Berry Blom

Analytics Consultant - Digital Marketing - Entrepreneurship

Portfolio

About

I studied at the Amsterdam University of Applied Sciences (AUAS) where I completed the bachelor Business IT & Managment. Previously I interned at Greetz where I fulfilled the role of Junior Business Intellegence Consultant. After that I studied Big Data for a semester where my passion for data came into being.

Currently I am a consultant at Avanade, a Microsoft company, where I give advise on how to deploy models in production. If you want to know more about my journey, have a look at my blog (top-right corner). Or download my new eBook where I discuss topics on Big Data, Analytics and Marketing intelligence!

Download Now!

Contact Me


Copyright © BerryBlom.com 2020

Data Cleaning in Python

For this project we are going to clean a dataset from Gapminder. It's about life expectancy for over 250 countries from the year 1800 to 2016. We are going to do some exploratory analysis and clean the dataset for further analysis.


The dataset looks like this:


Data cleaning


We want to clean the data:

  • Explore it and do some visuals
  • Check your assumptions
  • Reshape the data
  • Check if the columns are ready for analysis
  • What do you do with NaN values?
  • Check the average life expectancy over the years


    • All the details can be found in the Jupyter Notebook on my Github account.



Close Project

Exploratory Data Analysis

For this project we are going to explore dataset from The Guardian. It's about Olympic medals from 1896 to 2008 per country and athlete. We are going to do some exploratory analysis by pivoting the data, slicing it and reshaping the dataset and uncover some fascinating insights.


The dataset looks like this:


Data exploration


We want to explore the data:

  • Grouping and aggregating
  • Count medals
  • Drop duplicates
  • Locating suspicious data
  • Reshaping data
  • Visualize the data


    • All the details can be found in the Jupyter Notebook on my Github account.



    Close Project

Interactive Data Visualization

All the details can be found in the Jupyter Notebook on my Github account.

Close Project

Machine Learning

HR ANALYTICS

For this project we are going to develop a predictive model to analyze the employee turnover. The dataset contains data from almost 15000 employees from multiple companies.


The dataset looks like this:


ML data


We want to develop a model:

  • Discover the categorical variables
  • Make dummy variables
  • What's the current percentage?
  • Split the data
  • Make the Tree
  • Evaluation
  • Hyperparameter tuning


    • In the end we got an accuracy of 95%



    All the details can be found in the Jupyter Notebook on my Github account.

    Close Project

Data Mining and Cleaning

All the details can be found in the Jupyter Notebook on my Github account.

Close Project

Recommendation Case Study

All the details can be found in the Jupyter Notebook on my Github account.

Close Project