-
Notifications
You must be signed in to change notification settings - Fork 0
/
copper_modelling.py
294 lines (241 loc) · 13.5 KB
/
copper_modelling.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
import streamlit as st
from streamlit_option_menu import option_menu
from animation import*
import datetime
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
import pickle
from sklearn.model_selection import train_test_split
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.svm import SVC
import xgboost as xgb
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
#page congiguration
st.set_page_config(page_title= "Copper Modelling",
page_icon= 'random',
layout= "wide",)
#=========hide the streamlit main and footer
hide_default_format = """
<style>
#MainMenu {visibility: hidden; }
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_default_format, unsafe_allow_html=True)
left,right=st.columns([1,3])
with left:
url_link0="https://assets5.lottiefiles.com/packages/lf20_alg1vyxd.json"
st_lottie = lottie_home1(url_link0)
with right:
st.markdown("<h1 style='text-align: center; color: red;'>COPPER SELLING PRICE PREDICTION AND STATUS</h1>",
unsafe_allow_html=True)
selected = option_menu(None, ['HOME',"PRICE PREDICTION","STATUS",],
icons=["house",'cash-coin','trophy'],orientation='horizontal',default_index=0)
if selected=='HOME':
with left:
url_link1 = "https://assets1.lottiefiles.com/private_files/lf30_kit9njnq.json"
st_lottie = lottie_home1(url_link1)
st.write('## **WELCOME TO INDUSTRIAL COPPER MODELLING**')
st.markdown('##### ***This project focuses on modelling industrial copper data using Python and various libraries such as pandas, numpy, scikit-learn. The objective of the project is to preprocess the data, handle missing values, detect outliers, and handle skewness. Additionally, regression and classification models will be built to predict the selling price and determine if a sale was won or lost. The trained models will be saved as a pickle file for later use in a Streamlit application.***')
left ,right=st.columns([2,2])
with left:
st.write('### TECHNOLOGY USED')
st.write('- PYTHON (PANDAS, NUMPY)')
st.write('- SCIKIT-LEARN')
st.write('- DATA PREPROCESSING')
st.write('- EXPLORATORY DATA ANALYSIS')
st.write('- STREAMLIT')
with right:
st.write("### MACHINE LEARNING MODEL")
st.write('#### REGRESSION - ***:red[EXTRATREEREGRESSOR]***')
st.write('- The ExtraTree Regressor is an ensemble learning method that belongs to the tree-based family of models.')
st.write('#### CLASSIFICATION - ***:green[RANDOMFORESTCLASSIFIER]***')
st.write('- The RandomForestClassifier is an ensemble learning method that combines multiple decision trees to create a robust and accurate classification model.')
if selected=='PRICE PREDICTION':
with left:
url_link2 = "https://assets8.lottiefiles.com/packages/lf20_OPFirj1e4d.json"
st_lottie = lottie_price1(url_link2)
item_list=['W', 'S', 'Others', 'PL', 'WI', 'IPL']
status_list=['Won', 'To be approved', 'Lost', 'Not lost for AM', 'Wonderful', 'Revised','Offered', 'Offerable']
country_list=['28', '32', '38', '78', '27', '30', '25', '77', '39', '40', '26', '84', '80', '79','113', '89']
application_list=[10, 41, 28, 59, 15, 4, 38, 56, 42, 26, 27, 19, 20, 66,
29, 22, 40, 25, 67, 79, 3, 99, 2, 5,39, 69, 70, 65, 58, 68]
product_list=[1670798778, 1668701718, 628377, 640665, 611993, 1668701376,
164141591, 1671863738, 1332077137, 640405, 1693867550, 1665572374,
1282007633, 1668701698, 628117, 1690738206, 628112, 640400,
1671876026, 164336407, 164337175, 1668701725, 1665572032, 611728,
1721130331, 1693867563, 611733, 1690738219, 1722207579, 929423819,
1665584320, 1665584662, 1665584642]
st.write(
'##### ***<span style="color:yellow">Fill all the fields and Press the below button to view the :red[predicted price] of copper</span>***',
unsafe_allow_html=True)
c1,c2,c3=st.columns([2,2,2])
with c1:
quantity=st.text_input('Enter Quantity (Min:611728 & Max:1722207579) in tons')
thickness = st.text_input('Enter Thickness (Min:0.18 & Max:400)')
width = st.text_input('Enter Width (Min:1, Max:2990)')
with c2:
country = st.selectbox('Country Code', country_list)
status = st.selectbox('Status', status_list)
item = st.selectbox('Item Type', item_list)
with c3:
application = st.selectbox('Application Type', application_list)
product = st.selectbox('Product Reference', product_list)
item_order_date = st.date_input("Order Date", datetime.date(2023, 1, 1))
item_delivery_date = st.date_input("Estimated Delivery Date", datetime.date(2023, 1, 1))
with c1:
st.write('')
st.write('')
st.write('')
if st.button('PREDICT PRICE'):
data = []
with open('country.pkl', 'rb') as file:
encode_country = pickle.load(file)
with open('status.pkl', 'rb') as file:
encode_status = pickle.load(file)
with open('item type.pkl', 'rb') as file:
encode_item = pickle.load(file)
with open('scaling.pkl', 'rb') as file:
scaled_data = pickle.load(file)
with open('Extratreeregressor.pkl', 'rb') as file:
trained_model = pickle.load(file)
transformed_country = encode_country.transform(country_list)
encoded_ct = None
for i, j in zip(country_list, transformed_country):
if country == i:
encoded_ct = j
break
else:
st.error("Country not found.")
exit()
transformed_status = encode_status.transform(status_list)
encode_st = None
for i, j in zip(status_list, transformed_status):
if status == i:
encode_st = j
break
else:
st.error("Status not found.")
