My Published Works & Reports

Welcome to my publications page! Here you will find a collection of reports, analyses, and other works I have contributed to. Each entry details the scope, methodology, and key insights of the project.

  • 1. Web Scraping & Data Extraction

    This project report details the process of extracting data from dynamic web pages using Python libraries like BeautifulSoup and Requests. It covers techniques for navigating website structures, handling pagination, and storing the collected data in structured formats for further analysis.

    View Report

  • 2. Data Wrangling & Cleaning for Netflix Datasets (Kaggle)

    This report focuses on the critical data wrangling and cleaning phase for real-world datasets, specifically using Netflix data from Kaggle. It outlines methodologies for handling missing values, standardizing formats, removing duplicates, and transforming raw data into a clean, analysis-ready state, crucial for reliable insights.

    View Report

  • 3. Exploratory Data Analysis (EDA) - Titanic Survival Exploration

    An in-depth exploratory data analysis (EDA) report on the Titanic dataset. This project highlights techniques for understanding data distributions, identifying correlations, and visualizing patterns related to passenger survival. It demonstrates how EDA can uncover hidden insights and inform subsequent modeling decisions.

    View Report

  • 4. Business Intelligence Dashboard with Power BI

    This publication showcases the development of an interactive business intelligence dashboard using Microsoft Power BI. It details the process from data ingestion and transformation to creating insightful visualizations and key performance indicators (KPIs) for strategic decision-making in a business context.

    View Report

  • 6. Introduction to Machine Learning: Concepts and Applications

    This report provides a foundational understanding of core machine learning concepts, including supervised and unsupervised learning, model evaluation metrics, and common algorithms. It serves as an introductory guide to the principles governing intelligent systems and their practical applications.

    View Report

  • 7. Regression Models: Predictive Analytics for Continuous Outcomes

    A detailed exploration of various regression models, including Linear, Polynomial, and Ridge Regression. This report covers model theory, assumption checking, implementation techniques, and interpretation of results for predicting continuous variables in diverse datasets.

    View Report

  • 8. Classification Models: Algorithms for Categorical Prediction

    This publication delves into different classification algorithms such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines. It discusses their underlying mechanisms, training methodologies, and effectiveness in classifying data points into distinct categories, along with performance evaluation techniques.

    View Report

If you have any questions about these publications, feel free to contact me.