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shaguftaazad's Projects

bank-marketing-classification icon bank-marketing-classification

Bank Marketing Classification using scikit-learn library to train and validate classification models like Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Neural Network and Support Vector Machine. Also conducted comparative study on the above models when applied on different feature sets obtained via feature selection (Chi-Square Test), feature transformation (Principal Component Analysis) and feature elimination.

bank-marketing-dataset-machine-learning icon bank-marketing-dataset-machine-learning

The purpose of the data, on the other hand, is to predict whether the persons whose information is obtained can obtain a loan from the bank. Various data preprocessing, data analysis, data visualization and data scaling were performed in the project. The data brought into a certain form was finally tested with 5 different machine learning algorithms and selected according to the most appropriate accuracy and f-score ratio.

batche icon batche

All codes snippets and datasets

bird-species-classification icon bird-species-classification

Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.

birds icon birds

Polish bird species recognition - Bird song analysis and classification with MFCC and CNNs. Trained on EfficientNets with final score 0.88 AUC. Women in Machine Learning & Data Science project.

deloitte icon deloitte

Let's make this home for all the code snippets of this class!

ensemble-methods-for-image-classification icon ensemble-methods-for-image-classification

In this project, I implemented several ensemble methods (including bagging, AdaBoost, SAMME, stacking, snapshot ensemble) for a normal CNN model and Residual Neural Network.

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