Random Good Data Science Stuff

House price prediction using Linear Regression models (scikit learn and statsmodel)

Time Series Analysis

A data analytics project that utilizes PANDAS, Numpy, Matplotlib and statsmodel to analyze the results of hypothesis testing and regression modeling in determining whether a website update should be launched.

An implementation of an ARIMA time series forecast using Python statsmodels and scipy.

Udacity Data Analyst Nanodegree - Project III

A trading algorithm that identifies stocks with the largest potential for growth while heavily considering its volatility using quantopian

Our group chose this question to bring attention to the little knowledge that young loan applicants have. Based on our findings in our models we explore: Which age group is the least likely to apply for loans? Which group is most likely to default on loans?

This repository describes the implementation of Machine Learning techinques using the Statsmodels pacakge

A regression based modeling project to forecast the sales of Walmart

I am interested in predicting whether an individual will default on his or her credit card payment, on the basis of annual income and monthly credit card balance. First I will use Logistic regression with 1 feature only (balance) and then multiple logistic regression with 2 features (balance and income).

A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these.

This dataset provided by Udacity in their data analyst course proivdes a walkthrough of an AB test for customer conversion.

The objective is to build a ML-based solution (linear regression model) to develop a dynamic pricing strategy for used and refurbished smartphones, identifying factors that significantly influence it.

I used the New York Bike Counts dataset to formulate a hypothesis about the number of bikes crossing the Brooklyn Bridge. This dataset contains the number of bikes that crossed each bridge during each day. I first used this dataset to formulate a hypothesis and then used linear regression to test if my hypothesis was correct.

I perform a retrospective analysis on the linear regression analysis that I previously performed on the NYC Bike Counts dataset. Specifically, I analyze my linear regression analysis to identify anything that I could have done differently.

Prediction of nitrogen dioxide concentration in air using linear regression.

Analyze online shoppers' purchase intentions using Logistic Regression, K-means clustering & A/B Testing

Model to identify the potential lead by assigning a score for their rate of conversion. Therefore, reaching out to potential is no more a brainstorming task.