In this blog post, I will be walking you through an entire machine learning process from start to finish, focused primarily on building a classifier to predict whether SyriaTel customers will churn based on a number of different features. This blog post will walk through select steps I took to perform EDA on an unknown dataset, and ultimately iterate through a number of models to arrive at an optimal classifier.
To begin, load and import necessary libraries.
# import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import…
In this blog post, I will be walking you through my steps to clean data and build a multivariate regression to predict house sale prices in King’s County.
My goal behind this regression is to provide a tool for real estate investors (primarily in King’s County) to aid in asset valuation, during both buy and sell side activity. The idea is that this tool can be leveraged to identify lower priced houses for entry into the market. …
We’ve all been there. You throw some popcorn in the microwave, plop down on the couch with some friends, and start scrolling through Netflix, trying to decide what movie you’ll watch that evening. One friend wants to watch an action movie, another friend wants to watch a comedy, and a third just wants to watch a movie starring Denzel Washington. You may be different, but one factor that almost never influences my movie selection process is runtime. As the late film critic, Roger Ebert, once said: “No good movie is too long and no bad movie is short enough”. But…