The Prediction of Breast Cancer is a data science project and its dataset includes the measurements from the digitized images of needle aspirate of breast mass tissue.
The data has 100 examples of cancer biopsies with 32 features. From these features, we can predict whether the tumors are benign or malignant.
The steps to be done are:
- Data Preparation by checking the target variable and removing the ID variable.
- Visualization of the frequency of the type variable.
- Normalizing the variables by using min-max normalizations and z-score methods.
- Bifurcation of the train and test sets.
- Prediction of the type of tumors using the classification algorithm.
- Improving the models using different values of k and other methods of normalizations.
Other data Science Projects using python below:
1) Marketing Campaigns Prediction of the clientele subscribing to services in Bank.
2) Market Basket Analysis for the creation of Online Recommender System for Grocery Supermarket.
3) Movie Review Analysis using Natural Language Processing (NLP).
4) Analysis of Most Connected Hubs in Socially Connected People.
5) Analysis of Loan Sanction for particular customers in a Bank.
6) Prediction of Medical Expenses of Incoming Patients in a Particular Hospital.
7) Cluster Analysis of Collection of People impacting a Social Networking Site.
8) Text Mining with Predictive Analysis of Spam Filtration in Incoming Mails.
9) Expectation Maximization Analysis for Digit Classification.
10) Exploration of Murders according to Investigation Team Uniform Crime Reports.
Deep learning is a new area of machine learning research which has been introduced with the objective of moving machine learning closer to one of it’s goal i.e artificial intelligence.
There are various applications of deep learning. In this project we use deep learning for hand-written character recognition.
The challenges associated with hand-written character recognition are- word and line seperation,segmentation of words into characters, recognition of words when lexicons are large, and the use of the language models in aiding preprocessing and recognition, character extraction , types of hand writing, number of scriptors, size of vocabulary and spatial layout.
We would like to attempt them with deep learning. Multi-layer with layer wise abstraction will be used for this purpose.
Project Domain: Image processing and Machine learning
Project Title: Digit Recognition
Digit recognition is one of the active research topics in digital image processing. It is a classic machine learning problem. The goal of this project is to take an image of handwritten digits and determine what those digits are. The principal task in digit recognition is to extract HOG features from the database of handwritten digits and to build a classifier on it. Then we predict the digits of the database using this classifier. To build the classifier we write python scripts.
The current systems use resource intensive image processing methods and algorithms with varying accuracies and rejections.
The proposed system uses efficient pattern classification and machine learning approach to improve overall performance of predicting the digits.
This digit recognition can be further scaled to achieve big data processing levels.
Hardware and software requirements:
- RAM : 1 GB and above
- OS : Linux, Windows
Traffic management is the problem that most of the countries are taking special steps. Smart traffic management system project is a hardware and software application which will use latest technologies to calculate traffic and provide information to traffic police for taking steps.
Traffic Management System
This application is designed by using PLC and SCADA technology. Compare to existing method, in this project density of vehicles at each lane and their weight and then take required steps to control traffic. This system is useful for managing traffic in highways.
Sensors will detect weight of every vehicle and if vehicle weight is more than 80 sensors will send signals to PLC , PLC will guide vehicles to move to other lane by sending information to barricading.
Similarly if the weight of vehicle is less than 80 sensors will send message to PLC which will help vehicles to pass to respective LANE.
Download Smart Traffic Management System Project Source Code