def printYM():
print(x, y)
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from dm_function import sorted_profit
from dm_function import predicted_sales
import pickle
inv_mon_name = pickle.load(open("inv_mon_name.p", "rb"))
inv_monsoldper_name = pickle.load(open("inv_monsoldper_name.p", "rb"))
l = pickle.load(open("l.p", "rb"))
lis_inv = pickle.load(open("lis_inv.p", "rb"))
lis_inv_sold_per = pickle.load(open("lis_inv_sold_per.p", "rb"))
data = pickle.load(open("4yrsBMW.p", "rb"))
firstyear = pickle.load(open("firstyear.p", "rb"))
secondyear = pickle.load(open("secondyear.p", "rb"))
thirdyear = pickle.load(open("thirdyear.p", "rb"))
fourthyear = pickle.load(open("fourthyear.p", "rb"))
thirdfourth = pickle.load(open("thirdfourth.p", "rb"))
twothree4 = pickle.load(open("twothree4.p", "rb"))
twothree = pickle.load(open("twothree.p", "rb"))
onetwo = pickle.load(open("onetwo.p", "rb"))
onetwo3 = pickle.load(open("onetwo3.p", "rb"))
lastdata1 = pickle.load(open("last3mnthsBMW.p", "rb"))
dataset = pd.read_excel(
r"C:\Users\user\Desktop\dashboard\store-dataset1.xlsx")
dataset1 = dataset.sort_values('Order Date')
dataset1 = dataset1.reset_index(drop=True)
#x=int(input("Enter year"))
#y=int(input("Enter month "))
dataset1 = pd.DataFrame()
m = [1, 2, 3]
if (x == 2017 and y in m):
dataset1 = thirdfourth
elif (x == 2017 and y not in m):
dataset1 = fourthyear
elif (x == 2016 and y in m):
dataset1 = twothree
elif (x == 2016 and y not in m):
dataset1 = thirdyear
elif (x == 2015 and y in m):
dataset1 = onetwo
elif (x == 2015 and y not in m):
dataset1 = secondyear
else:
dataset1 = firstyear
dataset1 = dataset1.reset_index(drop=True)
a = pd.DatetimeIndex(dataset1['Order Date']).year == x
b = pd.DatetimeIndex(dataset1['Order Date']).month == y
dataset2 = pd.DataFrame()
for i in range(len(dataset1)):
if (a[i] == True and b[i] == True):
dataset2 = dataset2.append(dataset1.iloc[i, :])
dataset2 = dataset2.reset_index(drop=True)
l = list(dataset2['Product Name'].unique())
inv_dict = {}
inv_dict = lis_inv[inv_mon_name[((x - 2014) * 12 + y) - 1]]
sold_percent = {}
if (x == 2017):
if (y == 12):
data1 = lastdata1
else:
data1 = sorted_profit(x, y)
m = list(dataset1['Product Name'].unique())
import operator
new_l = []
dic = {}
for i in range(len(m)):
dic[m[i]] = 0
for i in range(0, len(m)):
new_l = []
new_l = predicted_sales(x, y, m[i])
#new_l= np.array(new_l).reshape(1, -1)
h = np.array(range(0, len(new_l)))
new_l = np.array(new_l).reshape(-1, 1)
h = np.array(h).reshape(-1, 1)
reg = LinearRegression()
reg.fit(h, new_l)
y_predict = int(abs(reg.predict(len(h))))
dic[m[i]] = y_predict
dic1 = sorted(dic.items(), key=lambda x: x[1])
for i in range(len(dataset2)):
sold_percent[dataset2['Product Name'][i]] = ((
(((inv_dict[dataset2['Product Name'][i]]) -
(inv_dict[dataset2['Product Name'][i]] -
int(dataset2['Quantity'][i]))) /
inv_dict[dataset2['Product Name'][i]]) * 100),
dataset2['Quantity'][i])
temp = int(dataset2['Quantity'][i])
inv_dict[dataset2['Product Name'][i]] -= int(dataset2['Quantity'][i])
if (sold_percent[dataset2['Product Name'][i]][1] >= 5.0):
try:
count = 0
dfb = data[data1['d_product'] == dataset2['Product Name']
[i]].index.values.astype(int)[0]
except:
count = 1
pass
if (count == 1):
dfb = data[data['d_product'] == dataset2['Product Name']
[i]].index.values.astype(int)[0]
if (dfb < 0.2 * (len(data))):
