# Data Science Simplified

Learning the Machine Learning, in a Human-friendly Way

### Time series Cross-validation and Forecasting Accuracy: Understand with Illustrations & Examples

In this post, let us review

• Standard statistical measures of forecasting accuracy
• Cross-validation in time series
• How to plot forecasts and,
• How to calculate forecasting accuracy in Python

### Exponential Smoothing Techniques: Learn with Examples and Illustrations

In this post, let us explore:

• Moving Averages
• Single Exponential Smoothing
• Double Exponential Smoothing
• Triple Exponential Smoothing

### Components of Time Series: A Beginner's Visual Guide

In this post, let us explore the four components of time series data.
1. Trend (T)
2. Cyclicality (C)
3. Seasonality (S)
4. Irregular component (I)

### Understand Basic concepts of Time Series with Examples and Visual Illustrations

In this post, let us explore the basic concepts about time series. We will also learn about resampling techniques, how to check for stationarity and ways to convert non stationary series into stationary series.

### Random Forest: A Beginner's Guide with Visual Illustrations & Examples

In this post, let us explore:
• Random Forest
• When to use
• Hyperparameters
• Examples

### Basics of Ensemble Models

In this post, let us explore:

• Ensemble Models
• Bagging
• Boosting
• Stacking

### Train-Test split and Cross-validation: Visual Illustrations & 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: Visual Examples

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

### Occam's Razor, Bias-Variance Tradeoff, No Free Lunch Theorem and The Curse of Dimensionality

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

• Occam's Razor (Law of Parsimony)
• No Free Lunch Theorem
• The curse of dimensionality

### Mastering Decision Trees with Visual Examples

Decision Tree models are simple and easy to interpret.

In this post, let us explore
• What are decision trees
• When to use decision trees
• Examples with code (Python)

### Mastering Exploratory Data Analysis: A Beginner's Guide with Visual Illustrations

"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: Transformation - Explained with Visual Examples

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

#### In this post, with the help of an example, let us explore transformation:

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

### Data Preprocessing - Creating Dummy Variables and Converting Ordinal Variables to Numbers with Examples

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

### Confusion Matrix, Accuracy, Precision, Recall, F score Explained with Intuitive Visual Examples

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

### Handling Outliers in Python: Explained with Visual Examples

In this post, we will discuss about
• How to identify outliers
• How to handle the outliers

### Handling Missing Values in Python: Different Methods Explained with Visual Examples

In this post, we will discuss:
• How to check for missing values
• Different methods to handle missing values

### Scales of Measurement - Data types: Nominal, Ordinal, Interval and Ratio scale

There are four measurement scales:

1. Nominal
2. Ordinal
3. Interval
4. Ratio scale.

### Importing data into Python

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