Demand-Prediction

Overview

This repository contains code and resources for predicting the demand of Yellow Taxi Trip Records. The goal of this project is to create a predictive model that can forecast the demand for yellow taxi trips in a given area and time frame. The model can be used by taxi companies, transportation authorities, and other stakeholders to optimize their fleet management and provide better service to customers.

Table of Contents

1. Introduction
2. Data
   • Labeling (Data Cleaning and Label Data)
   • Feature Selection
3. Methodology
   • Model Training
   • Model Evaluation
4. Results

Introduction

Demand prediction for yellow taxi trips is a crucial aspect of the transportation industry. Accurate forecasts enable taxi companies to anticipate surges in demand, allocate resources efficiently, and reduce customer waiting times. This project aims to build a robust predictive model using machine learning techniques to forecast the demand for yellow taxi trips in specific geographic areas and time intervals.

Data

The dataset used for this project consists of historical yellow taxi trip records. The data includes features such as pickup/drop-off locations, timestamps, and other relevant attributes.

Labeling (Data Cleaning and Label Data)

Before training the demand prediction model, the raw data undergoes a rigorous data cleaning process. This involves handling missing values, removing outliers, and addressing any data inconsistencies. Additionally, the data is labeled to indicate the demand (e.g., the number of taxi trips) in each time interval and geographic region.

Feature Selection

To build an effective prediction model, relevant features are selected from the preprocessed dataset. Feature selection involves identifying attributes that significantly contribute to the demand prediction task. In addition to the original dataset, we introduce two crucial features: "Number of Demand Last Day" and "Number of Demand Last Week."

Methodology

The demand prediction model is based on a combination of traditional time series forecasting techniques and machine learning algorithms. We preprocess the data, engineer relevant features, and split it into training and testing sets. The primary steps of the methodology are as follows:

Model Training

In this step, we utilize the preprocessed data to train the demand prediction model. Depending on the dataset characteristics, we may employ various machine learning algorithms such as regression, support vector machines, or deep learning models to capture complex relationships in the data.

Model Evaluation

For evaluating the performance of our demand prediction model, we utilize the Mean Absolute Percentage Error (MAPE) metric. MAPE is a widely-used evaluation metric in time series forecasting tasks, especially when dealing with demand prediction.

Mean Absolute Percentage Error (MAPE)

MAPE measures the accuracy of our predictions by calculating the absolute percentage difference between the predicted values and the actual demand. It is defined as follows:

$$MAPE = (1/n) * ∑(|(Actual - Predicted)| / Actual) * 100$$

Where:

• n is the number of data points in the test set.
• Actual represents the actual demand for yellow taxi trips.
• Predicted represents the demand values predicted by our model.

A lower MAPE value indicates better accuracy, with 0% being a perfect prediction and higher values indicating larger prediction errors.

During the model evaluation phase, we calculate the MAPE for our trained model on the test dataset. Additionally, we may provide visualizations that illustrate the performance of the model, comparing predicted demand against the actual demand over time.

The MAPE metric allows us to understand how well our demand prediction model performs and provides insights into any potential areas for improvement. We aim to achieve the lowest possible MAPE value to ensure our model accurately forecasts yellow taxi demand and contributes to efficient fleet management and service optimization.

Please note that as the project progresses, we may explore other complementary metrics to gain a comprehensive understanding of the model's performance, and we will provide a detailed analysis of the evaluation results in the project documentation.

Results

We will provide a summary of the model's performance and evaluation metrics based on the test dataset. Additionally, visualizations of the predictions against the actual demand will be included. Users can assess the model's accuracy and potential for practical implementation based on these results.