{
  "total": 209,
  "topics": [
    {
      "id": 25434,
      "title": "1st place solution",
      "comment_count": 24,
      "views": 3112,
      "votes": 80,
      "reused": true
    },
    {
      "id": 475366,
      "title": "failure prediction stage happen at early stage or after the product passes through the whole assembly line",
      "comment_count": 0,
      "views": 0,
      "votes": 0
    },
    {
      "id": 325860,
      "title": "Use for academic research (senior project)",
      "comment_count": 2,
      "views": 0,
      "votes": 3,
      "reused": true
    },
    {
      "id": 111787,
      "title": "2024 update - Process Mining based EDA,  data science toolbox: dbt, duckdb , bupaR",
      "comment_count": 5,
      "views": 0,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23909,
      "title": "Question on how to implement Matthews Correlation Coefficient as xgboost eval metrics",
      "comment_count": 5,
      "views": 1392,
      "votes": 1,
      "reused": true
    },
    {
      "id": 194957,
      "title": " Meaning of the fraction values recorded against each feature column.",
      "comment_count": 0,
      "views": 0,
      "votes": 1
    },
    {
      "id": 24319,
      "title": "Advice on Neural Network Approach.",
      "comment_count": 22,
      "views": 2309,
      "votes": null,
      "reused": true
    },
    {
      "id": 24418,
      "title": "XGboost interaction features",
      "comment_count": 15,
      "views": 2457,
      "votes": 6,
      "reused": true
    },
    {
      "id": 25359,
      "title": "#3 place solution",
      "comment_count": 20,
      "views": 3233,
      "votes": 37,
      "reused": true
    },
    {
      "id": 130207,
      "title": "Reduced size Production Line Performance availability inquiry",
      "comment_count": 1,
      "views": 0,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23295,
      "title": "Spark and R for this data set",
      "comment_count": 22,
      "views": 3343,
      "votes": 5,
      "reused": true
    },
    {
      "id": 96412,
      "title": "Bosch Production Line Performance Dataset in Research Articles",
      "comment_count": 2,
      "views": 0,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23973,
      "title": "Using bosch data for research purposes",
      "comment_count": 3,
      "views": 297,
      "votes": 11,
      "reused": true
    },
    {
      "id": 24065,
      "title": "The Magical Feature : from LB 0.3- to 0.4+",
      "comment_count": 215,
      "views": 26348,
      "votes": 10,
      "reused": true
    },
    {
      "id": 80558,
      "title": "Clarification: Usage of data outside competition",
      "comment_count": 3,
      "views": 0,
      "votes": null,
      "reused": true
    },
    {
      "id": 24138,
      "title": "my failure report",
      "comment_count": 7,
      "views": 1302,
      "votes": 6,
      "reused": true
    },
    {
      "id": 24171,
      "title": "How I’m doing cross-validation",
      "comment_count": 11,
      "views": 1848,
      "votes": 25,
      "reused": true
    },
    {
      "id": 25082,
      "title": "xgboost OMP: Error....Initializing libiomp5.dylib...",
      "comment_count": 3,
      "views": 189,
      "votes": 2,
      "reused": true
    },
    {
      "id": 66029,
      "title": "Master thesis: Survey companies about Kaggle competitions",
      "comment_count": 0,
      "views": 0,
      "votes": 3
    },
    {
      "id": 23319,
      "title": "Manufacturing Floor Visualization",
      "comment_count": 6,
      "views": 1163,
      "votes": 33,
      "reused": true
    },
    {
      "id": 24054,
      "title": "Training data has different column than test data?",
      "comment_count": 8,
      "views": 579,
      "votes": null,
      "reused": true
    },
    {
      "id": 25446,
      "title": "L2 ensembling",
      "comment_count": 5,
      "views": 633,
      "votes": 4,
      "reused": true
    },
    {
      "id": 22940,
      "title": "Any interest to write a paper here",
      "comment_count": 13,
      "views": 2773,
      "votes": 6,
      "reused": true
    },
    {
      "id": 23281,
      "title": "Using the data for academic research purposes",
      "comment_count": 4,
      "views": 606,
      "votes": 5,
      "reused": true
    },
    {
      "id": 25348,
      "title": "Solution Sharing",
