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ritumehrotra's Projects

appdichat icon appdichat

Appdichat is mobile chatting application with many features. You can register and login with the application. Also you can send friend request to your friends and accept or decline the requests received. You can also chat in real time with your friends and also see a list of the people using the application. A user can also build his profile by setting his display profile picture and set the status. Also you can view the profile of all your friends and know the number of mutual friends. The application uses Java, XML and the Firebase realtime database.

awesome-nlp icon awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

cats-vs-dogs-cnn-keras icon cats-vs-dogs-cnn-keras

The famous Cats-vs-Dogs dataset. I have used a self laid ConvNet to classify the image into 2 classes either a Dog or a Cat. The images used are of 100*100 pixels each. The images are first converted to the numpy array of pixel values using the python ZipFile module. The images are then divided into the training ,cross-validation,testing set containing 20000 , 5000 , 12500 images respectively. Also I have used data augmentation technique to avoid chances of overfitting the model. Finally I achieved a decent accuracy of about 88 % on the validation set.

collaborative-filtering-using-matrix-factorization-neural-network icon collaborative-filtering-using-matrix-factorization-neural-network

The Movielens 100K dataset. The dataset contains of around 1lac ratings given to about 9066 movies by around 671 users. I have implemented Collaborative Filtering using Matrix Factorization with Keras Embeddings to predict the unknown ratings. Also I have used a NN to make predictions. Finally I have tried to minimize the Mean squared Error on the training set. I have achieved a decent validation loss of 0.84.

flower-recognition-kaggle-cnn-keras icon flower-recognition-kaggle-cnn-keras

The dataset is Flower Recognition on Kaggle. The dataset consists of 4232 images each of different pixel values. Each of the image can be classified into either of 5 types-> 'Daisy','Rose' etc... . I have trained Convolutional Neural Network written in Keras to predict the flower on the validation set. Also used ImageDataGenerator to augment the training set and avoid overfitting problem .

gender-recognition-by-voice-val.-acc.-0.9908- icon gender-recognition-by-voice-val.-acc.-0.9908-

The Gender Recognition by Voice dataset from kaggle. The dataset consists of 3168 voice samples each of which has 20 different acoustic properties and the target variable is the 'gender' or the 'label'. I have done exhaustive EDA to analyze the data and the underlying trends. Also the outliers have been detected and removed for better performance. I have also done significant feature engineering by adding couple of new relevant features. Also I have normalized the data for better performance. Lastly I have used many classification algos. from the scikit to predict the 'gender' from the voice sample. For me SVM gives highest accuracy of about a little more than 99.1 %.

ibm-hr-analytics-employee-attrition-performance icon ibm-hr-analytics-employee-attrition-performance

The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.

movie-recsys-using-surprise-library icon movie-recsys-using-surprise-library

The dataset used is the Movielens100K dataset. I have then splitted the dataset into training and validation sets. Then I have studied the test and the train sets and also created the utility matrix. Finally I have used many models from the 'Surprise' package like SVD ,kNN etc... . I have compared the performance of all the models and then also tuned the parameters using the GridSearchCV.

the-iris-species-dataset icon the-iris-species-dataset

The famous Iris Species Dataset from Kaggle. I have normalized the features and also seen their distribution. I have also deployed many algos from scikit to predict on the dataset.

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