nupurgopali / traffic-prediction-using-svr-and-rfr

We have used Support Vector Regression and Random Forest Regression to predict traffic or congestion.

Traffic-Prediction-using-SVR-and-RFR

Our main aim was to predict the traffic using SVR and RFR model, then compare their error and accuracy rate.

We build our model in python using a real time dataset.

ABOUT THE DATA SET

Date:

The Date Column consists of the date on which the data were recorded in the format DD/MM/YYYY

Day:

The Day Column consists of the day of the week on which the data was recorded. This is done to facilitate the use of the dataset for further usability to predict the possibility of traffic based on what day of the week it is.

Coded Day:

The coded day gives code numbers to each day of the week. This makes the prediction of traffic based on the day much easier since we are not bound to implement string functions for converting the given days to codes. The day codes are as follows: -

Monday - 1 Tuesday - 2 Wednesday - 3 Thursday - 4 Friday - 5 Saturday - 6 Sunday - 7

Zone:

This Column is for the zone number from which the traffic data is recorded. Weather: The weather in this column is coded. This is based on the various standard weather conditions. The traffic varies based on the weather of the given zone. This includes aspects like humidity, mist, visibility, precipitation, etc.

Temperature:

This column has the recorded temperature for the given zone on the particular day. Temperature plays a major role in traffic prediction.

Traffic:

This is the column used as the training dataset, and is also used for prediction. The traffic in this column is coded in 5 levels. The levels are as follows: -

1 - Less than 5 cars. 2 - 5 to 15 cars. 3 - 15 to 30 cars. 4 - 30 to 50 cars. 5 - More than 50 cars.