      "comment_count": 33,
      "views": 3572,
      "votes": 41,
      "reused": true
    },
    {
      "id": 23301,
      "title": "Discover data with Java Spark",
      "comment_count": 1,
      "views": 554,
      "votes": 6,
      "reused": true
    },
    {
      "id": 25854,
      "title": "R Code",
      "comment_count": 1,
      "views": 540,
      "votes": null,
      "reused": true
    },
    {
      "id": 25382,
      "title": "8th place solution (team LAJMBURO)",
      "comment_count": 4,
      "views": 1193,
      "votes": 38,
      "reused": true
    },
    {
      "id": 25397,
      "title": "Solution using Apache Spark ML?",
      "comment_count": 5,
      "views": 635,
      "votes": 1,
      "reused": true
    },
    {
      "id": 25405,
      "title": "Struggling to find what makes my ML model overfit ",
      "comment_count": 10,
      "views": 588,
      "votes": null,
      "reused": true
    },
    {
      "id": 24200,
      "title": "Is This True Predictive Modeling?",
      "comment_count": 27,
      "views": 2036,
      "votes": 7,
      "reused": true
    },
    {
      "id": 24961,
      "title": "Shake-up???",
      "comment_count": 44,
      "views": 4852,
      "votes": 8,
      "reused": true
    },
    {
      "id": 25406,
      "title": "Imbalanced Classes",
      "comment_count": 2,
      "views": 311,
      "votes": null,
      "reused": true
    },
    {
      "id": 25360,
      "title": "What Hardware Did You Use?",
      "comment_count": 13,
      "views": 1010,
      "votes": 1,
      "reused": true
    },
    {
      "id": 25392,
      "title": "Some useful features",
      "comment_count": 2,
      "views": 434,
      "votes": 4,
      "reused": true
    },
    {
      "id": 25370,
      "title": "4th place solution",
      "comment_count": 3,
      "views": 902,
      "votes": 21,
      "reused": true
    },
    {
      "id": 25212,
      "title": "Request regarding consecutive/duplicate rows",
      "comment_count": 3,
      "views": 766,
      "votes": 19,
      "reused": true
    },
    {
      "id": 25349,
      "title": "Congratulations to winning teams and individuals!",
      "comment_count": 16,
      "views": 1249,
      "votes": 2,
      "reused": true
    },
    {
      "id": 25340,
      "title": "will top players share their idea or code after the match?",
      "comment_count": 1,
      "views": 352,
      "votes": 1,
      "reused": true
    },
    {
      "id": 25120,
      "title": "F# script: LB 0.43 using < 4GB RAM with F# XGBoost",
      "comment_count": 52,
      "views": 4025,
      "votes": 14,
      "reused": true
    },
    {
      "id": 25086,
      "title": "First submission deadline is in a few hours",
      "comment_count": 4,
      "views": 709,
      "votes": 2,
      "reused": true
    },
    {
      "id": 25295,
      "title": "How to bridge the category with dates",
      "comment_count": 2,
      "views": 377,
      "votes": null,
      "reused": true
    },
    {
      "id": 25313,
      "title": "Found this on the internet ...",
      "comment_count": 0,
      "views": 455,
      "votes": 2
    },
    {
      "id": 25243,
      "title": "F# script to create duplicate based features",
      "comment_count": 2,
      "views": 449,
      "votes": 4,
      "reused": true
    },
    {
      "id": 24224,
      "title": "unit of date features?",
      "comment_count": 2,
      "views": 596,
      "votes": 2,
      "reused": true
    },
    {
      "id": 25233,
      "title": "overfitting can be an issue",
      "comment_count": 0,
      "views": 317,
      "votes": 0
    },
    {
      "id": 25137,
      "title": "Are pre-processed selected features valid for Kaggle Rules?",
      "comment_count": 6,
      "views": 756,
      "votes": null,
      "reused": true
    },
    {
      "id": 25123,
      "title": "Submission file not getting stored in csv",
      "comment_count": 6,
      "views": 404,
      "votes": null,
      "reused": true
    },
    {
      "id": 23015,
      "title": "Load train_numeric in R (under 6GB RAM, 30 min -> 10s)",
      "comment_count": 18,
      "views": 3664,
      "votes": 38,
      "reused": true
    },
    {
      "id": 25032,
      "title": "Symposium Winners",
      "comment_count": 6,
      "views": 2689,
      "votes": 16,
      "reused": true
    },
    {
      "id": 25052,
