Upload app.py

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# ## Data Loading
+
+# Importing the necessary libraries, like pandas, numpy and some plotting libraries such as matplotlib and seaborn
+
+# In[ ]:
+
+
+import pandas as pd
+import numpy as np 
+import matplotlib
+import matplotlib.pyplot as plt
+import seaborn as sns
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+
+# Set the default font size, figure size and the grid in the plot
+
+# In[ ]:
+
+
+sns.set_style('darkgrid')
+matplotlib.rcParams['font.size'] = 14
+matplotlib.rcParams['figure.figsize'] = (10, 6)
+matplotlib.rcParams['figure.facecolor'] = '#00000000'
+
+
+# Reading of data as a pandas dataframe and named as **df**
+
+# In[ ]:
+
+
+df = pd.read_csv('Walmart.csv')
+
+
+# In[ ]:
+
+
+df
+
+
+# **About Data:**
+# *   Store - the store number
+# *   Date - the week of sales
+# *   Weekly_Sales - sales for the given store
+# *   Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week
+# *   Temperature - Temperature on the day of sale
+# *   Fuel_Price - Cost of fuel in the region
+# *   CPI – Prevailing consumer price index
+# *   Unemployment - Prevailing unemployment rate
+
+# **Insights:**
+# 
+# *   Here the target columns is Weekly_Sales.
+# *   The data is related to walmart store of united state of america. Where **Store**, **Holiday_Flag** are categorical in nature
+# *   The data is collected over a 45 stores and weekly sales gives the sales of the crossponding store.
+# 
+
+# ## Data Exploration and Modification
+
+# In[ ]:
+
+
+df.info() # it gives the information (like count and data type) of the dataset
+
+
+# Here Date columns is **object** and other remain columns are **interger or float** in nature. Now using the pandas I change the date column datatype(i.e. object) into a pandas-datetime. 
+
+# In[ ]:
+
+
+df.Date=pd.to_datetime(df.Date)
+
+
+# Using the date column i create three seperate columns of weekday, month and year and added to the existing dataset.
+
+# In[ ]:
+
+
+df['weekday'] = df.Date.dt.weekday
+df['month'] = df.Date.dt.month
+df['year'] = df.Date.dt.year
+
+
+# Now I drop the date columns because of no use of it.
+
+# In[ ]:
+
+
+df.drop(['Date'], axis=1, inplace=True)
+
+
+# Hence the modified dataset is look like:
+
+# In[ ]:
+
+
+df.head(3)
+
+
+# Explored the unique values of the weekday, month and year columns as follows:
+
+# In[ ]:
+
+
+print('years unique value', df.year.unique())
+print('months unique value', df.month.unique())
+print('weekday unique value', df.weekday.unique())
+
+
+# Months and weekday are as usual, but the data is taken from year 2010, 2011, 2012 only.
+
+# Now to get the idea of distribution of the dataset, I used describe function which gives a table of various statistical values of all the columns
+
+# In[ ]:
+
+
+df.describe()
+
+
+# **Insights:**
+# *   Temperature - has values ranges from (-2, 100.1) Fahrenhite.
+# *   CPI - is ranges from 126 to 227 with a standard deviation of 39.35
+# *   Unemployment - is ranges from 3.87 to 14.31 with a standard deviation of 1.87
+
+# In[ ]:
+
+
+original_df = df.copy() # made the copy of dataframe to check the dublicates values in the dataset
+
+
+# Checking of dublicates values : 
+
+# In[ ]:
+
+
+counter = 0
+rs,cs = original_df.shape
+
+df.drop_duplicates(inplace=True)
+
+if df.shape==(rs,cs):
+    print('The dataset doesn\'t have any duplicates')
+else:
+    print('Number of duplicates dropped/fixed ---> {rs-df.shape[0]}')
+
+
+# Checking of missing values : 
+
+# In[ ]:
+
+
+df.isnull().sum()
+
+
+# Dataset doesn't have null values
+
+# ## Data Visualization
+
+# In[ ]:
+
+
+df.head(3)
+
+
+# Here we have:
+# 
+# **Numerical columns:** Weekly_sales, temperature, fuel_price, cpi, unemployment
+# 
+# **Categorical columns:** Holiday_flag, Weekday, month, year
+# 
+# Now plotted the count plot to get the distribution or frequency of the columns
+
+# In[ ]:
+
+
+fig, axes = plt.subplots(2, 2, figsize=(16, 8))
+
+#axes[0,0].set_title('Holiday Count plot')
+sns.countplot(x='Holiday_Flag', data=df, ax= axes[0,0])
+
+#axes[0,1].set_title('Weekday Count plot')
+sns.countplot(x='weekday', data=df, ax= axes[0,1]);
+
+#axes[1,0].set_title('month Count plot')
+sns.countplot(x='month', data=df, ax= axes[1,0]);
+
+#axes[1,1].set_title('year Count plot')
+sns.countplot(x='year', data=df, ax= axes[1,1]);
+
+
+# **Insights:**
+# 
+# *   In Holiday flag most of the time there is no holiday in that week.
