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svm-to-amazon-reviews-data-set

SVM algorithm is applied on amazon reviews datasets to predict whether a review is positive or negative. Procedure to execute the above task is as follows: • Step1: Data Pre-processing is applied on given amazon reviews data-set.And Take sample of data from dataset because of computational limitations • Step2: Time based splitting on train and test datasets. • Step3: Apply Feature generation techniques(Bow,tfidf,avg w2v,tfidfw2v) • Step4: Apply SVM algorithm using each technique. • Step5: To find C(1/alpha) and gamma(=1/sigma) using gridsearch cross-validation and random cross-validation 0.2 Objective: • To classify given reviews (positive (Rating of 4 or 5) & negative (rating of 1 or 2)) using SVM algorithm. In [1]: # All necessary module %matplotlib inline import warnings warnings.filterwarnings("ignore") #import sys import re import math import sqlite3 import pandas as pd import numpy as np import pickle # modules for text processing import nltk import string from nltk.corpus import stopwords 1

t-sne-visualization-of-amazon-reviews-with-polarity-based-color--coding

Given Dataset consists of reviews of fine foods from amazon. Reviews describe (1)product and user information, (2)ratings, and (3) a plain text review.The main aim is To determine given review is positive (Rating of 4 or 5) or negative (rating of 1 or 2) and To visualize Amazon reviews with polarity based color-coding via t-SSNE

taxi-demand-prediction-in-new-york-city

Objectives: Task 1: Incorporate Fourier features as features into Regression models and measure MAPE. Task 2: Perform hyper-parameter tuning for Regression models. 2a. Linear Regression: Grid Search 2b. Random Forest: Random Search 2c. Xgboost: Random Search Task 3: Explore more time-series features using Google search/Quora/Stackoverflow to reduce the MAPE to < 12%

tensorflow-and-keras-build-various-mlp-architectures-for-mnist-dataset

titanic

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, 1. to complete the analysis of what sorts of people were likely to survive. 2.to apply the tools of machine learning to predict which passengers survived the tragedy.

ultimate-flask-front-end

blog post

various-cnn-networks-on-mnist-dataset

Three different architecture of CNN network on MNIST datasets.MNIST datasets contains handwritten images . Objective: 1) 3_ConvNets with kernel 3x3 2) 5_ConvNets with kernel 5x5 3) 7_ConvnNets with kernel 2x2

web-application-which-has-a-drop-down-to-select-one-file-and-one-or-more-fields-in-the-file-and-extr

I have a bunch of files in CSV format (with same set of fields) and to aid non-programmers I need to make it easy to access the same. Can you develop a web application which has a drop-down to select one file and one or more fields in the file and extract the information? Write both the front-end code and back-end code.

word-vectors-using-truncated-svd

Word Vectors using Truncated SVD is applied on amazon reviews datasets .From Different Types of word embedding ,here frequency based (TF_IDF word 2vec techniques ) is used. Procedure to execute the above task is as follows: Procedure: • Step1: Take Reviews data of amazon reviews data-set. And Ignore polarity column • Step2: To get Important Features using TF_IDF. • Step3: To calculate Co-occurance Matrix with Selected Important Features • Step4: To choose the n_components in truncated svd, with maximum explained variance and plotting of cumulative explained variance ratio. • Step5: To apply K-means clustering Algorithm&find Best number of cluster using Elbow method • Step6: To write a Function that takes a word and returns the most similar words using cosine similarity between the vectors 1.1 Objective: • To applyWord Vectors using Truncated SVD on Amazon reviews.