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Mohamed-Elsaeed5 / Python Data Analysis CourseA full Python course for data analysis with Jupyter notebooks. Covers data types, loops, functions, files, NumPy, Pandas, Matplotlib, Seaborn, and date/time. Perfect for beginners to intermediate learners. Includes examples, practice files, and a visual topic map.
orkunaaa111 / Football Betting OddsToday, I'm sharing with you an England football match analysis project I created using Python, Matplotlib and Plotly! 🎉 This work focuses on a detailed analysis of England league matches in the 2020-2021 season.
AparnaShankar / AttendanceAnalyzerA Django based application using python library matplotlib that would be used to display the attendance of students along with necessary graphs.It also has two handy metrics for students to calculate their makeup and future attendance.
Gaurikabhatt27 / Shark Attack Data Analysis Python EDA ProjectA comprehensive Exploratory Data Analysis (EDA) project using Python to analyze global shark attack incidents. The project involves data cleaning, transformation, and visualization to uncover patterns based on location, gender, activity, and time. Tools used: Pandas, NumPy, Matplotlib, and Seaborn.
omarbadrani / SpeedNet AnalyzerDesktop app in Python (PyQt5) for internet speed analysis (Wi-Fi/Ethernet). Features: real-time tests (DL/UL/Ping), auto/manual server choice, detailed results, averages, history table, dynamic graphs (Matplotlib), and export (Excel/PDF). Ideal for precise, fast, visual monitoring.
mohanrao06 / Telco Customer Churn Analysis02Analyzing customer churn trends using Python (Pandas,NumPy, Seaborn, Matplotlib). Key insights: higher churn for senior citizens, month-to-month contracts, and electronic check payments. Recommendations include promoting long-term contracts and auto-pay. Insights available in the Jupyter Notebook! 🚀
PAVAN301005 / COVID 19 Vaccination ImpactThis project analyzes COVID-19 vaccination impact across regions using real-world data. It explores trends in vaccination rates, cases, hospitalizations, and deaths, offering visual insights into how vaccines reduced the pandemic's severity. Built with Python, Pandas, Matplotlib, and Seaborn.
avinashreddy1235 / Chat Mining WhatsappEDA of my personal WhatsApp chat data to uncover insights like who sent the most messages, chat frequency, and usage trends. Built with Python, pandas, and matplotlib. A fun and educational project exploring communication patterns using real-world text data.
mrinalmayank7 / Terrorism Analysis DashboardData Science Model Framework : Rewinding Terrorist Events since 1970. It includes 5 Modules with Folium , Restful API & Data manipulation in Python (Pandas , Matplotlib , Seaborn ,Plotly). Provide Analysis of 900+ attacks with the implementation of 10 functional tools (Static + Dynamic) to analyze data.
rubenandrebarreiro / Studying Criminology And Forensics Worldwide🚨 👮 🕵️♂️ A simple project based in Data Science and Data Analysis for Criminology and Forensics worldwide. This project was built using Jupyter Notebook and Python, with the support of some libraries for Data Science and Data Visualisation, like NumPy, Matplotlib and Pandas. This project shows an intensive and detailed study case about some indexes, historics and data about Criminology in several countries, states and cities, among other aspects!
LynnFernandes23 / Diabetes Prediction System The Diabetes Prediction System is a comprehensive web application utilizing HTML for the frontend and Python's Flask framework for the backend. It leverages Pandas and NumPy for efficient data management, employing Scikit-Learn to develop robust machine learning models. Matplotlib is used for insightful data visualization.
