Statistical hypothesis testing, types of errors, and interpretation of p values
Learn how to propose null and alternate hypotheses, perform the statistical analysis, and interpret the results
Make discoveries with your data
Recent posts
Query pandas DataFrame to select rows based on value and condition matching
Learn how to query pandas DataFrame to select rows based on exact match, partial match, and conditional match in pandas DataFrame
Import small and large data using pandas (CSV, Excel, Tab, JSON, SQL, and Parquet files)
learn how to import CSV, Excel, Tab, JSON, and SQL files in pandas for data analysis and visualization
Checking and handling missing values (NaN) in pandas
Analyse and handle null or missing values in pandas series and dataframe
Left, right, outer and inner joins on pandas dataframes
learn to join pandas dataframes in multiple ways
Find probabilities using discrete and continuous probability distributions
Learn how to use probability distributions, probability mass function (PMF), cumulative distribution function (CDF), and probability density function (PDF)
Logistic regression in Python (feature selection, model fitting, and prediction)
Logistic regression for prediction of breast cancer, assumptions, feature selection, model fitting, model accuracy, and interpretation
Support Vector Machine (SVM) basics and implementation in Python
Implementation of Support vector machine (SVM) in Python for prediction of heart disease. Learn SVM basics, model fitting, model accuracy, and interpretation
Create scatter plots using Python (matplotlib pyplot.scatter)
Learn how to create 2D and 3D scatter plots from numerical arrays and pandas DataFrame using Python matplotlib package
Multicollinearity and variance inflation factor (VIF) in the regression model (with Python code)
Multicollinearity refers to the significant correlation among the independent variables in the regression model. Variance Inflation Factor (VIF) helps to dia...
Multiple linear regression (MLR)
Multiple regression analysis using statsmodels. Learn how to define regression model, assumptions, metrics evaluation, and interpretation
Linear Regression Using Neural Networks (PyTorch)
Linear regression using PyTorch
