prakhardogra921/Year-Prediction-using-Regression

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Python
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MIT License
Created over 6 years ago
Updated over 6 years ago

prakhardogra921/Year-Prediction-using-Regression

prakhardogra921

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Predicting Release Year of a Song using different Regression algorithms implemented from scratch using numpy

Vehicle-Detection-using-Faster-R-CNN

Vehicle Detection using state-of-the-art architecture Faster R-CNN with Pretrained models like VGG16 and ResNet50

Clustering-Analysis-on-customers-of-a-wholesale-distributor

Project in which unsupervised learning techniques are applied on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.

Jupyter Notebook

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.

Jupyter Notebook

Batch-Normalization

Batch Normalization using tf.layers and tf.nn libraries. Also comparing the performances with and without batch normalization.

Jupyter Notebook

Finding-Similar-Images-using-Locality-Sensitive-Hashing

Finding Similar Images using Locality Sensitive Hashing (using Min Hashing and Random Hyperplanes technique)

General-Adversarial-Network-MNIST

Building our own GAN and train it on MNIST dataset. Built the generator and discriminator networks using Tensorflow.

Jupyter Notebook

Youtube-Video-Label-Classification

Youtube Video Label Classification using Single Frame model and Long-term Recurrent Convolutional Networks (LRCN) model

Jupyter Notebook

Deep-Q-Learning

Tensorflow implementation of a deep neural network that can learn to play games via reinforcement learning. For this notebook the game used is the Cart-Pole game that is available in the OpenAI Gym library. Also the deep neural network can be used for other games as well. In order to run this on your system you will also need to clone the OpenAI gym repository.

Jupyter Notebook

Monte-Carlo-Methods-for-Reinforcement-Learning

Implementations of many Monte Carlo (MC) algorithms for updating policies of an environment using action values, greedy and epsilon-greedy procedures. Environment used for this notebook is the BlackJack Environment (can be seen in the OpenAI Gym library) and these functions can be used for other environments as well.

Jupyter Notebook