menanashat/electro_pi_Task2

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Created 12 months ago
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menanashat/electro_pi_Task2

menanashat

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this is simulation to ChatGPT

Market_Segmentation_with_Neural_Networks

Market_Segmentation_with_Neural_Networks

Jupyter Notebook

electro_pi_Task1

this about simple 10 tasks

Face-recongintion-System

system for detect ,add ,Track People Using MTCNN

Data-Science-Tools-and-Ecosystem

Jupyter Notebook

Arabic-_-OCR

in this project i upload the front face of Egyptian ID and back face of Egyptian then will produce the output in image

Webscraping-Test

the is implementation of webscraping

Jupyter Notebook

Eulerian-Video-Magnification-

this Algorithm is for motion magnification and color magnification

Chatbot

Building a Simple Chatbot in Python with a Knowledge Base

Analysis_of_Amsterdam

i make analysis of restaurant in Amsterdam

Jupyter Notebook

MOVIE_RATING_PREDICTION

machine learning project that involves building a model to predict the rating of a movie based on various features or characteristics of the movie. The project typically involves collecting and preparing a dataset of movies with their corresponding ratings and features, such as genre, director, cast, budget, and release date.

Jupyter Notebook

-Tic-Tac-Toe

this game is Build with python advanced

Package_using_pycaret

this package can calculate the accuracy of any dataset

image_Classifaction_App_Android-

tha app with kotlin language with tensorflow

My_Own_Package_of_ML

this code is responsible for Clean , Analysis , Visualize , Build ML Model all of that using Streamlit

menanashat

Global-Aligment-With-C-

This code with high Accuracy and efficiency with c++

Automated_EDA

this project can analyze any project and visualize different plots for each columns

React_Native_Porject

contain Login and Sign up and Product Home and Favourite screen and search and cart item

Final-Project-of-Computational

Email_Spam_Filtering

This project aims to automate the process of filtering out unwanted or unsolicited emails, commonly known as spam, from a user's email inbox.

Jupyter Notebook

electro_pi_Task3

this is Code Review Assistant

Analysis-of_zomato

this project make analysis of Restaurant

Jupyter Notebook

Desktop_Application

Vitalism is a cutting-edge project that utilizes video data analysis to extract vital signs from humans.

Sentiment_analysis

Sentiment analysis is the process of analyzing text data to determine the underlying emotion or opinion - positive, negative or neutral. In this project, we will build a sentiment analysis classifier using Python.

Jupyter Notebook

COVID-19_IMPACTS_ANALYSIS_EDA

is an approach used to analyze and understand the impact of the COVID-19 pandemic on different aspects of life. EDA is a process of discovering patterns, relationships, and insights from data by using statistical and graphical techniques.

Jupyter Notebook

Music_recommendation

The aim of the music recommendation project is to develop a system that suggests personalized music tracks to users based on their preferences and listening habits. The project utilizes machine learning algorithms to analyze user data, including track attributes, genres, and artist information, to generate accurate and relevant recommendations.

Jupyter Notebook

Face-Mask-Detection-project

computer vision application that uses TensorFlow and OpenCV to detect faces in a video stream and determine if the person is wearing a face mask or not.

-Flower-Classification_using_Decision_Tree_Algorithm

Project Goal: The goal is to develop a machine learning model that can classify images of flowers into different species/varieties with high accuracy. Data: Flower image dataset with around 100-1000 labeled images of different flowers belonging to 10-20 categories. Each image would be labeled with the flower species/variety it belongs to.

Jupyter Notebook

CREDIT_CARD_FRAUD_DETECTION

in this project i have explored how to use machine learning algorithms to detect credit card fraud. By following the main steps involved in the project, we can develop a machine learning model that can accurately detect fraudulent transactions and prevent financial losses for businesses and consumers.

Jupyter Notebook

TITANIC_SURVIVAL_PREDICTION

is a data science project that uses machine learning algorithms to predict the survival of passengers on the Titanic. The project is based on a dataset that contains information about the passengers on the Titanic, including their age, gender, class, and other factors.

Jupyter Notebook

Churn_Prediction

Churn prediction is a common data science project that involves predicting customer churn, which refers to the likelihood of customers discontinuing their relationship with a business or service provider. In this project, the goal is to develop a predictive model that can identify customers who are at risk of churning

Jupyter Notebook

SALES_PREDICTION

machine learning project that involves building a model to predict the sales of a product or service based on various features or characteristics of the product, such as price, promotion, advertising, and seasonality.

Jupyter Notebook

boston_House_Price_Predictions

machine learning project that aims to predict the prices of houses in the Boston area based on various factors such as the number of rooms, crime rate, accessibility to highways, and other features. The project involves using a dataset of historical Boston housing prices and corresponding features to train a machine learning model

Jupyter Notebook

ML_Facial_recognition_to_detect_mood_and_suggest_songs_accordingly

The goal is to develop a machine learning model that can analyze facial expressions in images/videos to detect emotions/mood such as happy, sad, angry etc. Based on the detected mood, suitable song recommendations will be provided to the user.

Jupyter Notebook

VIDEO_GAME_SALES_PREDICTION

is a data science project that uses machine learning algorithms to predict the sales of video games. The project is based on a dataset that contains information about various video games, including their genre, platform, publisher, and other factors.

Jupyter Notebook

Final-Presentation

this is Final Presentation for coursera about course IBM professional Data science

Customer-Segmentation-for-E-commerce-Personalization

Customer segmentation for e-commerce personalization is the process of dividing a customer base into distinct groups based on similar characteristics, behaviors, or preferences. By segmenting customers, e-commerce businesses can gain a deeper understanding of their target audience and tailor their marketing strategies.

Jupyter Notebook

Parkinsons-Disease-Detection-Using-ML-Algorithms

this Machine learning (ML) algorithms can be used to detect PD at an early stage, when it is more treatable. ML algorithms can be trained on data from patients with PD, such as their symptoms, medical history, and imaging scans. The algorithms can then be used to classify new patients as having PD or not having PD.

Jupyter Notebook

Dice-Roller-Android-Application

This app is developed by Koltin language with android studio

Sign_Language_Classification

This project is a hand sign detection system that uses the MediaPipe library and OpenCV to identify hand gestures and perform actions based on the gestures.

Jupyter Notebook

-Flower-Classification

The Flower Classification project is a deep learning project that involves training a model to classify images of flowers into one of 102 categories. The dataset used in this project is the Oxford Flower Dataset, which contains 8,189 images of flowers from 102 different categories. The images are of varying sizes.

Jupyter Notebook

Fake_News_Detection

In this project, we explore the use of Python in building a machine learning model to detect fake news. We delve into the key steps involved, including data collection, preprocessing, feature engineering, model selection, training, and evaluation. By leveraging Python's powerful libraries and tools for natural language processing

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Analyze_weather_data

Analyzing weather data using Python is a common project that involves processing and exploring weather-related datasets to gain insights and make predictions

Jupyter Notebook