Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, in Python programming Read more…
What is Pandas? pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, in Python programming language. It is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. Read more…
Complete Text Processing for Beginners Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value can be extracted from it. In theory, we can understand Read more…
NLP Tutorial – GloVe Vectors Embedding with TF2.0 and Keras GloVe stands for global vectors for word representation. It is an unsupervised learning algorithm developed by Stanford for generating word embeddings by aggregating a global word-word co-occurrence matrix from a corpus. The resulting embeddings show interesting linear substructures of the word in Read more…
Star Rating Prediction of Amazon Products Reviews Objective In this notebook, we are going to predict the Ratings of Amazon products reviews by the help of given reviewText column. Natural Language Processing (NLP) is a sub-field of artificial intelligence that deals understanding and processing human language. In light of new Read more…
Household Power Consumption Prediction using RNN-LSTM Power outage accidents will cause huge economic loss to the social economy. Therefore, it is very important to predict power consumption. Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of Read more…
Sentiment Analysis Objective In this notebook we are going to perform a binary classification i.e. we will classify the sentiment as positive or negative according to the `Reviews’ column data of the IMDB dataset. We will use TFIDF for text data vectorization and Linear Support Vector Machine for classification. Natural Read more…
Feature Dimension Reduction Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is LDA (Linear Discriminant Analysis)? The idea behind LDA is simple. Mathematically speaking, we need to find a new feature space to project the data in order to maximize classes separability Linear Discriminant Analysis is a supervised algorithm as it takes the Read more…
Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Linear Regression Let’s first understand what exactly linear regression is, it is a straight forward approach to predict the response y on the basis of different prediction variables such x and ε. . There is a linear relation between x and y. 𝑦𝑖 = 𝛽0 + Read more…
Recursive Feature Elimination (RFE) Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH As it’s name suggests, it eliminates the features recursively and build a model using remaining attributes then again calculates the model accuracy of the model..Moreover how it do it train the model on all the dataset and it tries to remove the least performing Read more…
What is Regularisation? Regularization adds a penalty on the different parameters of the model to reduce the freedom of the model. Hence, the model will be less likely to fit the noise of the training data and will improve the generalization abilities of the model. There are basically 3-types of Read more…
Principal Component Analysis(PCA) According to Wikipedia, PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. Principal components These Read more…
How does the KNN algorithm work? In KNN, K is the number of nearest neighbors. The number of neighbors is the core deciding factor. K is generally an odd number if the number of classes is 2. When K=1, then the algorithm is known as the nearest neighbor algorithm. This Read more…
What is K-Mean Clustering? Machine Learning can broadly be classified into three types: Supervised Learning Unsupervised Learning Semi-supervised Learning K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. The ‘means’ in the K-means refers Read more…
For detailed theory read An introduction to Statistical Learning: A decision tree is a flowchart-like tree structure where an internal node represents feature, the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It Read more…