Machine Learning Project – Iris Flower in Decision Tree

Program 1

Iris Dataset

import pandas as pd
import numpy as np
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split



df_iris=pd.read_csv("D://scikit_data/IrisData/Iris.csv")


df_iris


df_iris.shape


df_iris.info()


df_iris.isnull().sum()


df_iris


le=LabelEncoder()
df_iris['Species_new']=le.fit_transform(df_iris['Species'])


df_iris


df_iris=df_iris.drop('Species',axis='columns')


df_iris


df_iris=df_iris.drop('Id',axis='columns')


df_iris


df_input=df_iris.drop('Species_new',axis='columns')


df_input


df_output=df_iris.drop(['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm'],axis='columns')


df_output


# Model Creating
model=tree.DecisionTreeClassifier()


type(model)


# Train Model
model.fit(df_input,df_output)


model.predict([[4.9,2.5,4.5,1.7]])


model.score(df_input,df_output)


x=df_input  # Independed
y=df_output # Depended
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)


len(x_train)


len(x_test)


len(y_train)


len(y_test)


y_pred_train=model.predict(x_train)


y_pred_train


from sklearn.metrics import accuracy_score


data_acc=accuracy_score(y_pred_train,y_train)


data_acc


y_pred_test=model.predict(x_test)


y_pred_test


data_acc=accuracy_score(y_pred_test,y_test)


data_acc


model.predict([[6.3,2.3,4.4,1.3]])


spl=float(input("Enter value of SepalLength in Cm: "))
spw=float(input("Enter value of SepalWidth in Cm: "))  
ppl=float(input("Enter value of PetalLength in Cm: "))
ppw=float(input("Enter value of PetalWidth in Cm: "))
result=model.predict([[spl,spw,ppl,ppw]])
if(result==0):
    print("Type of Flower is Iris-setosa")
elif(result==1):
    print("Type of Flower is Iris-versicolor")
else:
    print("Type of Flower is Iris-virginica")
    
              
     
4.7	3.2	1.3	0.2