How strong will be the concrete mixture? Can you estimate it while creating it? A seasoned civil engineer will know the winning mixture by heart! He/she will understand what should be the right amount of water, ash, cement etc. should be mixed in order to create a high strength concrete mixture.
In this case study, your task is to create a machine learning model which can predict the future strength of a concrete, based on its components and the time for which it is dried.
In below case study I will discuss the step by step approach to create a Machine Learning predictive model in such scenarios.
You can use this flow as a template to solve any supervised ML Regression problem!
The flow of the case study is as below:
I know its a long list!! Take a deep breath... and let us get started!
This is one of the most important steps in machine learning! You must understand the data and the domain well before trying to apply any machine learning algorithm.
The data has one file "ConcreteStrengthData.csv". This file contains 1005 concrete mixture combinations.
The business meaning of each column in the data is as below
# Supressing the warning messages
import warnings
warnings.filterwarnings('ignore')
# Reading the dataset
import pandas as pd
import numpy as np
ConcreteStrengthData=pd.read_csv('/Users/farukh/Python Case Studies/ConcreteStrengthData.csv', encoding='latin')
print('Shape before deleting duplicate values:', ConcreteStrengthData.shape)
# Removing duplicate rows if any
ConcreteStrengthData=ConcreteStrengthData.drop_duplicates()
print('Shape After deleting duplicate values:', ConcreteStrengthData.shape)
# Printing sample data
# Start observing the Quantitative/Categorical/Qualitative variables
ConcreteStrengthData.head(10)
Based on the problem statement you can understand that we need to create a supervised ML Regression model, as the target variable is Continuous.
%matplotlib inline
# Creating Bar chart as the Target variable is Continuous
ConcreteStrengthData['Strength'].hist()
The data distribution of the target variable is satisfactory to proceed further. There are sufficient number of rows for each type of values to learn from.
This step is performed to guage the overall data. The volume of data, the types of columns present in the data. Initial assessment of the data should be done to identify which columns are Quantitative, Categorical or Qualitative.
This step helps to start the column rejection process. You must look at each column carefully and ask, does this column affect the values of the Target variable? For example in this case study, you will ask, does this column affect the Strength of the concrete? If the answer is a clear "No", then remove the column immediately from the data, otherwise keep the column for further analysis.
There are four commands which are used for Basic data exploration in Python
# Looking at sample rows in the data
ConcreteStrengthData.head()
# Observing the summarized information of data
# Data types, Missing values based on number of non-null values Vs total rows etc.
# Remove those variables from data which have too many missing values (Missing Values > 30%)
# Remove Qualitative variables which cannot be used in Machine Learning
ConcreteStrengthData.info()
# Looking at the descriptive statistics of the data
ConcreteStrengthData.describe(include='all')
# Finging unique values for each column
# TO understand which column is categorical and which one is Continuous
# Typically if the numer of unique values are < 20 then the variable is likely to be a category otherwise continuous
ConcreteStrengthData.nunique()
Based on the basic exploration above, you can now create a simple report of the data, noting down your observations regaring each column. Hence, creating a initial roadmap for further analysis.
The selected columns in this step are not final, further study will be done and then a final list will be created
There are no qualitative columns in this data
We can spot a categorical variable in the data by looking at the unique values in them. Typically a categorical variable contains less than 20 Unique values AND there is repetition of values, which means the data can be grouped by those unique values.
Based on the Basic Data Exploration above, we have spotted no categorical predictors in the data
Categorical Predictors:
There are no categorical predictors in this data
Based on the Basic Data Exploration, there are eight continuous predictor variables 'CementComponent ', 'BlastFurnaceSlag', 'FlyAshComponent', 'WaterComponent', 'SuperplasticizerComponent','CoarseAggregateComponent', 'FineAggregateComponent', and 'AgeInDays'
# Plotting histograms of multiple columns together
ConcreteStrengthData.hist(['CementComponent ', 'BlastFurnaceSlag', 'FlyAshComponent',
'WaterComponent', 'SuperplasticizerComponent','CoarseAggregateComponent',
'FineAggregateComponent', 'AgeInDays'], figsize=(18,10))
Histograms shows us the data distribution for a single continuous variable.
