├── README.md
└── house_rent
    ├── Procfile
    ├── model.pkl
    ├── House_Rent.pdf
    ├── house_rent.csv
    ├── request.py
    ├── requirements.txt
    ├── app.py
    ├── house_rent.py
    └── templates
        └── index.html


/README.md:
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1 | # House_Rent


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/house_rent/Procfile:
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1 | web: gunicorn --bind 0.0.0.0:$PORT app:app
2 | 


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/house_rent/model.pkl:
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https://raw.githubusercontent.com/Mouneshgouda/House_Rent/main/house_rent/model.pkl


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/house_rent/House_Rent.pdf:
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https://raw.githubusercontent.com/Mouneshgouda/House_Rent/main/house_rent/House_Rent.pdf


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/house_rent/house_rent.csv:
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 1 | city,type,bhk,rent
 2 | 0,0,1,20000
 3 | 0,0,2,30000
 4 | 0,1,2,40000
 5 | 1,0,3,50000
 6 | 1,0,4,60000
 7 | 1,1,4,70000
 8 | 0,1,5,80000
 9 | 1,1,5,90000
10 | 


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/house_rent/request.py:
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1 | import requests
2 | from collections.abc import Mapping
3 | 
4 | 
5 | url = 'http://localhost:5000/predict_api'
6 | r = requests.post(url,json={'city':1, 'type':1, 'bhk':2})
7 | 
8 | print(r.json())


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/house_rent/requirements.txt:
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 1 | Flask==2.1.2
 2 | gunicorn==19.9.0
 3 | itsdangerous==2.0
 4 | Jinja2==3.0
 5 | MarkupSafe==2.0.0rc2
 6 | Werkzeug==2.0
 7 | numpy>=1.9.2
 8 | scipy>=0.15.1
 9 | scikit-learn>=0.18
10 | matplotlib>=1.4.3
11 | pandas>=0.19
12 | 
13 | 


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/house_rent/app.py:
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 1 | import numpy as np
 2 | from flask import Flask, request, jsonify, render_template
 3 | import pickle
 4 | 
 5 | app = Flask(__name__)
 6 | model = pickle.load(open('model.pkl', 'rb'))
 7 | 
 8 | @app.route('/')
 9 | def home():
10 |     return render_template('index.html')
11 | 
12 | @app.route('/predict',methods=['POST'])
13 | def predict():
14 |     int_features = [int(x) for x in request.form.values()]
15 |     final_features = [np.array(int_features)]
16 |     prediction = model.predict(final_features)
17 | 
18 |     output = round(prediction[0], 2)
19 | 
20 |     return render_template('index.html', prediction_text='House rent approx Rs {}'.format(output))
21 | 
22 | 
23 | if __name__ == "__main__":
24 |     app.run(debug=True)


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/house_rent/house_rent.py:
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 1 | #!/usr/bin/env python
 2 | # coding: utf-8
 3 | 
 4 | # In[5]:
 5 | 
 6 | 
 7 | import numpy as np
 8 | import matplotlib.pyplot as plt
 9 | import pandas as pd
10 | from sklearn.linear_model import LinearRegression
11 | import pickle
12 | 
13 | 
14 | # In[2]:
15 | 
16 | 
17 | dataset = pd.read_csv('house_rent.csv')
18 | 
19 | 
20 | # In[3]:
21 | 
22 | 
23 | X = dataset.iloc[:, :3]
24 | y = dataset.iloc[:, -1]
25 | 
26 | 
27 | # In[6]:
28 | 
29 | 
30 | regressor = LinearRegression()
31 | 
32 | 
33 | # In[7]:
34 | 
35 | 
36 | regressor.fit(X, y)
37 | 
38 | 
39 | # In[8]:
40 | 
41 | 
42 | pickle.dump(regressor, open('model.pkl','wb'))
43 | 
44 | 
45 | # In[9]:
46 | 
47 | 
48 | model = pickle.load(open('model.pkl','rb'))
49 | 
50 | 
51 | # In[10]:
52 | 
53 | 
54 | print(model.predict([[1, 1, 2]]))
55 | 
56 | 
57 | # In[ ]:
58 | 
59 | 
60 | 
61 | 
62 | 


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/house_rent/templates/index.html:
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 1 | <!DOCTYPE html>
 2 | <html>
 3 | 
 4 | <head>
 5 |     <style>
 6 |         body {
 7 |             margin: 0;
 8 |             padding: 0;
 9 |             font-family: 'Arial', sans-serif;
10 |             background: url('your_background_image.jpg') center fixed;
11 |             background-size: cover;
12 |             height: 100vh;
13 |             display: flex;
14 |             align-items: center;
15 |             justify-content: center;
16 |         }
17 | 
18 |         .login {
19 |             background-color: rgba(255, 255, 255, 0.8); /* Add a semi-transparent white background to the form */
20 |             padding: 20px;
21 |             border-radius: 10px;
22 |             box-shadow: 0 0 10px rgba(0, 0, 0, 0.2);
23 |             text-align: center;
24 |         }
25 | 
26 |         .login h1 {
27 |             color: #333;
28 |         }
29 | 
30 |         input {
31 |             width: 100%;
32 |             padding: 10px;
33 |             margin: 5px 0;
34 |             box-sizing: border-box;
35 |             border: 1px solid #ccc;
36 |             border-radius: 5px;
37 |         }
38 | 
39 |         button {
40 |             width: 100%;
41 |             padding: 10px;
42 |             background-color: #4CAF50;
43 |             color: white;
44 |             border: none;
45 |             border-radius: 5px;
46 |             cursor: pointer;
47 |         }
48 | 
49 |         button:hover {
50 |             background-color: #45a049;
51 |         }
52 |     </style>
53 | </head>
54 | 
55 | <body>
56 |     <div>
57 | 		</p>
58 | 		<div align="center"></div>
59 | 		<p align="center"> 
60 | 			<a href="https://github.com/Mouneshgouda" target="_blank"><img alt="GitHub" src="https://img.shields.io/badge/-@MouneshPatil-181717?style=flat-square&logo=GitHub&logoColor=white"></a>
61 | 			<a href="https://www.linkedin.com/in/mounesh-gouda-858069246/" target="_blank"><img alt="LinkedIn" src="https://img.shields.io/badge/-MouneshPatil-0077B5?style=flat-square&logo=Linkedin&logoColor=white"></a>
62 | 	  </div>
63 |     <div class="login">
64 |         <h1>Prediction of House Rent By Mouneshgouda</h1>
65 |         <form action="{{ url_for('predict')}}" method="post">
66 |             <input type="text" name="city" placeholder="Name of the City" required="required" /><br><br>
67 |             <input type="text" name="type" placeholder="Type of house" required="required" /><br><br>
68 |             <input type="text" name="bhk" placeholder="No.of rooms" required="required" /><br><br>
69 | 
70 |             <button type="submit">Predict</button><br>
71 |         </form>
72 | 
73 |         <br>
74 |         <br>
75 |         {{ prediction_text }}
76 |     </div>
77 | </body>
78 | 
79 | </html>
80 | 


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