Chinmaya M.

Top skills - Website and mobile app development, data analysis, visualization of reports, building interactive dashboards Technologies - CSS, HTML, Java, R, Python, JavaScript, Node.JS, React.JS, SQL Strong expertise in machine learning and data analytics as well I am familiar with Data Analysis aspects such as – Univariate & Bivariate Analysis, Missing Value Imputation, Feature Engineering, Categorical Variable Encoding, Normalization & Standardization, Binning, Outlier Handling, pre-processing of data & selection of right machine learning models. • Develop data-driven insights & solutions with advanced analytics (machine learning, deep learning and statistical models) to extract insights from large proprietary data sources • Identify, implement, and present results of research on ways machine learning approaches can aid decision-making • Choosing appropriate Machine Learning/Data Mining algorithm • Deploying Machine Learning models into production - Deploying machine learning model as Web Service: Building & training machine learning model, Storing the trained model in a pickle file, Creating REST API using Flask • Deploying machine learning models to cloud - Building & training machine learning model, Storing the trained model in a pickle file (serialized format), Using Flask to build REST API based web-services, Writing Dockerfile to build the docker image • Working on large datasets & coordinating with internal teams and clients through Agile methodology • Experience in data visualization projects to build interactive business dashboards KEY EXPERIENCES: Client – Largest Investment Bank of UK • Project 1. Modeled trading strategies using Pandas, Numpy, Matplotlib, SciPy, Scikit-Learn • Responded to ad-hoc requests for insights from data with reports built using SAS, SPSS & Python • Identified scenarios to predict trends in price movements. Calculated probabilities & expected gains following “Buy/Sell” signals generated by ML model (Random Forest, RNN & LSTM) • Built a Random Forest with multiple Decision Trees to gauge the loan risk & to predict an interest rate Client – Largest Retailer of UK • Project 2. Did sales forecasting in Python with time series analysis tools such as ARIMA & SARIMA • Used k-means clustering to group consumers of beauty products. Used Discriminant Analysis to spot demographic/behavioral traits to distinguish between users & non-users of products/services • Built social media monitoring & analysis tools (NLTK) using NLP (Named Entity Recognition, Sentiment Analysis, Text Summarization, Aspect Mining & Topic Modeling) • Used PCA to reduce dimensionality of large datasets • Did Marketing Mix Modeling to analyze multi-channel promotional strategy. Created a random forest model with sales as the target variable and marketing channels as the feature inputs. Calculated the feature importance of each predictor and plotted it on a bar chart • Used LightGBM regression to build the marketing mix model