Taxi Fare Price Prediction Project
Micci micci
Taxi Fare Prediction Project
Project Overview
Automatidata team to build a multiple linear regression model to predict taxi fares using existing data that was collected by the team.Build a multiple linear regression model.
Objective
The goal is to build a multiple linear regression model and evaluate the model. It includes:
Conduct a complete exploratory data analysis.
Perform any data cleaning and data analysis steps to understand unusual variables (e.g., outliers).
Use descriptive statistics to learn more about the data.
Build and run a regression model.
Model Training
Develop a multiple regression model using different features to train the model with 80% of data and validate the model with 20% .
Result and Impact
Model Evaluation
mean_duration and mean_distance are the best predictor for taxi fares with R^2: 86% indicating how well the model fits the data.
rush_hour and passenger_count are also have affect the fare_amount but not much significant.
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Posted Sep 3, 2025
Build a multiple linear regression model to predict taxi fares using existing data that was collected by the team.Build a multiple linear regression model.
