Since the courses are needed to be purchased to get the dataset, I will study all the projects in this series from JifuZhao who shared the relative ipynb works on github. Reference link for this one: https://github.com/JifuZhao/DS-Take-Home/blob/master/01.%20Conversion%20Rate.ipynb

Some usual structure for data analysis works:

  • 1 State the Issues/Target
  • 2 Collect the Data
  • 3 Data cleaning and preprocessing
  • 4 Do the Exploratory Data Analysis
  • 5 Feature Engineering
  • 6 Build Model to Solve the Issue
  • 7 Model Evaluation
  • 8 Explain and Report

1.Issue/Goal

Optimizing conversion rate.
Build a model that predicts conversion rate and, based on the model, come up with ideas to improve revenue.
This challenge has no dates, no tables to join, no feature engineering required.

2.Collect Data

We have data about users who hit our site: whether they converted or not, as well as some of their characteristics.
The table is “conversion_data”. It has information about signed-in users during one session. Each row is a user session.

Columns:

  • country: user country based on the IP address
  • age: user age. Self-reported at sign-up step
  • new_user: whether the user created the account during this session or had already an account and simply came back to the site.
  • source: marketing channel source.
    • Ads: came to the site by clicking on advertisement
    • Seo: came to the site by clicking on search results
    • Direct: came to the site by directly typing the URL on the browser.
  • total_pages_visited: number of total pages visited during the session. This is a proxy for time spent on site and engagement during the session.
  • converted: this is our label. 1 - they converted within the session, 0 - they left without buying anything. The company goal is to increase conversion rate: # conversions / total sessions.
1
head(conversion_data, 1)
Field Value
country: UK
age: 25
new_user: 1
source: Ads
total_pages_visited: 1
converted: 0

3.Data cleaning and preprocessing

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data = pd.read_csv("./conversion_data.csv")

// breif view of table
data.head()

// info table
// give info of data numbers and datatype of each var.
data.info()

// get the statistical info of each var.
data.describe()

// get unique "levels" of each var.
for column in data.columns:
  uniques = sorted(data[column].unique*())
  print("{0:20s}{1:5d}\t".format(column, len(uniques)), uniques[:5])

// remove outliers - age
// age is typically < 100
// check
data[data["age"] > 100]

// delete
data = data[data["age"] < 100]

4.Do the Exploratory Data Analysis