A machine learning classification project to predict the repayment abilities of several clients of Home Credit

Classification - Term Deposit Opening Decision

Keras implementation of multi-label classification of movie genres from IMDB posters

Data visualization and one hot encoding of Kaggle dataset. Model trained with random forest classifier

Case-based Reasoning (CBR) System

The aim of this project is to classify the faces. Olivetti Faces dataset has been used. In this dataset there are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). The “target” for this database is an integer from 0 to 39 indicating the identity of the person pictured. Each of the sample images needs to be classified in the classes ranging from 0 to 39. PCA has been applied to reduce the dimensionality. Then various classification and regression techniques are used with and without using PCA and the accuracy and time taken by the algorithms are recorded. Algorithms used: SVM, KNN, logistic regression, neural networks, linear regression and random forests.

Understand the learning process of RNNs and discover the LSTM network architecture. Solve problems and perform Natural Language Processing using sequences of data

dummy variables & one hot encoding -- machine learning

The aim of this project is to classify the faces. Olivetti Faces dataset has been used. In this dataset there are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). The “target” for this database is an integer from 0 to 39 indicating the identity of the person pictured. Each of the sample images needs to be classified in the classes ranging from 0 to 39. PCA has been applied to reduce the dimensionality. Then various classification and regression techniques are used with and without using PCA and the accuracy and time taken by the algorithms are recorded. Algorithms used: SVM, KNN, logistic regression, neural networks, linear regression and random forests.

Feature engineering is the process of transforming raw data into features. Here are some basic ideas about feature engineering.

Predict the IMDB score of a new movie

A web application that generates an image for corresponding textual description. The user is required to enter a text description of a scene. The application will then generate an image that best corresponds to this description. The application uses Generative Adversarial Networks (GANs) trained on a large dataset of images consisting of multiple everyday-object categories.

tres veces

Recognize underfitting and overfitting, implement bagging and boosting, and build a stacked ensemble model using a number of classifiers.