+# *   In weekdays columns observations are mostly related to the day 4
+# *   Most of the observation in the data is from the month of april
+# *   Most of the observation in the data is from year 2011
+
+# To get the idea of how many observations are there in dataset crossponding to each store, I again plot a count plot.
+
+# In[ ]:
+
+
+plt.figure(figsize= (18,8))
+sns.countplot(x= 'Store', data= df);
+plt.show()
+
+
+# All the store have equal number of data in the set 
+
+# In[ ]:
+
+
+df.head(1)
+
+
+# To analyze the distribution of the data, I plotted the histogram and boxplot for Temperature, Unemployment, Fuel_Price, CPI.
+
+# In[ ]:
+
+
+fig, axes = plt.subplots(4, 2, figsize=(16, 16))
+# axes[0,0].set_title('Temperature')
+sns.histplot(x= 'Temperature', data= df, ax= axes[0,0])
+
+sns.boxplot(x= 'Temperature', data= df, ax= axes[0,1])
+
+# axes[1,0].set_title('Unemployment')
+sns.histplot(x= 'Unemployment', data= df, ax= axes[1,0])
+
+sns.boxplot(x= 'Unemployment', data= df, ax= axes[1,1])
+
+# axes[2,0].set_title('Fuel_Price')
+sns.histplot(x= 'Fuel_Price', data= df, ax= axes[2,0])
+
+sns.boxplot(x = 'Fuel_Price', data= df, ax= axes[2,1])
+
+# axes[3,0].set_title('CPI')
+sns.histplot(x= 'CPI', data= df, ax= axes[3,0])
+
+sns.boxplot(x= 'CPI', data= df, ax= axes[3,1]);
+
+
+# **Insights:**
+# 
+# *   Temperature:  Crossponding to the lower temperature, there is a presence of outlier.
+# *   Umemployment: The outlier is present in the dataset crossponding to higher and lower both values.
+# *   CPI: It is either very low or very high.
+
+# In[ ]:
+
+
+# Removing the outlier from Temperature column
+
+Q1 = df['Temperature'].quantile(0.25)
+Q3 = df['Temperature'].quantile(0.75)
+IQR = Q3 - Q1
+df = df[df['Temperature'] <= (Q3+(1.5*IQR))]
+df = df[df['Temperature'] >= (Q1-(1.5*IQR))]
+
+
+# In[ ]:
+
+
+# Removing the outlier from Unemployment column
+
+Q1 = df['Unemployment'].quantile(0.25)
+Q3 = df['Unemployment'].quantile(0.75)
+IQR = Q3 - Q1
+df = df[df['Unemployment'] <= (Q3+(1.5*IQR))]
+df = df[df['Unemployment'] >= (Q1-(1.5*IQR))]
+
+
+# In[ ]:
+
+
+df.shape
+
+
+# On the process of removing outlier, **484 data** points are removed from data-set
+
+# ## Encoding
+
+# Encoding is a process to convert the categorical columns into a numerical columns, as it is not a good preactice to train a model with categorical inputs.
+
+# In[ ]:
+
+
+cat_cols = ['Store', 'Holiday_Flag', 'weekday', 'month', 'year'] # these are the categorical columns
+
+
+# In[ ]:
+
+
+df[cat_cols].nunique() # Counting the unique value in each of the categorical columns.
+
+
+# In[ ]:
+
+
+# Imported OneHotEncoder to perfrom the encoding
+from sklearn.preprocessing import OneHotEncoder
+# Creating a object of the encoder function
+encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')
+# Fit the encoder object to the dataset which i want to convert into numerical form. 
