Chinmay Rane Chinmayrane16

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1. DeepRecommender

   Training Deep AutoEncoders for Collaborative Filtering

   Jupyter Notebook 9

2. Diamonds-In-Depth-Analysis

   Given dataset of Diamonds with features such as Cut, Carat, Clarity etc. I have used libraries such as Pandas, Numpy, Matplotlib, Seaborn to Analyse and Estimate the Price of Diamonds based on the …

   Jupyter Notebook 11 5

3. Recommender-Systems-with-Collaborative-Filtering-and-Deep-Learning-Techniques

   Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques

   Jupyter Notebook 10 2

4. Windows-Interface-for-Interacting-with-Neo-SmartPen-NWP-F110

   Developed Desktop Interface for Interacting with the IOT device (Neo SmartPen) FWP-F110. You can write any content on NCode Notebooks and receive the strokes on the Desktop screen in real time via …

   C# 7

5. MNIST-Digit-Recognizer-CNN-Keras-99.66

   Used the Dataset "MNIST Digit Recognizer" on Kaggle. Trained Convolutional Neural Networks on 42000 Training Images and predicted labels on 28000 Test Images with an Validation Accuracy of 99.52% a…

   Jupyter Notebook 10 3

6. Titanic-Survival-In-Depth-Analysis

   Used Pandas , Matplotlib , Seaborn libraries to Analyze , Visualize and Explore the data of people travelling on Titanic, and Used Scikit-learn Modelling Algorithms to predict their probability of …

   Jupyter Notebook 10 1