Netflix Filtering and Recomendation System
This project leverages Apache Hadoop and Apache Spark to collect, process, clean,
analyze, and deploy two Netflix Movies and Shows datasets. Built using Jupyter
Notebooks, Python, Pandas, Scikit-Learn, PySpark, and Tkinter, the project features a
GUI with two key systems: a filtering system that returns titles based on user-selected
parameters and a content recommendation system that suggests the top three most similar
titles using cosine similarity.
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Concrete Comprehensive Strength Regression Model
This project leverages machine learning to predict the compressive strength of concrete
based on its material composition. By applying regression models, it analyzes the impact
of different components on concrete strength to enhance predictive accuracy. Built using
Python, Jupyter Notebooks, Pandas, NumPy, Scikit-Learn, Matplotlib, and Seaborn, the
project includes data preprocessing, exploratory analysis, model training, and
performance evaluation.
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Plant Pulse Senior Project
Plant Pulse was my Computer Engineering senior desing project, developed as part of a
team of five. This smart gardening system integrates IoT and machine learning to monitor
plant health, using sensors to track temperature, humidity, soil nutrients, and pH
levels. The data is displayed in real-time through a web-based dashboard. Built with
Python, HTML, CSS, Flask, Arduino IDE, SQLAlchemy, Pandas, and Numpy. The system
provides predictive insights utilizing Random Forest Classifier to enhance plant care
and automate environmental adjustments.
Video Demonstration
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Ecommerce Funnel Analysis
Ecommerce Funnel Analysis is a personal data analytics project built to simulate
real-world e-commerce insights, where I analyze customer behavior throughout the online
purchase journey. Using SQL, I designed analytical queries and views to track user
interactions from product views to cart additions and completed purchases, enabling
measurement of drop-off rates and conversion patterns. The project features an
interactive Power BI dashboard that visualizes funnel progression, purchase behavior by
time of day, and performance across product categories, providing actionable insights to
optimize the customer experience.
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Guest Intelligence Dashboard
An interactive business intelligence dashboard built to simulate the role of a Guest
Intelligence Analyst. Using SQL Server, Python (Pandas), and Streamlit, I analyzed a
fast-food sales dataset to extract insights on product performance, customer behavior,
and time-based trends. The dashboard features KPIs, heatmaps, and visual breakdowns by
item type, time of day, and transaction method, helping stakeholders make data-driven
decisions to optimize sales and operations.
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Energy Consumption Prediction Model
Energy Consumption Prediction is a machine learning project developed as part of the
interview process for a Data Analyst role at Accenture Federal Services where I leverage
AI to optimize energy management and showcase this technology to the Deparment of Energy
. The model forecasts energy usage for the east part of the country using a Random
Forest Regressor over different time periods (days, months, and years) using historical
data. Built with Python, Flask, Scikit-Learn, Pandas, and Matplotlib, the project
includes a web-based application that allows users to input data and receive real-time
energy consumption predictions.
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