+encoder.fit(df[cat_cols])
+
+
+# In[ ]:
+
+
+# Creating a list of the encoded columns 
+encoded_cols = list(encoder.get_feature_names(cat_cols))
+print(encoded_cols)
+
+
+# In[ ]:
+
+
+# Now i added those encoded columns into the original dataset by transforming it into a categorical form.
+df[encoded_cols] = encoder.transform(df[cat_cols])
+
+
+# In[ ]:
+
+
+df.shape
+
+
+# ## Standardization
+
+# To scale all the column values to specific range of 0 - 1, I used standard scaler function. It is important to give the equal weights to all the columns.
+
+# In[ ]:
+
+
+# Importing a MinMaxScaler
+from sklearn.preprocessing import MinMaxScaler
+# Creating Scaler Object
+scaler = MinMaxScaler()
+# Fitted the scaler to the dataset
+scaler.fit(df)
+# Transformed the dataset using the fitted scaler object 
+scaled_df = scaler.transform(df)
+
+
+# In[ ]:
+
+
+# Converting the output scaled dataframe into a pandas dataframe
+scaled_df = pd.DataFrame(data = scaled_df, columns = df.columns)
+
+
+# In[ ]:
+
+
+# Checking the output dataframe
+scaled_df.head(3)
+
+
+# ## Train-Test-Split
+
+# Split the dataset into the two part: 
+# 1. Training dataset (used to train the model)
+# 2. Testing dataset (used to test the model)
+
+# In[ ]:
+
+
+# Drop the sales columns to get the input  features
+X = scaled_df.drop('Weekly_Sales', axis=1)
+# Use the sales column as a target columns
+y = scaled_df['Weekly_Sales']
+
+
+# In[ ]:
+
+
+# Importing train test split
+from sklearn.model_selection import train_test_split
+# dividing the dataset into the train and the test parts and each part has input feature and target features
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)
+
+
+# In[ ]:
+
+
+# Printin the shape of all the dataset
+X_train.shape, X_test.shape, y_train.shape, y_test.shape
+
+
+# ## Feature Selection
+
+# Out of all the 78 features all are not important and we have to choose the important feature out of all the features
+
+# In[ ]:
+
+
+# import a linear regerssion model
+from sklearn.linear_model import LinearRegression
+# import a Random Forest Regressor model
+from sklearn.ensemble import RandomForestRegressor
+# import a mean squared error for model evaluation
+from sklearn.metrics import mean_squared_error
+# import a r2 score for model evaluation
+from sklearn.metrics import r2_score
+# import a RFE model for feature selection
+from sklearn.feature_selection import RFE
+
+
+# In[ ]:
+
+
+# Creatint a list to store training and test error 
+Trr=[]; Tss=[]; n=3
+order=['ord-'+str(i) for i in range(2,n)]
+Trd = pd.DataFrame(np.zeros((10,n-2)), columns=order)
+Tsd = pd.DataFrame(np.zeros((10,n-2)), columns=order)
+
+m=df.shape[1]-2
+for i in range(m):
+    # creating a linear regression model object
+    lm = LinearRegression()
+    # creating a rfe model object with linear regression model and with a parameter of the number of features 
+    rfe = RFE(lm, n_features_to_select=X_train.shape[1]-i)
+    # fitting the rfe model to the trainig dataset
+    rfe = rfe.fit(X_train, y_train)
+    # creating a linear regression model object for prediction
+    LR = LinearRegression()
+    # fitted the lr model using the selected features
+    LR.fit(X_train.loc[:,rfe.support_], y_train)
+    # Made the prediction using the linear regression model
+    pred1 = LR.predict(X_train.loc[:,rfe.support_]) # make the prediction on the trainig dataset
+    pred2 = LR.predict(X_test.loc[:,rfe.support_]) # make the prediction on the test dataset
+    # Insert the mse into the Trr and Tss for train and test respectively
+    Trr.append(np.sqrt(mean_squared_error(y_train, pred1)))
+    Tss.append(np.sqrt(mean_squared_error(y_test, pred2)))
+
+
+# In[ ]:
+
+
+plt.plot(Trr, label= 'Train RMSE')
+plt.plot(Tss, label= 'Test RMSE')
+plt.legend()
+plt.show()
+
+
+# If we Recursively Eleminate at most **Ten** features then the score is maximum.
