09/06/2022 updated
AS
100 % available
Data Scientist
Ireland
Ireland
Agile MethodologyAlgorithmsData AnalysisBig DataCluster AnalysisGeneralized Linear ModelSpatial DatabasesR (Programming Language)Python (Programming Language)PostgreSQLLinear RegressionMachine LearningMicrosoft SQL ServersMySQLNumPy
Python, Apache Spark, R, SQL Server, PySpark, Machine Learning, Tableau, Spatial Database, Pandas, matplotlib, Naive Bayes, SAS, Postgres, Linear Regression, Decision Trees, Random Forest, Clustering, Numpy, Recommendation System, ggplot/ggplot2, MySQL, PyCharm/Jupyter, Agile, Anaconda, K-means algorithm, GLM, Regression, visualization, Data Analysis, big data, analytics
Experience
Professional experience0 - 5 years
Languages
EnglishGood
Project history
* Projected an idea to segregate the active bank accounts from inactive bank accounts which helped in increasing the
profit by providing offers to active account holders.
* Involved in feasibility study, design and development of the model to make sure that everything is done precisely and
efficiently.
* Concocted a model to predict credit decisions more accurately which helped to categorize loyal and active customers to
provide customized offers.
* Classified customers based on various factors to serve customers better using K-means algorithm.
* Used Generalized Linear Models (GLM) and Classification and Regression Trees (CART) to predict Customer Lifetime
Value (CLV) to predict customer with high future value.
* Used "User Based Collaborative Filtering" and ALS (Alternate Least Square) to recommend best products for loyal
customers, which helped in increasing customer retention.
* Involved in data refresh for creating a dashboard on monthly basis to reflect the correct visualization using Tableau.
* Worked with the big dataset for visualizing and creating models.
Project Details
Data Analysis Projects
* Project Name: Cross-selling Recommendation
Description: The Aim of this project was to use big data analytics technologies to conduct customer behaviour analysis
in terms of personalized cross-product buying and selling. Data set used for this project is from Amazon Product
Review and environment used is Spark and Python using Machine Learning methods.
* Project Name: Image Classification Using Neural Networks
Description: Image recognition is achieved by feeding an image into a neural network and obtaining some kind of label
for that image for the output. A label that matches the network output to a pre-defined class. There can be several
classes that can be labelled as an image, or only one.
* Project Name: London Housing Dataset
Description: Used the London Housing Dataset for predicting the price of house based on floor areas. To predict price
we used Python and Machine Learning Algorithms.
* Other Projects: Visualization Using R
* Dublin 2018 marathon dataset used to visualize factors affecting in winning marathon. Created a data model to
predict the winning trend.
* Speed dating dataset collected from dataworld compiled by Columbia Business School professors Ray Fisman and
Sheena Iyengar in 2002-2004. Applied random forest to find out which parameter effects the most in making a
decision for dating again the same person.
* Data Visualization using Tableau: Visualized the open source data set and created some of the meaningful
dashboards with it.
Achievements
* Worked as a student Ambassador in Maynooth University which includes responsibilities such as promoting the
university at various events, conduct guided campus tours and assisting recruiters visiting university for job fairs.
* Quite a few times became the performer of the month of the project while in TCS.
* I have been the hardest puncher of all my projects that I have worked on during my TCS career.
* Appeared and Passed several MCQs based competencies test on different technologies securing more than 80% on an
average in TCS.
* Volunteering in social and cultural events in school, college and university.
* Served as a School Captain.
profit by providing offers to active account holders.
* Involved in feasibility study, design and development of the model to make sure that everything is done precisely and
efficiently.
* Concocted a model to predict credit decisions more accurately which helped to categorize loyal and active customers to
provide customized offers.
* Classified customers based on various factors to serve customers better using K-means algorithm.
* Used Generalized Linear Models (GLM) and Classification and Regression Trees (CART) to predict Customer Lifetime
Value (CLV) to predict customer with high future value.
* Used "User Based Collaborative Filtering" and ALS (Alternate Least Square) to recommend best products for loyal
customers, which helped in increasing customer retention.
* Involved in data refresh for creating a dashboard on monthly basis to reflect the correct visualization using Tableau.
* Worked with the big dataset for visualizing and creating models.
Project Details
Data Analysis Projects
* Project Name: Cross-selling Recommendation
Description: The Aim of this project was to use big data analytics technologies to conduct customer behaviour analysis
in terms of personalized cross-product buying and selling. Data set used for this project is from Amazon Product
Review and environment used is Spark and Python using Machine Learning methods.
* Project Name: Image Classification Using Neural Networks
Description: Image recognition is achieved by feeding an image into a neural network and obtaining some kind of label
for that image for the output. A label that matches the network output to a pre-defined class. There can be several
classes that can be labelled as an image, or only one.
* Project Name: London Housing Dataset
Description: Used the London Housing Dataset for predicting the price of house based on floor areas. To predict price
we used Python and Machine Learning Algorithms.
* Other Projects: Visualization Using R
* Dublin 2018 marathon dataset used to visualize factors affecting in winning marathon. Created a data model to
predict the winning trend.
* Speed dating dataset collected from dataworld compiled by Columbia Business School professors Ray Fisman and
Sheena Iyengar in 2002-2004. Applied random forest to find out which parameter effects the most in making a
decision for dating again the same person.
* Data Visualization using Tableau: Visualized the open source data set and created some of the meaningful
dashboards with it.
Achievements
* Worked as a student Ambassador in Maynooth University which includes responsibilities such as promoting the
university at various events, conduct guided campus tours and assisting recruiters visiting university for job fairs.
* Quite a few times became the performer of the month of the project while in TCS.
* I have been the hardest puncher of all my projects that I have worked on during my TCS career.
* Appeared and Passed several MCQs based competencies test on different technologies securing more than 80% on an
average in TCS.
* Volunteering in social and cultural events in school, college and university.
* Served as a School Captain.