.37,0.375621,0.580833,0.138067,796,4663,5459 457 | 456,2012-03-31,2,1,3,0,6,0,2,0.424167,0.421708,0.738333,0.250617,2301,3934,6235 458 | 457,2012-04-01,2,1,4,0,0,0,2,0.425833,0.417287,0.67625,0.172267,2347,3694,6041 459 | 458,2012-04-02,2,1,4,0,1,1,1,0.433913,0.427513,0.504348,0.312139,1208,4728,5936 460 | 459,2012-04-03,2,1,4,0,2,1,1,0.466667,0.461483,0.396667,0.100133,1348,5424,6772 461 | 460,2012-04-04,2,1,4,0,3,1,1,0.541667,0.53345,0.469583,0.180975,1058,5378,6436 462 | 461,2012-04-05,2,1,4,0,4,1,1,0.435,0.431163,0.374167,0.219529,1192,5265,6457 463 | 462,2012-04-06,2,1,4,0,5,1,1,0.403333,0.390767,0.377083,0.300388,1807,4653,6460 464 | 463,2012-04-07,2,1,4,0,6,0,1,0.4375,0.426129,0.254167,0.274871,3252,3605,6857 465 | 464,2012-04-08,2,1,4,0,0,0,1,0.5,0.492425,0.275833,0.232596,2230,2939,5169 466 | 465,2012-04-09,2,1,4,0,1,1,1,0.489167,0.476638,0.3175,0.358196,905,4680,5585 467 | 466,2012-04-10,2,1,4,0,2,1,1,0.446667,0.436233,0.435,0.249375,819,5099,5918 468 | 467,2012-04-11,2,1,4,0,3,1,1,0.348696,0.337274,0.469565,0.295274,482,4380,4862 469 | 468,2012-04-12,2,1,4,0,4,1,1,0.3975,0.387604,0.46625,0.290429,663,4746,5409 470 | 469,2012-04-13,2,1,4,0,5,1,1,0.4425,0.431808,0.408333,0.155471,1252,5146,6398 471 | 470,2012-04-14,2,1,4,0,6,0,1,0.495,0.487996,0.502917,0.190917,2795,4665,7460 472 | 471,2012-04-15,2,1,4,0,0,0,1,0.606667,0.573875,0.507917,0.225129,2846,4286,7132 473 | 472,2012-04-16,2,1,4,1,1,0,1,0.664167,0.614925,0.561667,0.284829,1198,5172,6370 474 | 473,2012-04-17,2,1,4,0,2,1,1,0.608333,0.598487,0.390417,0.273629,989,5702,6691 475 | 474,2012-04-18,2,1,4,0,3,1,2,0.463333,0.457038,0.569167,0.167912,347,4020,4367 476 | 475,2012-04-19,2,1,4,0,4,1,1,0.498333,0.493046,0.6125,0.0659292,846,5719,6565 477 | 476,2012-04-20,2,1,4,0,5,1,1,0.526667,0.515775,0.694583,0.149871,1340,5950,7290 478 | 477,2012-04-21,2,1,4,0,6,0,1,0.57,0.542921,0.682917,0.283587,2541,4083,6624 479 | 478,2012-04-22,2,1,4,0,0,0,3,0.396667,0.389504,0.835417,0.344546,120,907,1027 480 | 479,2012-04-23,2,1,4,0,1,1,2,0.321667,0.301125,0.766667,0.303496,195,3019,3214 481 | 480,2012-04-24,2,1,4,0,2,1,1,0.413333,0.405283,0.454167,0.249383,518,5115,5633 482 | 481,2012-04-25,2,1,4,0,3,1,1,0.476667,0.470317,0.427917,0.118792,655,5541,6196 483 | 482,2012-04-26,2,1,4,0,4,1,2,0.498333,0.483583,0.756667,0.176625,475,4551,5026 484 | 483,2012-04-27,2,1,4,0,5,1,1,0.4575,0.452637,0.400833,0.347633,1014,5219,6233 485 | 484,2012-04-28,2,1,4,0,6,0,2,0.376667,0.377504,0.489583,0.129975,1120,3100,4220 486 | 485,2012-04-29,2,1,4,0,0,0,1,0.458333,0.450121,0.587083,0.116908,2229,4075,6304 487 | 486,2012-04-30,2,1,4,0,1,1,2,0.464167,0.457696,0.57,0.171638,665,4907,5572 488 | 487,2012-05-01,2,1,5,0,2,1,2,0.613333,0.577021,0.659583,0.156096,653,5087,5740 489 | 488,2012-05-02,2,1,5,0,3,1,1,0.564167,0.537896,0.797083,0.138058,667,5502,6169 490 | 489,2012-05-03,2,1,5,0,4,1,2,0.56,0.537242,0.768333,0.133696,764,5657,6421 491 | 490,2012-05-04,2,1,5,0,5,1,1,0.6275,0.590917,0.735417,0.162938,1069,5227,6296 492 | 491,2012-05-05,2,1,5,0,6,0,2,0.621667,0.584608,0.756667,0.152992,2496,4387,6883 493 | 492,2012-05-06,2,1,5,0,0,0,2,0.5625,0.546737,0.74,0.149879,2135,4224,6359 494 | 493,2012-05-07,2,1,5,0,1,1,2,0.5375,0.527142,0.664167,0.230721,1008,5265,6273 495 | 494,2012-05-08,2,1,5,0,2,1,2,0.581667,0.557471,0.685833,0.296029,738,4990,5728 496 | 495,2012-05-09,2,1,5,0,3,1,2,0.575,0.553025,0.744167,0.216412,620,4097,4717 497 | 496,2012-05-10,2,1,5,0,4,1,1,0.505833,0.491783,0.552083,0.314063,1026,5546,6572 498 | 497,2012-05-11,2,1,5,0,5,1,1,0.533333,0.520833,0.360417,0.236937,1319,5711,7030 499 | 498,2012-05-12,2,1,5,0,6,0,1,0.564167,0.544817,0.480417,0.123133,2622,4807,7429 500 | 499,2012-05-13,2,1,5,0,0,0,1,0.6125,0.585238,0.57625,0.225117,2172,3946,6118 501 | 500,2012-05-14,2,1,5,0,1,1,2,0.573333,0.5499,0.789583,0.212692,342,2501,2843 502 | 501,2012-05-15,2,1,5,0,2,1,2,0.611667,0.576404,0.794583,0.147392,625,4490,5115 503 | 502,2012-05-16,2,1,5,0,3,1,1,0.636667,0.595975,0.697917,0.122512,991,6433,7424 504 | 503,2012-05-17,2,1,5,0,4,1,1,0.593333,0.572613,0.52,0.229475,1242,6142,7384 505 | 504,2012-05-18,2,1,5,0,5,1,1,0.564167,0.551121,0.523333,0.136817,1521,6118,7639 506 | 505,2012-05-19,2,1,5,0,6,0,1,0.6,0.566908,0.45625,0.083975,3410,4884,8294 507 | 506,2012-05-20,2,1,5,0,0,0,1,0.620833,0.583967,0.530417,0.254367,2704,4425,7129 508 | 507,2012-05-21,2,1,5,0,1,1,2,0.598333,0.565667,0.81125,0.233204,630,3729,4359 509 | 508,2012-05-22,2,1,5,0,2,1,2,0.615,0.580825,0.765833,0.118167,819,5254,6073 510 | 509,2012-05-23,2,1,5,0,3,1,2,0.621667,0.584612,0.774583,0.102,766,4494,5260 511 | 510,2012-05-24,2,1,5,0,4,1,1,0.655,0.6067,0.716667,0.172896,1059,5711,6770 512 | 511,2012-05-25,2,1,5,0,5,1,1,0.68,0.627529,0.747083,0.14055,1417,5317,6734 513 | 512,2012-05-26,2,1,5,0,6,0,1,0.6925,0.642696,0.7325,0.198992,2855,3681,6536 514 | 513,2012-05-27,2,1,5,0,0,0,1,0.69,0.641425,0.697083,0.215171,3283,3308,6591 515 | 514,2012-05-28,2,1,5,1,1,0,1,0.7125,0.6793,0.67625,0.196521,2557,3486,6043 516 | 515,2012-05-29,2,1,5,0,2,1,1,0.7225,0.672992,0.684583,0.2954,880,4863,5743 517 | 516,2012-05-30,2,1,5,0,3,1,2,0.656667,0.611129,0.67,0.134329,745,6110,6855 518 | 517,2012-05-31,2,1,5,0,4,1,1,0.68,0.631329,0.492917,0.195279,1100,6238,7338 519 | 518,2012-06-01,2,1,6,0,5,1,2,0.654167,0.607962,0.755417,0.237563,533,3594,4127 520 | 