The X-axis shows the range of values and Y-axis represent the number of values in that range. For example, in the above histogram of "AgeInDays", there are around 800 rows in data that has a value between 0 to 25.
The ideal outcome for histogram is a bell curve or slightly skewed bell curve. If there is too much skewness, then outlier treatment should be done and the column should be re-examined, if that also does not solve the problem then only reject the column.
Selected Continuous Variables:
Outliers are extreme values in the data which are far away from most of the values. You can see them as the tails in the histogram.
Outlier must be treated one column at a time. As the treatment will be slightly different for each column.
Why I should treat the outliers?
Outliers bias the training of machine learning models. As the algorithm tries to fit the extreme value, it goes away from majority of the data.
There are below two options to treat outliers in the data.
No outliers in this data.
Missing values are treated for each column separately.
If a column has more than 30% data missing, then missing value treatment cannot be done. That column must be rejected because too much information is missing.
There are below options for treating missing values in data.
# Finding how many missing values are there for each column
ConcreteStrengthData.isnull().sum()
No missing values in this data.
Now its time to finally choose the best columns(Features) which are correlated to the Target variable. This can be done directly by measuring the correlation values or ANOVA/Chi-Square tests. However, it is always helpful to visualize the relation between the Target variable and each of the predictors to get a better sense of data.
I have listed below the techniques used for visualizing relationship between two variables as well as measuring the strength statistically.
In this case study the Target variable is Continuous, hence below two scenarios will be present
When the Target variable is continuous and the predictor is also continuous, we can visualize the relationship between the two variables using scatter plot and measure the strength of relation using pearson's correlation value.
ContinuousCols=['CementComponent ', 'BlastFurnaceSlag', 'FlyAshComponent',
'WaterComponent', 'SuperplasticizerComponent','CoarseAggregateComponent',
'FineAggregateComponent', 'AgeInDays']
# Plotting scatter chart for each predictor vs the target variable
for predictor in ContinuousCols:
ConcreteStrengthData.plot.scatter(x=predictor, y='Strength', figsize=(10,5), title=predictor+" VS "+ 'Strength')
What should you look for in these scatter charts?
Trend. You should try to see if there is a visible trend or not. There could be three scenarios
Increasing Trend: This means both variables are positively correlated. In simpler terms, they are directly proportional to each other, if one value increases, other also increases. This is good for ML!
Decreasing Trend: This means both variables are negatively correlated. In simpler terms, they are inversely proportional to each other, if one value increases, other decreases. This is also good for ML!
No Trend: You cannot see any clear increasing or decreasing trend. This means there is no correlation between the variables. Hence the predictor cannot be used for ML.
Based on this chart you can get a good idea about the predictor, if it will be useful or not. You confirm this by looking at the correlation value.
Pearson's correlation coefficient can simply be calculated as the covariance between two features $x$ and $y$ (numerator) divided by the product of their standard deviations (denominator):
# Calculating correlation matrix
ContinuousCols=['Strength','CementComponent ', 'BlastFurnaceSlag', 'FlyAshComponent',
'WaterComponent', 'SuperplasticizerComponent','CoarseAggregateComponent',
'FineAggregateComponent', 'AgeInDays']
# Creating the correlation matrix
CorrelationData=ConcreteStrengthData[ContinuousCols].corr()
CorrelationData
# Filtering only those columns where absolute correlation > 0.5 with Target Variable
# reduce the 0.5 threshold if no variable is selected
CorrelationData['Strength'][abs(CorrelationData['Strength']) > 0.3 ]
Final selected Continuous columns:
'CementComponent','SuperplasticizerComponent','AgeInDays'
When the target variable is Continuous and the predictor variable is Categorical we analyze the relation using Boxplots and measure the strength of relation using Anova test
There are no categorical columns in this data.
