1. Overview In this project, we will focus on New York City’s Ridesharing Trips Data Set and try to answer three interesting questions: (1) How do the residents of New York City use Ridesharing? (2) What usage patterns can we see? (3) Can we predict usage? To answer these questions, first we will clean up the trip data and integrate them with weather data and taxi zone geodata. Then we will conduct Exploratory Data Analysis (EDA) and Statistical Tests to find Temporal and Spatial Patterns, as well as weather effects and tipping behaviors.

1. Overview In this project, we will build a series of models to predict the probability of Customer Churn for a Mobile Phone Carrier. In the first part, we will conduct Exploratory Data Analysis (EDA). Here the Synthetic Minority Oversampling Technique (SMOTE) method is employed to address the imbalanced classification problem. Then we will develop customer churn prediction models based on (1) Logistic Regression, (2) Decision Tree, (3) Random Forest, (4) AdaBoost, (5) Gradient Boosting Decision Trees (GBDT), and (6) Neural Network.

1. Overview In this project, we will build a linear regression model to help people better understand the factors that can affect the price of a house in Beijing. The main sections of this article are: (1) Exploratory Data Analysis (EDA), (2) Developing Linear Regression model for predicting real estate price, and (3) Use that model to make predictions. The Python Notebook containing the complete model development process and the data used in this project can be found at Google Drive.