519,2012-06-02,2,1,6,0,6,0,1,0.583333,0.566288,0.549167,0.186562,2795,5325,8120 521 | 520,2012-06-03,2,1,6,0,0,0,1,0.6025,0.575133,0.493333,0.184087,2494,5147,7641 522 | 521,2012-06-04,2,1,6,0,1,1,1,0.5975,0.578283,0.487083,0.284833,1071,5927,6998 523 | 522,2012-06-05,2,1,6,0,2,1,2,0.540833,0.525892,0.613333,0.209575,968,6033,7001 524 | 523,2012-06-06,2,1,6,0,3,1,1,0.554167,0.542292,0.61125,0.077125,1027,6028,7055 525 | 524,2012-06-07,2,1,6,0,4,1,1,0.6025,0.569442,0.567083,0.15735,1038,6456,7494 526 | 525,2012-06-08,2,1,6,0,5,1,1,0.649167,0.597862,0.467917,0.175383,1488,6248,7736 527 | 526,2012-06-09,2,1,6,0,6,0,1,0.710833,0.648367,0.437083,0.144287,2708,4790,7498 528 | 527,2012-06-10,2,1,6,0,0,0,1,0.726667,0.663517,0.538333,0.133721,2224,4374,6598 529 | 528,2012-06-11,2,1,6,0,1,1,2,0.720833,0.659721,0.587917,0.207713,1017,5647,6664 530 | 529,2012-06-12,2,1,6,0,2,1,2,0.653333,0.597875,0.833333,0.214546,477,4495,4972 531 | 530,2012-06-13,2,1,6,0,3,1,1,0.655833,0.611117,0.582083,0.343279,1173,6248,7421 532 | 531,2012-06-14,2,1,6,0,4,1,1,0.648333,0.624383,0.569583,0.253733,1180,6183,7363 533 | 532,2012-06-15,2,1,6,0,5,1,1,0.639167,0.599754,0.589583,0.176617,1563,6102,7665 534 | 533,2012-06-16,2,1,6,0,6,0,1,0.631667,0.594708,0.504167,0.166667,2963,4739,7702 535 | 534,2012-06-17,2,1,6,0,0,0,1,0.5925,0.571975,0.59875,0.144904,2634,4344,6978 536 | 535,2012-06-18,2,1,6,0,1,1,2,0.568333,0.544842,0.777917,0.174746,653,4446,5099 537 | 536,2012-06-19,2,1,6,0,2,1,1,0.688333,0.654692,0.69,0.148017,968,5857,6825 538 | 537,2012-06-20,2,1,6,0,3,1,1,0.7825,0.720975,0.592083,0.113812,872,5339,6211 539 | 538,2012-06-21,3,1,6,0,4,1,1,0.805833,0.752542,0.567917,0.118787,778,5127,5905 540 | 539,2012-06-22,3,1,6,0,5,1,1,0.7775,0.724121,0.57375,0.182842,964,4859,5823 541 | 540,2012-06-23,3,1,6,0,6,0,1,0.731667,0.652792,0.534583,0.179721,2657,4801,7458 542 | 541,2012-06-24,3,1,6,0,0,0,1,0.743333,0.674254,0.479167,0.145525,2551,4340,6891 543 | 542,2012-06-25,3,1,6,0,1,1,1,0.715833,0.654042,0.504167,0.300383,1139,5640,6779 544 | 543,2012-06-26,3,1,6,0,2,1,1,0.630833,0.594704,0.373333,0.347642,1077,6365,7442 545 | 544,2012-06-27,3,1,6,0,3,1,1,0.6975,0.640792,0.36,0.271775,1077,6258,7335 546 | 545,2012-06-28,3,1,6,0,4,1,1,0.749167,0.675512,0.4225,0.17165,921,5958,6879 547 | 546,2012-06-29,3,1,6,0,5,1,1,0.834167,0.786613,0.48875,0.165417,829,4634,5463 548 | 547,2012-06-30,3,1,6,0,6,0,1,0.765,0.687508,0.60125,0.161071,1455,4232,5687 549 | 548,2012-07-01,3,1,7,0,0,0,1,0.815833,0.750629,0.51875,0.168529,1421,4110,5531 550 | 549,2012-07-02,3,1,7,0,1,1,1,0.781667,0.702038,0.447083,0.195267,904,5323,6227 551 | 550,2012-07-03,3,1,7,0,2,1,1,0.780833,0.70265,0.492083,0.126237,1052,5608,6660 552 | 551,2012-07-04,3,1,7,1,3,0,1,0.789167,0.732337,0.53875,0.13495,2562,4841,7403 553 | 552,2012-07-05,3,1,7,0,4,1,1,0.8275,0.761367,0.457917,0.194029,1405,4836,6241 554 | 553,2012-07-06,3,1,7,0,5,1,1,0.828333,0.752533,0.450833,0.146142,1366,4841,6207 555 | 554,2012-07-07,3,1,7,0,6,0,1,0.861667,0.804913,0.492083,0.163554,1448,3392,4840 556 | 555,2012-07-08,3,1,7,0,0,0,1,0.8225,0.790396,0.57375,0.125629,1203,3469,4672 557 | 556,2012-07-09,3,1,7,0,1,1,2,0.710833,0.654054,0.683333,0.180975,998,5571,6569 558 | 557,2012-07-10,3,1,7,0,2,1,2,0.720833,0.664796,0.6675,0.151737,954,5336,6290 559 | 558,2012-07-11,3,1,7,0,3,1,1,0.716667,0.650271,0.633333,0.151733,975,6289,7264 560 | 559,2012-07-12,3,1,7,0,4,1,1,0.715833,0.654683,0.529583,0.146775,1032,6414,7446 561 | 560,2012-07-13,3,1,7,0,5,1,2,0.731667,0.667933,0.485833,0.08085,1511,5988,7499 562 | 561,2012-07-14,3,1,7,0,6,0,2,0.703333,0.666042,0.699167,0.143679,2355,4614,6969 563 | 562,2012-07-15,3,1,7,0,0,0,1,0.745833,0.705196,0.717917,0.166667,1920,4111,6031 564 | 563,2012-07-16,3,1,7,0,1,1,1,0.763333,0.724125,0.645,0.164187,1088,5742,6830 565 | 564,2012-07-17,3,1,7,0,2,1,1,0.818333,0.755683,0.505833,0.114429,921,5865,6786 566 | 565,2012-07-18,3,1,7,0,3,1,1,0.793333,0.745583,0.577083,0.137442,799,4914,5713 567 | 566,2012-07-19,3,1,7,0,4,1,1,0.77,0.714642,0.600417,0.165429,888,5703,6591 568 | 567,2012-07-20,3,1,7,0,5,1,2,0.665833,0.613025,0.844167,0.208967,747,5123,5870 569 | 568,2012-07-21,3,1,7,0,6,0,3,0.595833,0.549912,0.865417,0.2133,1264,3195,4459 570 | 569,2012-07-22,3,1,7,0,0,0,2,0.6675,0.623125,0.7625,0.0939208,2544,4866,7410 571 | 570,2012-07-23,3,1,7,0,1,1,1,0.741667,0.690017,0.694167,0.138683,1135,5831,6966 572 | 571,2012-07-24,3,1,7,0,2,1,1,0.750833,0.70645,0.655,0.211454,1140,6452,7592 573 | 572,2012-07-25,3,1,7,0,3,1,1,0.724167,0.654054,0.45,0.1648,1383,6790,8173 574 | 573,2012-07-26,3,1,7,0,4,1,1,0.776667,0.739263,0.596667,0.284813,1036,5825,6861 575 | 574,2012-07-27,3,1,7,0,5,1,1,0.781667,0.734217,0.594583,0.152992,1259,5645,6904 576 | 575,2012-07-28,3,1,7,0,6,0,1,0.755833,0.697604,0.613333,0.15735,2234,4451,6685 577 | 576,2012-07-29,3,1,7,0,0,0,1,0.721667,0.667933,0.62375,0.170396,2153,4444,6597 578 | 577,2012-07-30,3,1,7,0,1,1,1,0.730833,0.684987,0.66875,0.153617,1040,6065,7105 579 | 578,2012-07-31,3,1,7,0,2,1,1,0.713333,0.662896,0.704167,0.165425,968,6248,7216 580 | 579,2012-08-01,3,1,8,0,3,1,1,0.7175,0.667308,0.6775,0.141179,1074,6506,7580 581 | 580,2012-08-02,3,1,8,0,4,1,1,0.7525,0.707088,0.659583,0.129354,983,6278,7261 582 | 581,2012-08-03,3,1,8,0,5,1,2,0.765833,0.722867,0.6425,0.215792,1328,5847,7175 583 | 582,2012-08-04,3,1,8,0,6,0,1,0.793333,0.751267,0.613333,0.257458,2345,4479,6824 