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
Secure Fee Management in Python
Secure Fee Management in Python is a web application developed with Flask, Python, MySQL, which has entities such as admin and Staff
Secure Fee Management in Python
Admin has the following features to do
- Add Student
- Add Staff
- View Report
- Fees details paid by students
Staff module has the following features to do
- View updated personal details
- View Fees report for students
The complete project demo is available in the following link
Plant Leaf Disease Detection using Deep learning algorithm
MODULES
The modules included in our implementation are as follows
- Dataset collection
- Data pre-processing
- Training and prediction using Regression Models
DATASET COLLECTION
The dataset is downloaded from kaggle.com with two classes ‘healthy’ and ‘diseased’. The dataset contains plant leaf image with training set and test set folders.
The dataset variable names are described below
| Variable name | Attribute Description |
| Class | Binary class ‘healthy’ and ‘diseased’ |
| Training set | 364 images in diseased 388 images in healthy |
| Test set | 60 images in diseased 60 images in healthy |
Project Demo Video
Heart Disease Prediction Using Hybrid Algorithm
IMPLEMENTATION METHODOLOGY
The proposed work is implemented in Python 3.6.4 with libraries scikit-learn, pandas, matplotlib and other mandatory libraries. We downloaded dataset from uci.edu. The data downloaded contains binary classes of heart disease. Machine learning algorithm is applied such as decision tree and random forest along with hybrid model.
DATA DICTIONARY
The dataset collected with attributes age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slop, ca, thal, pred_attribute.
Modules
The modules included in our implementation are as follows
- Decision Tree
- Random forest
- Hybrid RF & Linear model
Python – Demo
Lyrics Mood Prediction using Machine Learning
Lyrics Mood prediction using Machine Learning
Convolutional Neural Networks for Diabetic Retinopathy
Convolutional Neural Networks for Diabetic Retinopathy
The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a dicult and time consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages on the retina and consequently provide a diagnosis automatically and without user input. We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task.
Techniques Used
- Convolutional Neural Networks (CNN)
- Logistic regression
- Random Forest
Python Deep Learning project demo
Face detection and recognition and attendance using machine learning and deep learning
This project is proposed for real time face detection and recognition. The project is implemented in both machine learning and deep learning.
Implementation step:
Face is detected in real time, detected face is trained with atleast 1000 frames for good accuracy.
The training the collected data
Face recognition with input and mark attendance
Software used: Python
Python Project Demo