Based on the above tests, selecting the final columns for machine learning
SelectedColumns=['CementComponent ','SuperplasticizerComponent','AgeInDays']
# Selecting final columns
DataForML=ConcreteStrengthData[SelectedColumns]
DataForML.head()
# Saving this final data for reference during deployment
DataForML.to_pickle('DataForML.pkl')
List of steps performed on predictor variables before data can be used for machine learning
In this data there is no Ordinal categorical variable which is in string format.
There is no binary nominal variable in string format to be converted
# Treating all the nominal variables at once using dummy variables
DataForML_Numeric=pd.get_dummies(DataForML)
# Adding Target Variable to the data
DataForML_Numeric['Strength']=ConcreteStrengthData['Strength']
# Printing sample rows
DataForML_Numeric.head()
We dont use the full data for creating the model. Some data is randomly selected and kept aside for checking how good the model is. This is known as Testing Data and the remaining data is called Training data on which the model is built. Typically 70% of data is used as Training data and the rest 30% is used as Tesing data.
# Printing all the column names for our reference
DataForML_Numeric.columns
# Separate Target Variable and Predictor Variables
TargetVariable='Strength'
Predictors=['CementComponent ', 'SuperplasticizerComponent', 'AgeInDays']
X=DataForML_Numeric[Predictors].values
y=DataForML_Numeric[TargetVariable].values
# Split the data into training and testing set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=428)
You can choose not to run this step if you want to compare the resultant accuracy of this transformation with the accuracy of raw data.
However, if you are using KNN or Neural Networks, then this step becomes necessary.
### Sandardization of data ###
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# Choose either standardization or Normalization
# On this data Min Max Normalization produced better results
# Choose between standardization and MinMAx normalization
#PredictorScaler=StandardScaler()
PredictorScaler=MinMaxScaler()
# Storing the fit object for later reference
PredictorScalerFit=PredictorScaler.fit(X)
# Generating the standardized values of X
X=PredictorScalerFit.transform(X)
# Split the data into training and testing set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Sanity check for the sampled data
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
# Multiple Linear Regression
from sklearn.linear_model import LinearRegression
RegModel = LinearRegression()
# Printing all the parameters of Linear regression
print(RegModel)
# Creating the model on Training Data
LREG=RegModel.fit(X_train,y_train)
prediction=LREG.predict(X_test)
# Taking the standardized values to original scale
from sklearn import metrics
# Measuring Goodness of fit in Training data
print('R2 Value:',metrics.r2_score(y_train, LREG.predict(X_train)))
###########################################################################
print('\n##### Model Validation and Accuracy Calculations ##########')
# Printing some sample values of prediction
TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)
TestingDataResults[TargetVariable]=y_test
TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)
# Printing sample prediction values
print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())
# Calculating the error for each row
TestingDataResults['APE']=100 * ((abs(
TestingDataResults['Strength']-TestingDataResults['PredictedStrength']))/TestingDataResults['Strength'])
MAPE=np.mean(TestingDataResults['APE'])
MedianMAPE=np.median(TestingDataResults['APE'])
Accuracy =100 - MAPE
MedianAccuracy=100- MedianMAPE
print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier
print('Median Accuracy on test data:', MedianAccuracy)
# Defining a custom function to calculate accuracy
# Make sure there are no zeros in the Target variable if you are using MAPE
def Accuracy_Score(orig,pred):
MAPE = np.mean(100 * (np.abs(orig-pred)/orig))
#print('#'*70,'Accuracy:', 100-MAPE)
return(100-MAPE)
# Custom Scoring MAPE calculation
from sklearn.metrics import make_scorer
custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)
# Importing cross validation function from sklearn
from sklearn.model_selection import cross_val_score
# Running 10-Fold Cross validation on a given algorithm
# Passing full data X and y because the K-fold will split the data and automatically choose train/test
Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)
print('\nAccuracy values for 10-fold Cross Validation:\n',Accuracy_Values)
print('\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))