584 | 583,2012-08-05,3,1,8,0,0,0,1,0.769167,0.731079,0.6525,0.290421,1707,3757,5464 585 | 584,2012-08-06,3,1,8,0,1,1,2,0.7525,0.710246,0.654167,0.129354,1233,5780,7013 586 | 585,2012-08-07,3,1,8,0,2,1,2,0.735833,0.697621,0.70375,0.116908,1278,5995,7273 587 | 586,2012-08-08,3,1,8,0,3,1,2,0.75,0.707717,0.672917,0.1107,1263,6271,7534 588 | 587,2012-08-09,3,1,8,0,4,1,1,0.755833,0.699508,0.620417,0.1561,1196,6090,7286 589 | 588,2012-08-10,3,1,8,0,5,1,2,0.715833,0.667942,0.715833,0.238813,1065,4721,5786 590 | 589,2012-08-11,3,1,8,0,6,0,2,0.6925,0.638267,0.732917,0.206479,2247,4052,6299 591 | 590,2012-08-12,3,1,8,0,0,0,1,0.700833,0.644579,0.530417,0.122512,2182,4362,6544 592 | 591,2012-08-13,3,1,8,0,1,1,1,0.720833,0.662254,0.545417,0.136212,1207,5676,6883 593 | 592,2012-08-14,3,1,8,0,2,1,1,0.726667,0.676779,0.686667,0.169158,1128,5656,6784 594 | 593,2012-08-15,3,1,8,0,3,1,1,0.706667,0.654037,0.619583,0.169771,1198,6149,7347 595 | 594,2012-08-16,3,1,8,0,4,1,1,0.719167,0.654688,0.519167,0.141796,1338,6267,7605 596 | 595,2012-08-17,3,1,8,0,5,1,1,0.723333,0.2424,0.570833,0.231354,1483,5665,7148 597 | 596,2012-08-18,3,1,8,0,6,0,1,0.678333,0.618071,0.603333,0.177867,2827,5038,7865 598 | 597,2012-08-19,3,1,8,0,0,0,2,0.635833,0.603554,0.711667,0.08645,1208,3341,4549 599 | 598,2012-08-20,3,1,8,0,1,1,2,0.635833,0.595967,0.734167,0.129979,1026,5504,6530 600 | 599,2012-08-21,3,1,8,0,2,1,1,0.649167,0.601025,0.67375,0.0727708,1081,5925,7006 601 | 600,2012-08-22,3,1,8,0,3,1,1,0.6675,0.621854,0.677083,0.0702833,1094,6281,7375 602 | 601,2012-08-23,3,1,8,0,4,1,1,0.695833,0.637008,0.635833,0.0845958,1363,6402,7765 603 | 602,2012-08-24,3,1,8,0,5,1,2,0.7025,0.6471,0.615,0.0721458,1325,6257,7582 604 | 603,2012-08-25,3,1,8,0,6,0,2,0.661667,0.618696,0.712917,0.244408,1829,4224,6053 605 | 604,2012-08-26,3,1,8,0,0,0,2,0.653333,0.595996,0.845833,0.228858,1483,3772,5255 606 | 605,2012-08-27,3,1,8,0,1,1,1,0.703333,0.654688,0.730417,0.128733,989,5928,6917 607 | 606,2012-08-28,3,1,8,0,2,1,1,0.728333,0.66605,0.62,0.190925,935,6105,7040 608 | 607,2012-08-29,3,1,8,0,3,1,1,0.685,0.635733,0.552083,0.112562,1177,6520,7697 609 | 608,2012-08-30,3,1,8,0,4,1,1,0.706667,0.652779,0.590417,0.0771167,1172,6541,7713 610 | 609,2012-08-31,3,1,8,0,5,1,1,0.764167,0.6894,0.5875,0.168533,1433,5917,7350 611 | 610,2012-09-01,3,1,9,0,6,0,2,0.753333,0.702654,0.638333,0.113187,2352,3788,6140 612 | 611,2012-09-02,3,1,9,0,0,0,2,0.696667,0.649,0.815,0.0640708,2613,3197,5810 613 | 612,2012-09-03,3,1,9,1,1,0,1,0.7075,0.661629,0.790833,0.151121,1965,4069,6034 614 | 613,2012-09-04,3,1,9,0,2,1,1,0.725833,0.686888,0.755,0.236321,867,5997,6864 615 | 614,2012-09-05,3,1,9,0,3,1,1,0.736667,0.708983,0.74125,0.187808,832,6280,7112 616 | 615,2012-09-06,3,1,9,0,4,1,2,0.696667,0.655329,0.810417,0.142421,611,5592,6203 617 | 616,2012-09-07,3,1,9,0,5,1,1,0.703333,0.657204,0.73625,0.171646,1045,6459,7504 618 | 617,2012-09-08,3,1,9,0,6,0,2,0.659167,0.611121,0.799167,0.281104,1557,4419,5976 619 | 618,2012-09-09,3,1,9,0,0,0,1,0.61,0.578925,0.5475,0.224496,2570,5657,8227 620 | 619,2012-09-10,3,1,9,0,1,1,1,0.583333,0.565654,0.50375,0.258713,1118,6407,7525 621 | 620,2012-09-11,3,1,9,0,2,1,1,0.5775,0.554292,0.52,0.0920542,1070,6697,7767 622 | 621,2012-09-12,3,1,9,0,3,1,1,0.599167,0.570075,0.577083,0.131846,1050,6820,7870 623 | 622,2012-09-13,3,1,9,0,4,1,1,0.6125,0.579558,0.637083,0.0827208,1054,6750,7804 624 | 623,2012-09-14,3,1,9,0,5,1,1,0.633333,0.594083,0.6725,0.103863,1379,6630,8009 625 | 624,2012-09-15,3,1,9,0,6,0,1,0.608333,0.585867,0.501667,0.247521,3160,5554,8714 626 | 625,2012-09-16,3,1,9,0,0,0,1,0.58,0.563125,0.57,0.0901833,2166,5167,7333 627 | 626,2012-09-17,3,1,9,0,1,1,2,0.580833,0.55305,0.734583,0.151742,1022,5847,6869 628 | 627,2012-09-18,3,1,9,0,2,1,2,0.623333,0.565067,0.8725,0.357587,371,3702,4073 629 | 628,2012-09-19,3,1,9,0,3,1,1,0.5525,0.540404,0.536667,0.215175,788,6803,7591 630 | 629,2012-09-20,3,1,9,0,4,1,1,0.546667,0.532192,0.618333,0.118167,939,6781,7720 631 | 630,2012-09-21,3,1,9,0,5,1,1,0.599167,0.571971,0.66875,0.154229,1250,6917,8167 632 | 631,2012-09-22,3,1,9,0,6,0,1,0.65,0.610488,0.646667,0.283583,2512,5883,8395 633 | 632,2012-09-23,4,1,9,0,0,0,1,0.529167,0.518933,0.467083,0.223258,2454,5453,7907 634 | 633,2012-09-24,4,1,9,0,1,1,1,0.514167,0.502513,0.492917,0.142404,1001,6435,7436 635 | 634,2012-09-25,4,1,9,0,2,1,1,0.55,0.544179,0.57,0.236321,845,6693,7538 636 | 635,2012-09-26,4,1,9,0,3,1,1,0.635,0.596613,0.630833,0.2444,787,6946,7733 637 | 636,2012-09-27,4,1,9,0,4,1,2,0.65,0.607975,0.690833,0.134342,751,6642,7393 638 | 637,2012-09-28,4,1,9,0,5,1,2,0.619167,0.585863,0.69,0.164179,1045,6370,7415 639 | 638,2012-09-29,4,1,9,0,6,0,1,0.5425,0.530296,0.542917,0.227604,2589,5966,8555 640 | 639,2012-09-30,4,1,9,0,0,0,1,0.526667,0.517663,0.583333,0.134958,2015,4874,6889 641 | 640,2012-10-01,4,1,10,0,1,1,2,0.520833,0.512,0.649167,0.0908042,763,6015,6778 642 | 641,2012-10-02,4,1,10,0,2,1,3,0.590833,0.542333,0.871667,0.104475,315,4324,4639 643 | 642,2012-10-03,4,1,10,0,3,1,2,0.6575,0.599133,0.79375,0.0665458,728,6844,7572 644 | 643,2012-10-04,4,1,10,0,4,1,2,0.6575,0.607975,0.722917,0.117546,891,6437,7328 645 | 644,2012-10-05,4,1,10,0,5,1,1,0.615,0.580187,0.6275,0.10635,1516,6640,8156 646 | 645,2012-10-06,4,1,10,0,6,0,1,0.554167,0.538521,0.664167,0.268025,3031,4934,7965 647 | 