Devpost for Teams
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Predict the likelihood of a divorce from a questionnaire dataset and to determine key factors that cuase a divorce. In this way, we are able to help struggling coulpes.
We use historic drilling data to build a model that predicts the rate of penetration conditional on controllable drilling parameters and parameters that are known before the drilling process begins.
Understand what makes relationships work and fail through the power of data and machine learning.
Use Texas data to predict and map at-risk tracts for inadequate nutrition.
Using the data Chevron provided, we aim to predict the Rate of Penetration given various features
Analyzing pitch data and team statistics in order to understand the consequences of the Houston Astros and Boston Red Sox alleged cheating scandals.
Classifying severity of fires
Inside the mind of a student: in-depth course evaluation modeling and analytics
Use a random forest classifier to predict most threatening fires based on initial conditions.
Have you ever wanted marriage counseling but it's too expensive? Don't worry, others have already gone through that for you. We used divorce data taken by 170 spouses to measure marriage viability.
Using Perceptron Learning Model to predict divorce based on answers from a questionnaire
We used xgboost to predict ROP
An investigation into the underlying social issues relating to the lack of linguistic diversity in Harris County, and the proposal of solutions to
Data driven drilling!
This project uses data science to most accurately predict if a couple will get divorced or not based on a 54 question survey.
Exploring the data and discovering unique, interesting finds
Determine which US cities are most at risk of being Coronavirus positive based on international flight data
Rice Datathon 2020 Project
Predicting divorce using a weighted sum of data available on the UCI Machine Learning Database
A study on the relationship between gentrification and crime
Combating Forest Fires
Using SVM Algorithms to Optimize Divorce Prediction Scales
A proof-of-concept Markov model that uses machine-learning to enhance predictions for global disease spreading patterns
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