      "title": "Best MCC without row order ",
      "comment_count": 0,
      "views": 411,
      "votes": 0
    },
    {
      "id": 24884,
      "title": "XGBoost Custom Objective Function",
      "comment_count": 7,
      "views": 1370,
      "votes": 2,
      "reused": true
    },
    {
      "id": 24999,
      "title": "Worth Entering Competition With Leak",
      "comment_count": 2,
      "views": 523,
      "votes": null,
      "reused": true
    },
    {
      "id": 23067,
      "title": "Numeric Features: 2-D missing patterns (t-SNE)",
      "comment_count": 8,
      "views": 2215,
      "votes": 28,
      "reused": true
    },
    {
      "id": 24712,
      "title": "Feature Correlation Matrix / PCA Analysis on numerical Data",
      "comment_count": 2,
      "views": 869,
      "votes": 1
    },
    {
      "id": 24864,
      "title": "How to decide the best threshold?",
      "comment_count": 9,
      "views": 960,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24915,
      "title": "Team Member for Last 2 Weeks of Competition",
      "comment_count": 1,
      "views": 426,
      "votes": 2,
      "reused": true
    },
    {
      "id": 24883,
      "title": "Number of rows in submission file",
      "comment_count": 2,
      "views": 241,
      "votes": null,
      "reused": true
    },
    {
      "id": 24833,
      "title": "Can I use the data for academic use?",
      "comment_count": 5,
      "views": 502,
      "votes": null,
      "reused": true
    },
    {
      "id": 24882,
      "title": "Number of rows in submission file",
      "comment_count": 2,
      "views": 213,
      "votes": null,
      "reused": true
    },
    {
      "id": 24810,
      "title": "Over Sampling",
      "comment_count": 2,
      "views": 614,
      "votes": null,
      "reused": true
    },
    {
      "id": 24826,
      "title": "Same timestamp at many date columns, meaning ?",
      "comment_count": 1,
      "views": 487,
      "votes": 4,
      "reused": true
    },
    {
      "id": 23660,
      "title": "IEEE BigData 2016 Symposium Papers",
      "comment_count": 7,
      "views": 1354,
      "votes": null,
      "reused": true
    },
    {
      "id": 24838,
      "title": "strange date feature values",
      "comment_count": 1,
      "views": 409,
      "votes": null,
      "reused": true
    },
    {
      "id": 24809,
      "title": "Sampling for training and validation",
      "comment_count": 0,
      "views": 169,
      "votes": 0
    },
    {
      "id": 24787,
      "title": "Interpretation of date features",
      "comment_count": 1,
      "views": 359,
      "votes": null,
      "reused": true
    },
    {
      "id": 24775,
      "title": "Getting results after competition end",
      "comment_count": 1,
      "views": 306,
      "votes": null,
      "reused": true
    },
    {
      "id": 24784,
      "title": "What's your prediction's failure rate?",
      "comment_count": 1,
      "views": 268,
      "votes": null,
      "reused": true
    },
    {
      "id": 24587,
      "title": "failure report II",
      "comment_count": 2,
      "views": 861,
      "votes": null,
      "reused": true
    },
    {
      "id": 24694,
      "title": "Practical use ",
      "comment_count": 4,
      "views": 640,
      "votes": null,
      "reused": true
    },
    {
      "id": 24659,
      "title": "Team ? ",
      "comment_count": 5,
      "views": 761,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24687,
      "title": "IEEE BigData 2016 conference paper accepted",
      "comment_count": 0,
      "views": 505,
      "votes": 3
    },
    {
      "id": 24644,
      "title": "Failures",
      "comment_count": 0,
      "views": 460,
      "votes": 12
    },
    {
      "id": 24291,
      "title": "Best AUC and MCC without using magic feature(s)",
      "comment_count": 17,
      "views": 3238,
      "votes": null,
      "reused": true
    },
    {
      "id": 24588,
      "title": "SQL OLAP dataset analysis in Moscow, Russia",
      "comment_count": 1,
      "views": 538,
      "votes": -5,
      "reused": true
    },
    {
      "id": 24523,
      "title": "@Faron, Feature Evaluation",
      "comment_count": 0,
      "views": 587,
      "votes": 4
    },
    {
      "id": 24473,
      "title": "What is your best single model can do?",
      "comment_count": 4,