646,2012-10-07,4,1,10,0,0,0,2,0.415833,0.419813,0.708333,0.141162,781,2729,3510 648 | 647,2012-10-08,4,1,10,1,1,0,2,0.383333,0.387608,0.709583,0.189679,874,4604,5478 649 | 648,2012-10-09,4,1,10,0,2,1,2,0.446667,0.438112,0.761667,0.1903,601,5791,6392 650 | 649,2012-10-10,4,1,10,0,3,1,1,0.514167,0.503142,0.630833,0.187821,780,6911,7691 651 | 650,2012-10-11,4,1,10,0,4,1,1,0.435,0.431167,0.463333,0.181596,834,6736,7570 652 | 651,2012-10-12,4,1,10,0,5,1,1,0.4375,0.433071,0.539167,0.235092,1060,6222,7282 653 | 652,2012-10-13,4,1,10,0,6,0,1,0.393333,0.391396,0.494583,0.146142,2252,4857,7109 654 | 653,2012-10-14,4,1,10,0,0,0,1,0.521667,0.508204,0.640417,0.278612,2080,4559,6639 655 | 654,2012-10-15,4,1,10,0,1,1,2,0.561667,0.53915,0.7075,0.296037,760,5115,5875 656 | 655,2012-10-16,4,1,10,0,2,1,1,0.468333,0.460846,0.558333,0.182221,922,6612,7534 657 | 656,2012-10-17,4,1,10,0,3,1,1,0.455833,0.450108,0.692917,0.101371,979,6482,7461 658 | 657,2012-10-18,4,1,10,0,4,1,2,0.5225,0.512625,0.728333,0.236937,1008,6501,7509 659 | 658,2012-10-19,4,1,10,0,5,1,2,0.563333,0.537896,0.815,0.134954,753,4671,5424 660 | 659,2012-10-20,4,1,10,0,6,0,1,0.484167,0.472842,0.572917,0.117537,2806,5284,8090 661 | 660,2012-10-21,4,1,10,0,0,0,1,0.464167,0.456429,0.51,0.166054,2132,4692,6824 662 | 661,2012-10-22,4,1,10,0,1,1,1,0.4875,0.482942,0.568333,0.0814833,830,6228,7058 663 | 662,2012-10-23,4,1,10,0,2,1,1,0.544167,0.530304,0.641667,0.0945458,841,6625,7466 664 | 663,2012-10-24,4,1,10,0,3,1,1,0.5875,0.558721,0.63625,0.0727792,795,6898,7693 665 | 664,2012-10-25,4,1,10,0,4,1,2,0.55,0.529688,0.800417,0.124375,875,6484,7359 666 | 665,2012-10-26,4,1,10,0,5,1,2,0.545833,0.52275,0.807083,0.132467,1182,6262,7444 667 | 666,2012-10-27,4,1,10,0,6,0,2,0.53,0.515133,0.72,0.235692,2643,5209,7852 668 | 667,2012-10-28,4,1,10,0,0,0,2,0.4775,0.467771,0.694583,0.398008,998,3461,4459 669 | 668,2012-10-29,4,1,10,0,1,1,3,0.44,0.4394,0.88,0.3582,2,20,22 670 | 669,2012-10-30,4,1,10,0,2,1,2,0.318182,0.309909,0.825455,0.213009,87,1009,1096 671 | 670,2012-10-31,4,1,10,0,3,1,2,0.3575,0.3611,0.666667,0.166667,419,5147,5566 672 | 671,2012-11-01,4,1,11,0,4,1,2,0.365833,0.369942,0.581667,0.157346,466,5520,5986 673 | 672,2012-11-02,4,1,11,0,5,1,1,0.355,0.356042,0.522083,0.266175,618,5229,5847 674 | 673,2012-11-03,4,1,11,0,6,0,2,0.343333,0.323846,0.49125,0.270529,1029,4109,5138 675 | 674,2012-11-04,4,1,11,0,0,0,1,0.325833,0.329538,0.532917,0.179108,1201,3906,5107 676 | 675,2012-11-05,4,1,11,0,1,1,1,0.319167,0.308075,0.494167,0.236325,378,4881,5259 677 | 676,2012-11-06,4,1,11,0,2,1,1,0.280833,0.281567,0.567083,0.173513,466,5220,5686 678 | 677,2012-11-07,4,1,11,0,3,1,2,0.295833,0.274621,0.5475,0.304108,326,4709,5035 679 | 678,2012-11-08,4,1,11,0,4,1,1,0.352174,0.341891,0.333478,0.347835,340,4975,5315 680 | 679,2012-11-09,4,1,11,0,5,1,1,0.361667,0.355413,0.540833,0.214558,709,5283,5992 681 | 680,2012-11-10,4,1,11,0,6,0,1,0.389167,0.393937,0.645417,0.0578458,2090,4446,6536 682 | 681,2012-11-11,4,1,11,0,0,0,1,0.420833,0.421713,0.659167,0.1275,2290,4562,6852 683 | 682,2012-11-12,4,1,11,1,1,0,1,0.485,0.475383,0.741667,0.173517,1097,5172,6269 684 | 683,2012-11-13,4,1,11,0,2,1,2,0.343333,0.323225,0.662917,0.342046,327,3767,4094 685 | 684,2012-11-14,4,1,11,0,3,1,1,0.289167,0.281563,0.552083,0.199625,373,5122,5495 686 | 685,2012-11-15,4,1,11,0,4,1,2,0.321667,0.324492,0.620417,0.152987,320,5125,5445 687 | 686,2012-11-16,4,1,11,0,5,1,1,0.345,0.347204,0.524583,0.171025,484,5214,5698 688 | 687,2012-11-17,4,1,11,0,6,0,1,0.325,0.326383,0.545417,0.179729,1313,4316,5629 689 | 688,2012-11-18,4,1,11,0,0,0,1,0.3425,0.337746,0.692917,0.227612,922,3747,4669 690 | 689,2012-11-19,4,1,11,0,1,1,2,0.380833,0.375621,0.623333,0.235067,449,5050,5499 691 | 690,2012-11-20,4,1,11,0,2,1,2,0.374167,0.380667,0.685,0.082725,534,5100,5634 692 | 691,2012-11-21,4,1,11,0,3,1,1,0.353333,0.364892,0.61375,0.103246,615,4531,5146 693 | 692,2012-11-22,4,1,11,1,4,0,1,0.34,0.350371,0.580417,0.0528708,955,1470,2425 694 | 693,2012-11-23,4,1,11,0,5,1,1,0.368333,0.378779,0.56875,0.148021,1603,2307,3910 695 | 694,2012-11-24,4,1,11,0,6,0,1,0.278333,0.248742,0.404583,0.376871,532,1745,2277 696 | 695,2012-11-25,4,1,11,0,0,0,1,0.245833,0.257583,0.468333,0.1505,309,2115,2424 697 | 696,2012-11-26,4,1,11,0,1,1,1,0.313333,0.339004,0.535417,0.04665,337,4750,5087 698 | 697,2012-11-27,4,1,11,0,2,1,2,0.291667,0.281558,0.786667,0.237562,123,3836,3959 699 | 698,2012-11-28,4,1,11,0,3,1,1,0.296667,0.289762,0.50625,0.210821,198,5062,5260 700 | 699,2012-11-29,4,1,11,0,4,1,1,0.28087,0.298422,0.555652,0.115522,243,5080,5323 701 | 700,2012-11-30,4,1,11,0,5,1,1,0.298333,0.323867,0.649583,0.0584708,362,5306,5668 702 | 701,2012-12-01,4,1,12,0,6,0,2,0.298333,0.316904,0.806667,0.0597042,951,4240,5191 703 | 702,2012-12-02,4,1,12,0,0,0,2,0.3475,0.359208,0.823333,0.124379,892,3757,4649 704 | 703,2012-12-03,4,1,12,0,1,1,1,0.4525,0.455796,0.7675,0.0827208,555,5679,6234 705 | 704,2012-12-04,4,1,12,0,2,1,1,0.475833,0.469054,0.73375,0.174129,551,6055,6606 706 | 705,2012-12-05,4,1,12,0,3,1,1,0.438333,0.428012,0.485,0.324021,331,5398,5729 707 | 706,2012-12-06,4,1,12,0,4,1,1,0.255833,0.258204,0.50875,0.174754,340,5035,5375 708 | 707,2012-12-07,4,1,12,0,5,1,2,0.320833,0.321958,0.764167,0.1306,349,4659,5008 709 | 708,2012-12-08,4,1,12,0,6,0,2,0.381667,0.389508,0.91125,0.101379,1153,4429,5582 710 | 