Hi, I'm John Ruoro
Data Scientist | Machine Learning Engineer
I am a Junior Data Scientist who is results-oriented and possesses strong critical thinking and problem solving skills. I am equipped with technical skills necessary to extract data, build predictive models, and find solutions for business problems.
Contact MeAbout Me
My Introduction
I have experience working with large volumes of data to build and deploy machine learning models. I am proficient in data extraction, data mining, data visualization, and predictive modeling using scripting languages such as Python and SQL. I have a strong background in statistics with extensive skills in mathematics and statistical algorithms. I enjoy challenges, and I am always looking for opportunities to learn something new.
Skills
My SkillsData Scientist | Machine Learning Engineer
I am skilled in:Machine Learning
90%Python
90%SQL
75%Spark
50%Scikit Learn
90%TensorFlow
75%MS Excel
75%Microsoft Azure
50%Qualifications
My personal journeyMicrobiology
Jomo Kenyatta UniversityGrade: Second Class Honors Lower Division
- Pursued a Bachelor's degree in Microbiology. I acquired skills in various statistical areas such as data analysis, probability and hypothesis testing as well as time series analysis.(Epidemiology and Biostatistics)
Kenya Certificate of Secondary Education
The Kenya High SchoolGrade: B -
- I excelled in subjects such as Mathematics and Physics, attaining a grade A in each in my KCSE examination. I was the head of the Junior Achievement (JA) club whose main goal was to prepare students to succeed in the global economy.
Junior Data Scientist
Data GlacierInternship
Role Description
- Extracting data, building predictive models, and finding solutions for business problems as part of the data science
framework.
- Working with data in various ICL projects to build and deploy machine learning models.
- Performing data extraction, data mining, data visualisation, and predictive modelling using scripting languages
such as Python and SQL as required in various ICL projects.
Portfolio
Projects
Credit Card Fraud Detection Model
The project is a classification problem on credit card fraud detection. The data I used was preprocessed and PCA transformed. I conducted data mining and created visualizations using libraries such as matplotlib and seaborn to understand patterns in the data. I performed data balancing using simple oversampling and undersampling techniques and iterated various classification models over the balanced data . I then performed anomaly detection by using isolation forest to identify anomalous data points. The best performing model was the Random Forest model with an F1 score of 0.74.
View on GitHub
Bank Churn Model
The project is on bank churn prediction where I created a model to predict whether a bank customer will churn. I used visualizations such as countplots, pie charts and scatterplots to analyze the data. I also conducted data mining by checking correlations within the data. I encoded categorical features and standardized the data to put the variables on the same scale. I then balanced the data and fit it into a logistic regression model. The ROC AUC score improved after balancing the data from the performance of the base model by 0.11.
View on GitHub
Disaster Tweets Model
This is a Natural Language Processing(NLP) project where the goal was to create a model to predict tweets about real disasters and those that are not. I started with exploratory analysis where I checked the distribution of the text data and plotted visualizations to understand the underlying structure of the data. I then conducted text preprocessing techniques such as lower-casing and the removal of punctuation and tags. I used TfidfVectorizer to transform the text data and fit it on a logistic regression model that attained a ROC AUC score of 0.64.
View on GitHub