mrjohn1134/Tips_Model_Predictor1

1import numpy as np
2import pandas as pd
3import matplotlib.pyplot as plt
4import seaborn as sns
5import sklearn
6import os
7import warnings
8warnings.filterwarnings('ignore')
9from sklearn.linear_model import LinearRegression
10from sklearn.preprocessing import OneHotEncoder
11from sklearn.compose import ColumnTransformer
12from sklearn.pipeline  import Pipeline
13import joblib
14
15#Load dataset
16
17df=sns.load_dataset('tips')
18X = df[['total_bill','sex','smoker','day','size','time']]
19y = df['tip']
20
21df
22
23categorical = ['sex','smoker','day','size','time']
24
25#preprocessing steps
26preprocessor = ColumnTransformer([
27    ('cat', OneHotEncoder(handle_unknown='ignore'),categorical)
28],remainder='passthrough')
29
30#Define the  pipeline
31pipeline = Pipeline([
32    ('preprocessor',preprocessor),
33    ('regressor',LinearRegression())
34]) 
35
36
37
38#Fit amd save the trained pipeline
39pipeline.fit(X, y)
40joblib.dump(pipeline, 'tipsmodel.pkl')