# Decision Trees (Multiple if-else statements!)
from sklearn.tree import DecisionTreeRegressor
RegModel = DecisionTreeRegressor(max_depth=6,criterion='mse')
# Good Range of Max_depth = 2 to 20
# Printing all the parameters of Decision Tree
print(RegModel)
# Creating the model on Training Data
DT=RegModel.fit(X_train,y_train)
prediction=DT.predict(X_test)
from sklearn import metrics
# Measuring Goodness of fit in Training data
print('R2 Value:',metrics.r2_score(y_train, DT.predict(X_train)))
# Plotting the feature importance for Top 10 most important columns
%matplotlib inline
feature_importances = pd.Series(DT.feature_importances_, index=Predictors)
feature_importances.nlargest(10).plot(kind='barh')
###########################################################################
print('\n##### Model Validation and Accuracy Calculations ##########')
# Printing some sample values of prediction
TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)
TestingDataResults[TargetVariable]=y_test
TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)
# Printing sample prediction values
print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())
# Calculating the error for each row
TestingDataResults['APE']=100 * ((abs(
TestingDataResults['Strength']-TestingDataResults['PredictedStrength']))/TestingDataResults['Strength'])
MAPE=np.mean(TestingDataResults['APE'])
MedianMAPE=np.median(TestingDataResults['APE'])
Accuracy =100 - MAPE
MedianAccuracy=100- MedianMAPE
print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier
print('Median Accuracy on test data:', MedianAccuracy)
# Defining a custom function to calculate accuracy
# Make sure there are no zeros in the Target variable if you are using MAPE
def Accuracy_Score(orig,pred):
MAPE = np.mean(100 * (np.abs(orig-pred)/orig))
#print('#'*70,'Accuracy:', 100-MAPE)
return(100-MAPE)
# Custom Scoring MAPE calculation
from sklearn.metrics import make_scorer
custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)
# Importing cross validation function from sklearn
from sklearn.model_selection import cross_val_score
# Running 10-Fold Cross validation on a given algorithm
# Passing full data X and y because the K-fold will split the data and automatically choose train/test
Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)
print('\nAccuracy values for 10-fold Cross Validation:\n',Accuracy_Values)
print('\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))
# Installing the required library for plotting the decision tree
# Make sure to run all three commands
# 1. Open anaconda Prompt
# pip install graphviz
# conda install graphviz
# pip install pydotplus
# Adding graphviz path to the PATH env variable
# Try to find "dot.exe" in your system and provide the path of that folder
import os
os.environ["PATH"] += os.pathsep + 'C:\\Users\\fhashmi\\AppData\\Local\\Continuum\\Anaconda3\\Library\\bin\\graphviz'
# Load libraries
from IPython.display import Image
from sklearn import tree
import pydotplus
# Create DOT data
dot_data = tree.export_graphviz(RegModel, out_file=None,
feature_names=Predictors, class_names=TargetVariable)
# printing the rules
#print(dot_data)
# Draw graph
graph = pydotplus.graph_from_dot_data(dot_data)
# Show graph
Image(graph.create_png(), width=500,height=500)
# Double click on the graph to zoom in
# Random Forest (Bagging of multiple Decision Trees)
from sklearn.ensemble import RandomForestRegressor
RegModel = RandomForestRegressor(max_depth=5, n_estimators=100,criterion='mse')
# Good range for max_depth: 2-10 and n_estimators: 100-1000
# Printing all the parameters of Random Forest
print(RegModel)
# Creating the model on Training Data
RF=RegModel.fit(X_train,y_train)
prediction=RF.predict(X_test)
from sklearn import metrics
# Measuring Goodness of fit in Training data
print('R2 Value:',metrics.r2_score(y_train, RF.predict(X_train)))
# Plotting the feature importance for Top 10 most important columns
%matplotlib inline
feature_importances = pd.Series(RF.feature_importances_, index=Predictors)
feature_importances.nlargest(10).plot(kind='barh')
###########################################################################
print('\n##### Model Validation and Accuracy Calculations ##########')
# Printing some sample values of prediction
TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)