      "views": 1040,
      "votes": null,
      "reused": true
    },
    {
      "id": 24496,
      "title": "Segments of production parts with biclustering?",
      "comment_count": 0,
      "views": 413,
      "votes": 3
    },
    {
      "id": 24408,
      "title": "I am looking for a teammate",
      "comment_count": 1,
      "views": 449,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23363,
      "title": "compute mcc in R",
      "comment_count": 3,
      "views": 1461,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23042,
      "title": "Production Map",
      "comment_count": 20,
      "views": 6366,
      "votes": 110,
      "reused": true
    },
    {
      "id": 24346,
      "title": "Pandas.to_sparse() and converting to xgb.Dmatrix()",
      "comment_count": 1,
      "views": 389,
      "votes": null,
      "reused": true
    },
    {
      "id": 23575,
      "title": "Different days?",
      "comment_count": 9,
      "views": 1805,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24254,
      "title": "How to perform Data Analysis and reveal the gold in data?",
      "comment_count": 4,
      "views": 1372,
      "votes": null,
      "reused": true
    },
    {
      "id": 24325,
      "title": "Looking for team members",
      "comment_count": 5,
      "views": 603,
      "votes": null,
      "reused": true
    },
    {
      "id": 24328,
      "title": "AUC 99.94",
      "comment_count": 2,
      "views": 597,
      "votes": null,
      "reused": true
    },
    {
      "id": 24207,
      "title": "TO ADMIN: Will this contest be reset? ",
      "comment_count": 22,
      "views": 4339,
      "votes": 3,
      "reused": true
    },
    {
      "id": 24081,
      "title": "how can I download the dataset on a remote instance?",
      "comment_count": 18,
      "views": 1202,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24258,
      "title": "Similar Feature Values, Different Response",
      "comment_count": 4,
      "views": 592,
      "votes": 2,
      "reused": true
    },
    {
      "id": 24083,
      "title": "How many 1's in your submission?",
      "comment_count": 21,
      "views": 2671,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23064,
      "title": "Production Lines' Network",
      "comment_count": 6,
      "views": 1645,
      "votes": 12,
      "reused": true
    },
    {
      "id": 24120,
      "title": "Time, Effort and Reward",
      "comment_count": 16,
      "views": 1707,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24161,
      "title": "Question for model experts: xgboost",
      "comment_count": 3,
      "views": 555,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24192,
      "title": "LOL, folks, you are geniuses!",
      "comment_count": 0,
      "views": 541,
      "votes": 1
    },
    {
      "id": 24168,
      "title": "What's the meaning of \"T1\", \"T3\" in train_categorical.csv ?",
      "comment_count": 4,
      "views": 374,
      "votes": null,
      "reused": true
    },
    {
      "id": 23290,
      "title": "Turn categorical into numeric",
      "comment_count": 22,
      "views": 3565,
      "votes": 17,
      "reused": true
    },
    {
      "id": 24140,
      "title": "What is going on with R dada.table::fread and the datafiles ?",
      "comment_count": 6,
      "views": 458,
      "votes": null,
      "reused": true
    },
    {
      "id": 23908,
      "title": "Computer suggestions",
      "comment_count": 12,
      "views": 1666,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24050,
      "title": "best single model?",
      "comment_count": 7,
      "views": 2158,
      "votes": 10,
      "reused": true
    },
    {
      "id": 23137,
      "title": "Useful Linux csv pre-processing commands ",
      "comment_count": 4,
      "views": 1155,
      "votes": 25,
      "reused": true
    },
    {
      "id": 24063,
      "title": "Cross Validation Method",
      "comment_count": 2,
      "views": 487,
      "votes": null,
      "reused": true
    },
    {
      "id": 24056,
      "title": "File is too large to open, how do I get first look at the data?",
      "comment_count": 2,
      "views": 580,
      "votes": null,
      "reused": true
    },
    {
      "id": 24043,
      "title": "Similar to Telstra?",
      "comment_count": 2,
      "views": 779,
      "votes": 6,
