Random Forest

Practical 5: Random forest model

 

#First, start with importing necessary Python packages −

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

 

#Next, download the iris dataset from its weblink as follows −

path = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"

 

#Next, we need to assign column names to the dataset as follows −

headernames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']

 

#Now, we need to read dataset to pandas dataframe as follows −

dataset = pd.read_csv(path, names = headernames)

dataset.head()

 

#Data Preprocessing will be done with the help of following script lines.

X = dataset.iloc[:, :-1].values

y = dataset.iloc[:, 4].values

X

Y

#Next, we will divide the data into train and test split. The followingcode will split the dataset into 70% training data and 30% of testing data −

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30)

 

#Next, train the model with the help of RandomForestClassifier class of sklearn as follows −

from sklearn.ensemble import RandomForestClassifier

classifier = RandomForestClassifier(n_estimators = 50)

classifier.fit(X_train, y_train)

RandomForestClassifier(n_estimators=50)

#At last, we need to make prediction. It can be done with the help of following script −

y_pred = classifier.predict(X_test)

 

#Next, print the results as follows −

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

result = confusion_matrix(y_test, y_pred)

print("Confusion Matrix:")

print(result)

result1 = classification_report(y_test, y_pred)

print("Classification Report:",)

print (result1)

result2 = accuracy_score(y_test,y_pred)

print("Accuracy:",result2)

 

 

Building a Logistic Regression

  

Building a Logistic Regression

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Create a logistic regression based on the bank data provided.

The data is based on the marketing campaign efforts of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).

Note that the first column of the dataset is the index.

Import the relevant libraries

---

import pandas as pd

import numpy as np

import statsmodels.api as sm

import matplotlib.pyplot as plt

import seaborn as sns

sns.set()

 

# this part not be needed after the latests updates of the library

from scipy import stats

stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)

 

 

 Load the ‘Example_bank_data.csv’ dataset.

from google.colab import files

uploaded = files.upload()

raw_data = pd.read_csv('Example_bank_data.csv')

raw_data

 

We want to know whether the bank marketing strategy was successful, so we need to transform the outcome variable into 0s and 1s in order to perform a logistic regression.

# We make sure to create a copy of the data before we start altering itNote that we don't change the original data we loaded.

data = raw_data.copy()

 

# Removes the index column that came with the data

data = data.drop(['Unnamed: 0'], axis = 1)

 

# We use the map function to change any 'yes' values to 1 and 'no' values to 0. 

data['y'] = data['y'].map({'yes':1, 'no':0})

data

 

# Check the descriptive statistics

data.describe()

Declare the dependent and independent variables

 

y = data['y']

x1 = data['duration']

 

Simple Logistic Regression

x = sm.add_constant(x1)

reg_log = sm.Logit(y,x)

results_log = reg_log.fit()

 

# Get the regression summary

results_log.summary()

 

 

# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)

plt.scatter(x1,y,color = 'C0')

 

# Don't forget to label your axes!

plt.xlabel('Duration', fontsize = 20)

plt.ylabel('Subscription', fontsize = 20)

plt.show()

 

 

np.set_printoptions(formatter={'float': lambda x: "{0:0.2f}".format(x)})

#np.set_printoptions(formatter=None)

results_log.predict()

 

np.array(data['y'])

results_log.pred_table()

 

cm_df = pd.DataFrame(results_log.pred_table())

cm_df.columns = ['Predicted 0','Predicted 1']

cm_df = cm_df.rename(index={0: 'Actual 0',1:'Actual 1'})

cm_df

 

cm = np.array(cm_df)

accuracy_train = (cm[0,0]+cm[1,1])/cm.sum()

accuracy_train

 

Python List : Adding element to List

Adding an element to the list

We can add elements to the list using append, insert and extend functions.

#Initializing empty list

alist = [] 

Append function

It is used to add elements at the end of the list.
Append function takes only one argument at a time i.e using append function we can add only one element at a time.

#Adding element in the list using append function

alist.append(1)

alist.append(3)

alist.append(5)

print('List after addition of 3 elements :  ', alist ) 

Output :

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Insert function

It is used to add an element at the specified index position of the list.

