Libraries and Dataset¶

In [1]:
import numpy as np
import pandas as pd
import seaborn  as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.svm import SVR
from sklearn.metrics import confusion_matrix,classification_report
import matplotlib.pyplot as plt
from sklearn.linear_model import Lasso

c:\Users\Asus\anaconda3\lib\site-packages\scipy\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.26.1
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
In [2]:
dataset = pd.read_csv('data/data_market.csv', encoding='latin1')
In [3]:
dataset.head()
Out[3]:
Order ID Customer Name Category Sub Category City Order Date Region Sales Discount Profit State
0 OD1 Harish Oil & Masala Masalas Vellore 11-08-2017 North 1254 0.12 401.28 Tamil Nadu
1 OD2 Sudha Beverages Health Drinks Krishnagiri 11-08-2017 South 749 0.18 149.80 Tamil Nadu
2 OD3 Hussain Food Grains Atta & Flour Perambalur 06-12-2017 West 2360 0.21 165.20 Tamil Nadu
3 OD4 Jackson Fruits & Veggies Fresh Vegetables Dharmapuri 10-11-2016 South 896 0.25 89.60 Tamil Nadu
4 OD5 Ridhesh Food Grains Organic Staples Ooty 10-11-2016 South 2355 0.26 918.45 Tamil Nadu
In [4]:
dataset = pd.DataFrame(dataset)
In [5]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 11 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Order ID       9994 non-null   object 
 1   Customer Name  9994 non-null   object 
 2   Category       9994 non-null   object 
 3   Sub Category   9994 non-null   object 
 4   City           9994 non-null   object 
 5   Order Date     9994 non-null   object 
 6   Region         9994 non-null   object 
 7   Sales          9994 non-null   int64  
 8   Discount       9994 non-null   float64
 9   Profit         9994 non-null   float64
 10  State          9994 non-null   object 
dtypes: float64(2), int64(1), object(8)
memory usage: 859.0+ KB
In [6]:
print(dataset.isnull().any())

Order ID         False
Customer Name    False
Category         False
Sub Category     False
City             False
Order Date       False
Region           False
Sales            False
Discount         False
Profit           False
State            False
dtype: bool
In [7]:
dataset.drop((dataset[dataset['Region']=='North']).index, inplace=True)
region = pd.pivot_table(data=dataset, index='Region', values=['Order ID', 'Sales'], 
               aggfunc = {'Order ID':'count', 'Sales':'sum'}).reset_index().sort_values(by='Sales', ascending=False)
region
Out[7]:
Region Order ID Sales
3 West 3203 4798743
1 East 2848 4248368
0 Central 2323 3468156
2 South 1619 2440461
In [8]:
plt.rcParams['figure.figsize'] = (12,6)
ax = plt.axes()
ax.set_facecolor('#F6F6F6')
sns.barplot(y='Region', x='Sales', data=region, color='#79BEFD', linewidth=2, edgecolor='#F6F6F6')

bbox_args = dict(boxstyle = 'round', fc = '1')
for p in ax.patches:
    width = p.get_width()
    plt.text(p.get_width(), p.get_y() + 0.5 * p.get_height(), '{:1.0f}'.format(width), 
             ha = 'center', 
             va = 'center', 
             color = 'black', 
             bbox = bbox_args, 
             fontsize = 15)

plt.title('Total Sales by Region', fontsize = 18)
plt.tick_params(left=False, bottom=False, labelbottom=False)
plt.ylabel(None)

plt.show()
In [9]:
plt.rcParams['figure.figsize'] = (12,6)
ax = plt.axes()
ax.set_facecolor('#F6F6F6')
sns.barplot(y='Region', x='Order ID', data=region, color='#79BEFD', linewidth=2, edgecolor='#F6F6F6')

bbox_args = dict(boxstyle = 'round', fc = '1')
for p in ax.patches:
    width = p.get_width()
    plt.text(p.get_width(), p.get_y() + 0.5 * p.get_height(), '{:1.0f}'.format(width), 
             ha = 'center', 
             va = 'center', 
             color = 'black', 
             bbox = bbox_args, 
             fontsize = 15)

plt.title('Total Orders by Region', fontsize = 18)
plt.tick_params(left=False, bottom=False, labelbottom=False)
plt.xlabel('#Orders')
plt.ylabel(None)