      "reused": true
    },
    {
      "id": 24045,
      "title": "numeric features continous?",
      "comment_count": 0,
      "views": 269,
      "votes": 2
    },
    {
      "id": 23940,
      "title": "Is 48GB's of RAM good enough for now?",
      "comment_count": 20,
      "views": 2934,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24022,
      "title": "Train/Test event rate imbalance",
      "comment_count": 4,
      "views": 838,
      "votes": 1,
      "reused": true
    },
    {
      "id": 24024,
      "title": "Nan Structure",
      "comment_count": 0,
      "views": 293,
      "votes": 0
    },
    {
      "id": 24018,
      "title": "Categories and the power of 2",
      "comment_count": 5,
      "views": 645,
      "votes": 4,
      "reused": true
    },
    {
      "id": 23135,
      "title": "Questions about categorical.csv",
      "comment_count": 21,
      "views": 4323,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23969,
      "title": "date columns with no corresponding features",
      "comment_count": 3,
      "views": 701,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23546,
      "title": "Why all responses in train_numeric.csv are '0'?",
      "comment_count": 5,
      "views": 887,
      "votes": null,
      "reused": true
    },
    {
      "id": 23284,
      "title": "How to load data with using python",
      "comment_count": 8,
      "views": 2309,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23971,
      "title": "Rule Clarification",
      "comment_count": 7,
      "views": 1015,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23967,
      "title": "body parts with measurements",
      "comment_count": 1,
      "views": 300,
      "votes": null,
      "reused": true
    },
    {
      "id": 23943,
      "title": "Couple of questions about the data nature/structure.",
      "comment_count": 3,
      "views": 616,
      "votes": null,
      "reused": true
    },
    {
      "id": 23941,
      "title": "NaN meaning",
      "comment_count": 3,
      "views": 616,
      "votes": null,
      "reused": true
    },
    {
      "id": 23889,
      "title": "Top LB score > 0.467, leak or over-fitting ?",
      "comment_count": 34,
      "views": 6146,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23912,
      "title": "Is there a good way to tidy the data, or should we use sparse matrices?",
      "comment_count": 3,
      "views": 739,
      "votes": 2,
      "reused": true
    },
    {
      "id": 22929,
      "title": "Graphics showing directional relationships of each station",
      "comment_count": 14,
      "views": 4390,
      "votes": 6,
      "reused": true
    },
    {
      "id": 22987,
      "title": "Are feature numbers specific to the production line & station?",
      "comment_count": 4,
      "views": 1138,
      "votes": null,
      "reused": true
    },
    {
      "id": 23730,
      "title": "Dataset Size",
      "comment_count": 2,
      "views": 1026,
      "votes": null,
      "reused": true
    },
    {
      "id": 22917,
      "title": "Optimising probabilities -> binary prediction (script)",
      "comment_count": 8,
      "views": 3583,
      "votes": 29,
      "reused": true
    },
    {
      "id": 23870,
      "title": "CV vs Leaderboard scores",
      "comment_count": 9,
      "views": 1452,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23719,
      "title": "Figuring out which AWS instance will work the best",
      "comment_count": 4,
      "views": 811,
      "votes": null,
      "reused": true
    },
    {
      "id": 23826,
      "title": "Product vs Line vs Station ...argh!",
      "comment_count": 4,
      "views": 939,
      "votes": 5,
      "reused": true
    },
    {
      "id": 23867,
      "title": "Tunning For MCC",
      "comment_count": 4,
      "views": 458,
      "votes": null,
      "reused": true
    },
    {
      "id": 23362,
      "title": "Using a SQL database",
      "comment_count": 12,
      "views": 1728,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23850,
      "title": "Feature importance--various methods (vw vs Random Forest, XGB)",
      "comment_count": 10,
      "views": 1252,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23761,
      "title": "Categorical CV",
      "comment_count": 12,
      "views": 1823,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23830,