#Adding an element at a specific index using insert function. 

alist.insert(1,2)

print('List after performing insert operation :  ' , alist)

#Here we are adding element 2 at index position 1

Output :

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Extend function

It is used to add a sequence of elements to the list.
This function adds elements at the end of a list.
Extend function takes a list of elements as the argument.
#Adding multiple elements to the list
alist.extend( [ 4 , 'python' , 8.97 , (91,90,89) ] )
print('List after performing extend operation :  ' , alist)
#Here we are adding elements 4 , ' python ',8.97 and tuple (91,90,89)

Output :

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Python Lists : Creating List

List

The list is  simplest data structure in python which is used to store a collection of data.
The list is a collection of items like String, Numbers, Lists, Tuples, Dictionaries.
Lists are enclosed in square brackets [] .
Each value in the list is assigned to the index value.
An index value of the list is start from 0 .
Each item in list is separated by comma(,) .
List is Mutable data structure which means we can add , remove ,  replace , append the elements of list whenever we want.

Example : alist = [ 2 , 3 , 4 , 'a' , 7.67 , 'python' ]

How to create List...?

#Creating empty list
#Creating empty list means creating object of list data stucture with no values.
alist = []
print('Initialize empty list :  ' , alist )

#Creating a list of strings
alist = ['hii','this','is','Python']
print('List with string values :  ' , alist )

#Creating a list of integers
alist = [ 1 ,3, 5 , 7]
print('List with integer values :  ' , alist )
#You can also create a list with float , long , complex type integers

#Creating list having mixed type of values(stings , numbers)
alist = [ 345 , 'python' , 6.9836 , 218748682686836753656 , 'program' , 6+8j ]
print('List with mixed type of values :  ' , alist )

#Creating list with list type data structure
#List with lst type of items also called as nested lists or multidimensional list.
alist = [[3,4,5],['a','b','c'],[3.67,9,'hello']]
print('Multi-Dimensional List :  ' , alist )

#Creating list with the use of tuples
alist = [(4,5,6),('a','v','d'),(3,7,1)]
print('List with the use of tuples :  ' , alist )

#Creating list with the use od dictionaries
alist = [{1:'Python' , 2 :'Programming'},{'a' : 97 , 'b' :98 }]
print('List with the use of dictionaries :  ' , alist )

Output :

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A list may contain duplicate values with their distinct positions .
List store its element according to the index positions so we can store the same element multiple index positions, this can duplicate that element in a list.
If the list contains duplicate elements then that duplicate elements have different index positions.

#Creating a list with duplicate values
alist = [ 'a', 1 ,3 , 3 , 7 ,'a' , 5 , 7]
print('List with duplicate values :  ' , alist )

Output :

In above example ,three elements of list have duplicates
element 'a' have two index positions i.e. 0 and 5 .
element 3 have two index positions i.e. 2 and 3 .
element 7 also havetwo index positions i.e. 4 and 7 .
Because of different index positioning these elements considered as distinct elements not as same element .

Greatest number among 3 numbers which are user entered

#Greatest number among 3 numbers which are user entered

#Accepting 3 numbers from user
a=int(input('enter number:'))
b=int(input('enter number:'))
c=int(input('enter number:'))

# find which is greatest number
if a>b:
    if a>c:
        print(a,'is greatest number')
    else:
        print(c,'is greatest number')
else:
    if b>c:
        print(b,'is greatest number')
    else:
        print(c,'is greatest number')

OUTPUT:

Fibonacci series using for loop

#Fibonacci series using for loop

#Asking user that how much terms he want in the fibonacci series
n=int(input("How much terms you want:"))

#displaying fibonacci terms
a,b=0,1
print("fib series=\n",1)
for i in range(1,n):
    s=a+b
    print(s)
    a,b=b,s

OUTPUT:

Dictionary of Days and their Temperature

#Dictionary of Days and their Temperature

#defining an empty dictionary to add days and their temperature
d={}

#defining a list of name of days
days=['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']

#take entry of temperature from user
for i in days:      
    temp=int(input("enter temperature of "+i+" :"))
    d[i]=temp

#to display the day and its temperature in the form of dictionary
print('DAY & TEMPERATURE')
print(d)

#find maximum temperature
tmax=max(d.values())
print('maximum temperature' , tmax)

OUTPUT: