https://github.com/hariprasath-v/hackerearth_tredence-data-scientist-hiring-challenge

Machine Learning model to predict the weekly dispatch count of the warehouse.
https://github.com/hariprasath-v/hackerearth_tredence-data-scientist-hiring-challenge

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

Last synced: 8 months ago
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Machine Learning model to predict the weekly dispatch count of the warehouse.

• Host: GitHub
• URL: https://github.com/hariprasath-v/hackerearth_tredence-data-scientist-hiring-challenge
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2022-05-06T05:10:22.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-05-06T05:59:07.000Z (over 3 years ago)
• Last Synced: 2025-01-13T01:45:01.734Z (9 months ago)
• Topics: catboostregressor, exploratory-data-analysis, hackerearth, klib, machine-learning, matplotlib, pandas, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.89 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 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  

![Alt text](https://github.com/hariprasath-v/Hackerearth_tredence-data-scientist-hiring-challenge/blob/main/Daily_Dispatch_Model_Feature_Importance_Plot.png)

### Weely Dispatch Model Feature Importance  

![Alt text](https://github.com/hariprasath-v/Hackerearth_tredence-data-scientist-hiring-challenge/blob/main/Weekly_Dispatch_Model_Feature_Importance_Plot.png)

### Daily Dispatch Count Prediction Plot

![Alt text](https://github.com/hariprasath-v/Hackerearth_tredence-data-scientist-hiring-challenge/blob/main/Daily_Dispatch_Count_Prediction_Plot.png)

### Weekly Dispatch Count Prediction Plot

![Alt text](https://github.com/hariprasath-v/Hackerearth_tredence-data-scientist-hiring-challenge/blob/main/Weekly_Dispatch_Count_Prediction_Plot.png)