Quick Dive to data handeling and machine learning in Python

This model show a quick way to apply different classification machine learning algorithm on data strored in CSV formate.

Python provide very powerful tools like pandas, numpy, and sklearn. we exploit them a lot.

 Source Identification using 2 Class of image¶

In [72]:

from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import normalize
import numpy as np
import pandas as pd
# matplotlib.pyplot as plt

Understanding Data¶

In [2]:

sony=pd.read_csv("Sony_NONIQM.csv")
sony.head()

73

In [3]:

nikon=pd.read_csv("Nikons_NONIQM.csv")
nikon.head()

Out[3]:

Easiest way to create android supported hotspot in Linuxmint/Ubuntu/debian

This approach doesn't require any additional tools. You can easily create access point that can be use by any mobile devices.

Just fallow these simple steps:

1. menu>preferences>network connections
   

Quick Python Pandas Basics

Lets Learn Pandas

In [1]:

import pandas as pd

Pandas series

pandas series is similar to numpy array, But it support lots of extra functionality like Pandaseries.describe()
Basic access is similar to numpy array, it support access by index( s[5] ) or slicing ( s[5:10] ).
It also support vectorise operation and looping like numpy array.
Implemented in C so it works very fast.

Note : Get Code for offline testing  Github_Link or nbviewer.jupyter link

Learn Basic of Numpy

Benfits of Pandas series¶

In [8]:

s=pd.Series([2,3,4,5,6])
print s.describe()

count    5.000000
mean     4.000000
std      1.581139
min      2.000000
25%      3.000000
50%      4.000000
75%      5.000000
max      6.000000
dtype: float64