Author: Ajay Ohri

a few criteria that define a good data science project

• Learnability- What did you learn in the Project
• Capability – What capabilities were showcased in the project
• Difficulty- How difficult or easy was the project
• Potential Hireability- How likely are you going to be hired based on that project
• Ability- What creative approaches did you bring to the solution

A few datasets I liked only from a teaching purpose- iris, Boston, mtcars, Titanic, German Credit and mnist handwriting

A project is more than just a Kaggle dataset. hashtag#datascience hashtag#datasets hashtag#kaggle hashtag#machinelearning

In Seaborn a bar-chart can be created using the sns.countplot method and passing it the data

https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed

https://seaborn.pydata.org/generated/seaborn.barplot.html

It is quite easy to create buckets based on one column using query. Note you can also use qcut

bucket1 =df.query(‘Column_1 >=0 and Column_1 <0.25’)

bucket2 =df.query(‘Column_1 >=0.25 and Column_1 <0.5’)

bucket3 =df.query(‘Column_1 >=0.5 and Column_1 <0.75’)

bucket4 =df.query(‘Column_1 >=0.75 and Column_1 <=1’)

spark= SparkSession.builder \
.master(“local”) \
.appName(“Data Exploration”) \
.getOrCreate()

#load data as Spark DataFrame
data2=spark.read.format(“csv”) \
.option(“header”,”true”) \
.option(“mode”,”DROPMALFORMED”) \
.load(‘/home/Desktop//input/*.csv’)