      "title": "Vowpal Wabbit",
      "comment_count": 5,
      "views": 794,
      "votes": null,
      "reused": true
    },
    {
      "id": 23728,
      "title": "Questions about the Rules",
      "comment_count": 1,
      "views": 563,
      "votes": null,
      "reused": true
    },
    {
      "id": 23529,
      "title": "Initial Exploration and using missing values",
      "comment_count": 3,
      "views": 1345,
      "votes": 17
    },
    {
      "id": 23692,
      "title": "31 Features missing but measured in Date set",
      "comment_count": 3,
      "views": 764,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23645,
      "title": "Numeric features normalized?",
      "comment_count": 4,
      "views": 696,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23679,
      "title": "Dates for absent features",
      "comment_count": 1,
      "views": 391,
      "votes": 4,
      "reused": true
    },
    {
      "id": 23674,
      "title": "Product movement distribution in each level",
      "comment_count": 0,
      "views": 240,
      "votes": 0
    },
    {
      "id": 23673,
      "title": "HDF Files for download",
      "comment_count": 0,
      "views": 272,
      "votes": 0
    },
    {
      "id": 23066,
      "title": "understanding what is open source...",
      "comment_count": 2,
      "views": 803,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23590,
      "title": "Classify parts: a single assembly or multiple assemblies?",
      "comment_count": 3,
      "views": 852,
      "votes": null,
      "reused": true
    },
    {
      "id": 23626,
      "title": "Using this data for further research purposes",
      "comment_count": 0,
      "views": 182,
      "votes": 0
    },
    {
      "id": 23587,
      "title": "Looking for team",
      "comment_count": 1,
      "views": 272,
      "votes": null,
      "reused": true
    },
    {
      "id": 23603,
      "title": "kernel challenge",
      "comment_count": 2,
      "views": 506,
      "votes": 8,
      "reused": true
    },
    {
      "id": 23201,
      "title": "Understanding of train_date",
      "comment_count": 3,
      "views": 1331,
      "votes": null,
      "reused": true
    },
    {
      "id": 23191,
      "title": "Station Utilization Question",
      "comment_count": 5,
      "views": 917,
      "votes": null,
      "reused": true
    },
    {
      "id": 23285,
      "title": "Bosch Rexroth Multi Product Line",
      "comment_count": 3,
      "views": 847,
      "votes": 8,
      "reused": true
    },
    {
      "id": 23571,
      "title": "Error in prediction: New factor levels not present in the training data",
      "comment_count": 7,
      "views": 1190,
      "votes": null,
      "reused": true
    },
    {
      "id": 23405,
      "title": "Missing values distribution in Numeric train data",
      "comment_count": 6,
      "views": 1037,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23553,
      "title": "Sequential duplicates in test files?",
      "comment_count": 2,
      "views": 930,
      "votes": 6,
      "reused": true
    },
    {
      "id": 23532,
      "title": "Question on sparse formats",
      "comment_count": 3,
      "views": 938,
      "votes": null,
      "reused": true
    },
    {
      "id": 23322,
      "title": "AUC vs Matthews correlation coefficient",
      "comment_count": 12,
      "views": 2436,
      "votes": null,
      "reused": true
    },
    {
      "id": 23523,
      "title": "Are the stations related to each other ? Is it know how?",
      "comment_count": 0,
      "views": 272,
      "votes": 0
    },
    {
      "id": 23517,
      "title": "How to Factorize the categorical data",
      "comment_count": 0,
      "views": 448,
      "votes": 0
    },
    {
      "id": 22931,
      "title": "kernel",
      "comment_count": 12,
      "views": 2747,
      "votes": 5,
      "reused": true
    },
    {
      "id": 23493,
      "title": "Train vs Test data",
      "comment_count": 4,
      "views": 630,
      "votes": null,
      "reused": true
    },
    {
      "id": 23287,
      "title": "Reading by chunks in Python",
      "comment_count": 11,
      "views": 1575,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23394,
      "title": "Looking for a teammate to improve my 0.19+ score",
      "comment_count": 7,
