IEEE Project,Ns2 Project,Python Project
Machine Learning Projects , Python Projects

Citywide Bike Usage Prediction and Forecast

Citywide Bike Usage Prediction and Forecast

City wide bike usage prediction and forecast using machine learning is the aim of the project. Bike sharing demand is increasing nowadays. In this work, dataset from Capital Bikeshare system, Washington DC, USA is used.

Bike demand may affect or change due to various parameters such as weather, holiday or working day, temperature, humidity and more. These features are considered in this dataset.

Project methodology

  1. Data Pre-processing
  2. Data Visualization
  3. Training using Lasso
  4. and Ridge regression

As the dataset considered is bike demand, which is a variable parameter according to each condition and attributes. Regression is applied on this work.

Experimental analysis showed accuracy of bike demand prediction is around 62%.

Data science is useful when the dataset is having multiple influencing parameters. Here we have visualized data and done Explanatory data analysis as shown below

Citywide Bike Usage Prediction and Forecast

Project implementation demo is given below, source code for bike usage demand prediction and forecast is written in Python

Citywide Bike Usage Prediction and Forecast | ieee project demo

Similarly, forecasting is an interesting subject, it can be applied in given work check mode links

Stock Price prediction and Forecast

Air-pollution prediction through feature selection and deep learning
Deep Learning Projects , Python Projects

Air pollution prediction through feature selection and deep learning

Air pollution detection

Air pollution detection and monitoring is one the important area as these days air pollution is increasing through various parameters such as vehicles, industries etc. Air pollution prediction is made easier through artificial intelligence. This proposed work used feature selection algorithm for choosing important attributes from dataset.

The dataset from Central Air pollution board is used for this study. It has many attributes and the labels are considered as multi class classification as given below

  1. Residential
  2. Industrial
  3. RIRUO
  4. Sensitive

Regression algorithm and Neural Network algorithm is used for air pollution detection of above class type. Logistic regression gives best accuracy of around 63% for air pollution prediction.

Air Pollution Detection

The following link shows you the implementation process and methodology used. Source code used is Python and dataset downloaded from Central pollution board of India.

Air-pollution prediction through feature selection and deep learning | ieee project demo

More Machine Learning projects are available, some listed below

Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Learning

Human Action Recognition Using Deep Learning
Deep Learning Projects , Python Projects

Human Action Recognition Using Deep Learning

Human Action Recognition using Deep learning

Human Action Recognition using Deep learning is implemented with CNN algorithm. This achieves good accuracy for GUI single input Video file.

What is Human Action Recognition?

Human actions are simply hand weaving, clapping, boxing, jogging, running, walking etc. Human action recognition refers to automated detection of any of these activities from human. This is helpful for monitoring human through surveillance camera.

Human Action recognition PPT

Human action recognition is python project and PPT contains Abstract, Introduction, Existing study, Literature survey, UML diagram.

Human activity detection is one of the challenging job, however, artificial intelligence made it easier. This detection is more useful in surveillance and monitoring purposes. The dataset with 6 activities is considered for training, which includes boxing, hand waving, hand clapping, jogging, running, walking.

Human action recognition project source code download

Human action recognition project source code download is available with highest accuracy on action detection. Deep learning algorithm Convolutional Neural Network (CNN) is used for activity classification project. The accuracy of the model is as high as 50%. Source code execution needs keras and tensorflow environment. The demo of the activity recognition is can be seen below. For Human action recognition PPT, architecture diagram, project report are available. Contact us

Human Action Recognition Using Deep Learning | ieee project demo

More works on human monitoring is available are

Helmet wear detection using artificial intelligence

Human face emotion detection by deep learning

Driver drowsiness detection by deep learning model

Face detection and attendance monitoring through artificial intelligence

Human Speech emotion detection by deep learning

Heart Disease detection from ECG Signal Dataset using Machine learning
Machine Learning Projects , Python Projects

Heart Disease detection from ECG Signal Dataset using Machine learning

Electrocardiogram (ECG) data is quite useful to identify Heart Disease Detection. In this work, MITBIH dataset is taken for this study. Machine learning classification of heart disease is done using Decision Tree and Random forest algorithm.

