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def grop():
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
data = pd.read_csv('insurance.csv')
data_new = data.copy(deep = True)
data.head()
data.isnull().sum()
data.dropna(inplace = True)
X = data.drop('gross_premium', axis = 1)
y = data['gross_premium']
import re
obj_columns = list(data.select_dtypes("object").columns)
obj_columns
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")
data.head()
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)
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])
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)
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))
data.season.value_counts()
X = data.iloc[:,:-1]
y = data.iloc[:,-1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
from sklearn.metrics import r2_score
y_pred = model.predict(X_test)
r2 = r2_score(y_test, y_pred)
print(f'R2 Score: {round(r2*100, 2)}')
y_pred = model.predict(X_train)
r2 = r2_score(y_train, y_pred)
print(f'R2 Score: {round(r2*100, 2)}')
# There is no miss prediction hence model is not overfitted.........(i.e if its is overfitted than we use regularzation technique)
import pickle as pk
filename= 'crop_grosspremimum_Jp.pkl'
pk.dump(model,open(filename,'wb'))
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
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