A proficient Python Programmer, specializing in Machine Learning, Data Visualization, and Data Preprocessing, with internship experience at Nokia Bell Labs in Data Science, and an active learner. A Graduate Research Assistant in Machine Learning and Big Data, with a Master of Science Degree in Computer Science from the University of New Haven, 2018. A Google Analytics Certified Individual, 2019.
In my last role, I worked at Nokia Bell Labs as a Data Science and Machine Learning Intern focused on Data Preprocessing, which involved extracting and transforming raw time-series from the World Bank, International Labour Organization and OECD websites, aggregating over 2 million data points.
I am a recent graduate from the University of New Haven, with a Master's Degree in Computer Science and Graduate Research Assistant. I have earned Google Analytics Individual Qualification, Microsoft Technology Associate and have completed several online courses on Data Science . I am a constant learner, and I am updating my skill set regularly.
Email me at
chitralputhran@gmail.com
GitHub profile at
github.com/chitralputhran
LinkedIn profile at
linkedin.com/in/chitralputhran
Coursework:
Relevant Courses:
This is a web application made with the help of Flask, a microframework for Python based on Werkzeug, Jinja 2, and good intentions. On the backend, a deep learning model (ResNet-50) is classifying the given image file into various categories. ResNet-50 is a convolutional neural network that is trained on more than a million images from the ImageNet database.
Project LinkThis is a web application made with the help of Flask, a microframework for Python based on Werkzeug, Jinja 2, and good intentions. On the backend, a Machine Learning model is used for predicting the price of the car. The machine learning model was trained on the Automobile Dataset from the UCI Machine Learning Repository.
Live Project LinkThis is a web application made with the help of Flask, a microframework for Python based on Werkzeug, Jinja 2, and good intentions. On the backend, a machine learning model is classifying the wine sample entered as good or bad. The machine learning model was trained on the Wine Quality Dataset from the UCI Machine Learning Repository.
Live Project LinkThe project analyzes reviews by users of Amazon’s Alexa products. Using Natural Language Processing on the product reviews and some additional features, a machine learning model should be able to predict if the feedback is positive (1) or negative (0). The primary methods used are Random Forrest and Gradient Boosting for this data-set.
View ProjectDesigned a mobile phone database web application. Python was used to extract data of 883 mobile phone along with its features from fonoapi.
View ProjectApplied Support Vector Machine (Supervised learning model) on Heart Disease UCI Data set from Kaggle
View ProjectColumnTransformer was introduced in scikit-learn from version 0.20 onwards. The notebook file contains a quick and easy tutorial on ColumnTransformer to get you started.
View Project