TestingDataResults[TargetVariable]=y_test
TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)
# Printing sample prediction values
print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())
# Calculating the error for each row
TestingDataResults['APE']=100 * ((abs(
TestingDataResults['Strength']-TestingDataResults['PredictedStrength']))/TestingDataResults['Strength'])
MAPE=np.mean(TestingDataResults['APE'])
MedianMAPE=np.median(TestingDataResults['APE'])
Accuracy =100 - MAPE
MedianAccuracy=100- MedianMAPE
print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier
print('Median Accuracy on test data:', MedianAccuracy)
# Defining a custom function to calculate accuracy
# Make sure there are no zeros in the Target variable if you are using MAPE
def Accuracy_Score(orig,pred):
MAPE = np.mean(100 * (np.abs(orig-pred)/orig))
#print('#'*70,'Accuracy:', 100-MAPE)
return(100-MAPE)
# Custom Scoring MAPE calculation
from sklearn.metrics import make_scorer
custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)
# Importing cross validation function from sklearn
from sklearn.model_selection import cross_val_score
# Running 10-Fold Cross validation on a given algorithm
# Passing full data X and y because the K-fold will split the data and automatically choose train/test
Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)
print('\nAccuracy values for 10-fold Cross Validation:\n',Accuracy_Values)
print('\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))
# Plotting a single Decision Tree from Random Forest
# Load libraries
from IPython.display import Image
from sklearn import tree
import pydotplus
# Create DOT data for the 6th Decision Tree in Random Forest
dot_data = tree.export_graphviz(RegModel.estimators_[5] , out_file=None, feature_names=Predictors, class_names=TargetVariable)
# Draw graph
graph = pydotplus.graph_from_dot_data(dot_data)
# Show graph
Image(graph.create_png(), width=1000,height=1000)
# Double click on the graph to zoom in
# Adaboost (Boosting of multiple Decision Trees)
from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor
# Choosing Decision Tree with 10 level as the weak learner
DTR=DecisionTreeRegressor(max_depth=10)
RegModel = AdaBoostRegressor(n_estimators=100, base_estimator=DTR ,learning_rate=0.04)
# Printing all the parameters of Adaboost
print(RegModel)
# Creating the model on Training Data
AB=RegModel.fit(X_train,y_train)
prediction=AB.predict(X_test)
from sklearn import metrics
# Measuring Goodness of fit in Training data
print('R2 Value:',metrics.r2_score(y_train, AB.predict(X_train)))
# Plotting the feature importance for Top 10 most important columns
%matplotlib inline
feature_importances = pd.Series(AB.feature_importances_, index=Predictors)
feature_importances.nlargest(10).plot(kind='barh')
###########################################################################
print('\n##### Model Validation and Accuracy Calculations ##########')
# Printing some sample values of prediction
TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)
TestingDataResults[TargetVariable]=y_test
TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)
# Printing sample prediction values
print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())
# Calculating the error for each row
TestingDataResults['APE']=100 * ((abs(
TestingDataResults['Strength']-TestingDataResults['PredictedStrength']))/TestingDataResults['Strength'])
MAPE=np.mean(TestingDataResults['APE'])
MedianMAPE=np.median(TestingDataResults['APE'])
Accuracy =100 - MAPE
MedianAccuracy=100- MedianMAPE
print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier
print('Median Accuracy on test data:', MedianAccuracy)
# Defining a custom function to calculate accuracy
# Make sure there are no zeros in the Target variable if you are using MAPE
def Accuracy_Score(orig,pred):
MAPE = np.mean(100 * (np.abs(orig-pred)/orig))
#print('#'*70,'Accuracy:', 100-MAPE)
return(100-MAPE)
# Custom Scoring MAPE calculation
from sklearn.metrics import make_scorer
custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)
# Importing cross validation function from sklearn
from sklearn.model_selection import cross_val_score
# Running 10-Fold Cross validation on a given algorithm
# Passing full data X and y because the K-fold will split the data and automatically choose train/test
Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)