exit()
transformed_item = encode_item.transform(item_list)
encode_it = None
for i, j in zip(item_list, transformed_item):
if item == i:
encode_it = j
break
else:
st.error("Item type not found.")
exit()
order = datetime.datetime.strptime(str(item_order_date), "%Y-%m-%d")
delivery = datetime.datetime.strptime(str(item_delivery_date), "%Y-%m-%d")
day = delivery - order
data.append(quantity)
data.append(thickness)
data.append(width)
data.append(encoded_ct)
data.append(encode_st)
data.append(encode_it)
data.append(application)
data.append(product)
data.append(day.days)
x = np.array(data).reshape(1, -1)
pred_model= scaled_data.transform(x)
price_predict= trained_model.predict(pred_model)
predicted_price = str(price_predict)[1:-1]
st.write(f'Predicted Selling Price : :green[₹] :green[{predicted_price}]')
st.info("The Predicted selling price may be differ from various reason like Supply and Demand Imbalances,Infrastructure and Transportation etc..",icon='ℹ️')
if selected=='STATUS':
with left:
#url_link3 = "https://assets9.lottiefiles.com/packages/lf20_lw4olqnf.json"
url_link3='https://assets8.lottiefiles.com/private_files/lf30_vsr6pvvl.json'
lottie_status1(url_link3)
url_link4= "https://assets1.lottiefiles.com/private_files/lf30_by9lgy8q.json"
lottie_status1(url_link4)
item_list_cls = ['W', 'S', 'Others', 'PL', 'WI', 'IPL']
country_list_cls = ['28', '32', '38', '78', '27', '30', '25', '77', '39', '40', '26', '84', '80', '79', '113', '89']
application_list_cls = [10, 41, 28, 59, 15, 4, 38, 56, 42, 26, 27, 19, 20, 66,
29, 22, 40, 25, 67, 79, 3, 99, 2, 5, 39, 69, 70, 65, 58, 68]
product_list_cls = [1670798778, 1668701718, 628377, 640665, 611993, 1668701376,
164141591, 1671863738, 1332077137, 640405, 1693867550, 1665572374,
1282007633, 1668701698, 628117, 1690738206, 628112, 640400,
1671876026, 164336407, 164337175, 1668701725, 1665572032, 611728,
1721130331, 1693867563, 611733, 1690738219, 1722207579, 929423819,
1665584320, 1665584662, 1665584642]
st.write(
'##### ***<span style="color:yellow">Fill all the fields and Press the below button to view the status :red[WON / LOST] of copper in the desired time range</span>***',
unsafe_allow_html=True)
cc1, cc2, cc3 = st.columns([2, 2, 2])
with cc1:
quantity_cls = st.text_input('Enter Quantity (Min:611728 & Max:1722207579) in tons')
thickness_cls = st.text_input('Enter Thickness (Min:0.18 & Max:400)')
width_cls= st.text_input('Enter Width (Min:1, Max:2990)')
with cc2:
selling_price_cls= st.text_input('Enter Selling Price (Min:1, Max:100001015)')
item_cls = st.selectbox('Item Type', item_list_cls)
country_cls= st.selectbox('Country Code', country_list_cls)
with cc3:
application_cls = st.selectbox('Application Type', application_list_cls)
product_cls = st.selectbox('Product Reference', product_list_cls)
item_order_date_cls = st.date_input("Order Date", datetime.date(2023, 1, 1))
item_delivery_date_cls = st.date_input("Estimated Delivery Date", datetime.date(2023,1, 1))
with cc1:
st.write('')
st.write('')
st.write('')
if st.button('PREDICT STATUS'):
data_cls = []
with open('country.pkl', 'rb') as file:
encode_country_cls = pickle.load(file)
with open('item type.pkl', 'rb') as file:
encode_item_cls = pickle.load(file)
with open('scaling_classify.pkl', 'rb') as file:
scaled_data_cls = pickle.load(file)
with open('randomforest_classification.pkl', 'rb') as file:
trained_model_cls = pickle.load(file)
transformed_country_cls = encode_country_cls.transform(country_list_cls)
encoded_ct_cls = None
for i, j in zip(country_list_cls, transformed_country_cls):
if country_cls == i:
encoded_ct_cls = j
break
else:
st.error("Country not found.")
exit()
transformed_item_cls = encode_item_cls.transform(item_list_cls)
encode_it_cls = None
for i, j in zip(item_list_cls, transformed_item_cls):
if item_cls == i:
encode_it_cls = j
break
else:
st.error("Item type not found.")
exit()
order_cls = datetime.datetime.strptime(str(item_order_date_cls), "%Y-%m-%d")
delivery_cls = datetime.datetime.strptime(str(item_delivery_date_cls), "%Y-%m-%d")
day_cls = delivery_cls- order_cls
data_cls.append(quantity_cls)
data_cls.append(thickness_cls)
data_cls.append(width_cls)
data_cls.append(selling_price_cls)
data_cls.append(encoded_ct_cls)
data_cls.append(encode_it_cls)
data_cls.append(application_cls)
data_cls.append(product_cls)
data_cls.append(day_cls.days)
x_cls = np.array(data_cls).reshape(1, -1)
scaling_model_cls = scaled_data_cls.transform(x_cls)
pred_status = trained_model_cls.predict(scaling_model_cls)
if pred_status==6:
st.write(f'Predicted Status : :green[WON]')
else:
st.write(f'Predicted Status : :red[LOST]')
st.info("The Predicted Status may be differ from various reason like Supply and Demand Imbalances,Infrastructure and Transportation etc..",icon='ℹ️')