Feature Engineering Tutorial Series 4: Linear Model Assumptions

Linear models make the following assumptions over the independent variables X, used to predict Y: There is a linear relationship between X and the outcome Y The independent variables X are normally distributed There is no or little co-linearity among the independent variables Homoscedasticity (homogeneity of variance) Examples of linear Read more…

Matplotlib Crash Course

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is a cross-platform library for making 2D plots from data in arrays. It can be used in Python and IPython shells, Jupyter notebook and web application servers also. Matplotlib is written in Python and makes Read more…

Classify Dog or Cat by the help of Convolutional Neural Network(CNN)

Use of Dropout and Batch Normalization in 2D CNN on Dog Cat Image Classification in TensorFlow 2.0 We are going to predict cat or dog by the help of Convolutional neural network. I have taken the dataset from kaggle https://www.kaggle.com/tongpython/cat-and-dog. In this dataset there is two class cats and dogs Read more…

Deep Learning with Tensorflow 2.0 Tutorial – Getting Started with Tensorflow 2.0 and Keras for Beginners

Classification using Fashion MNIST  dataset What is TensorFlow? TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between Read more…

Complete Seaborn Python Tutorial for Data Visualization in Python

Visualizing statistical relationships Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Statistical analysis is a process of understanding how variables in a dataset relate to each other and how those relationships depend on other variables. Visualization Read more…

Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression | Machine Learning | KGP talkie

Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is ROC_AUC The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a Read more…

Feature Selection using Fisher Score and Chi2 (χ2) Test | Titanic Dataset | Machine Learning | KGP Talkie

Feature Selection using Fisher Score and Chi2 (χ2) Test Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Fisher Score and Chi2 ( χ2) Test Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which Read more…

Feature Selection Based on Univariate (ANOVA) Test for Classification | Machine Learning | KGP Talkie

Feature Selection Based on Univariate (ANOVA) Test for Classification Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Univariate (ANOVA) Test The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. An F-test is any statistical Read more…

Feature Selection Based on Mutual Information (Entropy) Gain for Classification and Regression | Machine Learning | KGP Talkie

Feature Selection Based on Mutual Information (Entropy) Gain Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Mutual Information The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. Mutual information (MI) is a measure of Read more…

Feature Selection with Filtering Method | Constant, Quasi Constant and Duplicate Feature Removal

Filtering method Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Unnecessary and redundant features not only slow down the training time of an algorithm, but they also affect the performance of the algorithm. There are several advantages of performing feature selection before training machine learning models: Models with less number of features have higher Read more…

Feature Dimention Reduction Using LDA and PCA with Python | Principal Component Analysis in Feature Selection | KGP Talkie

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…