In this post, let us explore:

- Random Forest
- When to use
- Advantages
- Disadvantages
- Hyperparameters
- Examples

Learning the Machine Learning, in a Human-friendly Way

- Random Forest
- When to use
- Advantages
- Disadvantages
- Hyperparameters
- Examples

Building an optimum model which **neither underfits nor overfits** the dataset takes effort. To know the performance of our model on unseen data, we can split the dataset into train and test sets and also perform cross-validation.

Heatmap depicts the two-dimensional data (matrix form) in the form of graph.

In this post, let us discuss some of the basic concepts/theorems used in Machine Learning:

- Occam's Razor (Law of Parsimony)
- What is Bias-variance Tradeoff
- No Free Lunch Theorem
- The curse of dimensionality

In this post, let us explore

- What are decision trees
- When to use decision trees
- Advantages
- Disadvantages
- Examples with code (Python)

"A picture is worth a thousand words"A complex idea can be understood effectively with the help of visual representations. Exploratory Data Analysis (EDA) helps us to understand the nature of the data with the help of summary statistics and visualizations capturing the details which numbers can't.

In this post, let us explore

- Visualizing the data
- Summarizing the data
- Correlation matrix

Data preprocessing is an important step before fitting any model. The following steps are performed under data preprocessing:

- Handling missing values
- Handling outliers
- Transforming nominal variables to dummy variables
- Converting ordinal data to numbers
- Transformation

- Standardization
- Normalization
- Log transformation
- How to transform data in Python

Data cleaning is a critical step before fitting any statistical model. It includes:

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- Handling missing values
- Handling outliers
- Transforming nominal variables to dummy variables (discussed in this post)
- Converting ordinal data to numbers (discussed in this post)
- Transformation (discussed in this post)

In this post, we will learn about

- What is accuracy
- What are precision, recall, specificity and F score
- How to manually calculate these measures
- How to interpret these measures
- What is confusion matrix and how to construct it
- What is the AUC score and its interpretation
- How to get confusion matrix and classification report in sklearn

In this post, we will discuss about

- How to identify outliers
- How to handle the outliers

In this post, we will discuss:

- How to check for missing values
- Different methods to handle missing values

There are four measurement scales:

- Nominal
- Ordinal
- Interval
- Ratio scale.

In this post, we will learn:

- How to import data into python
- How to import time series data
- How to handle different time series formats while importing

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Posts (Atom)

- ARIMA/SARIMA with Python: Understand with Real-life Example, Illustrations and Step-by-step Descriptions
- Feature Selection: Filter method, Wrapper method and Embedded method
- Demystifying Principal Component Analysis (PCA): A Beginner's Guide with Intuitive Examples & Illustrations
- Scales of Measurement - Data types: Nominal, Ordinal, Interval and Ratio scale
- Confusion Matrix, Accuracy, Precision, Recall, F score Explained with Intuitive Visual Examples
- Time series Cross-validation and Forecasting Accuracy: Understand with Illustrations & Examples
- Train-Test split and Cross-validation: Visual Illustrations & Examples
- Handling Outliers in Python: Explained with Visual Examples
- Handling Missing Values in Python: Different Methods Explained with Visual Examples
- Components of Time Series: A Beginner's Visual Guide