Astha Allawadhi's repositories
Statistical-Analysis
Statistics is the heart of Analytics, and doing that using python requires a number of libraries and functions which makes it easy to apply the statistical concepts on machine learning models.
Classification-ML-models
Classification problems are the ones where our target column is categorical in simple words we have to predict discrete value - whether the bank will give loan (yes/no) , will the given person is terrorist or not, the person will take credit car or not etc etc. Classification is also a supervised learning technique , there are many machine learning models for the same , some of them discussed here are : Decision trees(works as both regressor and classifier) , KNN( , Naive bayes , logistic regression
Extrpolatory-Data-Analysis
EDA or Extrapolatory Data Analysis is the process of reading the data , importing data so we can analyse data , extract important information from it and clean it if necessary then perform the predictive model building on it.
Introduction-to-Python-using-Jupyter
For a newbie in data science , it is necessary to take up a programming language to build your models on. So python gives very user friendly platform for the same
Linear-Regression
Linear Regression is where it all starts - it is the basic statistical model approach which can be used fro building a basic predictive model for predicting a continuous target eg, sale price , salary , etc. Linear regression is the supervised learning algorithm - supervised means where we have defined dependent and independent variables, in simple language we have a target feature for which we are building model in order to predict that target column