A highly analytical professional with a background in Civil Engineering and specialized training in Data Science and Machine Learning, driven by a passion for solving real-world challenges with data. Experienced in applying machine learning, predictive modeling, and data visualization to solve real-world problems, with a strong record of managing complex projects and delivering actionable insights.
- Data Analysis & Visualization: Transforming raw data into actionable insights using Pandas, NumPy, Matplotlib, and Seaborn.
- Machine Learning & Predictive Modeling: Building and evaluating models (classification, regression, decision trees, random forests) for real-world applications.
- Data Preprocessing & Feature Engineering: Cleaning data, handling missing values, and preparing datasets for robust analysis.
- Collaboration & Reporting: Documenting workflows and sharing insights clearly via Jupyter/Colab and visual reports.
- Languages: Python, SQL
- Data Analysis: Pandas, NumPy, Matplotlib, Seaborn
- Machine Learning: Scikit-learn, Logistic Regression, Decision Trees, Random Forests, Classification & Regression Models
- Data Handling: Jupyter Notebook, Google Colab, Excel, CSV/JSON handling
- Version Control: Git, GitHub
- Currently Learning: TensorFlow, Keras, Deep Learning
Explore some of my work where I apply data science principles to diverse challenges:
- Loan Eligibility Prediction — Built a machine learning model for Dream Housing Finance to automate loan eligibility decisions. The optimized Random Forest Classifier reached 85% accuracy, streamlining approvals and improving decision reliability.
- Customer_Segmentation_Analysis — Applied unsupervised machine learning (K-Means, Hierarchical Clustering) to segment a credit card company's customer base into distinct profiles. This analysis successfully identified a critical "High-Risk Defaulter" segment, enabling the development of targeted risk mitigation strategies.
- Footwear Sales Data Analysis — Performed visual analytics on sales data to identify key trends and patterns for a footwear company. Found top-performing brands that generated 60%+ of net profit, guiding inventory allocation and marketing strategies.
- Global Literacy Rates Analysis — Conducted an in-depth data analysis to uncover regional disparities and gender gaps in global literacy, providing strategic recommendations for a non-profit organization's targeted educational interventions.
I am committed to continuous learning and expanding my expertise in data science and machine learning:
- Publishing impactful ML projects on GitHub (e.g., loan prediction, literacy analysis, customer segmentation).
- Practicing end-to-end workflows: Data Collection → EDA → Feature Engineering → Model Building → Evaluation → Insights.
- Expanding into Deep Learning & Neural Networks with TensorFlow and Keras.
- Strengthening SQL for Data Science for advanced querying and data manipulation.
I'm always open to discussing data science, new opportunities, or collaborations. Feel free to reach out!
- Email: [email protected]