chrisaguirre3/automatidata

A/B Testing, Multiple Linear Regression, Machine Learning, EDA, Statistical Testing, Data Cleaning, Feature Engineering

Automatidata

Description

Project working working with the NYC Taxi & Limousine Commission (TLC) to build a model that predicts taxi fares ahead of rides for an app they were building and to build a model that predicts whether or not customers would leave a tip to generate more revenue for taxi drivers.

Problem Statement

How can we predict taxi fares and whether a customer will leave a tip ahead of rides?

Task

Build, evaluate, and choose a predictive model that predicts taxi fares ahead of rides and another model that predicts whether a customer will leave a tip.

The Dataset

• Time Period: 2017
• Rows: 22,699
• Columns: 18

Link to Dataset

Analysis Plan

1. Import necessary libraries/modules
2. Perform initial EDA
3. Conduct data cleaning
   • Nulls
   • Duplicates
   • Outliers
4. Perform full EDA
   • Data wrangling and feature engineering
5. Conduct A/B test to determine whether there is a relationship between payment type and fare amount
6. Build/evaluate Multiple Linear Regression model to predict taxi fares ahead of rides
7. Build/evaluate/choose between Random Forest and XGBoost models to predict whether a customer will leave a tip
8. Provide final recommendations/next steps

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Repository Files

• Initial EDA
• Data Cleaning, Wrangling, Feature Engineering, and EDA
• A/B Test
• Multiple Linear Regression Model
• Feature Engineering and Machine Learning Models