+
+# In[2]:
+
+
+# Eleminating 10 features and using Linear Regresion model the error printed as follows which is the best possible score.
+
+# creating a linear regression model object
+lm = LinearRegression()
+# creating a rfe model object with linear regression model and with number of features equal to 10.
+rfe = RFE(lm,n_features_to_select=X_train.shape[1]-9) 
+# fitting the rfe model to the trainig dataset
+rfe = rfe.fit(X_train, y_train)
+# creating a linear regression model object for prediction
+LR = LinearRegression()
+# fitted the lr model using the selected features
+LR.fit(X_train.loc[:,rfe.support_], y_train)
+# Made the prediction using the linear regression model
+pred1 = LR.predict(X_train.loc[:,rfe.support_])
+pred2 = LR.predict(X_test.loc[:,rfe.support_])
+# Printing the results as a MSE and r2_score.
+print("MSE train",np.sqrt(mean_squared_error(y_train, pred1)))
+print("MSE test",np.sqrt(mean_squared_error(y_test, pred2)))
+print("r2_score train - {}".format(r2_score(y_train, pred1)))
+print("r2_score test - {}".format(r2_score(y_test, pred2)))
+
+
+# Now Removing the 10 features and create the New training and test dataset
+
+# In[ ]:
+
+
+X_train = X_train.loc[:,rfe.support_]
+X_test = X_test.loc[:,rfe.support_]
+
+
+# Now onwards I am going to use various models 
+
+# ## Linear Regression
+
+# In[ ]:
+
+
+lr =LinearRegression()
+lr.fit(X_train, y_train)
+pred1 = lr.predict(X_train)
+pred2 = lr.predict(X_test)
+
+print("Root Mean Squared Error train {}".format(np.mean(mean_squared_error(y_train, pred1))))
+print("Root Mean Squared Error test {}".format(np.sqrt(mean_squared_error(y_test, pred2))))
+print("r2_score train {}".format(r2_score(y_train, pred1)))
+print("r2_score test {}".format(r2_score(y_test, pred2)))
+
+
+# **Ridge Regression**
+
+# In[ ]:
+
+
+from sklearn.linear_model import Ridge
+rr = Ridge()
+rr.fit(X_train, y_train)
+predrr1 = rr.predict(X_train)
+predrr2 = rr.predict(X_test)
+print("Root Mean Squared Error train {}".format(np.mean(mean_squared_error(y_train, predrr1))))
+print("Root Mean Squared Error test {}".format(np.sqrt(mean_squared_error(y_test, predrr2))))
+print("r2_score train {}".format(r2_score(y_train, predrr1)))
+print("r2_score test {}".format(r2_score(y_test, predrr2)))
+
+
+# **Lasso Regression**
+
+# In[ ]:
+
+
+from sklearn.linear_model import Lasso
+lr = Lasso()
+lr.fit(X_train, y_train)
+predlr1 = lr.predict(X_train)
+predlr2 = lr.predict(X_test)
+print("Root Mean Squared Error train {}".format(np.mean(mean_squared_error(y_train, predlr1))))
+print("Root Mean Squared Error test {}".format(np.sqrt(mean_squared_error(y_test, predlr2))))
+print("r2_score train {}".format(r2_score(y_train, predlr1)))
+print("r2_score test {}".format(r2_score(y_test, predlr2)))
+
+
+# **ElasticNet Regression**
+
+# In[ ]:
+
+
+from sklearn.linear_model import ElasticNet
+en = ElasticNet()
+en.fit(X_train, y_train)
+predlr1 = en.predict(X_train)
+predlr2 = en.predict(X_test)
+print("Root Mean Squared Error train {}".format(np.mean(mean_squared_error(y_train, predlr1))))
+print("Root Mean Squared Error test {}".format(np.sqrt(mean_squared_error(y_test, predlr2))))
+print("r2_score train {}".format(r2_score(y_train, predlr1)))
+print("r2_score test {}".format(r2_score(y_test, predlr2)))
+
+
+# **Polynomial Regression**
+
+# In[ ]:
+
+
+from sklearn.preprocessing import PolynomialFeatures
+
+
+# In[ ]:
+
+
+Trr = []
+Tss = []
+for i in range(2,4):
+  poly_reg = PolynomialFeatures(degree = i)
+  pl_X_train = poly_reg.fit_transform(X_train)
+  pl_X_test = poly_reg.fit_transform(X_test)
+  lr = LinearRegression()
+  lr.fit(pl_X_train, y_train)
+  pred_poly_train = lr.predict(pl_X_train)
+  Trr.append(np.sqrt(mean_squared_error(y_train, pred_poly_train)))
+  pred_poly_test = lr.predict(pl_X_test)
+  Tss.append(np.sqrt(mean_squared_error(y_test, pred_poly_test)))
+
+
+# In[ ]:
+
+
+plt.figure(figsize=[15,6])
+plt.subplot(1,2,1)
+plt.plot(range(2,4), Trr, label= 'Training')
+plt.plot(range(2,4), Tss, label= 'Testing')
+plt.title('Polynomial Feature on training data')
+plt.xlabel('Degree')
