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Published 20 hours ago • verified publisher icon sksoumik
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Forecasting weather Using Multinomial Logistic Regression, Decision Tree, Naïve Bayes Multinomial, and Support Vector Machine

Forecasting Weather Using Machine Learning

Forecasting Weather Using Multinomial Logistic Regression, Decision Tree, Naïve Bayes Multinomial, and Support Vector Machine

Data set

Our dataset looks like below which we collected from Bangladesh Meteorological Department alt text

We had last 30 years [1988-2017] of weather data.The training and test set is divided into two segments having 70% and 30% data split across the two categories.

Parameters:

  1. Day
  2. Month
  3. Year
  4. Humidity(%)
  5. Max Temp(in ⁰C)
  6. Min Temp(in ⁰C)
  7. Rainfall (in mm)
  8. Sea Level Pressure (in mb)
  9. Sunshine (hours)
  10. Wind Speed(knot)
  11. Cloud (in okta)

Train and Test accuracy of the models tested:

Model Training Accuracy (%) Testing Accuracy (%)
Logistic Regression 74.2 76.9
Decision Tree 76.8 74.05
Multinomial NB 54.27 54.34
SVM 76.42 77.52

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Forecasting weather Using Multinomial Logistic Regression, Decision Tree, Naïve Bayes Multinomial, and Support Vector Machine

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