Supervised Machine Learning - Introduction, Linear regression and its Pearson’s coefficient, Linear regression mathematically and coefficient of determination, Exploratory Data Analysis (EDA), Model analysis and squared errors, Descriptive analysis on the dataset, Analyse the distribution of the dependent column, Missing values imputation, Bivariate analysis, Building model using all information, Exploratory Data Analysis (EDA), Fluke correlation.
- Supervised Machine Learning - Introduction
- Linear Regression and its Pearson’s Coefficient
- Linear Regression Mathematically and Coefficient of Determinant
- Model Analysis and Squared Errors
- Summary and Lab Exercise of Linear Regression
- Descriptive Analysis on the Dataset
- Analyse the Distribution - Dependent Column
- Missing Values Imputation
- Bivariate Analysis
- Building Model Using All Information
- Exploratory Data Analysis (EDA)
- Fluke Correlation