Recently I started working on a Python package which is everything time series, with specific focus on EDA, forecasting, classification and anomaly detection. It will leverage other Python libraries wherever appropriate. My first realization [...]
Portfolio balancing is a complex optimization problem. The problem can be stated as assignment of weights to different stocks in the portfolio so that a metric called Sharpe Ratio is maximized. In this post we will [...]
There are two main kinds of problems in Reinforcement Learning (RL), evaluation of a policy aka the prediction problem and finding the best policy aka the control problem. A policy dictates the action to be taken given [...]
Feature selection is the process of selection a subset of features most relevant from a given set of features for a supervised machine learning problem. There are many techniques for feature selection. in this post we will use [...]
Deep Learning model architecture aligns with a specific structure of the data e.g RNN or LSTM for linear data like text, CNN for grid data like image. The structure of the data in these [...]
With Covid-19 ravaging the world, a lot of people are exploring ways AI and ML can help in combating the virus spread and infection. Viruses like Covid-19 is a complex socio-economic and public health [...]
Auto Training and Parameter Tuning for a ScikitLearn based Model for Leads Conversion Prediction This is a sequel to my last blog on CRM leads conversion prediction using Gradient Boosted Trees as implemented in ScikitLearn. The focus of [...]
Predicting CRM Lead Conversion with Gradient Boosting using ScikitLearn Sales leads are are generally managed and nurtured in CRM systems. It will be nice if we could predict the likelihood of any lead [...]
