Taxi demand prediction in New York City - Scaler Topics

--- title: Taxi demand prediction in New York City - Scaler Topics description: Learn about a classic problem aimed at Taxi demand prediction in New York City case study using aritificial intelligence on scaler Topics. category: Miscellaneous author: Himanshu Gupta --- :::section{.abstract} ## About this Taxi demand prediction in New York City Course Embark on a data-driven journey through the bustling streets of New York City with our Taxi Demand Prediction course. Explore the intricacies of forecasting taxi demand using cutting-edge machine learning techniques. From understanding spatiotemporal patterns to predicting peak hours, this course equips learners with the tools and expertise needed to navigate the dynamic landscape of urban transportation. Whether you're a novice or seasoned professional, join us in unraveling the secrets of taxi demand prediction and making a tangible impact on urban mobility. ### Key Features of this Taxi demand prediction in New York City Course Embark on a transformative journey into the realm of Taxi demand prediction in New York City equipped with the knowledge and skills. By enrolling, you will: 1. Gain a comprehensive understanding of taxi demand prediction in New York City's dynamic environment. 2. Define clear objectives and constraints to guide model development. 3. Learn to map real-world data to ML problems using Dask dataframes. 4. Explore time series forecasting and regression techniques tailored to taxi demand prediction. 5. Evaluate model performance using appropriate metrics. 6. Master data cleaning techniques for handling various data types and outliers. ### Pre-requisites for Taxi demand prediction in New York City Course Prior to embarking on this course, familiarity with the following concepts is recommended: 1. Basic understanding of machine learning concepts and algorithms. 1. Proficiency in Python programming language for data manipulation and model implementation. 1. Familiarity with data preprocessing techniques, including cleaning and feature engineering. 1. Understanding of regression analysis and time series forecasting. 1. Prior experience with data analysis and visualization using libraries like pandas and matplotlib. ### Who should learn this Taxi demand prediction in New York City Course? This course is ideal for those who are: 1. Interested in exploring the intersection of data science and transportation analytics. 2. Aspiring data scientists seeking practical experience in predictive modeling. 3. Urban planners or transportation professionals looking to leverage data-driven approaches for demand forecasting. 4. Students or enthusiasts eager to understand the application of machine learning in real-world scenarios. 5. Anyone curious about the dynamics of taxi demand and its implications for urban mobility and infrastructure planning. ::: :::section{.main} ## FAQs Q: **Do I need prior experience in data science to enroll in this course?** No prior experience is required, but basic knowledge of machine learning concepts and Python programming is recommended. Q: **What software or tools do I need for the course?** You will need Python installed on your computer along with libraries like pandas, numpy, and scikit-learn for data analysis and modeling. Q: **Will I receive a certificate upon completing the course?** Yes, upon successful completion, you will receive a certificate of completion, validating your skills in taxi demand prediction. Q: **Are there any prerequisites for enrolling in this course?** A:Basic knowledge of machine learning concepts and Python programming is recommended, but not mandatory. Q: **Can I access the course materials at any time?** A:Yes, you will have access to the course materials 24/7, allowing you to learn at your own pace. :::