You will get Predict NYC Taxi Trip Duration using Machine Learning
Project details
I will build a robust Machine Learning model to accurately predict NYC Taxi Trip Durations. Unlike basic scripts that overfit, I use a data-first approach driven by deep Exploratory Data Analysis (EDA) and feature engineering.
My solution leverages Ridge Regression with carefully tuned regularization (Alpha=1) to handle the high correlation between trip distance and duration. This ensures the model remains stable and generalizes well to unseen data, rather than just memorizing the training set.
I don't just hand over code; I provide a complete analysis pipeline. This includes cleaning outliers (like unrealistic trip times), engineering geospatial features from GPS coordinates, and delivering a comprehensive technical report. Whether you need this for fleet management, logistics planning, or academic research, you will receive a high-quality, transparent, and documented solution.
My solution leverages Ridge Regression with carefully tuned regularization (Alpha=1) to handle the high correlation between trip distance and duration. This ensures the model remains stable and generalizes well to unseen data, rather than just memorizing the training set.
I don't just hand over code; I provide a complete analysis pipeline. This includes cleaning outliers (like unrealistic trip times), engineering geospatial features from GPS coordinates, and delivering a comprehensive technical report. Whether you need this for fleet management, logistics planning, or academic research, you will receive a high-quality, transparent, and documented solution.
Machine Learning Tools
Azure Machine Learning, BERT, ChatGPT, Databricks Platform, Databricks MLflow, GitHub Copilot, Google Sheets, GPT-3, Keras, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Sonnet, SQL, TensorFlow, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$60
|
Standard
$250
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 6 | 10 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $150
Additional Revision
+$40
Model Documentation
(+ 3 Days)
+$60Frequently asked questions
About Boules
Machine Learning Engineer | Computer Vision & NLP Specialist
Sohag, Egypt - 1:07 am local time
Whether you need to analyze video data, generate captions for images, or classify complex text patterns, I deliver clean, documented, and high-performance code.
My Core Services:
Computer Vision: Developing models for action recognition, object detection, and image classification (CNNs, ResNet, Transformers).
Natural Language Processing: Building text classification systems (e.g., spam detection, sentiment analysis) and utilizing LSTM/RNN architectures.
Model Optimization: Fine-tuning pre-trained models and creating custom Datasets/DataLoaders in PyTorch.
Featured Projects:
Group Activity Recognition: Built a system to analyze and classify group dynamics in sports (e.g., Volleyball) using spatio-temporal modeling.
Image Captioning: Developed an encoder-decoder model (CNN + Transformer) to automatically generate descriptive captions for images.
Fraud & Spam Detection: Created high-accuracy classification models for detecting anomalies in financial data and text messages.
I am passionate about turning complex data into actionable solutions. If you are looking for a dedicated engineer to bring your AI project to life, let’s connect.
Steps for completing your project
After purchasing the project, send requirements so Boules can start the project.
Delivery time starts when Boules receives requirements from you.
Boules works on your project following the steps below.
Revisions may occur after the delivery date.
Data Cleaning & Exploratory Analysis
I will perform a systematic EDA to understand traffic distributions and strictly filter data outliers (e.g., negative durations) to ensure a clean, reliable baseline for modeling.
Geospatial Feature Engineering
I will transform raw timestamps and coordinates into predictive features, such as calculating Haversine distances and encoding temporal indicators (Rush Hour, Weekend).