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.

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.

Develop a multiple regression model using different features to train the model with 80% of data and validate the model with 20% .

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.