709,2012-12-09,4,1,12,0,0,0,2,0.384167,0.390146,0.905417,0.157975,441,2787,3228 711 | 710,2012-12-10,4,1,12,0,1,1,2,0.435833,0.435575,0.925,0.190308,329,4841,5170 712 | 711,2012-12-11,4,1,12,0,2,1,2,0.353333,0.338363,0.596667,0.296037,282,5219,5501 713 | 712,2012-12-12,4,1,12,0,3,1,2,0.2975,0.297338,0.538333,0.162937,310,5009,5319 714 | 713,2012-12-13,4,1,12,0,4,1,1,0.295833,0.294188,0.485833,0.174129,425,5107,5532 715 | 714,2012-12-14,4,1,12,0,5,1,1,0.281667,0.294192,0.642917,0.131229,429,5182,5611 716 | 715,2012-12-15,4,1,12,0,6,0,1,0.324167,0.338383,0.650417,0.10635,767,4280,5047 717 | 716,2012-12-16,4,1,12,0,0,0,2,0.3625,0.369938,0.83875,0.100742,538,3248,3786 718 | 717,2012-12-17,4,1,12,0,1,1,2,0.393333,0.4015,0.907083,0.0982583,212,4373,4585 719 | 718,2012-12-18,4,1,12,0,2,1,1,0.410833,0.409708,0.66625,0.221404,433,5124,5557 720 | 719,2012-12-19,4,1,12,0,3,1,1,0.3325,0.342162,0.625417,0.184092,333,4934,5267 721 | 720,2012-12-20,4,1,12,0,4,1,2,0.33,0.335217,0.667917,0.132463,314,3814,4128 722 | 721,2012-12-21,1,1,12,0,5,1,2,0.326667,0.301767,0.556667,0.374383,221,3402,3623 723 | 722,2012-12-22,1,1,12,0,6,0,1,0.265833,0.236113,0.44125,0.407346,205,1544,1749 724 | 723,2012-12-23,1,1,12,0,0,0,1,0.245833,0.259471,0.515417,0.133083,408,1379,1787 725 | 724,2012-12-24,1,1,12,0,1,1,2,0.231304,0.2589,0.791304,0.0772304,174,746,920 726 | 725,2012-12-25,1,1,12,1,2,0,2,0.291304,0.294465,0.734783,0.168726,440,573,1013 727 | 726,2012-12-26,1,1,12,0,3,1,3,0.243333,0.220333,0.823333,0.316546,9,432,441 728 | 727,2012-12-27,1,1,12,0,4,1,2,0.254167,0.226642,0.652917,0.350133,247,1867,2114 729 | 728,2012-12-28,1,1,12,0,5,1,2,0.253333,0.255046,0.59,0.155471,644,2451,3095 730 | 729,2012-12-29,1,1,12,0,6,0,2,0.253333,0.2424,0.752917,0.124383,159,1182,1341 731 | 730,2012-12-30,1,1,12,0,0,0,1,0.255833,0.2317,0.483333,0.350754,364,1432,1796 732 | 731,2012-12-31,1,1,12,0,1,1,2,0.215833,0.223487,0.5775,0.154846,439,2290,2729 733 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Exploratory Data Analysis 2 | 3 | These projects were done in the first phase of my data science learning. Many of the datasets are from FiveThirtyEight public data, such as [Analyzing Movie Reviews](https://github.com/Tahsin-Mayeesha/Data-science-mini-projects/tree/master/Analyzing-Movie-Reviews) and [Police Killings](https://github.com/Tahsin-Mayeesha/Data-science-mini-projects/tree/master/Police-Killings). The other ones are from various sources such as Reddit and course work from Udacity. 4 | 5 | -------------------------------------------------------------------------------- /Star-Wars-survey-from-Five-ThirtyEight-data/To-do.txt: -------------------------------------------------------------------------------- 1 | 1.Segment the data by columns like Education, Location (Census Region), and Which character shot first?, which aren't binary. Are they any interesting patterns? 2 | 2.Clean up columns 15 to 29, which have to do with what characters are viewed favorably and unfavorably. 3 | 3.Which character is the most liked? 4 | 4.Which character is the most disliked? 5 | 5.hich character creates the most controversy? (split between dislikes and likes) 6 | 6. Do similar analysis for star trek. 7 | -------------------------------------------------------------------------------- /Udacity-Descriptive-Statistics-Final-Project/Descriptive statistics final project.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd\n", 12 | "import numpy as np\n", 13 | "import matplotlib.pyplot as plt\n", 14 | "import seaborn as sns\n", 15 | "%matplotlib inline" 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [ 22 | "This experiment will require the use of a standard deck of playing cards. This is a deck of fifty-two cards divided into four suits (spades (♠), hearts (♥), diamonds (♦), and clubs (♣)), each suit containing thirteen cards (Ace, numbers 2-10, and face cards Jack, Queen, and King). For the purposes of this task, assign each card a value: The Ace takes a value of 1, numbered cards take the value printed on the card, and the Jack, Queen, and King each take a value of 10.\n" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 2, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "suits = [\"Spades\", \"Hearts\",\"Diamonds\", \"Clubs\"]" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 3, 39 | "metadata": { 40 | "collapsed": false 41 | }, 42 | "outputs": [], 43 | "source": [ 44 | "unique_cards = {\"Ace\":1,\"Two\":2,\"Three\":3,\"Four\":4,\"Five\":5,\"Six\":6,\"Seven\":7,\"Eight\":8,\"Nine\":9,\"Ten\":10,\"Jack\":10,\"Queen\":10,\n", 45 | " \"King\":10}" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 4, 51 | "metadata": { 52 | "collapsed": false 53 | }, 54 | "outputs": [], 55 | "source": [ 56 | "# Creates the Deck\n", 57 | "Deck = {}\n", 58 | "\n", 59 | "for i in range(4):\n", 60 | " for key,val in unique_cards.items():\n", 61 | " Deck[key + \" of \" + suits[i] + \" \"] = val" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 5, 67 | "metadata": { 68 | "collapsed": false 69 | }, 70 | "outputs": [], 71 | "source": [ 72 | "df = pd.DataFrame({\"Cards\": Deck.keys(), \"Values\":Deck.values()})" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 6, 78 | "metadata": { 79 | "collapsed": false 80 | }, 81 | "outputs": [ 82 | { 83 | "data": { 84 | "text/html": [ 85 | "
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CardsValues
