Learn Data Science using Python

Another post starts with you beautiful people! Hope you have enjoyed my last post about  kaggle submission   and you also tried to build your own machine learning model. To continue the same spirit today I will discuss about my model submission for the  Wallmart Sales Forecasting   where I got a score of 3077 (rank will be 196) in kaggle. Challenge : In this challenge, we are provided with historical sales data for 45 Walmart stores located in different regions since 2010-02-05 to 2012-11-01. Each store contains a number of departments, and we are tasked with predicting the department-wide sales for each store. My Sollution : To solve this machine learning regression problem I followed below steps- 1) load the datasets .  While loading the datasets, I ensured that required attributes only should be there in train and feature datasets. The important key feature which affects the sales of a store mostly is 'markdown' because this feature cont...

Another post starts with you beautiful people! First of all thank you everyone for visiting my blog and showing your keen interest in Linear  and Logistic  Regression topics of Machine Learning track! Since many of you have asked a common but most important question- How to know when and where apply either Linear or Logistic regression? Therefore I am going to share this post where I will try to resolve your doubt. Linear and Logistic regressions are usually the first algorithms people learn in predictive modeling. Each form has its own importance and a specific condition where they are best suited to apply- What is Regression Analysis? Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor).  This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables.  For example, relatio...

Another post starts with you beautiful people! In my previous posts we have learnt the Python basics and advanced, statistics techniques for the Data Science track. I suggest you to please read previous post just for 10-15 min. before sleeping daily and then there is no any obstacle to stop you to become a great Data Scientist. In this post we will start our Machine Learning  track with the  Linear Regression   topic. I Have highlighted the both so please click on the link to know the formal definition of those. Machine learning-  More specifically the field of predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability. In applied machine learning we will borrow, reuse and steal algorithms from many different fields, including statistics and use them towards these ends. Linear Regression was developed in the field of statistics and is studied as a model ...