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Python Basics, Machine Learning and Deep Learning
We implemented two Centralized approaches- NMPC and Velocity Obstacle Algorithm, along with two DeCentralized approaches- Priority Safe Interval Path Planning (SIPP), and Conflict Based Search (CBS) planning.
Automobile Data Insights: A data analysis project exploring trends in vehicle prices, fuel efficiency, and other key attributes. Uses Python, Pandas, Matplotlib, and Seaborn for data visualization and insights.
Welcome to my project. OpenPyVision is a real time videoMixer based on opencv and pyqt6.
A collection of Python libraries for data analysis and visualization, including NumPy, Pandas, Matplotlib, and Seaborn.
This project was done in association with the Solar-Terrestrial Research Department(CSTR) in the New Jersey Institute of Technology (NJIT) and was purposely crafted to find the phenomenon STEVE.
Numpy ( Numerical Python ) Python Package.
Analyzing the customer dataset and developing a machine learning solution to segment customers into meaningful groups based on their purchasing behavior, demographics, and interaction with past marketing campaigns.
Showing my daily progress in understanding machine learning.
A structured collection of Jupyter notebooks exploring NumPy from the ground up; covering array creation, manipulation, broadcasting, indexing, and data visualization for scientific computing and data analysis.
A journey through 100 Days of Python, featuring multiple projects and games. Each day brings new coding challenges and fun, building skills and habits for consistent Python practice!
The TARDIS Train Dashboard is a data analysis and visualization tool designed to process, clean, and analyze train delay data. It provides insights into train schedules, delays, and their causes, and offers predictive modeling for future delays.
13 Data Analysis Examples - MovieLens 25M Dataset
The objective of this project is to examine a user's screen usage through Python, with a focus on identifying the applications and websites they utilize and the duration of usage. The project employs a range of libraries including pandas, numpy, plotly.express, and plotly.graph_objects for data processing, visualization, and analysis purposes.
NumPy (short for Numerical Python) is a powerful Python library used for working with arrays, matrices, and numerical computations.
Conducted sentiment analysis and data visualization on YouTube comments using the TextBlob library, uncovering insights on audience engagement, emoji usage, and video performance metrics. Implemented linear regression models to explore correlations between views, likes, video titles, and audience engagement.
Conducted in-depth time series analysis on stock market data for major tech companies like Amazon, Google, Apple, and Microsoft. Utilized multivariable analysis to explore the inter-relationship between stock closing prices and daily % return, visualized findings using Seaborn library, and performed value at risk calculations for each company.
A complete collection of NumPy operations, data types, and examples for Python learners. Simple explanations, clean code, and real practice for beginners to master NumPy. Learn NumPy easily — from arrays to advanced operations. Includes examples on data types, reshaping, slicing, and mathematical functions.
This repository contains a Jupyter/Colab notebook `g_numpy.ipynb` where I have learned the **fundamentals of NumPy**, the core library for numerical computing in Python. It covers array creation, operations, and important techniques used in data science and scientific computing.
A comprehensive guide to mastering NumPy with practical examples and applications in machine learning. Perfect for learners and developers looking to deepen their knowledge of numerical computations in Python.
A portfolio project that analyzes survey data from 5,000 employees around the world to investigate the impact of work-related factors on mental health and job satisfaction. It examines various variables and identifies trends and correlations. Using SQL, Python, Tableau, and Excel, the project demonstrates data analysis and visualization techniques.
This repository is not just a collection of tutorials and practice exercises, but also a showcase of innovative projects that highlight the incredible potential of NUMPY in various fields.
Análisis exploratorio ligero de las ventas de cuatro tiendas Alura Store en Latinoamérica, empleando Python y pandas para obtener insights rápidos de facturación, categorías y logística.
Healthcare Data Analysis Project using Python & Power BI. I've extracted impactful insights on Patents & Exclusivities granted by the US FDA to drug products.
Realizar un estudio para la empresa farmacéutica BIOGENESYS para ayudar en su estrategia de expansión en Latinoamérica. Realicé un análisis exploratorio de datos sobre la incidencia de COVID-19 y otros factores relevantes, identificando tendencias y oportunidades mediante estadísticas, mediciones y visualizaciones.
🌊🚨 Sistema de Alertas Tempranas - Golfo de Cádiz Sistema profesional de monitorización y alertas para el Golfo de Cádiz.
math function plotter coded in Python using Numpy, Matplotlib and Tkinter
small pieces of Python code
OmniNumPy is an experimental compatibility layer that sits on top of modern NumPy (≥2.0) and lets you run the same code across multiple array backends — NumPy, PyTorch, CuPy, and JAX — with minimal changes.
Attendance Management System using Face Recognition
Here I analysed data and made pipeline out of it making model making/training/testing/selecting and hyperparameters selecting more user friendly and visualised, like an application. I have worked for this project ~2 weeks.
Adaptive Learning Recommendation System using collaborative filtering to suggest personalized learning materials based on student engagement and performance metrics.
As an introduction in the field of "Data Science", I have worked upon creating and analysis and visualizing a given data set of Amazon sales, using Python. Loading loading raw sales data into pandas DataFrames, cleaning and preprocessing the dataset for accuracy, applying descriptive statistics with NumPy, and visualizing via Matplotlib and Seaborn
The Airbnb Price Prediction project focuses on analyzing and predicting Airbnb listing prices based on various factors such as location, amenities, room type, customer reviews, and host reputation.