0Six of Hearts6
1Seven of Spades7
2Queen of Diamonds10
3Queen of Hearts10
4Four of Spades4
5King of Diamonds10
6Ace of Diamonds1
7Six of Diamonds6
8Five of Diamonds5
9Three of Hearts3
10Jack of Spades10
11Three of Spades3
12Six of Clubs6
13Three of Clubs3
14Ten of Spades10
15Two of Clubs2
16Ten of Clubs10
17Ten of Hearts10
18Two of Diamonds2
19Two of Spades2
20Nine of Hearts9
21Jack of Diamonds10
22Four of Clubs4
23Eight of Diamonds8
24Seven of Clubs7
25Queen of Clubs10
26Eight of Clubs8
27Six of Spades6
28Four of Diamonds4
29Two of Hearts2
30Ace of Spades1
31King of Spades10
32Queen of Spades10
33Four of Hearts4
34King of Hearts10
35Ace of Hearts1
36Nine of Spades9
37Eight of Spades8
38Seven of Diamonds7
39Nine of Clubs9
40Seven of Hearts7
41Ace of Clubs1
42Five of Hearts5
43King of Clubs10
44Three of Diamonds3
45Ten of Diamonds10
46Jack of Hearts10
47Eight of Hearts8
48Five of Spades5
49Nine of Diamonds9
50Jack of Clubs10
51Five of Clubs5
\n", 357 | "
" 358 | ], 359 | "text/plain": [ 360 | " Cards Values\n", 361 | "0 Six of Hearts 6\n", 362 | "1 Seven of Spades 7\n", 363 | "2 Queen of Diamonds 10\n", 364 | "3 Queen of Hearts 10\n", 365 | "4 Four of Spades 4\n", 366 | "5 King of Diamonds 10\n", 367 | "6 Ace of Diamonds 1\n", 368 | "7 Six of Diamonds 6\n", 369 | "8 Five of Diamonds 5\n", 370 | "9 Three of Hearts 3\n", 371 | "10 Jack of Spades 10\n", 372 | "11 Three of Spades 3\n", 373 | "12 Six of Clubs 6\n", 374 | "13 Three of Clubs 3\n", 375 | "14 Ten of Spades 10\n", 376 | "15 Two of Clubs 2\n", 377 | "16 Ten of Clubs 10\n", 378 | "17 Ten of Hearts 10\n", 379 | "18 Two of Diamonds 2\n", 380 | "19 Two of Spades 2\n", 381 | "20 Nine of Hearts 9\n", 382 | "21 Jack of Diamonds 10\n", 383 | "22 Four of Clubs 4\n", 384 | "23 Eight of Diamonds 8\n", 385 | "24 Seven of Clubs 7\n", 386 | "25 Queen of Clubs 10\n", 387 | "26 Eight of Clubs 8\n", 388 | "27 Six of Spades 6\n", 389 | "28 Four of Diamonds 4\n", 390 | "29 Two of Hearts 2\n", 391 | "30 Ace of Spades 1\n", 392 | "31 King of Spades 10\n", 393 | "32 Queen of Spades 10\n", 394 | "33 Four of Hearts 4\n", 395 | "34 King of Hearts 10\n", 396 | "35 Ace of Hearts 1\n", 397 | "36 Nine of Spades 9\n", 398 | "37 Eight of Spades 8\n", 399 | "38 Seven of Diamonds 7\n", 400 | "39 Nine of Clubs 9\n", 401 | "40 Seven of Hearts 7\n", 402 | "41 Ace of Clubs 1\n", 403 | "42 Five of Hearts 5\n", 404 | "43 King of Clubs 10\n", 405 | "44 Three of Diamonds 3\n", 406 | "45 Ten of Diamonds 10\n", 407 | "46 Jack of Hearts 10\n", 408 | "47 Eight of Hearts 8\n", 409 | "48 Five of Spades 5\n", 410 | "49 Nine of Diamonds 9\n", 411 | "50 Jack of Clubs 10\n", 412 | "51 Five of Clubs 5" 413 | ] 414 | }, 415 | "execution_count": 6, 416 | "metadata": {}, 417 | "output_type": "execute_result" 418 | } 419 | ], 420 | "source": [ 421 | "df" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": 7, 427 | "metadata": { 428 | "collapsed": false 429 | }, 430 | "outputs": [ 431 | { 432 | "data": { 433 | "text/plain": [ 434 | "count 52.000000\n", 435 | "mean 6.538462\n", 436 | "std 3.183669\n", 437 | "min 1.000000\n", 438 | "25% 4.000000\n", 439 | "50% 7.000000\n", 440 | "75% 10.000000\n", 441 | "max 10.000000\n", 442 | "Name: Values, dtype: float64" 443 | ] 444 | }, 445 | "execution_count": 7, 446 | "metadata": {}, 447 | "output_type": "execute_result" 448 | } 449 | ], 450 | "source": [ 451 | "df[\"Values\"].describe()" 452 | ] 453 | }, 454 | { 455 | "cell_type": "markdown", 456 | "metadata": {}, 457 | "source": [ 458 | "1. First, create a histogram depicting the relative frequencies of the card values." 459 | ] 460 | }, 461 | { 462 | "cell_type": "code", 463 | "execution_count": 8, 464 | "metadata": { 465 | "collapsed": false 466 | }, 467 | "outputs": [ 468 | { 469 | "data": { 470 | "text/plain": [ 471 | "" 472 | ] 473 | }, 474 | "execution_count": 8, 475 | "metadata": {}, 476 | "output_type": "execute_result" 477 | }, 478 | { 479 | "data": { 480 | "image/png": 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481 | "text/plain": [ 482 | "" 483 | ] 484 | }, 485 | "metadata": {}, 486 | "output_type": "display_data" 487 | } 488 | ], 489 | "source": [ 490 | "\n", 491 | "df[\"Values\"].hist(bins=10)\n" 492 | ] 493 | }, 494 | { 495 | "cell_type": "markdown", 496 | "metadata": {}, 497 | "source": [ 498 | "1. First, create a histogram depicting the relative frequencies of the card values.\n", 499 | "2. Now, we will get samples for a new distribution. To obtain a single sample, shuffle your deck of cards and draw three cards from it. (You will be sampling from the deck without replacement.) Record the cards that you have drawn and the sum of the three cards’ values. Repeat this sampling procedure a total of at least thirty times." 500 | ] 501 | }, 502 | { 503 | "cell_type": "code", 504 | "execution_count": 9, 505 | "metadata": { 506 | "collapsed": false 507 | }, 508 | "outputs": [ 509 | { 510 | "name": "stdout", 511 | "output_type": "stream", 512 | "text": [ 513 | "[21L, 11L, 25L, 12L, 25L, 14L, 18L, 18L, 8L, 16L, 14L, 24L, 28L, 25L, 23L, 26L, 26L, 26L, 12L, 26L, 30L, 22L, 16L, 22L, 24L, 13L, 25L, 19L, 19L, 21L]\n" 514 | ] 515 | } 516 | ], 517 | "source": [ 518 | "samples_sum = []\n", 519 | "\n", 520 | "for i in range(30):\n", 521 | " item = df.sample(n=3,replace = False)[\"Values\"].sum()\n", 522 | " samples_sum.append(item)\n", 523 | "\n", 524 | "print samples_sum" 525 | ] 526 | }, 527 | { 528 | "cell_type": "markdown", 529 | "metadata": {}, 530 | "source": [ 531 | "#### Let’s take a look at the distribution of the card sums. Report descriptive statistics for the samples you have drawn. Include at least two measures of central tendency and two measures of variability.