Forecasting Time Series Data with FbProphet in Python

    FbProphet, an open source software released by Facebook, provides a procedure for forecasting time series data based on an additive model. In this tutorial, I'll briefly explain how to forecast time series data by using FbProphet API in Python.

    The tutorial covers:

  1. Preparing time series data
  2. Defining the model and forecasting
  3. Performance evaluation
  4. Source code listing

    Let's start by loading the required packages for this tutorial.

 
import yfinance as yf
import pandas as pd

from fbprophet import Prophet
from fbprophet import plot
from fbprophet.diagnostics import cross_validation, performance_metrics
  
 

Smoothing Example with Savitzky-Golay Filter in Python

     Savitzky-Golay filter is used in signal processing to eliminate noise in a signal and improve the smoothness of a signal trend. The filter calculates a polynomial fit of each window based on polynomial degree and window size. 

    SciPy API provides the savgol_filter() function to implement Savitzky-Golay filter in Python. In this tutorial, we'll briefly learn how to smooth the signal data by using savgol_filter() function in Python. 

    The tutorial covers:

  1. Preparing signal data
  2. Smoothing with Savitzky-Golay filter
  3. Source code listing

    We'll start by loading the required libraries.

 
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt 
 
 

LightGBM Multi-class Classification Example in R

     Muti-class or multinomial classification is type of classification that involves predicting the instance out of three or more available classes. 

    LightGBM is an open-source gradient boosting framework that based on tree learning algorithm and designed to process data faster and provide better accuracy. LightGBM can be used for regression, classification, ranking and other machine learning tasks.  

    In this tutorial, we'll briefly learn how to classify multi-class data by using LightGBM in R. The tutorial covers:

  1. Preparing the data
  2. Fitting the model and prediction
  3. Accuracy checking
  4. Source code listing
    We'll start by installing R interface package of LightGBM API and loading the required packages.

 
install.packages("lightgbm")

library(caret)
library(lightgbm) 
 

LightGBM Regression Example in R

    LightGBM is an open-source gradient boosting framework that based on tree learning algorithm and designed to process data faster and provide better accuracy. LightGBM can be used for regression, classification, ranking and other machine learning tasks.  

    In this tutorial, we'll briefly learn how to fit and predict regression data by using LightGBM in R. The tutorial covers:

  1. Preparing the data
  2. Fitting the model and prediction
  3. Accuracy checking
  4. Source code listing
    We'll start by installing R interface package of LightGBM API and loading the required packages.

 
install.packages("lightgbm")
 
library(lightgbm)
library(caret)
library(ggplot2) 
 

Differential Evolution Optimization Example in Python

    Differential Evolution (DE) is a population-based metaheuristic search algorithm to find the global minimum of a multivariate function. DE is a kind of evolutionary computing algorithm that starts with an initial set of candidate solution and updates it iteratively. 

    SciPy provides differential_evolution() function to implement differential evolution method in Python. In this tutorial, we'll briefly learn how to implement and solve optimization problem with differential evolution method by using this differential_evolution() function.

    The tutorial covers:

  1. Understanding the problem
  2. Differential Evolution implementation
  3. Source code listing

 We'll start by loading the required libraries.


import numpy as np
from scipy.optimize import differential_evolution
import matplotlib.pyplot as plt
from matplotlib import cm
 

LightGBM Classification Example in Python

     LightGBM is an open-source gradient boosting framework that based on tree learning algorithm and designed to process data faster and provide better accuracy. It can handle large datasets with lower memory usage and supports distributed learning. You can find all the information about the API in this link.

    LightGBM can be used for regression, classification, ranking and other machine learning tasks. In this tutorial, you'll briefly learn how to fit and predict classification data by using LightGBM in Python. The tutorial covers:

  1. Preparing the data
  2. Building the model
  3. Prediction and accuracy check
  4. Source code listing
   We'll start by loading the required libraries for this tutorial.