      "views": 893,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23487,
      "title": "Submission Problem",
      "comment_count": 4,
      "views": 335,
      "votes": null,
      "reused": true
    },
    {
      "id": 23438,
      "title": "train_categorical.csv is all NAN ?",
      "comment_count": 2,
      "views": 523,
      "votes": null,
      "reused": true
    },
    {
      "id": 23435,
      "title": "MCC for xgboost",
      "comment_count": 12,
      "views": 1589,
      "votes": 8,
      "reused": true
    },
    {
      "id": 23410,
      "title": "My EDA on 25% training data",
      "comment_count": 4,
      "views": 1222,
      "votes": 5,
      "reused": true
    },
    {
      "id": 23448,
      "title": "Missing Map on Numeric Data",
      "comment_count": 0,
      "views": 332,
      "votes": 1
    },
    {
      "id": 23447,
      "title": "Time for quality control for each ID",
      "comment_count": 0,
      "views": 327,
      "votes": 0
    },
    {
      "id": 23228,
      "title": "Team up  with  somewone with experience in big  data",
      "comment_count": 4,
      "views": 853,
      "votes": null,
      "reused": true
    },
    {
      "id": 23375,
      "title": "Python : Scikit Random Forest on Decision trees",
      "comment_count": 2,
      "views": 807,
      "votes": null,
      "reused": true
    },
    {
      "id": 23090,
      "title": " Underlying Product groups",
      "comment_count": 2,
      "views": 792,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23312,
      "title": "timestamp for when each measurement was taken",
      "comment_count": 5,
      "views": 1071,
      "votes": null,
      "reused": true
    },
    {
      "id": 23420,
      "title": "A few commands on the cli to merge the files",
      "comment_count": 0,
      "views": 223,
      "votes": 5
    },
    {
      "id": 23398,
      "title": "Holdout vs. CV",
      "comment_count": 3,
      "views": 639,
      "votes": 1,
      "reused": true
    },
    {
      "id": 22909,
      "title": "Expeditive exploration+models on data",
      "comment_count": 25,
      "views": 6975,
      "votes": 72,
      "reused": true
    },
    {
      "id": 23354,
      "title": "So, is there a leak?",
      "comment_count": 15,
      "views": 2702,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23385,
      "title": "My approach towards chunking it up",
      "comment_count": 0,
      "views": 394,
      "votes": 0
    },
    {
      "id": 23291,
      "title": "Python: Using Pandas to Concatenate numerical and categorical data",
      "comment_count": 3,
      "views": 851,
      "votes": null,
      "reused": true
    },
    {
      "id": 23279,
      "title": "part type",
      "comment_count": 3,
      "views": 515,
      "votes": null,
      "reused": true
    },
    {
      "id": 23227,
      "title": "Vowpal wabbit format",
      "comment_count": 5,
      "views": 809,
      "votes": null,
      "reused": true
    },
    {
      "id": 22986,
      "title": "Any recommendation on using Hadoop or spark clusters to do the analysis? ",
      "comment_count": 10,
      "views": 2715,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23146,
      "title": "XGBoost simple starter AUC 0.712",
      "comment_count": 10,
      "views": 3110,
      "votes": 21,
      "reused": true
    },
    {
      "id": 23321,
      "title": "Why Decision Tree beats Random Forest ??",
      "comment_count": 7,
      "views": 1338,
      "votes": 2,
      "reused": true
    },
    {
      "id": 23337,
      "title": "Some features missing?(Date entry[Dn]present, feature[F(n-1)] missing)",
      "comment_count": 1,
      "views": 354,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23323,
      "title": "Getting started with R",
      "comment_count": 1,
      "views": 430,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23242,
      "title": "The dataset is so big that...",
      "comment_count": 3,
      "views": 1343,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23238,
      "title": "Not enough memory in R",
      "comment_count": 4,
      "views": 868,
      "votes": null,
      "reused": true
    },
    {
      "id": 23164,
      "title": "Teaming up!",
      "comment_count": 18,
      "views": 1124,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23260,