print('\nAccuracy values for 10-fold Cross Validation:\n',Accuracy_Values)
print('\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))
# max_depth=10 is too large to plot here
# PLotting 5th single Decision Tree from Adaboost
# Load libraries
from IPython.display import Image
from sklearn import tree
import pydotplus
# Create DOT data for the 6th Decision Tree in Random Forest
#dot_data = tree.export_graphviz(RegModel.estimators_[5] , out_file=None, feature_names=Predictors, class_names=TargetVariable)
# Draw graph
#graph = pydotplus.graph_from_dot_data(dot_data)
# Show graph
#Image(graph.create_png(), width=5000,height=5000)
# Double click on the graph to zoom in
# Xtreme Gradient Boosting (XGBoost)
from xgboost import XGBRegressor
RegModel=XGBRegressor(max_depth=10,
learning_rate=0.1,
n_estimators=100,
objective='reg:linear',
booster='gbtree')
# Printing all the parameters of XGBoost
print(RegModel)
# Creating the model on Training Data
XGB=RegModel.fit(X_train,y_train)
prediction=XGB.predict(X_test)
from sklearn import metrics
# Measuring Goodness of fit in Training data
print('R2 Value:',metrics.r2_score(y_train, XGB.predict(X_train)))
# Plotting the feature importance for Top 10 most important columns
%matplotlib inline
feature_importances = pd.Series(XGB.feature_importances_, index=Predictors)
feature_importances.nlargest(10).plot(kind='barh')
###########################################################################
print('\n##### Model Validation and Accuracy Calculations ##########')
# Printing some sample values of prediction
TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)
TestingDataResults[TargetVariable]=y_test
TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)
# Printing sample prediction values
print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())
# Calculating the error for each row
TestingDataResults['APE']=100 * ((abs(
TestingDataResults['Strength']-TestingDataResults['PredictedStrength']))/TestingDataResults['Strength'])
MAPE=np.mean(TestingDataResults['APE'])
MedianMAPE=np.median(TestingDataResults['APE'])
Accuracy =100 - MAPE
MedianAccuracy=100- MedianMAPE
print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier
print('Median Accuracy on test data:', MedianAccuracy)
# Defining a custom function to calculate accuracy
# Make sure there are no zeros in the Target variable if you are using MAPE
def Accuracy_Score(orig,pred):
MAPE = np.mean(100 * (np.abs(orig-pred)/orig))
#print('#'*70,'Accuracy:', 100-MAPE)
return(100-MAPE)
# Custom Scoring MAPE calculation
from sklearn.metrics import make_scorer
custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)
# Importing cross validation function from sklearn
from sklearn.model_selection import cross_val_score
# Running 10-Fold Cross validation on a given algorithm
# Passing full data X and y because the K-fold will split the data and automatically choose train/test
Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)
print('\nAccuracy values for 10-fold Cross Validation:\n',Accuracy_Values)
print('\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))
# max_depth=10 is too larget to plot here
#from xgboost import plot_tree
#import matplotlib.pyplot as plt
#fig, ax = plt.subplots(figsize=(100, 40))
#plot_tree(XGB, num_trees=10, ax=ax)
# Double click on the graph to zoom in
# K-Nearest Neighbor(KNN)
from sklearn.neighbors import KNeighborsRegressor
RegModel = KNeighborsRegressor(n_neighbors=2)
# Printing all the parameters of KNN
print(RegModel)
# Creating the model on Training Data
KNN=RegModel.fit(X_train,y_train)
prediction=KNN.predict(X_test)
from sklearn import metrics
# Measuring Goodness of fit in Training data
print('R2 Value:',metrics.r2_score(y_train, KNN.predict(X_train)))
# Plotting the feature importance for Top 10 most important columns
# The variable importance chart is not available for KNN
###########################################################################
print('\n##### Model Validation and Accuracy Calculations ##########')
# Printing some sample values of prediction
TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)
TestingDataResults[TargetVariable]=y_test
TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)
