Predicting Apartment Prices in Mexico City

The focus of this project is to create a wrangle function that takes the name of a CSV file as input and returns a DataFrame, a machine learning model that predicts apartment prices in Mexico City, to show the 10 most influential coefficients for the model Subset the data in the CSV file and return only apartments in Mexico City (“Distrito Federal”) that cost less than $100,000, Remove outliers by trimming the bottom and top 10% of properties in terms of “surface_covered_in_m2”.

Predicting Building Damage in Kavrepalanchok, Nepal

In this project, data was collected from querying a SQL database. Data was explored and cleaned to build a classification model to predict building damage for the district of Kavrepalanchok. • Wrote a wrangle function that create a “severe_damage” column, where all buildings with a damage grade greater than 3 should be encoded as 1. All other buildings should be encoded at 0, drop any columns that could cause issues with leakage or multicollinearity in your model.

Predictive Model to Predict Bankruptcy of a Company in Taiwan

In this project, I have explored data collected by a team of Taiwanese economists studying bankruptcy. The goal is to build a model that can predict whether a company will go bankrupt or not. Data extracted from a JSON file. Load and save files using Python Explore some of the features of the dataset, use visualizations to help us understand those features, and develop a model that solves the problem of imbalanced data by under- and over-sampling.