Investigating the relationship between crime and housing prices across 304 English districts (2010–2020) using 430,000+ records. Python-based data pipeline with Pearson correlation, PCA, regression analysis, and Power BI dashboards.

Data analysis comparing the performance of Facebook and AdWords ad campaigns in 2019, Using Statical Methods such as A/B test focusing on clicks, conversions, cost-effectiveness, and seasonal trends.

Attempting to determine value in the NFL Draft

Objective: Cleaned, analyzed, and detected anomalies in financial data to support decision-making. Utilized Power BI, Tableau, and Excel for dashboard creation and SQL for data extraction. Key insights included 12k room revenue and a 58.80% occupancy rate, driving financial decisions.

Python Learning Series

Cartometric analysis QGIS Plugin: Tribute to MapAnalyst

Exploratory Data Analysis (EDA) and Statistical Investigation of customer behavior using Python, Pandas, NumPy, Matplotlib, Seaborn, and SciPy to uncover spending patterns and demographic insights.

✈️ Airfare Insights – A data analytics project on Indian flight ticket prices. Analyzed using Python, SQL & Statistics, this project explores how airline, stops, routes, duration, and departure time affect airfare. Includes data cleaning, EDA, statistical testing, SQL queries, and key insights to uncover real-world pricing patterns.

In this Project I Used Hypothesis testing To gain insights and Explore strategies to enhance taxi revenue through an in-depth analysis of payment methods. This repo dives into the correlation between payment types and fare pricing, offering valuable recommendations for optimizing earnings without compromising customer satisfaction.

Python-based implementation of Mean, Median, and Mode for basic statistical analysis.

Conducted data cleaning, exploratory data analysis (EDA), and using SQL for efficient data exploration and pattern identification. Used basic statistical analysis to understand relationships between variables. •Tools/Technologies: Python (Pandas, Matplotlib, Seaborn), SQL ,Jupyter Notebook and SQL Workbench.

Questa repository contiene il codice e i materiali relativi alla tesi magistrale, con un focus su analisi statistiche ed analisi predittive. Include strumenti e metodi per esplorare e modellare i dati, con tecniche statistiche avanzate come la regressione logistica, analisi di clustering, e metodi di ML e DL per la previsione e classificazione

Matrix factorization recommender system utilizing SVD and PMF for collaborative filtering. Features deep interpretability of latent factors on highly sparse datasets, anti-overfitting techniques, and a real-time Streamlit UI.