sanithps98 · mkrawat751999 · Jun 13, 2021
diff --git a/app.py b/app.py
@@ -0,0 +1,44 @@
+# -*- 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()
diff --git a/final_work.py b/final_work.py
@@ -0,0 +1,69 @@
+# -*- 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'))
diff --git a/model_1.sav b/model_1.sav
diff --git a/requirements.txt b/requirements.txt
@@ -0,0 +1,13 @@
+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
@@ -0,0 +1,152 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Document</title>
+	<meta name="viewport" content="width=device-width, initial-scale=1">
+
+</head>
+
+
+<body>
+
+    <div style="color:white">
+
+        <form action="{{ url_for('predict')}}" method="post">
+		    <h1>PRICE PREDICION</h1>
+			<h2></h2>
+            <h3>Hihgway milage</h3><input id="first" name="milage" type="number ">
+            <h3>Engine Size</h3><br><input id="second" name="size_of_the_engine" required="number">
+            <h3>Curb weight</h3><input id="third" name="weight_of_a_car" required="number">
+            <h3>Horse Power</h3><br><input id="fourth" name="power" required="number">
+
+            <br><br><button id="sub" type="submit ">Calculate</button>
+            <br>
+
+
+
+
+        </form>
+
+
+
+        <br><br><h3>{{ prediction_text }}<h3>
+    </div>
+
+
+
+
+    <style>
+        body {
+            background-color:green;
+            text-align: center;
+            padding: 0px;
+        }
+
+        #research {
+            font-size: 18px;
+            width: 100px;
+            height: 23px;
+            top: 23px;
+        }
+
+        #box {
+            border-radius: 60px;
+            border-color: 45px;
+            border-style: solid;
+            font-family: cursive;
+            text-align: center;
+            background-color: rgb(168, 131, 61);
+            font-size: medium;
+            position: absolute;
+            width: 700px;
+            bottom: 9%;
+            height: 850px;
+            right: 30%;
+            padding: 0px;
+            margin: 0px;
+            font-size: 14px;
+        }
+
+        #fuel {
+            width: 83px;
+            height: 43px;
+            text-align: center;
+            border-radius: 14px;
+            font-size: 20px;
+        }
+
+        #fuel:hover {
+            background-color: coral;
+        }
+
+        #research {
+            width: 99px;
+            height: 43px;
+            text-align: center;
+            border-radius: 14px;
+            font-size: 18px;
+        }
+
+        #research:hover {
+            background-color: coral;
+        }
+
+        #resea {
+            width: 99px;
+            height: 43px;
+            text-align: center;
+            border-radius: 14px;
+            font-size: 18px;
+        }
+
+        #resea:hover {
+            background-color: coral;
+        }
+
+        #sub {
+            width: 120px;
+            height: 43px;
+            text-align: center;
+            border-radius: 14px;
+            font-size: 18px;
+        }
+
+        #sub:hover {
+            background-color: darkcyan;
+        }
+
+        #first {
+            border-radius: 14px;
+            height: 25px;
+            font-size: 20px;
+            text-align: center;
+			font-family: serif;
+        }
+
+        #second {
+            border-radius: 14px;
+            height: 25px;
+            font-size: 20px;
+            text-align: center;
+        }
+
+        #third {
+            border-radius: 14px;
+            height: 25px;
+            font-size: 20px;
+            text-align: center;
+        }
+
+        #fourth {
+            border-radius: 14px;
+            height: 25px;
+            font-size: 20px;
+            text-align: center;
+        }
+    </style>
+</body>
+
+</html>