Background
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 an understanding of cab supply and demand could increase the efficiency of their service and enhance user experience by minimizing waiting time.
Objective of this project is to combine historical usage pattern along with the open data sources like weather data to forecast cab booking demand in a city.
Process Flow
You will be provided with hourly renting data span of two years. Data is randomly divided into train and test set. You 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. You need to append the train_label dataset to train.csv as ‘Total_booking’ column.
Dataset Description
Please find the descriptions of the columns present in the dataset as below:
o datetime - hourly date + timestamp
o season - spring, summer, autumn, winter
o holiday - whether the day is considered a holiday
o workingday - whether the day is neither a weekend nor holiday
o weather - Clear , Cloudy, Light Rain, Heavy temp - temperature in Celsius
o atemp - "feels like" temperature in Celsius
o humidity - relative humidity
o windspeed - wind speed
o Total_booking - number of total booking
Tasks to be done:
1. Import the required libraries and load the training and testing dataset
2. Analyze the dataset and write your observations
a. Check the shape of the training and testing set
b. Print the data types of each column
c. Check the missing values present in the dataset
3. Perform Feature Engineering:
a. Create new columns date, hour, weekDay, month from datetime column
b. Coerce the datatype of season, holiday, workingday, and weather to category
c. Drop the datetime column as we have already extracted useful features from it
4. Perform Outlier Analysis:
a. Plot Box plots across various features like season, hour of the day, working_day, etc
to see if there are any Outlier and note down your inference
b. Remove the outliers present in the dataset
5. Perform Correlation Analysis:
a. Plot a correlation plot between "total booking" and ["temp", "atemp", "humidity",
"windspeed"]
b. Write down your inference in the markdown cell
6. Perform Data Visualization:
a. Visualize distribution of data: total_booking column and plot the probability
distribution plot for the column as well
b. Visualize total_booking vs (Month, Season, Hour, Weekday, Usertype)
c. Use Histograms to plot all the continuous variables present in the data
7. Convert the categorical variables into one hot vector
8. Split your dataset for training and testing
9. Fit various models (Random Forest Regressor, Ada Boost Regressor, Bagging Regressor, SVR, and K-Neighbors Regressor)
10. Display a Factor plot to visualize the RMSE values achieved by different modeling
algorithm
11. Perform Hyper-parameter tuning on the best model using GridSearchCV and print the
best parameters using model.best_params_
12. Perform prediction on the test set and print the mean_squared_log_error
This project can be used as final year project, capstone project, personal portfolio project, resume, proof of concept.
If you need implementation for the above problem or any of its variants, feel free to contact us.
