Scikit-learn, Numpy, Pandas, Matplotlib, Plotly, Bokeh and Seaborn are some of the common Python libraries used in the field of data science. Let’s see some other libraries that can be useful for data science projects.

Feature-Engine is a library for feature engineering and it allows us to select the variables we want to transform so it’s very easy to apply different engineering procedures to different feature subsets. Also, Feature-Engine transformers can be assembled within the Scikit-learn pipeline.

Feature-Engine includes transformers for:

- Variable transformation
- Variable selection
- Categorical variable encoding
- Missing data imputation
- Discretisation
- Outlier capping or removal
- Variable creation

For the…

Genetic algorithm is a probabilistic search algorithm based on the modeling of genetic processes in living things. It was inspired by the science of genetics. Some of the concepts defined in genetic science are used by modeling in genetic algorithm. Let’s talk about these concepts briefly.

**Gene**: the smallest structure that carries meaningful genetic information in its own right in the course of natural evolution is called a gene. In genetic algorithms, the gene represents the property of each element that makes up the chromosomes.

**Chromosome** : It is formed by a combination of genes. …

Folium is a library that uses JavaScript leaflet.js module in the background and it enables interactive map visualizations in Python. Now let’s examine some features in Folium.

Firstly, let’s take the necessary steps to use the Folium library.

`pip install folium `

#conda install -c conda-forge folium

import folium

We use the Map() function to create a map. For this, we need to give latitude and longitude information to the function. For example, let us enter the approximate latitude and longitude of Istanbul.

`folium.Map(location=[41,29])`

Let’s look at the output without any parameters.

By default, “tiles” appears as ”OpenStreetMap” in the Map()…

When working with time series data, the date information may not be in the format we want, or we may want to produce new variables that can make our model better with the time information we have. Now let’s see how we can do this in Pandas.

In this article, we will use the electricity consumption data (January 2015 — June 2020) from Epiaş Şeffaflık Platformu. You can access the data** ****here.**

Let’s start by loading the necessary libraries and data.

import pandas as pd # 1.0.3

import numpy as np

data=pd.read_csv("/kaggle/input/electricity-consumption-20152020/GercekZamanliTuketim-01072015-30062020.csv",

encoding= 'unicode_escape')df=data.copy() # to copy just in…

SymPy is a Python library that we can perform symbolic math operations. SymPy benefits in many fields such as derivative, integral, differential, equation solving, matrix operations, and statistical operations. It also has useful functions in the fields of physics and chemistry.

First, let’s install our SymPy library and other necessary libraries.

To do some basic mathematical operations, let’s first define two symbols as x and y, then let’s look at the output of some symbolic mathematical operations.

Local outlier factor is one of the methods used to detect outlier observations.Outlier detection methods can be distribution-based,depth-based,clustering-based and density-based. LOF allows to define outliers by doing density-based scoring. It is similar to the KNN (nearest neighbor search) algorithm. The difference is that we’re trying to find observations that are close together in KNN, but we’re trying to find observations that are not alike in LOF.

Local outlier factor algorithm can be divided into four parts:

- K-Distance and K-Neighbors
- Reachability Distance
- Local Reachability Density
- Local Outlier Factor Calculation

In order to find the distance between observations, it is necessary to…

**Büyük sayılar yasası** bir rassal değişkenin uzun vadeli kararlılığını tanımlayan bir olasılık teoremidir. Örneğin bir parayı attığımızda yazı ve tura olarak iki olasılık mevcuttur ve yazı gelme olasılığımız 0.5'tir.Şimdi bu parayı 5 kere attığımızı düşünelim ve ilk 4 atışta tura ve son atışta ise yazı geldiğini varsayalım.Şimdi ise olasılığımız 0.2 oldu.Bu parayı 100,1000 hatta 10000 kere atarsak yazı ve tura gelme durumlarının birbirine yaklaştığını görürüz.Gözlem sayısı arttıkça yazıların (ya da turaların) sıklığı %50'ye yaklaşacaktır.Bunu örnek bir uygulamayla gözlemleyelim.

Görüldüğü gibi 10488576.atışta yazı olasılığının %50'ye çok yaklaştığını görüyoruz.

Industrial Engineering Student at Sakarya University