Add files

Salary_Data.csv

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+YearsExperience,Salary
+1.1,39343.00
+1.3,46205.00
+1.5,37731.00
+2.0,43525.00
+2.2,39891.00
+2.9,56642.00
+3.0,60150.00
+3.2,54445.00
+3.2,64445.00
+3.7,57189.00
+3.9,63218.00
+4.0,55794.00
+4.0,56957.00
+4.1,57081.00
+4.5,61111.00
+4.9,67938.00
+5.1,66029.00
+5.3,83088.00
+5.9,81363.00
+6.0,93940.00
+6.8,91738.00
+7.1,98273.00
+7.9,101302.00
+8.2,113812.00
+8.7,109431.00
+9.0,105582.00
+9.5,116969.00
+9.6,112635.00
+10.3,122391.00
+10.5,121872.00

model.py

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+# Simple Linear Regression
+
+'''
+This model predicts the salary of the employ based on experience using simple linear regression model.
+'''
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import pickle
+import requests
+import json
+
+# Importing the dataset
+dataset = pd.read_csv('Salary_Data.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 1].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)
+
+# Fitting Simple Linear Regression to the Training set
+from sklearn.linear_model import LinearRegression
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+# Predicting the Test set results
+y_pred = regressor.predict(X_test)
+
+# Saving model to disk
+pickle.dump(regressor, open('model.pkl','wb'))
+
+# Loading model to compare the results
+model = pickle.load( open('model.pkl','rb'))
+print(model.predict([[1.8]]))

request.py

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+# URL
+url = 'http://localhost:5000/api'
+
+# Change the value of experience that you want to test
+r = requests.post(url,json={'exp':1.8,})
+print(r.json())

server.py

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+# Create API of ML model using flask
+
+'''
+This code takes the JSON data while POST request an performs the prediction using loaded model and returns
+the results in JSON format.
+'''
+
+# Import libraries
+import numpy as np
+from flask import Flask, request, jsonify
+import pickle
+
+app = Flask(__name__)
+
+# Load the model
+model = pickle.load(open('model.pkl','rb'))
+
+@app.route('/api',methods=['POST'])
+def predict():
+    # Get the data from the POST request.
+    data = request.get_json(force=True)
+
+    # Make prediction using model loaded from disk as per the data.
+    prediction = model.predict([[np.array(data['exp'])]])
+
+    # Take the first value of prediction
+    output = prediction[0]
+
+    return jsonify(output)
+
+if __name__ == '__main__':
+    app.run(port=5000, debug=True)