Upload 6 files

Lasso Regression.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b07b1d5479f558ac9a6494f837b04309561a988ab3bb93c8b39f37e5b9bb4099
+size 129

feedbacko.csv

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+timestamp,briefit,feedbacko
+2024-01-15 14:15:13,5,super

feedbacko.py

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+import streamlit as st
+import pandas as pd
+from datetime import datetime
+def run_feedback():
+    df = pd.read_csv('feedbacko.csv')
+    st.title('Feedback Form')
+    brief = st.slider('Rate your experience ⭐️', min_value=1, max_value=5, value=3)
+    feedback_text = st.text_area('Provide additional comments or feedback:')
+
+    if st.button('Submit Feedback'):
+        # Check if 'feedbacko' is empty and replace it with None
+        feedback_text = None if not feedback_text else feedback_text
+
+        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+        new_data = {
+            'timestamp': timestamp,
+            'briefit': brief,
+            'feedbacko': feedback_text
+        }
+        new_df = pd.DataFrame([new_data])
+        combined_df = pd.concat([df, new_df], ignore_index=True, axis=0)
+        combined_df = combined_df.drop_duplicates(subset=['feedbacko'])
+        combined_df = combined_df.dropna(subset=['briefit'])
+        combined_df.to_csv('feedbacko.csv', index=False)
+        st.success('Thank You for your feedback')
+
+        

gross_premimum.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[145]:
+
+
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import LabelEncoder
+import pandas as pd
+
+
+# In[146]:
+
+
+data = pd.read_csv('insurance.csv')
+data_new = data.copy(deep = True)
+
+
+# In[147]:
+
+
+data.head()
+
+
+# In[148]:
+
+
+data.isnull().sum()
+
+
+# In[149]:
+
+
+data.dropna(inplace = True)
+
+
+# In[150]:
+
+
+X = data.drop('gross_premium', axis = 1)
+y = data['gross_premium']
+
+
+# In[151]:
+
+
+import re
+
+obj_columns = list(data.select_dtypes("object").columns)
+obj_columns
+
+
+# In[152]:
+
+
+import re
+
+for col in obj_columns:
+    data[col] = data[col].astype("str")
+    data[col] = data[col].apply(lambda x: re.sub(r'[^a-zA-Z0-9]', '', x.lower())).astype("str")
+
+
+# In[153]:
+
+
+season_catogory = list(data.season.values)
+scheme_catogory = list(data.scheme.values)
+state_catogory  = list(data.state_name.values)
+district_catogory = list(data.district_name.values)
+
+
+# In[154]:
+
+
+columns = ['season','scheme','state_name','district_name']
+from sklearn.preprocessing import LabelEncoder
+encoder = LabelEncoder()
+for col in columns:
+    data[col] = encoder.fit_transform(data[col])
+
+
+# In[155]:
+
+
+season_label = list(data.season.values)
+scheme_label = list(data.scheme.values)
+state_label = list(data.state_name.values)
+district_label = list(data.district_name.values)
+
+
+# In[156]:
+
+
+season_category_label_dict = dict(zip(season_catogory, season_label))
+
+scheme_category_label_dict = dict(zip(scheme_catogory, scheme_label))
+
+state_category_label_dict = dict(zip(state_catogory, state_label))
+
+district_category_label_dict = dict(zip(district_catogory, district_label))
+
+
+# In[157]:
+
+
+
+# In[163]:
+
+
+# X = data.iloc[:,:-1]
+# y = data.iloc[:,-1]
+
+
+# In[164]:
+
+
+# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+
+# # In[165]:
+
+
+# from sklearn.linear_model import LinearRegression
+
+
+# In[166]:
+
+
+# model = LinearRegression()
+
+
+# # In[167]:
+
+
+# model.fit(X, y)
+
+
+# In[168]:
+
+
+# import pickle as pk
+# filename= 'crop_grosspremimum_Jp.pkl'
+# pk.dump(model,open(filename,'wb'))
+
+
+# In[169]:
+
+
+def encoding(input_data):
+      input_data[0] = season_category_label_dict[input_data[0].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      input_data[1] = scheme_category_label_dict[input_data[1].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      input_data[2] = state_category_label_dict[input_data[2].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      input_data[3] = district_category_label_dict[input_data[3].lower().replace(" ","").replace(" ","").replace(" ","").replace(" ","")]
+      return input_data
+
+
+# In[170]:
+
+
+# crop_grosspremimum = pk.load(open(filename, "rb"))
+
+
+# # In[172]:
+
+
+# data = ["kharif","PMFBY","Andhra Pradesh","Chittoor",18.82,22410.65,792.39,50.93,50.93,614]
+# crop_grosspremimum.predict([encoding(data)])[0]
+
+
+# In[ ]:
+
+
+
+