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Hackerearth_tredence-data-scientist-hiring-challenge

Competition hosted on Hackerearth

About

Machine Learning model to predict the weekly dispatch count of the warehouse based on the different warehouses' product-wise daily dispatch data.

Final Score 82.4

Evaluation Metric is Mean Absolute Percentage Error.

File information

  • tredence-data-scientist-hiring-challenge_EDA.ipynb

    Packages Used,

     * seaborn
 * Pandas
 * klib
 * Numpy
 * Matplotlib
 * re

    Basic Exploratory Data Analysis

  • tredence-data-scientist-hiring-challenge_Model.ipynb

    Packages Used,

      * Sklearn
  * re
  * Pandas
  * Numpy
  * Matplotlib
  * catboost

    Data Pre-processing

    For the test data daily dispatch count is to be predicted. So first build the catboost regressor model to predict the daily dispatch count and then by using the daily dispatch count build the final catboost regressor model for weekly dispatch count prediction.

Daily Dispatch Model Feature Importance

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Weely Dispatch Model Feature Importance

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Daily Dispatch Count Prediction Plot

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Weekly Dispatch Count Prediction Plot

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About

Machine Learning model to predict the weekly dispatch count of the warehouse.

Topics

machine-learning sklearn exploratory-data-analysis pandas seaborn matplotlib klib hackerearth catboostregressor

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Readme

License

Apache-2.0 license
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