Sunday, 24 January 2021

Fake Product Review Detection using Machine Learning


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Online reviews play a very important role for decision-making in today's e-commerce. Large parts of the population, i.e. customers read product or store reviews before deciding what to buy or where to buy and whether to buy or not. Because writing fake / fraudulent reviews comes with monetary gain, online review websites there has been a huge increase in tricky opinion spam. Basically, an untruthful review is a fake review or fraudulent review or opinion spam. Positive reviews of a target object can attract more customers and increase sales; negative reviews of a target object can result in lower demand and lower sales. Fake review detection has attracted considerable attention in recent years. Most review sites, however, still do not filter fake reviews publicly. Yelp is an exception that over the past few years

has filtered reviews. Yelp's algorithm, however, is a business secret. In this work, by analyzing their filtered reviews, we try to find out what Yelp could do. The results will be useful in their filtering effort for other review hosting sites. Filtering has two main approaches: supervised and unmonitored learning. There are also about two types in terms of the characteristics used: linguistic characteristics and behavioral characteristics. Through supervised learning approach we have tried to make a model which can identify the fake review with almost 70 percent accuracy.

As the Internet continues to grow in size and importance, the quantity and impact of online reviews is increasing continuously. Reviews can influence people across a wide range of industries, but they are particularly important in e-commerce, where comments and reviews on products and services are often the most convenient, if not the only, way for a buyer to decide whether to buy them.

 

Model training

Refer to the Jupyter notebooks in research folder to know the steps taken for preprocessing, model development and algorithms used. Although we experemented with different models, we found Naive Bayes to be most accurate with F1 score of 77%. 

Installing and running this app:

  1. Requirements: Use pip install/conda install to download following packages
  • Numpy, pandas
  • sklearn
  • spacy
  • Django 2.1
  • pickle
  • tqdm
  1. running the app:
  • Go to folder containing manage.py and run command: python manage.py runserver
  • Once the server starts, open browser. The app runs on http://127.0.0.1:8000/
  • fake_reviews.txt and real_reviews.txt contains some reviews that can be used to test the working of model.

Thursday, 21 January 2021

Fake News Detection using Machine Learning | Natural Language Processing...


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Fake News Detection using Machine Learning Natural Language Processing . A NLP and Machine Learning based web application used for detecting fake news. Uses NLP for preprocessing the input text. Uses XGBoost model for predicting whether the input news is Fake or Real.

here are tons of stories articles, where the news is fake or cooked up. With numerous advances in tongue Processing and machine learning, we will actually build an ml model which is in a position to detect if a bit of stories ... Here we'll be using artificial neural network models to verify the genuinity of the article.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Dataset Link: https://www.kaggle.com/c/fake-news/data

Training Model File 

Fake_News_Classifier_Using_LSTM.ipynb

Fake_News_Classifier_using_Machine_Learning.ipynb

Output Generated File

xgb_fake_news_predictor.pkl

Driver Distraction Prediction Using Deep Learning, Machine Learning


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Driver Distraction Prediction Using Machine Learning”, where given driver images, each taken during a car with a driver doing something within the car (texting, eating, talking on the phone, makeup, reaching behind, etc). The goal was to predict the likelihood of what the driving force is doing in each picture.

Driving a car may be a complex task, and it requires complete attention. Distracted driving is any activity that takes away the driver’s attention from the road. Several studies have identified three main sorts of distraction: visual distractions (driver’s eyes off the road), manual distractions (driver’s hands off the wheel) and cognitive distractions (driver’s mind off the driving task).