      "title": "Where  are public scripts?",
      "comment_count": 0,
      "views": 318,
      "votes": 2
    },
    {
      "id": 23150,
      "title": "Looking for teammates ",
      "comment_count": 17,
      "views": 920,
      "votes": null,
      "reused": true
    },
    {
      "id": 23258,
      "title": "reading test_categorical.csv ",
      "comment_count": 1,
      "views": 433,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23166,
      "title": "Wired-looking PCA results. 3 clusters?",
      "comment_count": 10,
      "views": 1716,
      "votes": null,
      "reused": true
    },
    {
      "id": 23249,
      "title": "Dealing with NA",
      "comment_count": 2,
      "views": 487,
      "votes": null,
      "reused": true
    },
    {
      "id": 23138,
      "title": "Chunked processing R",
      "comment_count": 4,
      "views": 809,
      "votes": null,
      "reused": true
    },
    {
      "id": 23216,
      "title": "Robot Overlords - Looking for teamates",
      "comment_count": 2,
      "views": 291,
      "votes": null,
      "reused": true
    },
    {
      "id": 23222,
      "title": "How big is dataset and nulls..",
      "comment_count": 0,
      "views": 442,
      "votes": 0
    },
    {
      "id": 23109,
      "title": "Looking for Teammates",
      "comment_count": 54,
      "views": 2595,
      "votes": 1,
      "reused": true
    },
    {
      "id": 23037,
      "title": " L0_S0_D1 <===> L0_S0_F0",
      "comment_count": 10,
      "views": 2260,
      "votes": 10,
      "reused": true
    },
    {
      "id": 23128,
      "title": "Using split",
      "comment_count": 3,
      "views": 998,
      "votes": 5,
      "reused": true
    },
    {
      "id": 23070,
      "title": "Handling Dataset This Big with Limited Memory",
      "comment_count": 15,
      "views": 3174,
      "votes": 6,
      "reused": true
    },
    {
      "id": 23116,
      "title": "Building a team",
      "comment_count": 0,
      "views": 299,
      "votes": 0
    },
    {
      "id": 23098,
      "title": "Any advice on how to handle badly formatted data?",
      "comment_count": 4,
      "views": 790,
      "votes": null,
      "reused": true
    },
    {
      "id": 23095,
      "title": "Using Python to store data",
      "comment_count": 1,
      "views": 1007,
      "votes": 3,
      "reused": true
    },
    {
      "id": 22908,
      "title": "Datasets' size uncompressed (14.3GB)",
      "comment_count": 21,
      "views": 6382,
      "votes": 12,
      "reused": true
    },
    {
      "id": 23044,
      "title": "Some basic statistics on features",
      "comment_count": 2,
      "views": 1360,
      "votes": 11,
      "reused": true
    },
    {
      "id": 23003,
      "title": "\"internal failures\"",
      "comment_count": 1,
      "views": 794,
      "votes": 10,
      "reused": true
    },
    {
      "id": 23058,
      "title": "This dataset have too many columns for HDF5. What else to use?",
      "comment_count": 5,
      "views": 1382,
      "votes": 3,
      "reused": true
    },
    {
      "id": 23057,
      "title": "Aggregating station patterns from numerical file",
      "comment_count": 4,
      "views": 854,
      "votes": 7,
      "reused": true
    },
    {
      "id": 23056,
      "title": "Shrinking the *date files",
      "comment_count": 0,
      "views": 718,
      "votes": 6
    },
    {
      "id": 23046,
      "title": "Submission file",
      "comment_count": 8,
      "views": 786,
      "votes": null,
      "reused": true
    },
    {
      "id": 23029,
      "title": "Question about loading data in Python",
      "comment_count": 2,
      "views": 1140,
      "votes": null,
      "reused": true
    },
    {
      "id": 23022,
      "title": "Out of the doldrums...",
      "comment_count": 2,
      "views": 908,
      "votes": 3,
      "reused": true
    },
    {
      "id": 22974,
      "title": "constant features?",
      "comment_count": 6,
      "views": 1310,
      "votes": null,
      "reused": true
    },
    {
      "id": 22981,
      "title": "Want to Run you code rapidly?",
      "comment_count": 8,
      "views": 2446,
      "votes": -13
    },
    {
      "id": 22910,
      "title": "Beat the benchmark",
      "comment_count": 3,
      "views": 2453,
      "votes": 11,
      "reused": true
    }
  ],
  "errors": []
}