1)Create model and train
# importing Librarys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import requests
from pickle import dump,load
# Load Dataset
url="http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ["sepal_length", "sepal_width", "petal_length", "petal_width", "species"]
# Loading Dataset
df=pd.read_csv(url,names=names)
df.tail(11)
df.columns
test=[
{'sepal_length':5.1, 'sepal_width':3.5, 'petal_length':1.4, 'petal_width':0.2},
{'sepal_length':6.9, 'sepal_width':3.1, 'petal_length':5.4, 'petal_width':2.1}
]
df.info()
# Checking null values
df.isna().sum()
df.describe()
# visulization
plt.figure(figsize=(10,8))
sns.scatterplot(x='sepal_length',y='sepal_width',data=df,hue='species')
plt.hist(df['species'])
# Label Encoding
# Selecting dependent and indepedent variable
x=df.drop('species',axis=1)
y=df['species']
# split data
from sklearn.model_selection import train_test_split
xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=0)
# fit and train model
from sklearn.ensemble import RandomForestClassifier
model=RandomForestClassifier()
model.fit(xtrain,ytrain)
ypred=model.predict(xtest)
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
print("accuray is: ",accuracy_score(ytest,ypred))
cm=confusion_matrix(ytest,ypred)
sns.heatmap(cm,annot=True)
print(classification_report(ytest,ypred))
# conclusion:
#In this task we have we explored the iris dataset and predicted the classification using RandomForestClassifier algorithm.
dump(model,open('model.pkl','wb'))
2) Create Flask app (filenae=app.py)
import pickle
#create flask app
app= Flask(__name__)
#Load the picke model
model=pickle.load(open('model.pkl','rb'))
@app.route('/predict', methods = ['POST'])
def predict():
json_ = request.json
query_df = pd.DataFrame(json_)
prediction = model.predict(query_df)
return jsonify({"Prediction":list(prediction)})
if __name__ == "__main__":
app.run(debug=True)
3) Using postman we can deploy test
