Chaitanya Kaul's repositories
Flight_Fare_Prediction
Aim of the project is to predict the fare of the flight.
Heart-Disease--Classification
We have a data which classified if patients have heart disease or not according to features in it. We will try to use this data to create a model which tries predict if a patient has this disease or not. We will use logistic regression (classification) algorithm.
Credit-Card-Fraud-Classification
Creating and comparing accuracies of different machine learning classification models to classify whether a transaction is fraud or not on imbalanced dataset. Imbalanced datasets are those where there is a severe skew in the class distribution, such as 1:100 or 1:1000 examples in the minority class to the majority class
DIABETES-PREDICTION
Predicting if a person has Diabetes or not using Logistic Regression Classifier
Titanic-Disaster
The aim of the project was to predict which passengers survived the Titanic Disaster. The type of machine learning we will be doing is called classification, because when we make predictions we are classifying each passenger as ‘survived’ or not.
DNA-classifier
DNA classifier using Natural Language Processing. Used K-mer method to convert sequence strings into fixed size words
FAKE-NEWS-DETECTION
Detection of Fake news using Passive Agressive classifier and TfidfVectorizer. Accuracy achieved with this model is 92.98% .
Loan-Prediction
Its a Binary Classification problem where aim is to predict whether an indivisual will get a loan or not. using different machine learning classifiers.
machine-learning-basics
My first step towards Machine Learning - Knowing basics of all the important machine learning algorithms
MALARIA_DETECTION
Its a Deep Learning problem statement where aim of the project is to detect whether a person has Malaria or not. We do this by using Transfer Learning technique(VGG 19)
Stock-Sentiment-Analysis
stock sentiment analysis using headlines
Predicting-Lung-disease
Built a CNN model to Predict whether a person have a normal lung disease or Pneumonia by providing X-ray images of lungs as training data.
Sentiment-Analysis
Performing sentiment analysis on the data set given to us. Accuracy achieved with our model is 70.91%
Spam-Detection-classification
Classfying whether a message is spam or not by applying natural language processing and classification methods on the given dataset.