taylorplumer

# The Best Medium-Hard Data Analyst SQL Interview Questions  

By Zachary Thomas ([zthomas.nc@gmail.com](mailto:zthomas.nc@gmail.com), [Twitter](https://twitter.com/zach_i_thomas), [LinkedIn](https://www.linkedin.com/in/thomaszi/)) 

**Tip: **See the Table of Contents (document outline) by hovering over the vertical line on the right side of the page 

## Background & Motivation

> The first 70% of SQL is pretty straightforward but the remaining 30% can be pretty tricky.

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git clone https://github.com/taylorplumer/classifiers-dash

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from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier

# modify depending on needs for sklearn classifiers and yellowbrick visualizers
MODELS = [GradientBoostingClassifier(), RandomForestClassifier(), LogisticRegression(max_iter=1000), GaussianNB() ]
VISUALIZERS = ['ROCAUC','PrecisionRecallCurve', 'ClassificationReport','ConfusionMatrix']

# modify depending on needs for filesystem structure
INPUT_DATA_FILEPATH = 'Data/Input/'

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{
  "basics": {
    "name": "Taylor Plumer",
    "label": "Business Intelligence Analyst",
    "summary": "I’m a business intelligence analyst who loves building reactive data visualization tools. I recently graduated from Udacity's Data Scientist Nanodegree program and am open to opportunities to help organizations utilize machine learning for good.",
    "website": "https://thefoxandtheforest.github.io",
    "location": {
      "city": "San Jose",
      "countryCode": "USA",
      "region": "California"

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# Create a histogram of listings

neighbourhood_list = df['neighbourhood_cleansed'].unique().tolist()
hist_data = []

for neighbourhood in neighbourhood_list:
    trace = go.Histogram(x = df.loc[df['neighbourhood_cleansed'] == neighbourhood].price.tolist(), opacity=0.75, name = neighbourhood)
    hist_data.append(trace)

layout = go.Layout(barmode='stack', xaxis = dict(title='Listing Price'), yaxis = dict(title='Count'))