Time-Series---Project-13

Sweet Lift Taxi: Taxi Order Prediction

Description

The Sweet Lift Taxi company aims to predict the number of taxi orders for the next hour at airports to attract more drivers during peak hours. This project involves building a predictive model to forecast the number of orders using historical data. The model's performance is evaluated using the RMSE metric, with a goal to achieve an RMSE of no more than 48 on the test set.

Project Structure

• notebook.ipynb: Contains the entire workflow, including data preprocessing, exploratory data analysis (EDA), model training, and evaluation.
• /datasets/taxi.csv: The dataset used in this project, containing historical taxi orders with the target column num_orders.
• /output/: Stores model predictions, evaluation metrics, and visualizations.

Objectives

The primary objectives of the project are:

1. Data Resampling:

   • Resample the taxi order data by one hour to prepare it for modeling.

2. Exploratory Data Analysis (EDA):

   • Analyze the dataset to understand trends and patterns.

3. Model Training:

   • Train different machine learning models with various hyperparameters to predict the number of taxi orders.
   • Use 90% of the dataset for training and 10% for testing.

4. Model Evaluation:

   • Evaluate the models using the RMSE metric.
   • Ensure that the final model achieves an RMSE of no more than 48 on the test set.

Data Description

The dataset is stored in the /datasets/taxi.csv file and contains historical data on taxi orders at airports. The target variable for the prediction task is the num_orders column, which represents the number of taxi orders in a given time interval.

Findings

• After resampling the data by one hour and analyzing it, significant trends were identified that helped in feature engineering.
• The final model achieved an RMSE of XX (to be updated), meeting the goal of an RMSE less than 48.
• The model predictions can be used to inform the company of peak hours, allowing them to attract more drivers during high-demand periods.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/sweet-lift-taxi-order-prediction.git

2. Install the required dependencies pip install -r requirements.txt
3. Launch Jupyter Notebook jupyter notebook