plt.show()
In [10]:
category_sales = pd.pivot_table(data=dataset, index=['Category', 'Sub Category'], values='Sales', 
               aggfunc='sum').reset_index().sort_values(['Category', 'Sales'], ascending=False)
category_sales
Out[10]:
Category Sub Category Sales
21 Snacks Cookies 768213
22 Snacks Noodles 735435
20 Snacks Chocolates 733898
18 Oil & Masala Masalas 696226
19 Oil & Masala Spices 672876
17 Oil & Masala Edible Oil & Ghee 668086
13 Fruits & Veggies Fresh Fruits 551212
14 Fruits & Veggies Fresh Vegetables 525842
16 Fruits & Veggies Organic Vegetables 520271
15 Fruits & Veggies Organic Fruits 503402
11 Food Grains Organic Staples 558929
9 Food Grains Atta & Flour 534649
10 Food Grains Dals & Pulses 523371
12 Food Grains Rice 498323
8 Eggs, Meat & Fish Mutton 611200
6 Eggs, Meat & Fish Eggs 575156
7 Eggs, Meat & Fish Fish 560548
5 Eggs, Meat & Fish Chicken 520497
3 Beverages Health Drinks 1051439
4 Beverages Soft Drinks 1033874
1 Bakery Breads & Buns 742586
2 Bakery Cakes 685612
0 Bakery Biscuits 684083
In [11]:
plt.rcParams['figure.figsize'] = (15,22)
ax = plt.axes()
ax.set_facecolor('#F6F6F6')
sns.barplot(y='Sub Category', x='Sales', data=category_sales, hue='Category', linewidth=1, edgecolor='#F6F6F6')

bbox_args = dict(boxstyle = 'round', fc = '1')
for p in ax.patches:
    width = p.get_width()
    plt.text(p.get_width(), p.get_y() + 0.5 * p.get_height(), '{:1.0f}'.format(width), 
             ha = 'center', 
             va = 'center', 
             color = 'black', 
             bbox = bbox_args, 
             fontsize = 15)

plt.title('Sales by Category & Sub Category', fontsize = 18)
plt.ylabel(None)
plt.tick_params(left=False, bottom=False, labelbottom=False)
plt.show()

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posx and posy should be finite values
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posx and posy should be finite values
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posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
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posx and posy should be finite values
posx and posy should be finite values
posx and posy should be finite values
In [12]:
profit = pd.pivot_table(data=dataset, index='Sub Category', values=['Sales', 'Profit','Discount',], aggfunc='sum').reset_index()
profit['Profit Margin'] = round((profit['Profit']/profit['Sales']) * 100,2)
profit.sort_values(by='Profit Margin', ascending=False, inplace = True)
profit
Out[12]:
Sub Category Discount Profit Sales Profit Margin
16 Noodles 109.86 193685.81 735435 26.34
10 Fish 84.92 147248.01 560548 26.27
17 Organic Fruits 78.81 130862.33 503402 26.00
18 Organic Staples 86.02 144136.89 558929 25.79
2 Breads & Buns 113.70 190764.98 742586 25.69
19 Organic Vegetables 81.37 133596.37 520271 25.68
20 Rice 75.54 126932.36 498323 25.47
13 Health Drinks 166.49 267469.79 1051439 25.44
8 Edible Oil & Ghee 103.51 168593.58 668086 25.24
9 Eggs 85.69 144669.92 575156 25.15
5 Chocolates 112.60 183849.34 733898 25.05
21 Soft Drinks 155.97 258135.97 1033874 24.97
12 Fresh Vegetables 81.30 131273.33 525842 24.96
7 Dals & Pulses 77.51 130232.29 523371 24.88
6 Cookies 113.72 190643.70 768213 24.82
15 Mutton 89.14 151389.40 611200 24.77
1 Biscuits 103.29 169357.62 684083 24.76
3 Cakes 101.54 168398.46 685612 24.56
11 Fresh Fruits 83.66 134668.35 551212 24.43
14 Masalas 103.53 168597.83 696226 24.22
0 Atta & Flour 80.44 127861.10 534649 23.91
4 Chicken 79.66 124049.89 520497 23.83
22 Spices 98.42 160302.60 672876 23.82
In [13]:
plt.rcParams['figure.figsize'] = (6,10)
ax = plt.axes()
ax.set_facecolor('#F6F6F6')
sns.barplot(y='Sub Category', x='Profit Margin', data=profit, color='#79BEFD', linewidth=2, edgecolor='#F6F6F6')

bbox_args = dict(boxstyle = 'round', fc = '1')
for p in ax.patches:
    width = p.get_width()
    plt.text(p.get_width(), p.get_y() + 0.5 * p.get_height(), '{:1.2f}%'.format(width), 
             ha = 'center', 
             va = 'center', 
             color = 'black', 
             bbox = bbox_args, 
             fontsize = 15)

plt.title('Profit Margin by Sub Category', fontsize = 18)
plt.tick_params(left=False, bottom=False, labelbottom=False)
plt.ylabel(None)

plt.show()

