Prediction_of_cab_booking

Basic details about problem :

Cab booking system is the process where renting a cab is automated through an app throughout a city. Using this app,people can book a cab from one location to another location. Being a cab booking app company, exploiting theunderstanding ofcab supply and demand could increase the efficiency of their service and enhance user experience by minimizing waiting time.

Objective of this work is to combine historical usage pattern along with the open data sources like weather data to forecast cab booking demand in a city.

Data :

Hourly renting data span of two years is provided. Data is randomly divided into train and test set. We must predict the total count of cabs booked in each hour covered by the test set, using the information available prior to the booking period.

Here train labels i.e. ‘Total_booking’ are provided in seperarte csv file & we must append to train dataset before building the model.

Descriptions of the columns present in the datasets as below.

• datetime-hourly date +timestamp

• season-spring, summer, autumn, winter

• holiday-whether the day is considered a holiday

• workingday-whether the day is neither a weekend nor holiday

• weather-Clear , Cloudy, Light Rain, Heavy temp-temperature in Celsius

• atemp-"feels like" temperature in Celsius

• humidity-relative humidity

• windspeed-wind speed

Total_booking-number of total booking

Model building:

Following tasks are performed for building the efficient model & executing the project-

Task1:

1.Visualize data using different visualizations to generate interesting insights.

2.Outlier Analysis

3.Missing value analysis

4.Visualizing Total_booking Vs other features to generate insights

5.Correlation Analysis

Task2:

1.Feature Engineering

2.Grid search

3.Regression Analysis

4.Ensemble Model

Finally one best model has been choosen for predicting the total cab bookings of test data.

Dependencies:

Python 3+, jupyter notebbook, Pandas, Numpy, EDA, Sklearn, Supervised learning, Linear regression, Ensemble learning, Decision tree, Random forest, SVR, Bagging, Adaboost, Gradientboost

Purpose:

The purpose of this project is to gain insights in following topics

Pratical implementation of -

• EDA analysis of data
• Outlier detection & removal
• finding missing values & replacing with suitable values
• plotting of various features on graphs for better intution using seaborn & matplotlib
• correlation analysis using heatmap
• feature engineering
• ML alogarithms & Ensemble learning

Conclusion:

• This work can be directly used for predicting the cab booking system for any practical applications

• Above procedure will be same for any ML model building & it can be taken as reference for other works also