\n", 532 | "\n", 533 | "Median and mean can be the measures of central tendency while standard deviation and IQR reports can be the measures of variability. Given this is a small sample, it seems the variance is pretty high compared to standard error." 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 10, 539 | "metadata": { 540 | "collapsed": false 541 | }, 542 | "outputs": [ 543 | { 544 | "name": "stdout", 545 | "output_type": "stream", 546 | "text": [ 547 | "median 21.5\n" 548 | ] 549 | }, 550 | { 551 | "data": { 552 | "text/plain": [ 553 | "count 30.000000\n", 554 | "mean 20.300000\n", 555 | "std 5.790301\n", 556 | "min 8.000000\n", 557 | "25% 16.000000\n", 558 | "50% 21.500000\n", 559 | "75% 25.000000\n", 560 | "max 30.000000\n", 561 | "dtype: float64" 562 | ] 563 | }, 564 | "execution_count": 10, 565 | "metadata": {}, 566 | "output_type": "execute_result" 567 | } 568 | ], 569 | "source": [ 570 | "samples_sum = pd.Series(samples_sum)\n", 571 | "print \"median \" + str(samples_sum.median())\n", 572 | "samples_sum.describe()" 573 | ] 574 | }, 575 | { 576 | "cell_type": "markdown", 577 | "metadata": {}, 578 | "source": [ 579 | "#### Create a histogram of the sampled card sums you have recorded. Compare its shape to that of the original distribution. How are they different, and can you explain why this is the case?\n", 580 | "\n", 581 | "According to central limit theorem , the sampling distribution always assumes a normal distribution while the first histogram reported actual frequencies of the deck of the card." 582 | ] 583 | }, 584 | { 585 | "cell_type": "code", 586 | "execution_count": 11, 587 | "metadata": { 588 | "collapsed": false 589 | }, 590 | "outputs": [ 591 | { 592 | "data": { 593 | "text/plain": [ 594 | "" 595 | ] 596 | }, 597 | "execution_count": 11, 598 | "metadata": {}, 599 | "output_type": "execute_result" 600 | }, 601 | { 602 | "data": { 603 | "image/png": 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604 | "text/plain": [ 605 | "" 606 | ] 607 | }, 608 | "metadata": {}, 609 | "output_type": "display_data" 610 | } 611 | ], 612 | "source": [ 613 | "samples_sum.hist(bins=10)" 614 | ] 615 | }, 616 | { 617 | "cell_type": "markdown", 618 | "metadata": {}, 619 | "source": [ 620 | "##### Make some estimates about values you will get on future draws. Within what range will you expect approximately 90% of your draw values to fall? What is the approximate probability that you will get a draw value of at least 20? Make sure you justify how you obtained your values." 621 | ] 622 | }, 623 | { 624 | "cell_type": "code", 625 | "execution_count": 12, 626 | "metadata": { 627 | "collapsed": false 628 | }, 629 | "outputs": [ 630 | { 631 | "name": "stdout", 632 | "output_type": "stream", 633 | "text": [ 634 | "29.8250452212\n", 635 | "10.7749547788\n" 636 | ] 637 | } 638 | ], 639 | "source": [ 640 | "#For the top 5% or bottom 5% the corresponding Z score would be\n", 641 | "\n", 642 | "z = 1.645\n", 643 | "\n", 644 | "# Calculating SE from the original dataframe's std with a sample size of 3.\n", 645 | " \n", 646 | "print samples_sum.mean() + z*samples_sum.std()\n", 647 | "print samples_sum.mean() - z*samples_sum.std()\n", 648 | "\n" 649 | ] 650 | }, 651 | { 652 | "cell_type": "markdown", 653 | "metadata": {}, 654 | "source": [ 655 | "10-29 should contain approximately 90% of the sample draw values where we calculate the sum." 656 | ] 657 | }, 658 | { 659 | "cell_type": "code", 660 | "execution_count": 141, 661 | "metadata": { 662 | "collapsed": false 663 | }, 664 | "outputs": [ 665 | { 666 | "data": { 667 | "text/plain": [ 668 | "count 52.000000\n", 669 | "mean 6.538462\n", 670 | "std 3.183669\n", 671 | "min 1.000000\n", 672 | "25% 4.000000\n", 673 | "50% 7.000000\n", 674 | "75% 10.000000\n", 675 | "max 10.000000\n", 676 | "Name: Values, dtype: float64" 677 | ] 678 | }, 679 | "execution_count": 141, 680 | "metadata": {}, 681 | "output_type": "execute_result" 682 | } 683 | ], 684 | "source": [ 685 | "df[\"Values\"].describe()" 686 | ] 687 | }, 688 | { 689 | "cell_type": "code", 690 | "execution_count": 13, 691 | "metadata": { 692 | "collapsed": false 693 | }, 694 | "outputs": [ 695 | { 696 | "data": { 697 | "text/plain": [ 698 | "-0.051810777643623906" 699 | ] 700 | }, 701 | "execution_count": 13, 702 | "metadata": {}, 703 | "output_type": "execute_result" 704 | } 705 | ], 706 | "source": [ 707 | "(20-samples_sum.mean())/samples_sum.std()" 708 | ] 709 | }, 710 | { 711 | "cell_type": "code", 712 | "execution_count": 14, 713 | "metadata": { 714 | "collapsed": true 715 | }, 716 | "outputs": [], 717 | "source": [ 718 | "# From Z chart we get \n", 719 | "\n", 720 | "p = 1- 0.47934 # for Z = -0.35906\n", 721 | "\n", 722 | "# 0.47934 is the probability that the drawn sample will be less than or equal to 20, so we do 1 - 0.47934 to get the probability \n", 723 | "# that the drawn sample will be larger than p." 724 | ] 725 | }, 726 | { 727 | "cell_type": "code", 728 | "execution_count": 15, 729 | "metadata": { 730 | "collapsed": false 731 | }, 732 | "outputs": [ 733 | { 734 | "data": { 735 | "text/plain": [ 736 | "0.52066" 737 | ] 738 | }, 739 | "execution_count": 15, 740 | "metadata": {}, 741 | "output_type": "execute_result" 742 | } 743 | ], 744 | "source": [ 745 | "p" 746 | ] 747 | }, 748 | { 749 | "cell_type": "markdown", 750 | "metadata": {}, 751 | "source": [ 752 | "Given the sample mean is around 20, this seems reasonable." 