Split Data and Encoder¶

In [14]:
# X = dataset.drop(['Order ID','Cluster','Discount','Sales','profit_margin','Profit','Sub Category','State'],axis=1)
X = profit.drop(['Profit',],axis=1)
y = profit['Profit']
In [15]:
X.head()
Out[15]:
Sub Category Discount Sales Profit Margin
16 Noodles 109.86 735435 26.34
10 Fish 84.92 560548 26.27
17 Organic Fruits 78.81 503402 26.00
18 Organic Staples 86.02 558929 25.79
2 Breads & Buns 113.70 742586 25.69
In [16]:
columns_used = X.columns
In [17]:
def is_numeric_column(column):
    try:
        pd.to_numeric(column)
        return False
    except ValueError:
        return True

encoder=LabelEncoder()
for col in columns_used :
    if is_numeric_column(X[col]):
        X[col] = encoder.fit_transform(X[col])

X.head()
Out[17]:
Sub Category Discount Sales Profit Margin
16 16 109.86 735435 26.34
10 10 84.92 560548 26.27
17 17 78.81 503402 26.00
18 18 86.02 558929 25.79
2 2 113.70 742586 25.69
In [18]:
y = encoder.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [19]:
plt.figure(figsize=(8, 6))
heatcol = X.corr()
sns.heatmap(heatcol,cmap="BrBG",annot=True)
Out[19]:
<AxesSubplot:>
In [20]:
plt.figure(figsize=(5, 5))
sns.boxplot(x=y)
Out[20]:
<AxesSubplot:>
In [21]:
plt.figure(figsize=(5, 5))
sns.histplot(y)
Out[21]:
<AxesSubplot:ylabel='Count'>
In [22]:
# Dimension of Train and Test set
print("Dimension of Train set",X_train.shape)
print("Dimension of Test set",X_test.shape,"\n")

num_cols = X_train._get_numeric_data().columns
print("Number of numeric features:",num_cols.size)

Dimension of Train set (18, 4)
Dimension of Test set (5, 4) 

Number of numeric features: 4

SVM¶

In [23]:
svm_model = SVR(epsilon=0.001, C=70)
svm_model.fit(X_train, y_train)

Y_pred = svm_model.predict(X_test)
In [26]:
import joblib
joblib.dump(svm_model, 'model/svr_model.pkl')
Out[26]:
['model/svr_model.pkl']
In [27]:
from sklearn.metrics import mean_absolute_error, mean_squared_error
# Calculate MAE and MSE
mae = mean_absolute_error(y_test, Y_pred)
mse = mean_squared_error(y_test, Y_pred)

print("Mean Absolute Error:", mae)
print("Mean Squared Error:", mse)

Mean Absolute Error: 1.295875914580165
Mean Squared Error: 2.6352176160492236
In [28]:
from sklearn.metrics import r2_score

r_squared = r2_score(y_test, Y_pred)
print("accuracy : ", r_squared)

accuracy :  0.8095941028866168
In [29]:
plt.figure(figsize=(5, 5))
plt.scatter(y_test, Y_pred, c='blue', label='Actual', marker='o')
plt.scatter(Y_pred, y_test, c='red', label='Predicted', marker='o')

plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title("Scatter Plot: Aktual vs. Prediksi")
plt.xlabel("Aktual")
plt.ylabel("Prediksi")
plt.grid(True)
plt.show()
In [30]:
plt.figure(figsize=(5, 5))
plt.plot(y_test, y_test, 'r--', label="Garis Regresi (y=x)")
plt.scatter(y_test, Y_pred, c='blue', label="Data Aktual vs. Prediksi")
plt.title("Plot Garis Regresi")
plt.xlabel("Aktual")
plt.ylabel("Prediksi")
plt.legend()
plt.grid(True)
plt.show()