Machine Learning Data Analyst

ROHIT ADIKE +1-475-***-**** ************@*****.*** Fairfield, CT

PROFESSIONAL SUMMARY

Data-driven professional with 2+ years of experience in data analysis, machine learning, and process optimization. Skilled in designing end-to-end data solutions, including ETL pipelines, predictive modeling, and data visualization. Proficient in Python, SQL, and machine learning frameworks, with a proven ability to translate complex data into actionable insights. Experienced in cross-functional collaboration, process improvement, and ensuring compliance with regulatory standards. Passionate about leveraging data science to solve real-world problems and drive business efficiency. TECHNICAL SKILLS

Programming Languages: Python (Pandas, NumPy, Scikit-learn, TensorFlow, Keras), Java, SQL

Machine Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost, K- Means Clustering

Data Visualization: Tableau, Matplotlib, Seaborn, Plotly

Databases: PostgreSQL, MySQL, SQLite, MongoDB

Tools: Apache Airflow, Git, Jupyter Notebook, Microsoft Excel, Microsoft Visio

Frameworks: Django, Flask

Other Skills: ETL Processes, Feature Engineering, NLP (Tokenization, NER, Sentiment Analysis), Statistical Analysis PROFESSIONAL EXPERIENCE

Cognizant Technology Solutions, Hyderabad, India

Data analyst October 2020 - December 2022

Analyzed and interpreted large datasets to provide actionable insights, leading to a 15% improvement in customer retention.

Developed and automated SQL-based ETL pipelines, reducing manual reporting time by 40%.

Designed interactive Tableau dashboards, enabling department heads to effectively monitor key performance indicators

(KPIs).

Collaborated with cross-functional teams to support predictive modeling for marketing campaigns, resulting in a 25% increase in ROI.

Conducted A/B testing to evaluate new product features, ensuring data-driven decision-making and continuous optimization. Utilized Python (Pandas, Scikit-learn) and statistical methods to drive data analysis and enhance business strategies.

PROJECTS

Real-Time Sales Forecasting System - Sacred Heart University

Designed and implemented a scalable data science architecture for predicting sales trends using historical sales data from Kaggle’s Rossmann Store Sales Dataset.

Built an ETL pipeline using Python (Pandas, NumPy) to extract, transform, and load data into PostgreSQL, ensuring data integrity and consistency.

Developed and evaluated machine learning models (Linear Regression, Random Forest, XGBoost), achieving an RMSE of 0.15.

Created an interactive dashboard using Tableau to visualize daily, weekly, and monthly sales trends.

Automated the data pipeline using Apache Airflow for scheduled updates and real-time insights. Tools & Technologies: Python, PostgreSQL, Tableau, Apache Airflow, XGBoost Smart Healthcare Management System - Sacred Heart University

Designed and implemented a relational database system to manage hospital operations, including patient records, appointments, and billing.

Developed a role-based access control system using Python (Django) to restrict access to sensitive data.

Designed an appointment scheduling system with real-time availability tracking, reducing appointment conflicts by 30%.

Tools & Technologies: PostgreSQL, Python (Django), SQL, ERD tools (Lucidchart) Flight Path Analysis - Sacred Heart University

Collected and cleaned flight data from multiple sources, including flight logs, weather data, and airport schedules, leveraging Python and SQL for statistical analysis and data visualization.

Performed exploratory data analysis (EDA) to identify patterns, trends, and anomalies in flight paths, such as frequent delays and inefficient routes.

Developed predictive models using machine learning algorithms (Linear Regression, Random Forest) to optimize flight routes and reduce fuel consumption by 15%.

Engineered features such as flight distance, weather conditions, and airport congestion to improve model accuracy.

Utilized time-series analysis to predict potential delays and inefficiencies in flight paths, enabling proactive decision- making.

Created interactive dashboards using Tableau for stakeholders to monitor key performance metrics, such as on-time arrivals, fuel efficiency, and delay predictions.

Collaborated with aviation experts to validate findings and suggest operational improvements, leading to a 10% reduction in flight delays.

Deployed the model as a web application using Flask, allowing airlines to input flight data and receive real-time route optimization recommendations.

Tools & Technologies: Python, SQL, Tableau, Machine Learning (Linear Regression, Random Forest), Pandas, NumPy, Flask BigMart Sales Prediction – Malla Reddy college of engineering

Developed a predictive model using Python and machine learning algorithms (Linear Regression, Random Forest) to forecast sales and optimize inventory management.

Performed data preprocessing, including handling missing values, outlier detection, and feature scaling, to improve model performance.

Engineered features such as product weight, fat content, and store location to enhance model accuracy.

Achieved an accuracy of 85% in predicting sales, enabling better inventory planning and reducing stockouts by 20%.

Conducted hyperparameter tuning using GridSearchCV to optimize model performance.

Visualized sales trends and model predictions using Matplotlib and Seaborn for stakeholder presentations. Tools & Technologies: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn Phishing Website Detection - Malla Reddy college of engineering

Built a machine learning model using Logistic Regression and SVM to identify phishing websites, achieving an accuracy of 92%.

Collected and preprocessed a dataset of legitimate and phishing URLs, performing tokenization and feature extraction.

Engineered features such as URL length, presence of special characters, and domain age to improve model performance.

Evaluated model performance using precision, recall, and F1-score, ensuring high reliability in detecting malicious websites.

Deployed the model as a web application using Flask, allowing users to input URLs and receive real-time phishing predictions.

Conducted cross-validation to ensure model robustness and generalizability. Tools & Technologies: Python, Scikit-learn, Pandas, NumPy, Flask INTERNSHIP EXPERIENCE

Electronics Corporation of India Limited (ECIL)

Intern – AADHAR Based Online Voting System

Developed a secure electronic voting machine using Java, CSS, MySQL, and Apache framework.

Implemented fingerprint identification for voter verification using AADHAR database integration. Agribuds

Intern - Campus Ambassador

Assisted in addressing and resolving agricultural challenges faced by farmers by providing innovative solutions and support.

EDUCATION

Sacred Heart University, Fairfield, CT

Master’s in computer science (Data Science) January 2023 - March 2024 Key Project: Flight Path Analysis – Developed predictive models to optimize flight routes and reduce fuel consumption. Malla Reddy College of Engineering, JNTUH, Hyderabad, India Bachelor of Technology (Computer Science & IT) December 2020 Key Projects: BigMart Sales Prediction, Phishing Website Detection KEY ACHIEVEMENTS

Improved process efficiency by 15% through data-driven process optimization at Cognizant.

Reduced flight delays by 10% and fuel consumption by 15% through predictive modeling in the Flight Path Analysis project.

Achieved 92% accuracy in detecting phishing websites using machine learning models. VOLUNTEER WORK

I participated in monthly community food bank meal preparation.

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