#Matplotlib-pyplot
Showing 34 of 34 repositories tagged #matplotlib-pyplot, ranked by stars
Transform your favorite cities into beautiful, minimalist designs. MapToPoster lets you create and export visually striking map posters with code.
The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.
Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram, etc
Resources for teaching & learning practical data visualization with python.
In this project I am Cleaning, Analysing and creating Visualizations to make data easily accessible of ease use.
The "House Price Prediction" project focuses on predicting housing prices using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, and XGBoost, this project provides an end-to-end solution for accurate price estimation.
Kickstart AI through Machine Learning and Deep Learning Projects (20+)
Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.
This repository contains jupyter notebook and other resources made by me during learning Data Science
Como obter os dados históricos e analisar ações da bolsa utilizando o Pandas
This repository contains code for collecting pose data of various yoga poses using the MediaPipe Pose model. The collected data includes the angles of different body joints in each yoga pose.
This repository is meant to store the code examples used in articles posted on www.insidelearningmachines.com. Each article on the website will have an associated jupyter notebook here with the same name.
🐍 Materiály, Zdrojové Kódy, Prezentácie ku kurzom Python, OOP, R, BI, Data Science, AI/ML, ChatGPT
Engezny is a python package that quickly generates all possible charts from your dataframe and saves them for you, and engezny is only supporting now uni-parameter visualization using the pie, bar and barh visualizations.
The aim of this project is to develop a solution using Data science and machine learning to predict the compressive strength of a concrete with respect to the its age and the quantity of ingredients used.
Data cleaning, analysis and visualization of Paris metro traffic (Python, Pandas, Matplotlib, iPyLeaflet, Kepler.gl).
A visually interactive AI-powered sales forecasting dashboard using Streamlit, Plotly, and Python. Visualize KPIs, trends, and export filtered data with ease.
Using K-Means algorithm for customer segmentation due to credit card behavior
Exploratory Analysis of Indian Airline's Ticket Prices using Python and Power BI
This project focuses on predicting gold prices using historical data and machine learning techniques. It demonstrates a complete data science workflow, including data preprocessing, exploratory data analysis, feature engineering, model training, evaluation, and result visualization using Python.
Co-occurrence analysis in pubmed and faers of two lists of terms.
Pytorch implementation of the "Dermatologist-level classification of skin cancer with deep neural networks" research paper. Smaller dataset supplied by Udacity.
📊 A practical toolkit for feature engineering and selection in Python. Explore EDA, handle missing data and outliers, scale/encode/transform features, discretize, and select via filter, wrapper, embedded, shuffling, and hybrid methods. Comes with datasets, clean notebooks, reusable utils, and clear visuals for fast, reproducible ML. Workflows.
In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.
Collection of notebooks describing common use cases of some important Python libraries for Data Science and Machine learning
📘 This repository offers a complete K-Nearest Neighbors (KNN) tutorial, guiding you from core theory to hands-on practice. Learn to implement KNN from scratch with NumPy, apply it using scikit-learn, and explore visualizations, datasets, and Jupyter notebooks to fully understand, test, and optimize the algorithm.
End to end implementation and deployment of Machine Learning Car Price Prediction using python, flask, gunicorn, scikit-Learn, etc. on Heroku web application platform.
🔬 Reproducible sandbox for Gaussian Naive Bayes (GNB) applied to cancer cell classification — includes an interactive notebook, data layout and preprocessing guidance, feature-extraction tips, a lightweight scikit-learn pipeline, evaluation protocols for small/imbalanced biomedical datasets, and example scripts for prepare/train/evaluate.
Black Friday Sales Analysis explores customer demographics, purchasing behaviors, and product trends to uncover insights and patterns driving sales during Black Friday events.
Samsung Innovation Campus Python Course
This project offers two Flask applications that utilize ImageAI's image prediction algorithms and object detection models. These apps enable users to upload images and videos for object recognition, detection and analysis, providing accurate prediction results, confidence scores, raw data of detected objects at frame-level, and object insights.
The purpose of this project is to explore and analyze Goods and Services Tax (GST) revenue data collected from Indian states across months. The analysis reveals patterns, correlations, and trends in tax revenue to help in policy-making and financial planning.
A Perfect Repository For Data Anaysis with Jupyter Notebook.:chart_with_upwards_trend::chart_with_downwards_trend: