A data-driven analysis of movie success factors, including genre popularity, production company performance, language trends, and financial success. This project explores influences on a movie’s ratings, popularity, and revenue through feature engineering, data preprocessing, and visualizations.
Python-based ETL pipeline that automates data transfer from Google Sheets to PostgreSQL.
Our project aims to bring together teachers and students and provide a platform for better connectivity.
Analysis of Swiggy's food delivery data across Indian cities, examining restaurant metrics, cuisines, and customer preferences.
SQL practice problems solved across platforms for Data Analyst preparation
The project utilizes Social Network Analysis (SNA) to comprehensively analyze global air travel dynamics and assess India's position in the aviation market.
This project aims to develop a deep learning-based system for classifying diatom images, which can be used for water quality monitoring. Dataset sourced from KAGGLE (URL provided below.)
🙋Completed all the assignments based on SQL, EXCEL, PYTHON, EDA, MACHINE_LEARNING, LINEAR REGRESSION,
Python implementation of K-Means clustering data science algorithm
An in-depth analysis of a movie rental store using SQL & Python to uncover trends in customer behavior, rental patterns, and revenue insights. Features data cleaning, EDA, SQL queries, and visualizations for data-driven decision-making. 🚀
Process Mining Dashboard developed using Python, Pandas, Streamlit, and Matplotlib as part of the Celonis Process Mining Virtual Internship. The project analyzes event log data, calculates activity durations, and visualizes workflow performance through an interactive dashboard.
A data-driven task tracker using pandas and matplotlib for task analysis and visualization.
Analyzes and Visualizes the potential correlation between public sentiment on the r/Bitcoin subreddit and the historical price of Bitcoin.
Predicting discounted prices of the listed products from Amazon & Flipkart based on their ratings, reviews and actual prices using models like Random Forest Regressor, KNN Regressor, etc.
This project aims to compare the performance of Facebook and AdWords advertising campaigns through A/B testing. By analyzing key metrics such as ad views, clicks, conversions, and costs, we seek to identify the most effective platform for optimizing advertising strategies.
A machine learning model to predict whether clients of a Portuguese banking institution will subscribe to a term deposit, based on data from direct marketing campaigns involving phone calls.
Repositorio para el trabajo práctico final de Introducción a Ciencia de Datos - Lic. Ciencia de Datos - Universidad de San Martín.
AI Data Analyst Agent with CSV/Excel support, automated EDA, ML task suggestion, OpenAI/Gemini fallback, and Markdown report generation.
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