Libraries and Dataset¶

In [1]:
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn  as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler,MinMaxScaler
In [2]:
# dataset = pd.read_csv('data/data_market.csv', encoding='latin1')
dataset = pd.read_csv('data/market_cluster.csv', encoding='latin1')
In [3]:
dataset.head()
Out[3]:
Order ID Customer Name Category Sub Category City Order Date Region Sales Discount Profit State profit_margin Cluster
0 OD1 Harish Oil & Masala Masalas Vellore 11-08-2017 North 1254 0.12 401.28 Tamil Nadu 0.32 Medium
1 OD2 Sudha Beverages Health Drinks Krishnagiri 11-08-2017 South 749 0.18 149.80 Tamil Nadu 0.20 Medium
2 OD3 Hussain Food Grains Atta & Flour Perambalur 06-12-2017 West 2360 0.21 165.20 Tamil Nadu 0.07 Low
3 OD4 Jackson Fruits & Veggies Fresh Vegetables Dharmapuri 10-11-2016 South 896 0.25 89.60 Tamil Nadu 0.10 Low
4 OD5 Ridhesh Food Grains Organic Staples Ooty 10-11-2016 South 2355 0.26 918.45 Tamil Nadu 0.39 High

Data Cleaning & Preprocessing¶

In [4]:
dataset.drop(['Order ID'], axis=1, inplace=True)
In [5]:
dataset.isna().sum()
Out[5]:
Customer Name    0
Category         0
Sub Category     0
City             0
Order Date       0
Region           0
Sales            0
Discount         0
Profit           0
State            0
profit_margin    0
Cluster          0
dtype: int64
In [6]:
def remove_outliers(data: pd.DataFrame, column: str) -> pd.Series:
    q3, q1 = np.nanpercentile(data[column], [75, 25])
    iqr = q3 - q1
    upper_bound = q3 + 1.5 * iqr
    lower_bound = q1 - 1.5 * iqr
    data = data[(data[column] > lower_bound) & (data[column] < upper_bound)]

    return data

dataset = remove_outliers(dataset, 'Discount')
dataset = remove_outliers(dataset, 'Sales')
dataset = remove_outliers(dataset, 'Profit')
In [7]:
dataset.dropna(inplace=True)
In [8]:
dataset.head()
Out[8]:
Customer Name Category Sub Category City Order Date Region Sales Discount Profit State profit_margin Cluster
0 Harish Oil & Masala Masalas Vellore 11-08-2017 North 1254 0.12 401.28 Tamil Nadu 0.32 Medium
1 Sudha Beverages Health Drinks Krishnagiri 11-08-2017 South 749 0.18 149.80 Tamil Nadu 0.20 Medium
2 Hussain Food Grains Atta & Flour Perambalur 06-12-2017 West 2360 0.21 165.20 Tamil Nadu 0.07 Low
3 Jackson Fruits & Veggies Fresh Vegetables Dharmapuri 10-11-2016 South 896 0.25 89.60 Tamil Nadu 0.10 Low
4 Ridhesh Food Grains Organic Staples Ooty 10-11-2016 South 2355 0.26 918.45 Tamil Nadu 0.39 High
In [9]:
sns.histplot(dataset['Cluster'])
Out[9]:
<AxesSubplot:xlabel='Cluster', ylabel='Count'>
In [10]:
encoder = LabelEncoder()
scaler = StandardScaler()
onehot = OneHotEncoder()
minmaxscaler = MinMaxScaler()
In [11]:
dataset["Order Date"] = pd.to_datetime(dataset["Order Date"])
dataset["Order Date"] = dataset["Order Date"].dt.month

dataset["Customer Name"] = encoder.fit_transform(dataset["Customer Name"])
dataset["Category"] = encoder.fit_transform(dataset["Category"])
dataset["City"] = encoder.fit_transform(dataset["City"])
dataset["Region"] = encoder.fit_transform(dataset["Region"])
dataset["State"] = encoder.fit_transform(dataset["State"])
dataset["Sub Category"] = encoder.fit_transform(dataset["Sub Category"])