Dataset details -

  • Image Size - 480 X 640 pixels
  • Training Images count - 22424 images
  • Test Images count - 79726 images
  • Image type - RGB
  • Image field of view - Dashboard images with view of Driver and passenger
  • The 10 classes to predict are:
    • c0: safe driving
    • c1: texting - right
    • c2: talking on the phone - right
    • c3: texting - left
    • c4: talking on the phone - left
    • c5: operating the radio
    • c6: drinking
    • c7: reaching behind
    • c8: hair and makeup
    • c9: talking to passenger
  • Loss - multi-class logarithmic loss

State-Farm-Distracted-Driver-Detection

Kaggle hosted the challenge few years ago which focused on identifying distracted drivers using Computer Vision
Details of challenge can be found here - 
https://www.kaggle.com/c/state-farm-distracted-driver-detection

Implementation Details

  • DL Model - CNN's build from scratch ( 6 Conv Layer, 5 Dropout Layer, 3 Dense Layer)
  • Framework - Keras / Pytorch version in the process.
  • CNN Model Visualization/Model Interpretability - GradCAM
  • Final Accuracy -Train acc - 99.06%, Val acc-99 .46%

GRAD-CAM implementation for a test image with label drinking

GRAD-CAM is a technique to highlight how a model classifies new instanes by creating a heat map which highlights only the area which has contributed the most in prediction.
As seen in below image model classifies driver as distracted by drinking by highlighting the hand and glass.

Multiple Disease Prediction using Machine Learning


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Multiple Disease Prediction using Machine Learning . This Web App was developed using Python Flask Web Framework . The models won’t to predict the diseases were trained on large Datasets. All the links for datasets and therefore the python notebooks used for model creation are mentioned below during this readme. The WebApp can predict following Diseases:

  • Diabetes
  • Breast Cancer
  • Heart Disease
  • Kidney Disease
  • Liver Disease
  • Malaria
  • Pneumonia

Models with their Accuracy of Prediction

DiseaseType of ModelAccuracy
DiabetesMachine Learning Model98.25%
Breast CancerMachine Learning Model98.25%
Heart DiseaseMachine Learning Model85.25%
Kidney DiseaseMachine Learning Model99%
Liver DiseaseMachine Learning Model78%
MalariaDeep Learning Model(CNN)96%
PneumoniaDeep Learning Model(CNN)95%

 

Steps to run the WebApp in local Computer

Step-1: Download the files in the repository.
Step-2: Get into the downloaded folder, open command prompt in that directory and install all the dependencies using following command

pip install -r requirements.txt

Step-3: After successfull installation of all the dependencies, run the following command

python app.py

Dataset Links

All the datasets were used from kaggle.

Buy Now ₹1501

Tuesday, 12 January 2021

Crime Data Analysis Project in Machine Learning ,Python ,Django ,Pandas


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Crime Data Analysis Project in Machine Learning .Crime analyses is one among the important application of knowledge mining. data processing contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to spot crimes faster and help to form faster decisions.
The main objective of crime analysis is to seek out the meaningful information from great deal of knowledge and disseminates this information to officers and investigators within the field to help in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is employed for crime data analysis.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

Tuesday, 5 January 2021

Movie Recommendation System Project Using Collaborative Filtering, Pytho...


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( Note : Project Included with Complete Source code Database Plus Documentation, Synopsis, Report)

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.

A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.

A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.

Algorithms Implemented 

  • Content based filtering
  • Collaborative Filtering
    • Memory based collaborative filtering
      • User-Item Filtering
      • Item-Item Filtering
    • Model based collaborative filtering
      • Single Value Decomposition(SVD)
      • SVD++
  • Hybrid Model
    • Content Based + SVD

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements
python 3.6

pip3

virtualenv

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

Thursday, 24 December 2020

ONLINE GERAGE MANAGEMENT SYSTEM IN PHP MYS


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This Garage Management System PHP shall help the user to keep track of all the activities in the garage. It is a web based application which shall have users like Manager, Supervisor, Security, Customer and Mechanic. The admin shall give access for specific modules for the other users. The users shall login and manage with the activities of the system. The supervisor shall be able to check for the inventory of vehicle spares in the garage. He shall be able to check for the vehicles that are serviced currently and the ones which has to be notified for services. The user also shall be able to note the mechanic shop time hours. The system will also allow the payment for the repair or service done. The system also can check for the vehicle spares that are sold from the garage. The interface is developed using html and PHP. It has user friendly web interface.

Login User Roles :

  • Manager
  • Supervisor
  • Security
  • Customer
  • Mechanic

Brief overview of the technology:

Front end: HTML, CSS, JavaScript

  1. HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
  2. CSS : (Cascading Style Sheets) Create attractive Layout
  3. Bootstrap : responsive design mobile freindly site
  4. JavaScript: it is a programming language, commonly use with web browsers.