Random forest accuracy is higher than decision tree. Heart disease prediction is done with highest accuracy around 97% using MITBIH dataset. There are 5 different classes are labelled in the dataset.

Heart Disease detection from ECG Signal Dataset using Machine learning | ieee project demo

Heart disease prediction is also done with Cleveland dataset for this check link

College Admission Through ML and AI
Deep Learning Projects , Machine Learning Projects , Python Projects

College Admission Through ML and AI

This is a web application in Python Flask. This application enable users to make use of Artificial Intelligence to get the students details in admission portal. Face recognition is used to capture students face and train them. For further student update data, face recognition used to retrieve data from database.

Check demo under following link

College Admission Process through ML and AI | ieee project demo
Skin Disease Detection using deep learning
Deep Learning Projects , Python Projects

Skin Disease Detection using deep learning

Skin disease / lesion detection through artificial intelligence is the growing area of study. In this work, Skin disease is identified by deep learning algorithm. Convolutional Neural Network (CNN) was used to train.

This work has implemented with 3 different pre-trained algorithms models and compared their performance.

  1. Alexnet
  2. VGG-16
  3. ResNet-18

Below figure shows the UML diagram of Skin disease detection project using CNN algorithm

Uml Skin disease

The performance shows that Alexnet and ResNet was giving good accuracy more than 90%. The demo video of the project is given below.

Skin Disease detection using Deep learning | ieee project demo

As like skin disease various disease can be classified with deep learning, some of the are listed with corresponding links below

Liver Disease detection using Machine learning

Breast cancer prediction source code download

Diabetes detection through machine learning Project

Lung cancer detection and classification project in python

P2P Transaction in Blockchain – Python Implementation
Python Projects

P2P Transaction in Blockchain – Python Implementation

Blockchain is one of growing research area and used in various applications including Bitcoin and more banking applications. In this work, blockchain of Peer to peer node is considered. The security key handled is using SHA algorithm for hash code generation.

Below is the working model of Blockchain in Peer to Peer network

P2P Transaction in Blockchain – Python Implementation | ieee project demo
Fruit Quality Assessment using Artificial Intelligence
Deep Learning Projects , Python Projects

Fruit Quality Assessment using Artificial Intelligence

Artificial Intelligence in Agricultural and food packaging industry is growing nowadays. Fruit quality detection using machine learning is more easier for packaging business. As the demand for food increasing with growing population, fast detection of fruit quality is needed. The is handled binary classification “healthy” and “damaged”.

Image features are extracted from images of healthy and damaged fruit dataset are trained with Support Vector Machine (SVM) algorithm. Demo of the working model is show in below link. Source code of the project implementation in python

Fruit Quality Assessment using Artificial Intelligence | ieee project demo

Looking for similar project, don’t forget to check the

Fruit Stage Monitoring and Quality detection using Artificial intelligence

A Semi-Supervised Learning Approach for Twitter Spam Drift detection
Machine Learning Projects , Python Projects

A Semi-Supervised Learning Approach for Twitter Spam Drift detection

Twitter Spam Drift detection

Twitter Spam Drift detection is analyzed from live stream of twitter data. Drift detection is the problem of identifying spam over the time. That is, through the course of time and it is dynamic. Machine learning algorithms are analyzed in the project

Twitter Spam Drift detection

Which algorithms used?

Naive Bayes, Logistic regression, KNN and SVM are used for detection.

The following modules are implemented
Data Collection
Data Pre-processing
Label data using YATSI
Twitter spam prediction

What is Unique in this project?

The uniqueness of the project is that the data is dynamic and thus the spam drift can be identified over the time.

Project Demo video

A Semi-Supervised Learning Approach for Twitter Spam Drift detection | ieee project demo

More related projects on twitter data are available as follows

Location Prediction in Twitter using Machine learning Techniques

Hate Speech detection on Twitter

Location Prediction in Twitter using Machine learning Techniques

Twitter sentiment analysis using five machine learning techniques