Built Model For Cab Data

Cab_Model.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Apr 29 07:11:15 2022
+
+@author: allenayodeji
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import xlrd
+
+df = pd.read_csv('data_cleaning.csv')
+
+date=[]
+for i in df['Date of Travel']:
+    xl_date = i
+    datetime_date = xlrd.xldate_as_datetime(xl_date, 0)
+    date_object = datetime_date.date()
+    date.append(date_object)
+df['Date']= date   
+
+#To Delete a whole column
+df = df.drop(['Unnamed: 0'], axis = 1)
+df = df.drop(['Date of Travel'], axis = 1)
+
+#To remove spaces in columns
+df.columns = df.columns.str.replace(' ','_')
+profit = (df.Price_Charged - df.Cost_of_Trip)
+df['Profit_of_Cabs'] = profit.apply(lambda x: x if x>=0 else 0)
+
+
+
+# choose relevant columns
+df.columns
+
+df_model = df[['Company', 'City', 'Date', 'Profit_of_Cabs',]]
+# get dummy data
+df_dum = pd.get_dummies(df_model)
+# train test split
+from sklearn.model_selection import train_test_split
+
+X = df_dum.drop('Profit_of_Cabs', axis =1)
+y = df_dum.Profit_of_Cabs.values
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# multiple linear regression 
+import statsmodels.api as sm
+X_sm =  X = sm.add_constant(X)
+model = sm.OLS(y,X_sm)
+model.fit().summary()
+
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import cross_val_score
+
+lm = LinearRegression()
+lm.fit(X_train, y_train)
+
+np.mean(cross_val_score(lm, X_train, y_train))
+
+
+# lasso regression
+from sklearn.linear_model import Lasso
+lm_l = Lasso()
+lm_l.fit(X_train, y_train)
+np.mean(cross_val_score(lm_l, X_train, y_train))
+
+#Xgboost
+from xgboost import XGBRegressor
+xgb_model = XGBRegressor(random_state= 42)
+
+search_space = {
+    "n_estimators" : [100,200,300],
+    "max_depth" : [3,6,9],
+    "gamma" : [0.01, 0.1],
+    "learning_rate" : [0.001, 0.01, 0.1, 1]
+
+    }
+# tune Models on GridSearchCV
+from sklearn.model_selection import GridSearchCV
+#make a GridSearchCV object
+GS = GridSearchCV(estimator = xgb_model, 
+                  param_grid = search_space,
+                  scoring = ["r2", "neg_root_mean_squared_error"],
+                  refit = "r2",
+                  cv =2,
+                  verbose = 4)
+GS.fit(X_train, y_train)
+
+GS.best_score_
+
+
+# test ensembles
+tpred_lm = lm.predict(X_test)
+tpred_lml = lm_l.predict(X_test)
+
+
+from sklearn.metrics import mean_absolute_error
+
+mean_absolute_error = (y_test,tpred_lm)
+mean_absolute_error = (y_test,tpred_lml)
+