Data Science

Data Analyst

Freelancer having 4 years of hands-on experience in machine learning, deep learning and predictive analytics.

Experienced in Statistical Analysis, Visualization, Hypothesis Testing, Feature Engineering, Hyperparameter Tuning, Model Analysis and Evaluation.
Sound knowledge of ML and DL algorithms - Regression, Neural Networks, CNNs, RNNs, Decision Trees, Random Forest, Gradient Boost, XGBoost, AdaBoost.
Solid3years of hands-on experience in Pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly, Scikitlearn, OpenCV, Tensorflow and Keras Framework
Experienced in deployment tools- Flask and gunicorn.
More than 3 years of experience in SQL scripting on SQL Server and MySQL, data-extraction, manipulation, pipelining, automation and query-tuning.
Experienced in VCS and remote repository managers such as GitLab and Github

Projects Summary:

Role: Python Developer
Environment: Tensorflow2.x, OpenCV, DLIB, Python, pandas, sklearn, gunicorn
Outline: Developed, Tested and Deployed multiple ML classification models for image skin tone mapping and skin issue prediction. Developed image processing algorithms to eliminate bad lighting conditions from image.

2. Project:- Supervised Machine Learning Prediction for Customer Attrition

Role: Solution Integrator, Ericsson
Environment: Python, Numpy, Pandas, Matplotlib, Seaborn, Sickit Learn (Logistic Regression, Decision Tree-based Learning)
Outline: The data set was collated from operator contained a mix of categorical and numerical attributes of postpaid customers. The goal was to predict whether the customers switched away or not.
Performed data exploratory analysis(Feature Distribution Graphs, Visual Analysis),data cleaning, missing value imputation and deduplication using Numpy, Matplotlib, Seaborn and Plotly and Categorization libraries. Generated correlation heatmaps, used PCA to combine features showing high multi-collinearity.
Implemented Label Encoders, Standard Scaler and Min Max Scaler
Baseline Model was prepared using Logistic Regression on default hyperparameters
Implemented Univariate Selection techniques (Chi-squared, RFE, Select KBest, Decision Trees)to select the most useful features
Evaluated over - sampling models built using SMOTE+ Logistic Regression and Recursive Feature Elimination + Logistic Regression model
Evaluated all decision-treebased ML models(includinghyper-parameter tuning)-AdaBoost, Random Forest, Gradient Boost, XGboost, Cat boost and generated their performance metrics respectively.
Model was deployed on the Development environment and then on Production cluster using Pickle, Flask and gunicorn

3- Project: Analysis of LTE internet usage

Role: Solution Integrator
Environment: Python - Pandas, Numpy, SciPy, Statsmodels, Matplotlib, Plotly and Seaborn
Outline: Region data collated from multiple operators. Developed models on LTE usage for a festive season
Generated visualizations and performed residual analysis of internet activity (of festive season against normal)across time and location using MatplotlibandSeaborn.
Generated graphs on exploratory data analysis using Matplotlib and Seaborn on univariate and bivariate analysis, distribution of features, Hypothesis testing for dependence (ANOVA, Chi-Squared, Tukey HSD)among features, correlation matrix.

4-Project: Web Scrapping of MS affiliated companies in USA

Environment: Beautiful Soup, Selenium (Python), pandas
Outline: Developed multiple scripts to scrape and map various Microsoft affiliated companies across USA

Prabar Dash