Скачать или смотреть Predict Taxi Fares with Neural Networks in Google Colab (2025)

Build a real-world taxi fare prediction model in Python using Google Colab—end to end. We start with messy data and show every step of data cleaning, feature engineering, and a simple neural network that predicts taxi prices fast and reliably.

What you’ll learn

Importing and sanity-checking raw ride data

Cleaning essentials: missing values, duplicates, outliers (lat/lon, distance, fare)

Feature engineering: pickup/dropoff distance, time-of-day/day-of-week, basic geo features

Train/validation split, scaling/normalization, and baseline vs. NN

Building a dense neural network (Keras) and tuning key hyperparameters

Evaluating with MAE/RMSE and interpreting results

Saving the model and tips for improving accuracy

What’s in the Colab notebook

Clean, commented code you can run line-by-line

Helper functions for distance (Haversine) and quick EDA plots

Reproducible pipeline from raw CSV → predictions

#MachineLearning #GoogleColab #Python #NeuralNetworks #DataScience #TaxiFarePrediction