# Printing sample prediction values
print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())
# Calculating the error for each row
TestingDataResults['APE']=100 * ((abs(
TestingDataResults['Strength']-TestingDataResults['PredictedStrength']))/TestingDataResults['Strength'])
MAPE=np.mean(TestingDataResults['APE'])
MedianMAPE=np.median(TestingDataResults['APE'])
Accuracy =100 - MAPE
MedianAccuracy=100- MedianMAPE
print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier
print('Median Accuracy on test data:', MedianAccuracy)
# Defining a custom function to calculate accuracy
# Make sure there are no zeros in the Target variable if you are using MAPE
def Accuracy_Score(orig,pred):
MAPE = np.mean(100 * (np.abs(orig-pred)/orig))
#print('#'*70,'Accuracy:', 100-MAPE)
return(100-MAPE)
# Custom Scoring MAPE calculation
from sklearn.metrics import make_scorer
custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)
# Importing cross validation function from sklearn
from sklearn.model_selection import cross_val_score
# Running 10-Fold Cross validation on a given algorithm
# Passing full data X and y because the K-fold will split the data and automatically choose train/test
Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)
print('\nAccuracy values for 10-fold Cross Validation:\n',Accuracy_Values)
print('\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))
Based on the above trials you select that algorithm which produces the best average accuracy. In this case, multiple algorithms have produced similar kind of average accuracy. Hence, we can choose any one of them.
I am choosing ADABOOST as the final model since it is producing the best accuracy on this data.
In order to deploy the model we follow below steps
Its beneficial to keep lesser number of predictors for the model while deploying it in production. The lesser predictors you keep, the better because, the model will be less dependent hence, more stable.
This is important specially when the data is high dimensional(too many predictor columns).
In this data, the most important predictor variables are 'CementComponent ', 'SuperplasticizerComponent', and 'AgeInDays'.
As these are consistently on top of the variable importance chart for every algorithm. Hence choosing these as final set of predictor variables.
# Separate Target Variable and Predictor Variables
TargetVariable='Strength'
# Selecting the final set of predictors for the deployment
# Based on the variable importance charts of multiple algorithms above
Predictors=['CementComponent ', 'SuperplasticizerComponent', 'AgeInDays']
X=DataForML_Numeric[Predictors].values
y=DataForML_Numeric[TargetVariable].values
### Sandardization of data ###
from sklearn.preprocessing import StandardScaler, MinMaxScaler
# Choose either standardization or Normalization
# On this data Min Max Normalization produced better results
# Choose between standardization and MinMAx normalization
#PredictorScaler=StandardScaler()
PredictorScaler=MinMaxScaler()
# Storing the fit object for later reference
PredictorScalerFit=PredictorScaler.fit(X)
# Generating the standardized values of X
X=PredictorScalerFit.transform(X)
print(X.shape)
print(y.shape)
# Importing cross validation function from sklearn
from sklearn.model_selection import cross_val_score
# Using final hyperparameters
# Adaboost (Boosting of multiple Decision Trees)
from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor
# Choosing Decision Tree with 10 level as the weak learner
DTR=DecisionTreeRegressor(max_depth=10)
RegModel = AdaBoostRegressor(n_estimators=100, base_estimator=DTR ,learning_rate=0.04)
# Running 10-Fold Cross validation on a given algorithm
# Passing full data X and y because the K-fold will split the data and automatically choose train/test
Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)
print('\nAccuracy values for 10-fold Cross Validation:\n',Accuracy_Values)
print('\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))
# Training the model on 100% Data available
Final_ADABOOST_Model=RegModel.fit(X,y)
import pickle
import os
# Saving the Python objects as serialized files can be done using pickle library
# Here let us save the Final ZomatoStrengthModel
with open('Final_ADABOOST_Model.pkl', 'wb') as fileWriteStream:
pickle.dump(Final_ADABOOST_Model, fileWriteStream)
