Movie Recommendation using Machine learning
Movie Recommendation using Machine learning
Movie Recommendation using Machine learning and collaborative filtering techniques is implemented in Python
Python demo
Liver Disease prediction with 99% accuracy using Convolutional Neural Network and ML model
Introduction:
As a final year student, choosing a compelling and impactful project is crucial for showcasing your skills and knowledge. Liver disease prediction with 99% accuracy is good to choose, an area of growing significance in the healthcare field, offers an excellent opportunity to apply advanced machine learning techniques. In this blog post, we will explore how Convolutional Neural Networks (CNNs) and Decision Trees (DT) can be harnessed to predict liver disease, presenting an exciting and relevant final year project idea.
- Understanding Liver Disease:
Liver disease, is the more life threatening disease. It is important to detect early. The early detection helps in extending life of patients. - Convolutional Neural Networks (CNNs):
2.1. Introduction to CNNs: CNNs as a powerful deep learning technique widely used in computer vision applications. CNN has convolutional layers, pooling layers, and fully connected layers.
2.2. CNNs for Liver Disease Prediction: Explore how CNNs can be adapted for liver disease prediction by utilizing medical images such as ultrasound or MRI scans. Discuss the steps involved in data preprocessing, model architecture design, training, and evaluation.
2.3. Benefits and Limitations of CNNs: The advantages of CNNs in capturing intricate patterns in medical images, enabling high accuracy in disease prediction. Potential limitations are the need for sufficient data and computational resources. - Decision Trees (DT):
3.1. Introduction to Decision Trees: Decision trees is a versatile machine learning algorithm used for classification tasks.
3.2. DT for Liver Disease Prediction: The application uses decision trees in predicting liver disease .
3.3. Benefits and Limitations of DT: Decision trees has ease of interpretability, ease of implementation, and handling missing values. It has the potential limitations, such as overfitting and sensitivity to small changes in the training data. - Comparative Analysis:
Compare the performance and characteristics of CNNs and decision trees for liver disease prediction. Liver disease prediction with 99% accuracy whereas Decision tree achieved very less than this. Discuss their strengths and weaknesses in terms of accuracy, interpretability, computational requirements, and suitability for different data types. Encourage students to consider the trade-offs when choosing between these two approaches. - Conclusion:
Liver disease prediction with 99% accuracy using Convolutional Neural Networks in predicting liver disease is more accurate than using machine learning technique Decision Tree.
Python demo video
Check some of the related project demo
Baby cry detection and analysis
This project is developed on Python programming with Machine Learning algorithm, K-Nearest Neighbor (KNN) algorithm
The project used Audio pre-processing with MFCC technique to extract feature.
ML is used to train and detect the infant cry type. There are five types used in this project are
Belly Pain
Burping
Discomfort
Hungry
Tired
The project gets accuracy of 88% with KNN algorithm
Python Demo video
Landslide prediction using Machine Learning algorithms
Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in all parts of the world. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased.
Python Project demo video
Stock Price Prediction using Hyper tuned ML models
Stock price prediction has long been a challenging problem in the financial industry, with researchers and practitioners continuously seeking more accurate and robust methods. This study explores the application of hyperparameter-tuned machine learning (ML) models for stock price prediction. Leveraging the power of advanced ML algorithms and hyperparameter optimization techniques, this research aims to improve the accuracy of stock price forecasts and provide insights into the effectiveness of different models.
Algorithms Used
Decision Tree algorithm
KNN algorithm
Random forest algorithm
The research begins by collecting historical stock price data, along with relevant financial indicators and market sentiment features. Hyperparameter optimization techniques such as grid search optimization are then applied to fine-tune the models and enhance their predictive performance.
Python Demo
The stock price prediction project also implemented using
Clustering Analysis
www.finalsemprojects.com/stock-prediction-using-clustering-and-classification-model/
Stock price prediction using deep learning project demo can be seen here
www.finalsemprojects.com/stock-price-prediction-in-machine-learning-and-deep-learning/
stock price prediction in machine learning and Deep learning
Stock price prediction using machine learning and deep learning
Stock price prediction using machine learning and deep learning is the python project implemented with Decision tree algorithm for ML and Convolutional Neural Network for DL. This also gives stock price forecast for next 5 days.
How to Download dataset for Stock Price prediction?
We have used NSE real time data and it can be download with the use of mpl_finance library through python code. The data we have downloaded from 2000 to till date. We can able to download any stock data depending on the symbol fiven by NSE India.
What is the algorithm Used for this project?
The algorithm used are Decision Tree and CNN algorithm
What is the output of the project?
Stock price prediction is the ultimate output of the project. Whereas we also evaluated the performance of algorithm by measures such as MAE, MSE, RMSE and R-Squared error values.
What is the application of Stock price prediction?
Stock price prediction is useful for traders and investors for the predicting and forecasting the price. The volatility nature of stock price may reduce the performance of the algorithm.
What is the advantages of this project?
In this project, any stock price prediction can be performed. The stock symbol represented by NSE should be given. Thus any NSE stock can be given and download real time data for prediction.
Demo of the project can be viewed below
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
- Data Pre-processing
- Data Visualization
- Training using Lasso
- 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
Project implementation demo is given below, source code for bike usage demand prediction and forecast is written in Python
Similarly, forecasting is an interesting subject, it can be applied in given work check mode links
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 prediction is also done with Cleveland dataset for this check link
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
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
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
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