Code&Data Insights

[ Hierarchical Clustering ] Hierarchical Clustering : Hierarchical clustering is a data analysis technique that groups data hierarchically based on similarity or distance - Use Euclidean distance or Manhattan distance - 2 approachs for hierarchical Clustering : 1) Agglomaerative- Top-down 2) Divisive - Bottom-up [ Agglomerative Hierarchical Clustering ] ( Agglomerative Hierarchical Clustering : ..

[ K-Nearest Neighbours ] K Nearest Neighbors (KNN) => KNN is a supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. How It Works? Step 1) Choose the number K of neighbors Step 2) Take the K nearst neighbors of the new data point, according to the Euclidean distance - Euclidean Distance : √((x₂ - x₁)² + (y₂ - y..

[ Ensemble Learning ] Ensemble Learning : when we take multiple algorithms or the same algorithm multiple times and we put them together that results in a much more powerful version. => It helps to improve the performance and accuracy of machine learning algorithms. => Putting multiple ML algorithms together to create one bigger ML algorithm that leverages many other ML algorithms. Type of Ensem..

[ R-squared  ]R-squared  - a measure of the goodness-of-fit of a regression model. - It represents the proportion of the variance in the dependent variable that is predictable from the independent variables. - the percentage of variation explained by the relationship between two variables.  => range : 0 to 1 => R² = 1 - (SSR/SST)SSR = the sum of squared residuals (the sum of the squared differen..

Linear Model - Easy to optimize, fast training and prediction - Good Interpretability - ONLY suitable for linearly separable classes => The capacity of the linear model depends on the input dimensionality D. => VC dimensions : D + 1 for Logistic regression VC dimension? : a measure of the capacity or complexity of a hypothesis space Linear Regression - Parameter space is convex - Objective funct..

In Multiple Iinear Regression Model, there are many variables. To build a model, we need to choose right variables ! ( Using all the variables given in the data, it's NOT a good idea ) [ 5 methods of building models ] 1. All-in - Prior knowledge - Preparing for Backward Elimination 2. Backward Elimination Step 1) Select a significance level to stay in the model (ex) SL = 0.05 ----> SL = Signific..

[ Type of Data ] [ Sampling Methods ] 1) Random Sampling : sample choice made without any pattern and would be completely unrelated 2) Simple Random Sampling : all of the selections are equally likely, for example drawing one name and each name has the same chance of being selected 3) Systematic Random Sampling : more organized in sample selection, create pattern to choose the samples 4) Stratif..

Pandas : a Python library used for working with data sets. -> Pandas has functions for analyzing, cleaning, exploring, and manipulating data. [ DataFrame ] DataFrame : a Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns in RDB(relational database-SQL) [ Series ] Series : Series is a one-dimensional array holding data of any type, lik..