# dataset["Customer Name"] = onehot.fit_transform(dataset["Customer Name"].values.reshape(-1, 1)).toarray()
# dataset["Category"] = onehot.fit_transform(dataset["Category"].values.reshape(-1, 1)).toarray()
# dataset["City"] = onehot.fit_transform(dataset["City"].values.reshape(-1, 1)).toarray()
# dataset["Region"] = onehot.fit_transform(dataset["Region"].values.reshape(-1, 1)).toarray()
# dataset["State"] = onehot.fit_transform(dataset["State"].values.reshape(-1, 1)).toarray()
# dataset["Sub Category"] = onehot.fit_transform(dataset["Sub Category"].values.reshape(-1, 1)).toarray()

dataset["Order Date"] = encoder.fit_transform(dataset["Order Date"])
In [12]:
dataset[["Sales", "Discount", "profit_margin","Profit"]] = scaler.fit_transform(dataset[["Sales", "Discount", "profit_margin","Profit"]])
In [13]:
class_to_numeric = {'Low': 0, 'Medium': 1, 'High': 2}
dataset['Cluster'] = [class_to_numeric[label] for label in dataset['Cluster']]
In [14]:
dataset.head()
Out[14]:
Customer Name Category Sub Category City Order Date Region Sales Discount Profit State profit_margin Cluster
0 12 5 14 21 10 2 -0.414559 -1.430908 0.124389 0 0.595874 1
1 37 1 13 8 10 3 -1.291968 -0.627370 -0.941183 0 -0.416872 1
2 14 3 0 13 5 4 1.507054 -0.225601 -0.875930 0 -1.514014 0
3 15 4 12 4 9 3 -1.036563 0.310092 -1.196262 0 -1.260827 0
4 28 3 18 12 9 3 1.498367 0.444015 2.315743 0 1.186643 2

Splitting data train & test¶

In [15]:
X = dataset.drop(['Cluster','Sub Category','State','profit_margin'],axis=1)
y = dataset['Cluster']
In [16]:
X.head()
Out[16]:
Customer Name Category City Order Date Region Sales Discount Profit
0 12 5 21 10 2 -0.414559 -1.430908 0.124389
1 37 1 8 10 3 -1.291968 -0.627370 -0.941183
2 14 3 13 5 4 1.507054 -0.225601 -0.875930
3 15 4 4 9 3 -1.036563 0.310092 -1.196262
4 28 3 12 9 3 1.498367 0.444015 2.315743
In [17]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
In [18]:
X_train.shape , X_test.shape
Out[18]:
((7960, 8), (1991, 8))
In [19]:
y_train.shape , y_test.shape
Out[19]:
((7960,), (1991,))

Decision Tree with gini index¶

In [20]:
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split, RandomizedSearchCV
In [21]:
from sklearn.metrics import accuracy_score
In [22]:
clf_gini = DecisionTreeClassifier(criterion='gini', max_depth=3)

# clf_gini = GridSearchCV(estimator=df, param_grid=param_grid, cv=5, scoring='accuracy')
# clf_gini.fit(X_train, y_train)
plt.figure(figsize=(12,8))

tree.plot_tree(clf_gini.fit(X_train, y_train))
Out[22]:
[Text(0.5, 0.875, 'x[7] <= -0.542\ngini = 0.666\nsamples = 7960\nvalue = [2723, 2720, 2517]'),
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 Text(0.75, 0.625, 'x[7] <= 1.391\ngini = 0.594\nsamples = 4992\nvalue = [580, 2058, 2354]'),
 Text(0.625, 0.375, 'x[5] <= 0.377\ngini = 0.605\nsamples = 4081\nvalue = [580, 1987, 1514]'),
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 Text(0.6875, 0.125, 'gini = 0.535\nsamples = 1689\nvalue = [506, 1022, 161]'),
 Text(0.875, 0.375, 'x[5] <= 1.465\ngini = 0.144\nsamples = 911\nvalue = [0, 71, 840]'),
 Text(0.8125, 0.125, 'gini = 0.037\nsamples = 699\nvalue = [0, 13, 686]'),
 Text(0.9375, 0.125, 'gini = 0.397\nsamples = 212\nvalue = [0, 58, 154]')]
In [23]:
clf = DecisionTreeClassifier(random_state=42)
params = { 
    "max_depth": range(5,41,5), 
    "criterion": ["gini", "entropy"]
}
In [24]:
model_dt =  GridSearchCV(estimator=clf, #
    param_grid=params,
    cv=2, 
    n_jobs=-1,
    verbose=1
)
In [25]:
model_dt.fit(X_train, y_train) # model fit

