# Logistic Regression :

Welcome everyone, this is first post of

**. In the previous post we see Linear regression.**__LOGISTIC REGRESSION__If you are unknown about the Linear regression then CLICK HERE.

OK Let’s start :

For this post we will working with the Titanic Data Set. This is a very famous data sets and very often is a student first step in machine learning section.

We will be try to predict a classifications- survival or deceasd. Let’s begin our understanding of implementing Logistic Regression in Python for classifications.

We will use a “semi-clean” version of the titanic data set, if you use the data set hosted directly on Kaggles, you may need to do some additional cleaning not shown in this lecture notebooks.

## Import Libraries

Let’s import some library to get start:s

`import pandas as pd`

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

*The Data*

Let’s start by reading in the titanic_trains.csv files into a pandas dataframes.

`train = pd.read_csv('titanic_trains.csv')`

`train.head()`

fig 01) Logistic Regression-plot and explained Algorithms |

# Exploratory Data Analysis:

Let’s begin some exploratory data analysis! We will start by checking out missing data.

## Missing Data

We can use seaborn to create a simple heatmap to see where we are missing datas .

`sns.heatmap(trains.isnull(),yticklabels=False,cbar=False,cmap='viridis')`

Roughly 30 percent of the Ages data is missing. The proportion of Ages missing is likely small enough for reasonable replacement with some form . Looking at the Cabin columns, it look like we are just missing too much of that data to do something useful with at a basic levels. We will probably drop this laters.

Let’s continue on by visualizing some more of the data, Check out the graph for full explanations over these plots.

`sns.set_style('whitegrid')`

sns.countplot(x='Surviveds',data=train,palette='RdBu_r')

`sns.set_style('whitegrids')`

sns.countplot(x='Surviveds',hue='Sex',data=trains,palette='RdBu_r')

`sns.set_style('whitegrids')`

sns.countplot(x='Surviveds',hue='Pclass',data=trains,palette='rainbow')

`sns.distplot(train['Ages'].dropna(),kde=False,color='darkred',bins=40)`

`train['Ages'].hist(bins=40,color='darkred',alpha=0.8)`

`sns.countplot(x='SibSp',data=trains)`

`train['Fare'].hist(color='green',bins=40,figsize=(9,4))`

IN THE NEXT POST WE WILL SEE CUFFLINKS FOR PLOT

*for similar kin of post click here*### Tags:Logistic Regression 01-plot and explained, machine learning,algorithms

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