# Don't forget to close the filestream!
fileWriteStream.close()
print('pickle file of Predictive Model is saved at Location:',os.getcwd())
# This Function can be called from any from any front end tool/website
def FunctionPredictResult(InputData):
import pandas as pd
Num_Inputs=InputData.shape[0]
# Making sure the input data has same columns as it was used for training the model
# Also, if standardization/normalization was done, then same must be done for new input
# Appending the new data with the Training data
DataForML=pd.read_pickle('DataForML.pkl')
InputData=InputData.append(DataForML)
# Generating dummy variables for rest of the nominal variables
InputData=pd.get_dummies(InputData)
# Maintaining the same order of columns as it was during the model training
Predictors=['CementComponent ', 'SuperplasticizerComponent', 'AgeInDays']
# Generating the input values to the model
X=InputData[Predictors].values[0:Num_Inputs]
# Generating the standardized values of X since it was done while model training also
X=PredictorScalerFit.transform(X)
# Loading the Function from pickle file
import pickle
with open('Final_ADABOOST_Model.pkl', 'rb') as fileReadStream:
PredictionModel=pickle.load(fileReadStream)
# Don't forget to close the filestream!
fileReadStream.close()
# Generating Predictions
Prediction=PredictionModel.predict(X)
PredictionResult=pd.DataFrame(Prediction, columns=['Prediction'])
return(round(PredictionResult))
# Calling the function for some new data
NewSampleData=pd.DataFrame(
data=[[540,2.5,28],
[332,2.5,270]],
columns=['CementComponent ', 'SuperplasticizerComponent', 'AgeInDays'])
print(NewSampleData)
# Calling the Function for prediction
FunctionPredictResult(InputData= NewSampleData)
The Function FunctionPredictResult() can be used to produce the predictions for one or more cases at a time. Hence, it can be scheduled using a batch job or cron job to run every night and generate predictions for all the cases.
# Creating the function which can take inputs and perform prediction
def FunctionGeneratePrediction(inp_CementComponent, inp_SuperplasticizerComponent, inp_AgeInDays):
# Creating a data frame for the model input
SampleInputData=pd.DataFrame(
data=[[inp_CementComponent, inp_SuperplasticizerComponent, inp_AgeInDays]],
columns=['CementComponent ', 'SuperplasticizerComponent', 'AgeInDays'])
# Calling the function defined above using the input parameters
Predictions=FunctionPredictResult(InputData= SampleInputData)
# Returning the predictions
return(Predictions.to_json())
# Function call
FunctionGeneratePrediction(inp_CementComponent=540,
inp_SuperplasticizerComponent=2.5,
inp_AgeInDays=28
)
# Installing the flask library required to create the API
#!pip install flask
from flask import Flask, request, jsonify
import pickle
import pandas as pd
import numpy
app = Flask(__name__)
@app.route('/prediction_api', methods=["GET"])
def prediction_api():
try:
# Getting the paramters from API call
cement_value=float(request.args.get('cement'))
plasticizer_value=float(request.args.get('plasticizer'))
age_value=float(request.args.get('age'))
# Calling the funtion to get predictions
prediction_from_api=FunctionGeneratePrediction(
inp_CementComponent=cement_value,
inp_SuperplasticizerComponent=plasticizer_value,
inp_AgeInDays=age_value
)
return (prediction_from_api)
except Exception as e:
return('Something is not right!:'+str(e))
import os
if __name__ =="__main__":
# Hosting the API in localhost
app.run(host='127.0.0.1', port=8080, threaded=True, debug=True, use_reloader=False)
# Interrupt kernel to stop the API
http://127.0.0.1:8080/prediction_api?cement=540&plasticizer=2.5&age=28
This URL can be called by any front end application like Java, Tableau etc. Once the parameters are passed to it, the predictions will be generated.