Demand prediction of driver availability using multistep time series analysis

Demand prediction of driver availability using multistep time series analysis

In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

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What will you learn

Converting a Time Series problem to Supervised Learning problem
What is Multi-Step Time Series Forecast?
Understanding of problem statement and use case where this concept of predicting driver hours can be applied.
Data Pre-processing in Time Series
Exploratory data analysis on Time-Series
Feature Engineering in Time Series: Breaking Time Features to [Dayofweek, Weekend]
Concept of Lead-Lag
Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) in Time Series
Rolling Mean Concept
Different strategies to solve Multi-Step Time Series problem explained
How recursive multistep prediction strategy works?
Solving time-series with a Regressor Model
Regressor Models Spot Checking
Driver Online Hours Prediction with Ensemble Models (Random Forest and Xgboost)
Evaluation Metrics: Root Mean Square Error

Project Description

Food delivery supported through advanced applications has emerged as one of the fastest growing developments in the e-commerce space. We all love to order online, one thing that we don't like to experience is variable pricing for delivery charges. Delivery charges highly depend on the availability of riders in your area, demand of orders in your area, and distance covered. Due to driver unavailability, there is a surge in delivery pricing and many customers drop off resulting in loss to the company.

To tackle such issues if we track the number of hours a particular delivery executive is active, we can efficiently allocate certain drivers to a particular area depending on demand.

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Curriculum For This Mini Project

The Business Problem
04m
Overview of the Dataset
04m
Solution Workflow
01m
Data Preprocessing - Driver Data-1
02m
Data Preprocessing - Driver Data-2
03m
Time Series Concepts - Lead Lag
02m
Data Preprocessing - Pings Data
08m
Exploratory Data Analysis - Pings Data
02m
Data Insights
04m
Preparing Training Data
02m
DriverTrainingEDA
03m
Mode Training - Iteration 1
05m
Multi-Step Time Series
10m
Iteration2 LagFeatures
02m
Plot and Lag Features
04m
Results by Lag Feature Iteration 2
02m
Iteration3_rollingmean
03m
Iteration4_lagfeature_rollingmean
02m
Test_Script
05m
Data Pipeline
04m