diff --git a/app.py b/app.py
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--- /dev/null
+++ b/app.py
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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jun 1 10:23:24 2021
+
+@author: Acer
+"""
+from flask import Flask, render_template, request , jsonify
+import requests
+import numpy as np
+import sklearn
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LinearRegression
+from joblib import load
+
+app = Flask(__name__)
+app.config['SECRET_KEY']='mukul'
+
+@app.route('/',methods=['GET'])
+def Home():
+ return render_template('mainpafe.html')
+
+
+
+standard_to = StandardScaler()
+@app.route("/predict", methods=['POST'])
+def predict():
+
+ if request.method == 'POST':
+ Highway_mpg=float(request.form['milage'])
+ engine_size=int(request.form['size_of_the_engine'])
+ curb_weight=int(request.form['weight_of_a_car'])
+ horse_power=int(request.form['power'])
+ model =load('model_final.joblib')
+ prediction=model.predict([[Highway_mpg,engine_size,curb_weight,horse_power]])
+ output=round(prediction[0],2)
+ if output<0:
+ return render_template('mainpafe.html',prediction_texts="Sorry you cannot sell this car")
+ else:
+ return render_template('mainpafe.html',prediction_text="You Can Sell The Car at ${}".format(output))
+ else:
+ return render_template('mainpafe.html')
+
+if __name__=="__main__":
+ app.run()
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diff --git a/final_work.py b/final_work.py
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--- /dev/null
+++ b/final_work.py
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+# -*- coding: utf-8 -*-
+"""final2.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+ https://colab.research.google.com/drive/1Z01aBybX4M0oInJkPut3yzwSX0GI63EL
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+path = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/automobileEDA.csv'
+df = pd.read_csv(path)
+df.head()
+
+from sklearn.linear_model import LinearRegression
+#Create the linear regression object
+lm = LinearRegression()
+lm
+
+Z = df[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']]
+#Fit the linear model
+lm.fit(Z, df['price'])
+
+lm.fit(Z, df['price'])
+# Find the R^2
+lm.score(Z, df['price'])
+
+from sklearn.metrics import mean_squared_error
+# Produce a prediction
+Y_predict_multifit = lm.predict(Z)
+# Compare the predicted results with the actual results
+# The mean square error of price and predicted value using multifit is:
+mean_squared_error(df['price'], Y_predict_multifit)
+
+import pandas as pd
+import numpy as np
+
+# Import clean data
+path = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/module_5_auto.csv'
+df = pd.read_csv(path)
+
+df.to_csv('module_5_auto.csv')
+df = df._get_numeric_data()
+df.head()
+
+y_data = df['price']
+x_data=df.drop('price',axis=1)
+
+from sklearn.model_selection import train_test_split
+
+x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.15, random_state=1)
+
+print("number of test samples :", x_test.shape[0])
+print("number of training samples:",x_train.shape[0])
+
+lr = LinearRegression()
+lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)
+
+yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])
+yhat_train[0:5]
+
+yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])
+yhat_test[0:5]
+
+import pickle
+filename='model_1.sav'
+pickle.dump(lr,open(filename,'wb'))
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diff --git a/model_1.sav b/model_1.sav
new file mode 100644
index 0000000..928e45c
Binary files /dev/null and b/model_1.sav differ
diff --git a/requirements.txt b/requirements.txt
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index 0000000..f5389d9
--- /dev/null
+++ b/requirements.txt
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+Flask==1.1.1
+gunicorn==19.9.0
+itsdangerous==1.1.0
+jinja2==2.10.1
+MarkupSafe==1.1.1
+Werkzeug==0.15.5
+numpy>=1.9.2
+scipy>=0.15.1
+scikit-learn>=0.18
+matplotlib>=1.4.3
+pandas>=0.19
+tldextract==3.1.0
+
diff --git a/templates/mainpafe.html b/templates/mainpafe.html
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index 0000000..aaf593b
--- /dev/null
+++ b/templates/mainpafe.html
@@ -0,0 +1,152 @@
+
+
+
+
+
+
+ Document
+
+
+
+
+
+
+
+
+
+
+
+
+
+
{{ prediction_text }}
+
+
+
+
+
+
+
+
+
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