753 | ] 754 | }, 755 | { 756 | "cell_type": "code", 757 | "execution_count": null, 758 | "metadata": { 759 | "collapsed": true 760 | }, 761 | "outputs": [], 762 | "source": [] 763 | } 764 | ], 765 | "metadata": { 766 | "kernelspec": { 767 | "display_name": "Python 2", 768 | "language": "python", 769 | "name": "python2" 770 | }, 771 | "language_info": { 772 | "codemirror_mode": { 773 | "name": "ipython", 774 | "version": 2 775 | }, 776 | "file_extension": ".py", 777 | "mimetype": "text/x-python", 778 | "name": "python", 779 | "nbconvert_exporter": "python", 780 | "pygments_lexer": "ipython2", 781 | "version": "2.7.11" 782 | } 783 | }, 784 | "nbformat": 4, 785 | "nbformat_minor": 0 786 | } 787 | -------------------------------------------------------------------------------- /Udacity-Descriptive-Statistics-Final-Project/Readme.md: -------------------------------------------------------------------------------- 1 | # Descriptive Statistics Final Project 2 | 3 | Note: This course is currently only available for free, so it's not possibleto submit your work for review. 4 | 5 | # Overview: 6 | 7 | This experiment will require the use of a standard deck of playing cards. This is a deck of fifty-two cards divided into four suits (spades (♠), hearts (♥), diamonds (♦), and clubs (♣)), each suit containing thirteen cards (Ace, numbers 2-10, and face cards Jack, Queen, and King). 8 | 9 | You can use either a physical deck of cards for this experiment or you may use a virtual deck of cards such as that found on random.org (http://www.random.org/playing-cards/). 10 | 11 | For the purposes of this task, assign each card a value: The Ace takes a value of 1, numbered cards take the value printed on the card, and the Jack, Queen, and King each take a value of 10. 12 | 13 | 1. First, create a histogram depicting the relative frequencies of the card values. 14 | 2. Now, we will get samples for a new distribution. To obtain a single sample, shuffle your deck of cards and draw three cards from it. (You will be sampling from the deck without replacement.) Record the cards that you have drawn and the sum of the three cards’ values. Repeat this sampling procedure a total of at least thirty times. 15 | 3. Let’s take a look at the distribution of the card sums. Report descriptive statistics for the samples you have drawn. Include at least two measures of central tendency and two measures of variability. 16 | 4. Create a histogram of the sampled card sums you have recorded. Compare its shape to that of the original distribution. How are they different, and can you explain why this is the case? 17 | 5. Make some estimates about values you will get on future draws. Within what range will you expect approximately 90% of your draw values to fall? What is the approximate probability that you will get a draw value of at least 20? Make sure you justify how you obtained your values. 18 | -------------------------------------------------------------------------------- /Visualizing-Pixar-movie-data/PixarMovies.csv: -------------------------------------------------------------------------------- 1 | Year Released,Movie,Length,RT Score,IMDB Score,Metacritic Score,Opening Weekend,Worldwide Gross,Domestic Gross,Adjusted Domestic Gross,International Gross,Domestic %,International %,Production Budget,Oscars Nominated,Oscars Won 2 | 1995,Toy Story,81,100,8.3,92,29.14,362,191.8,356.21,170.2,52.98%,47.02%,30,3,0 3 | 1998,A Bug's Life,96,92,7.2,77,33.26,363.4,162.8,277.18,200.6,44.80%,55.20%,45,1,0 4 | 1999,Toy Story 2,92,100,7.9,88,57.39,485,245.9,388.43,239.2,50.70%,49.32%,90,1,0 5 | 2001,"Monsters, Inc.",90,96,8.1,78,62.58,528.8,255.9,366.12,272.9,48.39%,51.61%,115,3,1 6 | 2003,Finding Nemo,104,99,8.2,90,70.25,895.6,339.7,457.46,555.9,37.93%,62.07%,94,4,1 7 | 2004,The Incredibles,115,97,8,90,70.47,631.4,261.4,341.28,370,41.40%,58.60%,92,4,2 8 | 2006,Cars,116,74,7.2,73,60.12,462,244.1,302.59,217.9,52.84%,47.16%,70,2,0 9 | 2007,Ratatouille,111,96,8,96,47,623.7,206.4,243.65,417.3,33.09%,66.91%,150,5,1 10 | 2008,WALL-E,97,96,8.4,94,63.1,521.3,223.8,253.11,297.5,42.93%,57.07%,180,6,1 11 | 2009,Up,96,98,8.3,88,68.11,731.3,293,318.9,438.3,40.07%,59.93%,175,5,2 12 | 2010,Toy Story 3,103,99,8.4,92,110.31,1063.2,415,423.88,648.2,39.03%,60.97%,200,5,2 13 | 2011,Cars 2,113,39,6.3,57,109,559.9,191.5,194.43,368.4,34.20%,65.80%,200,0,0 14 | 2012,Brave,100,78,7.2,69,66.3,539,237.3,243.39,301.7,44.03%,55.97%,185,1,1 15 | 2013,Monsters University,107,78,7.4,65,82.43,743.6,268.5,269.59,475.1,36.11%,63.89%,200,0,0 16 | 2015,Inside Out,102,98,8.8,93,90.4,677.1,340.5,340.5,336.6,50.29%,49.71%,175,NA,NA -------------------------------------------------------------------------------- /Visualizing-Pixar-movie-data/To-do.txt: -------------------------------------------------------------------------------- 1 | Explore more variables 2 | --------------------------------------------------------------------------------