Heart Failure PredictionLast Updated on May 3, 2021
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide.
Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.
Most cardiovascular diseases can be prevented by addressing behavioral risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.
People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidemia or already established disease) need early detection and management wherein a machine learning model can be of great help.
Created a model for predicting mortality caused by Heart Failure.
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Heart Disease Classification Via Svm KernelsLast Updated on May 3, 2021
The aim of the project is to develop a model where where we can predict the Heart Disease in the person.
This will help us to detect the disease whether the person is having the Heart Disease or not. This model will help many people all over the globe to know about the heart disease and it can save many lives.
In this project, a good dataset was necessary to implement and execute the problem statement. Then a good analysis was required to find out the correlations and various distributions among the features. Using various types of plots and charts to find out the number of people affected with this till now and generate a model where we can achieve a good score. So, the problem statement was so clear that we need to find whether the person has heart disease or not. SVM classifier helps us to distinguish and get the results. There are various types of SVM kernels in today's era, then we gave to thought to do on different SVM kernels rather than one-two kernels. In total we applied 14 different types of kernels. The highest accuracy on testing was 90.163% and the kernel was Generalized Hist Intersection SVM kernel. SVM classifier can be used to predict the heart disease and solve the required problems.
Enterprise AiLast Updated on May 3, 2021
Enterprise AI is about enhancing the customer satisfaction index and to ensure customer stickiness to your organization. By infusing emerging technologies like artificial intelligence to engage and retain the set of customers. Using AI algorithm, we should address the use case. The business operation
processes like determining the customer sentiments from various different media like - Social media, Audio Calls, Video Calls, Images, Emails & Chats, interact with customers to provide quick and effortless
solutions, analyze and learn from buying behaviour to generate next best offer, ascertain customer retention and ensure lesser churn, derive AI-based Customer Segmentation, manage customer
touchpoints, evaluate customer feedback and engage with the customers. We provide a membership card to all the customers who purchase stocks in the store. By scanning the QR code the customer can fill the
feedback. Through the user can easily complete the feedback (Bad, Good, Very good) after purchasing. We are providing three categories (Bronze, Gold and Platinum) for our customers to categorize their
purchasing list to calculate the purchasing efficiency based on their quality ,they purchase. The customer who gives feedback as very good, they come under platinum category, best offers are provided to
them (free purchase for Rs.1000). Notifications will be sent to customers through the messages about the new products available along with its price. Best offers are also provided on festival occasions. We classify the feedback using classification algorithms like random forest to get the positive and negative feedbacks.
Negative feedback will be collected and rectified soon. Through this approach, the shopkeeper is able to get clear feedback about his shop easily.
Churn PredictionLast Updated on May 3, 2021
Predicting Customer Churn at a Fictitious Wireless Telecom Company
Churn Management has gotten great attention among the telecommunication Industry because it is proved that instead of going for advertisements to find new customers it’s better to find a technique, solution, and all the available resources in our service to figure out a pattern to make customers stay in the company. Every telecommunication company has huge competition and due to easy access of the plans and services provided by all the companies, a customer can switch the company anytime. For churn Prediction, it is most required to identify the customer who has the highest probability of leaving the service of the company and it will be effective if it’s done at the right time. Through this company can make a decision on what service to provide to make the customer not leave the service.
Here, we can reformulate the given problem as a Classification problem. My goal is to build a Classification model that can predict if Customers will stay with the company or not from the given features. To achieve this, first, I did data analysis and data cleaning, data preparation for training, and then model building. After this, based on the performance I find the best parameters of our model through GridSearchCV which best suits for the given data and gave the expected result.
False Alarm Detection SystemLast Updated on May 3, 2021
This project was made for a chemical industry which had sensors installed in various parts of the factory to detect H2S gas which is hazardous to health. Every time one or multiple sensors detected the H2S leak, an emergency alarm rings to alert the workers. For every alarm, the industry calls a team which sanitizes the place and checks for the leak and this was a big cost to the company.
A few of the alarms that ring are not even hazardous. The company gave us the data for each alarm with a final column stating the alarm was dangerous or not.
Unwanted substance deposition (0/1)
The data was first pre-processed and analysis libraries like Numpy and Pandas were used to make it ready to be utilized by a machine learning algorithm.
Problems like standard scaling, categorical data and missing values were handled with appropriate techniques.
Then, we used Logistic Regression model to make a classifier with first five column as independent columns and dangerous column as dependent/target column.
Now whenever, there is a leakage and the alarm rings, the data is sent to us and we predict if it is dangerous or not. If found dangerous then only the team is called to sanitize the place and fix the leak. This saved a lot of money for the company.
Research Paper On Face Detection Using Haar Cascade ClassifierLast Updated on May 3, 2021
In the last several years, face detection has been listed as one of the most engaging fields in research. Face detection algorithms are used for the detection of frontal human faces. Face detection finds use in many applications such as face tracking, face analysis, and face recognition. In this paper, we are going to discuss face detection using a haar cascade classifier and OpenCV. In this study, we would be focusing on some of the face detection technology in use.
In this study, we covered and studied in detail about face detection technique using haar cascades classifier and OpenCV to get the desired output. Using the OpenCV library, the haar cascade classifier was able to perform successful face detection with high accuracy and efficiency. We also used the OpenCV package to extract some of the features of the face to compare them. Also, we discussed some popular face detection methods. Further, we discussed the scope of face detection in the future and some of its applications. At last, we conclude that the future of facial detection technology is bright Security and surveillance is the major segments that will be deeply influenced. Other areas that are now welcoming it are private industries, public buildings, and schools