Back end: PHP, MySQL

  1. PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software.
  2. MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases.

Software Requirement(any one) : 

Installation Steps

1. Download zip file and Unzip file on your local server.
2. Put this file inside "c:/xampp/htdocs/" .
3. Database Configuration
Open phpmyadmin
Create Database named - garage_management_system
Import database - garage_management_system.sql from downloaded folder(inside database)
4. Open Your browser put inside "http://localhost/Project Folder Name/"

Buy Now ₹501

Thursday, 17 December 2020

Flutter Medicine Reminder App Project using Android Studio


A Flutter application, which you can save medicines and app will be send remind when you have to take it 🔔

A few resources to get you started if this is your first Flutter project:

For help getting started with Flutter, view our online documentation, which offers tutorials, samples, guidance on mobile development, and a full API reference.

Used Technology:

  • flutter cupertino icons 📱 link
  • flutter intl 📆 link
  • flutter sqflite 📋 link
  • flutter auto size text 📝 link
  • flutter path 🚀 link
  • flutter local notifications 🔔 link
  • flutter timezone 🕧 link
  • flutter animated text kit 📝 link
  • flutter animated widgets 👁️🗨️ link

App features :

  •  Save medicines in local database
  •  Show notification in correct time
  •  Delete medicines

Download Link

Thursday, 3 December 2020

Flutter Quiz App Project with Source Code | Android Studio


This is a quiz app that generates 3 questions per quiz.The result is finally displayed depending upon the number of questions answered correctly by the user. Questions are saved in lib/ques.dart files in json format. Android Studio IDE use for run this project android emulator.

QuizApp is an Flutter ,  android, IOS  based application, and enables the user to undertake a series of questions on Dart language. The app is user friendly, and the user shall find it extremely easy to answer the multiple-choice questions. At the end of the quiz, a result-report is generated which states the score. The app also presents an option to the current user to play the question-round again or quit in between.

 

 

Download Link

Steps To Run App

  1. cd QUIZ-APP-FLUTTER
  2. flutter run

Generate Output Files

  1. For Android - https://flutter.dev/docs/deployment/android
  2. For Ios- https://flutter.dev/docs/deployment/ios

Getting Started With Flutter

For help getting started with Flutter, view our online documentation, which offers tutorials, samples, guidance on mobile development, and a full API reference.

Tuesday, 1 December 2020

Flutter Scientific Calculator App Project | Android Studio


flutter Scientific Calculator is useful for situations where we need to calculate some complex things like logs or trigonometry. In such cases, the normal calculator won’t be useful for us. So therefore, we are here to develop a Scientific Calculator.

The scientific calculator is a type of an electronic calculator in which different complex calculating methods are involved. These methods contain mathematical, scientific and some methods related to engineering. Other than that, the scientific calculators have some features similar to ordinary calculator in which many kinds of basic calculations can be performed like addition, subtraction, multiplication, division etc. The functions which are involved in scientific calculator are scientific notations, floating point values, trigonometric functions, logarithmic function, fraction, factorial etc. The scientific calculator is used in various fields for example in astronomy, geology, physics, chemistry, somehow in biology as well. This calculator is mostly used by the students studying in school, college and university and also engineers of different sectors.

Flutter Scientific calculator application is an Android project built in Android Studio and or you can also use vs code. As you know by the name of the project it functions fully as similar to a normal calculator. Here you can perform all of your calculation with ease. This whole project is designed using Android Studio. Dart programming language is used for field validations. This project also can be implemented in different gadgets like the mobile phones and the watch gears also platform independent .

Requirements to develop project

To develop this application there are certain things that you need to know beforehand. So let us see its requirements and the platform that we’ll use for this project. Let us begin with the tool that we’ll use for our Flutter Scientific Calculator. The latest Version of Android Studio Or VS Code and also Dart and Flutter plugins Shoul  be install will be chosen as the Application Development Platform for it. So, you must be fond of Android Studio VS Code and also Dart and Flutter  and have good hands on it.

Download Link