I am an Analytics graduate with a concentration in Statistical Modeling, passionate about leveraging my data science skills for bridging the gap between business and analytics. I have a strong background in Analytics and Statistics with an ability to effectively communicate a story through data.
I'm passionate about building ML and NLP models that can extract value from text data and deliver valuable insights to businesses for improved decision making.
I am confident about my knowledge and experience in the following data science & analytical techniques and tools:
- Experience with Python Packages: Numpy, Pandas, Scipy, Seaborn, Matplotlib, Scikit Learn, TensorFlow, PCA, nltk, Plotly
- Experience in working with R: ggplot2, shiny, glmnet, caret, tidyverse, dplyr, healthcareai, Mediana, officer, flextable
- Supervised & Unsupervised Machine Learning Techniques: Regression, Clustering, Classification, PCA, Reinforcement Learning
- Tableau and PowerBI
- Time Series Analysis & Econometrics
- MySQL
- Hadoop, Hive, Azure
I have a Bachelor's honors and a Master's degree in Economics. During my graduate program in economics, I gained a strong experience in conducting statistical analysis for solving various research problems using SPSS.
I am passionate about working in an environment where statistical analysis and analytical techniques such as ML and NLP models can be applied for delivering valuable business insights.
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Experience
Data Analytics Intern | Inspire Network for Environment
▪ Analyzed district-level crop yield data to equip farmers with information on how much yield to expect from three crops, assess financial expenditure on water irrigation systems and fertilizers for bringing in sustainability
▪ Gathered data from multiple sources, performed data mining/feature engineering and created visualizations to assess the relationship between factors affecting crop yield in Python and Tableau
▪ Built linear and non-linear regression models for crop yield prediction for each district in the state of Uttarakhand and compared their respective RMSEs
▪ Policies and schemes launched by the State Government based on prediction models contributed to a 15.7% increase in yield of crops
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Projects
Building artificial Health Personality for Intelligent Marketing Github
▪ Built a mock ‘Health’ persona that takes articles as inputs and gives an interest score on the subject as output
▪ Developed the model in Python using WordNet and Wu & Palmer Similarity that gave scores beyond 80% for health-related subjects
▪ The model led to an increase of 11% in ROI of a fitness product client of Persona Panels
Optimizing the security system of GE Aviation with ML (Subject to NDA)
▪ Developed a model for reducing False Positive information in the cybersecurity data using ML prediction that can be used by GE to reduce their load of manual elimination and optimize business costs
▪ Developed the models in Python using Logistic Regression and Random Forest with accuracies of 77% and 89%, respectively
▪ The model reduced time spent on manual handling of alerts by 36%