ThomasAFink / Visualization Of The Solar System On An Interstellar ScaleVisualize the Solar System: A Python script that plots the orbits of the major planets, with a special focus on Pluto and the Kuiper Belt. Utilizing NumPy for calculations and Matplotlib for visualization, this script provides an educational tool to explore the dynamics of our solar system, highlighting Pluto's unique orbit and the vast Kuiper Belt
nakigoe / NakigoeFull Stack Web Developer ASP.NET Core Blazor | Russian | Tajik | Mandarin | Japanese | English
gushi4421 / Douban Top250 Spider基于 Python Flask 的豆瓣电影 Top250 全栈数据分析项目。集成爬虫抓取、数据清洗 (Pandas)、多格式存储 (CSV/Excel/JSON)、可视化分析 (Matplotlib) 及情感词云生成，并通过 Web 界面进行交互式展示。
PSindhuri / Research ProjectThe goal of this project was to predict road surface quality score on scale of 0 to 3 with 0 for unworn roads up to 3 for heavily worn-out roads using deep learning regression models. Real time videos of the road surface around Bay Area, CA and KITTI image dataset was used for training and testing. Bokeh and Google Maps API was used for visualization of results and the data. LSTM, CNN-LSTM, SVR, Linear regression were used. Tools and Programming Languages: Python (Scikit, Pandas, NumPy, matplotlib, csv etc.), Keras with TensorFlow background, OpenCV, Jupyter notebook
amoodaniel / Recommendation System Using Twitter DataThis project attempted to improve product recommendation in e-commerce by developing a recommendation system that applied collaborative filtering algorithm using Twitter data as a basis for making recommendations to other members of an e-commerce store. The recommendation system was built using Python 3.8. The major modules in the recommendation system includes the Data Extraction Module, the Network Module, and The Conjunction module (which combines the metrics from the network and the metrics from the website database). The python packages that played a vital role in the conclusion of this project include, Tweepy, Networkx, Pandas, intertools, matplotlib, re, scipy.spatial.distance and csv. The project provided interesting recommendations to users based on their closeness with other Twitter users registered on our e-commerce store. It also eradicated the problem of cold-start finish, which has been a persistent major problem in recommender systems.
liu-kaining / MathplotterMathPlotter 是一款基于Flask框架的数学函数可视化工具，专为教育、科研场景设计。用户通过网页输入以x为变量的数学公式（支持sin、sqrt等常见函数），系统自动解析并生成高精度二维图像，实时展示函数形态。采用SymPy安全表达式解析与Matplotlib绘图，支持跨平台运行，适配Python 3.9+环境。界面简洁直观，左栏操作、右栏绘图，可自定义X轴范围，自动检测复数/非法输入，帮助用户快速验证数学模型的几何特征，适用于课堂教学、函数分析及算法调试等场景
shashwat23 / Titanic Survival PredictionTitanic-Machine-Learning-from-Disaster This repository contains a machine learning project for predicting survival of passengers who travelled on Titanic Ship in 1912. Problem Description- This project highlights my approach to the introductory machine learning competition on Kaggle website- Titanic: Machine Learning from Disaster [1]. The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. This project analyses which people were likely to survive. In particular, tools of machine learning have been used to predict which passengers survived the tragedy. Project Description This project has been made in Python v3.4. It uses various data processing, visualisation and machine learning packages such as numpy, pandas, matplotlib, scikit-learn etc. which should be installed if the code is run on a local machine. The project uses a 5 step process (general procedure) for it's predicting task which is as follows [2]: Perform a statistical analysis of the data and look over it's characteristics such as data type of columns, number of instances, correlation of each attribute with the output variable, finding mean and other information about data, correlation matrix etc. After performing statistical analysis, do a visual analysis by plotting the data. Do analyse the scatter_matrix, plot box plots etc. so as to know which attributes are relevant and which are not. Remove irrelevant attributes from the dataset for further analysis. Make a list of all machine learning algorithms that can give good prediction results and spot check each one of them (apply each one of them on the dataset) to find which one is better for prediction. Use k-fold cross validation to calculate performance characteristics of each of the learners (accuracy, precision, recall, area under ROC curve etc.). Take some of the good performing algorithms and perform a grid search/ randomised search over it's hyperparameters to find the optimal hyperparameters for the prediction task. Ensure that the optimal hyperparameters do not overfit the data, by performing k-fold cross validations on learners using these tuned hyperparametes as well. Use an ensemble or Voting Classifier on the above selected algorithms to achieve better performance or use any one of the above algorithm directly to perform predictions. Keep iterating over the above steps again and again and tune them according to the need so as to achieve better performance. File Description titanic_predictor - contains python code for predicting survival. my_solution.csv - contains sample output file generated from algorithm. train.csv- contains training data test.csv - contains testing data for making predictions readme.md - for guide to this project.
shnuwl / Python Matplotlib使用python第三方库wxPython、matplotlib、pcap、numpy等，从udp包里提取数据并计算出天线的广播权，最终形成beam forming图像。增加了UI控制台，可控制图像的暂停、图像参数的实时输入、图像坐标系的选择。过程中解决了绑定网卡数据包、图像窗口的动态刷新、设置说明文字的位置大小及自定义极坐标轴的刻度等问题。
thecraigd / Avocado PricesMapping Avocado Prices in the US using Python - GeoPandas, GeoPy and Matplotlib
