Handwritten Digits Classification - CNN

Classification of the handwritten digits (0-9) has been performed using CNN. The data set includes 60,000 training samples and 10,000 testing samples. The dataset is collected from MNIST. Tensorflow 2.0 is used. The model includes 2 conv, 2 maxpool, 1 dense, 1 dropout layers. Earlystopping is performed. 99% accuracy is achieved. This Project’s GitHub Repository

March 23, 2017

Rain Prediction - ANN

The purpose of this project is to predict if it will rain tomorrow. The data includes weather information (i.e., temperature, evaporation, wind speed, humidity, pressure and cloud status) of different locations in Australia. The data is quite imbalanced having a few instances of rain information. The objective is to train a neural network to predict rain tomorrow from the given information. Pytorch is used for training. Feature selection is performed using Pearson’s correlation.

March 22, 2017

Customer Churn Detection - Spark, MLib

The dataset includes different information about customers. The objective is to predict customer churn from the data. The input data is highly imbalanced consisting 150 churn (i.e., churn = 1) and 750 no churn (i.e., churn = 0) customers. Check the customer_churn.csv dataset for details. MLlib and PySpark is used to build the model. Feature vectorization is performed to convert the categorical features. Random undersampling is performed to the majority class (i.