+plt.ylabel('RMSE')
+plt.legend()
+
+
+# It is clear that in between 2-4 degree polynomial regression 2 has Bais-variance tradeoff
+
+# In[ ]:
+
+
+poly_reg = PolynomialFeatures(degree = 2)
+pl_X_train = poly_reg.fit_transform(X_train)
+pl_X_test = poly_reg.fit_transform(X_test)
+lr = LinearRegression()
+lr.fit(pl_X_train, y_train)
+pred_poly_train = lr.predict(pl_X_train)
+print("r2_score train {}".format(r2_score(pred_poly_train, y_train)))
+pred_poly_test = lr.predict(pl_X_test)
+print("r2_score test {}".format(r2_score(pred_poly_test, y_test)))
+print("Root Mean Squared Error train {}".format(np.mean(mean_squared_error(y_train, pred_poly_train))))
+print("Root Mean Squared Error test {}".format(np.sqrt(mean_squared_error(y_test, pred_poly_test))))
+
+
+# In[ ]:
+
+
+#creating a tabel 
+tabel = {
+        'Train R2': [0.9324387485162124, 0.9323641360074176, 0.0, 0.0, 0.9563932198334125],
+        'Test R2' : [0.9223162582948724, 0.9219331606995953, -0.00014816618161050954, -0.00014816618161050954, -0.0005599911350040454],
+        'Train RMSE' : [0.0016695395619648289, 0.0016713833486400986, 0.024711495499242828, 0.024711495499242828, 0.0010346077251656776 ],
+        'Test RMSE' : [0.04569350618906344, 0.04580603645234492, 0.16395383804559885, 0.16395383804559885, 730742413.004261 ]
+        }
+
+
+# In[ ]:
+
+
+df_new = pd.DataFrame(tabel) 
+
+
+# In[ ]:
+
+
+df_new.index = ['Linear Regression', 'Ridge Regression', 'Lasso Regression', 'ElasticNet Regression', 'Polynomial Regression']
+
+
+# In[ ]:
+
+
+df_new
+
+
+# It is clear that Linear Regression is the Best Model in the dataset, with test accuracy of 92%(approx). 
+# 
+# To improve the accuracy further we can apply other regressor i.e. Random Forest, G
+
+# Now I am going to imporve the accuracy till 98% - 99%. For this I have to use Decision Tree or Random Forest etc.
+
+# **Decision Tree Regressor**
+
+# In[ ]:
+
+
+from sklearn.tree import DecisionTreeRegressor
+dt = DecisionTreeRegressor()
+dt.fit(X_train, y_train)
+
+
+# In[ ]:
+
+
+pred_dt1 = dt.predict(X_train)
+pred_dt2 = dt.predict(X_test)
+print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_dt1))))
+print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_dt2))))
+print('Accuracy Score train: ', dt.score(X_train, y_train))
+print('Accuracy Score test: ', dt.score(X_test, y_test))
+
+
+# In[ ]:
+
+
+max_depth_range = np.arange(1,40,1)
+for x in max_depth_range:
+  dt = DecisionTreeRegressor(max_depth= x)
+  dt.fit(X_train, y_train)
+  pred_dt1 = dt.predict(X_train)
+  pred_dt2 = dt.predict(X_test)
+  print('for max_depth: ', x)
+  print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_dt1))))
+  print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_dt2))))
+  print('Accuracy Score train: ', dt.score(X_train, y_train))
+  print('Accuracy Score test: ', dt.score(X_test, y_test))
+  print()
+
+
+# Decision Tree has maximum accuracy at **maximum depth 39** 
+
+# **Random Forest Regressor**
+
+# In[ ]:
+
+
+from sklearn.ensemble import RandomForestRegressor
+rfc = RandomForestRegressor()
+rfc.fit(X_train, y_train)
+
+
+# In[ ]:
+
+
+pred_rfc1 = rfc.predict(X_train)
+pred_rfc2 = rfc.predict(X_test)
+print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_rfc1))))
+print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_rfc2))))
+print('Accuracy Score train: ', dt.score(X_train, y_train))
+print('Accuracy Score test: ', dt.score(X_test, y_test))
+
+
+# In[ ]:
+
+
+max_depth_range = np.arange(1,40,1)
+for x in max_depth_range:
+  dt = RandomForestRegressor(max_depth= x)
+  dt.fit(X_train, y_train)
+  pred_xg1 = dt.predict(X_train)
+  pred_xg2 = dt.predict(X_test)
+  print('for max_depth: ', x)
+  print('for max_depth: ', x)
+  print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_xg1))))
+  print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_xg2))))
+  print('Accuracy Score train: ', dt.score(X_train, y_train))
+  print('Accuracy Score test: ', dt.score(X_test, y_test))
+  print()
+
+
+# In the depth of **36** the** Random Forest Regressor** has its maximum value of accuracy.