Fitting 2 folds for each of 16 candidates, totalling 32 fits
Out[25]:
GridSearchCV(cv=2, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': range(5, 41, 5)},
             verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=2, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': range(5, 41, 5)},
             verbose=1)
DecisionTreeClassifier(random_state=42)
DecisionTreeClassifier(random_state=42)
In [26]:
cv_results_dt = pd.DataFrame(model_dt.cv_results_)
cv_results_dt.sort_values("rank_test_score").head(1)
Out[26]:
mean_fit_time std_fit_time mean_score_time std_score_time param_criterion param_max_depth params split0_test_score split1_test_score mean_test_score std_test_score rank_test_score
9 0.029328 0.003273 0.005254 0.002253 entropy 10 {'criterion': 'entropy', 'max_depth': 10} 0.972613 0.973869 0.973241 0.000628 1
In [27]:
best_params = model_dt.best_params_
best_maxDepth = best_params['max_depth']
best_Criterion = best_params['criterion']

print(f'The best Max Depth : {best_maxDepth}')
print(f'The best Criterion : {best_Criterion}')

The best Max Depth : 10
The best Criterion : entropy
In [28]:
#Using the Decision Tree Classifier with splitting criterion as Gini impurity, the maximum depth of the tree is 3.
clf_gini = DecisionTreeClassifier(criterion=best_Criterion, max_depth=best_maxDepth, random_state=0)


# fit the model
clf_gini.fit(X_train, y_train)
#Plot the tree
plt.figure(figsize=(12,8))

tree.plot_tree(clf_gini.fit(X_train, y_train))
Out[28]:
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 Text(0.9538461538461539, 0.6818181818181818, 'x[7] <= 1.502\nentropy = 0.018\nsamples = 610\nvalue = [0, 1, 609]'),
 Text(0.9461538461538461, 0.5909090909090909, 'x[5] <= 1.272\nentropy = 0.159\nsamples = 43\nvalue = [0, 1, 42]'),
 Text(0.9384615384615385, 0.5, 'entropy = 0.0\nsamples = 42\nvalue = [0, 0, 42]'),
 Text(0.9538461538461539, 0.5, 'entropy = 0.0\nsamples = 1\nvalue = [0, 1, 0]'),
 Text(0.9615384615384616, 0.5909090909090909, 'entropy = 0.0\nsamples = 567\nvalue = [0, 0, 567]'),
 Text(0.9846153846153847, 0.6818181818181818, 'x[7] <= 1.775\nentropy = 0.745\nsamples = 222\nvalue = [0, 47, 175]'),
 Text(0.9769230769230769, 0.5909090909090909, 'x[7] <= 1.682\nentropy = 0.556\nsamples = 54\nvalue = [0, 47, 7]'),
 Text(0.9692307692307692, 0.5, 'entropy = 0.0\nsamples = 34\nvalue = [0, 34, 0]'),
 Text(0.9846153846153847, 0.5, 'x[5] <= 1.582\nentropy = 0.934\nsamples = 20\nvalue = [0, 13, 7]'),
 Text(0.9769230769230769, 0.4090909090909091, 'entropy = 0.0\nsamples = 7\nvalue = [0, 0, 7]'),
 Text(0.9923076923076923, 0.4090909090909091, 'entropy = 0.0\nsamples = 13\nvalue = [0, 13, 0]'),
 Text(0.9923076923076923, 0.5909090909090909, 'entropy = 0.0\nsamples = 168\nvalue = [0, 0, 168]')]
In [29]:
import joblib
joblib.dump(clf_gini, 'model/decisiontree_model.pkl')
Out[29]:
['model/decisiontree_model.pkl']
In [30]:
#Predict the values 
y_pred_gini = clf_gini.predict(X_test)
#Predict the value using X train for accuracy comparision 
y_pred_train_gini = clf_gini.predict(X_train)
y_pred_train_gini
#Determine the accuracy score
print('Model accuracy score with criterion gini index: {0:0.4f}'. format(accuracy_score(y_test, y_pred_gini)))
#Accuracy Score for training set
print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train_gini)))

Model accuracy score with criterion gini index: 0.9769
Training-set accuracy score: 0.9991
In [31]:
from sklearn.metrics import confusion_matrix,classification_report, ConfusionMatrixDisplay
from sklearn.metrics import  f1_score
In [32]:
cm = confusion_matrix(y_test, y_pred_gini)

print('Confusion matrix\n\n', cm)

Confusion matrix

 [[682   6   0]
 [ 15 642  16]
 [  0   9 621]]
In [33]:
cm = confusion_matrix(y_test, y_pred_gini)
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot(cmap='Blues', values_format='d')
plt.title("Confusion Matrix: Decision Tree")
plt.show()

print(classification_report(y_test, y_pred_gini))

              precision    recall  f1-score   support

           0       0.98      0.99      0.98       688
           1       0.98      0.95      0.97       673
           2       0.97      0.99      0.98       630

    accuracy                           0.98      1991
   macro avg       0.98      0.98      0.98      1991
weighted avg       0.98      0.98      0.98      1991