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Thyroid disease is a common cause of medical diagnosis and prediction, with an onset that is difficult to forecast in medical research. The thyroid gland is one of our body's most vital organs. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are one of the two common diseases of the thyroid that releases thyroid hormones in regulating the rate of body's metabolism. The main goal is to predict the estimated risk on a patient's chance of obtaining thyroid disease or not.
Description: My purpose of this project is to predict the fault of Scania truck due to air pressure system or not. For it, I used various types of ML-based classification models.
Model Used: Random Forest Classifier, Xgboost Classifier, Decision Tree, KNeighbours Classifier, Gradient Boosting, Adaboost Classifier, Logistic Regression.
Shopping malls and Marts keep track of individual item sales data in order to forecast future client demand and adjust inventory management. In a data warehouse, these data stores hold significant consumer information and particular item details. By mining the data stored in the data warehouse, more anomalies, and common patterns can be discovered.
In this project, I predict the car’s selling price with 88.6% accuracy. The price of a car depends on a lot of factors like the goodwill of the brand of the car, features of the car, horsepower and the mileage it gives, and many more….
Accuracy Using Linear Regression
Accuracy Using Linear Regression
The Accuracy Using Linear Regression is about 88.5%
Accuracy Using Lasso Regression
Accuracy Using Lasso Regression
The Accuracy Using Lasso Regression is about 88.6%
Accuracy Using Ridge Regression
Accuracy Using Ridge Regression
The Accuracy Using Ridge Regression is about 88.6%.
Detecting fraud transactions is of great importance for any credit card company. We are tasked by a well-known company to detect potential frauds so that customers are not charged for items that they did not purchase. So the goal is to build a classifier that tells if a transaction is a fraud or not.
Confusion Matrix
The data is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.
The accuracy of this model is 84%
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. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict possible heart disease.
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.
Confusion Matrix
Confusion Matrix
In this project, our objective is to predict whether the patient has diabetes or not based on various features like Glucose level, Insulin, Age, BMI and various other features. We will perform all the steps from Data gathering to Model deployment. During Model evaluation, we compare various machine learning algorithms on the basis of accuracy_score metric and find the best one. Then we create a web app using streamlit.
Diabetes is an increasingly growing health issue due to our inactive lifestyle. If it is detected in time then through proper medical treatment, adverse effects can be prevented. To help in early detection, technology can be used very reliably and efficiently. Using machine learning we have built a predictive model that can predict whether the patient is diabetes positive or not.
In this project I deploy a app for Exploratory Data Analysis on streamlit for .csv format upto 250MB.
In today’s world, data is power. With News companies having terabytes of data stored in servers, everyone is in the quest to discover insights that add value to the organization. With various examples to quote in which analytics is being used to drive actions, one that stands out is news article classification.
Nowadays on the Internet, there are a lot of sources that generate immense amounts of daily news. In addition, the demand for information by users has been growing continuously, so it is crucial that the news is classified to allow users to access the information of interest quickly and effectively. This way, the machine learning model for automated news classification could be used to identify topics of untracked news and/or make individual suggestions based on the user’s prior interests.
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