+
+# In[ ]:
+
+
+rfc = RandomForestRegressor(max_depth = 36)
+rfc.fit(X_train, y_train)
+pred_rfc1 = rfc.predict(X_train)
+pred_rfc2 = rfc.predict(X_test)
+print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_rfc1))))
+print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_rfc2))))
+print('Accuracy Score train: ', rfc.score(X_train, y_train))
+print('Accuracy Score test: ', rfc.score(X_test, y_test))
+
+
+# **XG Boost Regressor**
+
+# In[ ]:
+
+
+from xgboost import XGBRegressor
+xg = XGBRegressor()
+xg.fit(X_train, y_train)
+
+
+# In[ ]:
+
+
+pred_xg1 = xg.predict(X_train)
+pred_xg2 = xg.predict(X_test)
+print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_xg1))))
+print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_xg2))))
+
+
+# In[ ]:
+
+
+max_depth_range = np.arange(1,15,1)
+for x in max_depth_range:
+  dt = XGBRegressor(max_depth= x)
+  dt.fit(X_train, y_train)
+  pred_xg1 = dt.predict(X_train)
+  pred_xg2 = dt.predict(X_test)
+  print('for max_depth: ', x)
+  print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_xg1))))
+  print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_xg2))))
+  print('Accuracy Score train: ', dt.score(X_train, y_train))
+  print('Accuracy Score test: ', dt.score(X_test, y_test))
+  print()
+
+
+# It means **maximun depth 9** has best value of Accuracy
+
+# In[ ]:
+
+
+xg = XGBRegressor(max_depth = 9)
+xg.fit(X_train, y_train)
+pred_xg1 = xg.predict(X_train)
+pred_xg2 = xg.predict(X_test)
+print("RMSE for train {}".format(np.sqrt(mean_squared_error(y_train, pred_xg1))))
+print("RMSE for test {}".format(np.sqrt(mean_squared_error(y_test, pred_xg2))))
+print('Accuracy Score train: ', xg.score(X_train, y_train))
+print('Accuracy Score test: ', xg.score(X_test, y_test))
+
+
+# In[ ]:
+
+
+tabel1 = {
+        'Train Score': [0.9679040861170889, 0.9637587048322853, 0.9601543222728802], 
+        'Test Score' : [0.8808466556220073, 0.9028060343874318, 0.9115195955339979],
+        'Train RMSE' : [0.02816270639447925, 0.02992618589836856, 0.03137907401148098],
+        'Test RMSE' :  [0.05659037012899937, 0.051110374979016944, 0.04876553192516943]
+        }
+
+
+# In[ ]:
+
+
+df1 = pd.DataFrame(tabel1) 
+
+
+# In[ ]:
+
+
+df1
+
+
+# In[ ]:
+
+
+df1.index = ['Decision Tree', 'Random Forest', 'XGBoost']
+
+
+# In[ ]:
+
+
+df1
+
+
+# Among the method XGBoost is the best method for the data set
+
+# By Comparising the Linear and XGBoost we can conclude that linear Regression the best suited for the above data set
+
+# In[ ]:
+
+
+
+