print('HB')
inv_dict[dataset2['Product Name'][i]] += (80 * temp) // 100
if (dfb >= 0.2 * (len(data))):
print('HMW')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
else:
if (dfb < 0.2 * (len(data1))):
print('HB1')
inv_dict[dataset2['Product Name'][i]] += (80 * temp) // 100
if (dfb >= 0.2 * (len(data1))):
print('HMW1')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
if (sold_percent[dataset2['Product Name'][i]][1] <= 5.0
and sold_percent[dataset2['Product Name'][i]][1] >= 3.0):
try:
count = 0
dfb = data[data1['d_product'] == dataset2['Product Name']
[i]].index.values.astype(int)[0]
except:
count = 1
pass
if (count == 1):
dfb = data[data['d_product'] == dataset2['Product Name']
[i]].index.values.astype(int)[0]
if (dfb < 0.2 * (len(data))):
print('MB')
inv_dict[dataset2['Product Name'][i]] += (80 * temp) // 100
if (dfb > 0.2 * (len(data) and dfb < 0.8 * (len(data)))):
print('MM')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
if (dfb >= 0.8 * (len(data))):
print('MW')
inv_dict[dataset2['Product Name'][i]] += (20 * temp) // 100
else:
if (dfb < 0.2 * (len(data1))):
print('MB1')
inv_dict[dataset2['Product Name'][i]] += (80 * temp) // 100
if (dfb > 0.2 * (len(data1) and dfb < 0.8 * (len(data1)))):
print('MM1')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
if (dfb >= 0.8 * (len(data1))):
print('MW1')
inv_dict[dataset2['Product Name'][i]] += (20 * temp) // 100
if (sold_percent[dataset2['Product Name'][i]][1] <= 3.0):
try:
count = 0
dfb = data[data1['d_product'] == dataset2['Product Name']
[i]].index.values.astype(int)[0]
except:
count = 1
pass
if (count == 1):
dfb = data[data['d_product'] == dataset2['Product Name']
[i]].index.values.astype(int)[0]
if (dfb < 0.2 * (len(data))):
print('WB')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
if (dfb > 0.2 * (len(data) and dfb < 0.8 * (len(data)))):
print('WM')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
if (dfb >= 0.8 * (len(data))):
print('WW')
inv_dict[dataset2['Product Name'][i]] += (0 * temp) // 100
else:
if (dfb < 0.2 * (len(data1))):
print('WB1')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
if (dfb > 0.2 * (len(data1) and dfb < 0.8 * (len(data1)))):
print('WM1')
inv_dict[dataset2['Product Name'][i]] += (50 * temp) // 100
if (dfb >= 0.8 * (len(data1))):
print('WW1')
inv_dict[dataset2['Product Name'][i]] += (0 * temp) // 100
new_data = data1
new_dict = {}
new_list = list(dataset['Product Name'].unique())
l_best = {}
l_moderate = {}
l_worst_high = {}
l_worst_low = {}
not_sold = {}
discount = int(input('Enter the Discount in % '))
for i in range(0, len(new_data)):
if (i < .2 * len(new_data)):
#new_dict[new_data['d_product'][i]]= 'B'
#new_data['d_MRP'][i]=new_data['d_inventory'][i]+new_data['d_inventory'][i]*discount
#new_data['d_profit'][i]=(new_data['d_MRP'][i]-new_data['d_inventory'][i])*new_data['d_quantity'][i]
l_best[(new_data['d_product'][i])] = new_data['d_profit'][i]
elif (i >= .8 * len(new_data)):
#new_dict[new_data['d_product'][i]]= 'W'
if (new_data['d_quantity'][i] >= 5):
#new_data['d_MRP'][i]= new_data['d_MRP'][i]+new_data['d_MRP'][i]*0.2
#new_data['d_MRP'][i]=new_data['d_inventory'][i]+new_data['d_inventory'][i]*discount
#print(new_data['d_product'][i])
#new_data['d_profit'][i]=(new_data['d_MRP'][i]-new_data['d_inventory'][i])*new_data['d_quantity'][i]
l_worst_high[(
new_data['d_product'][i])] = new_data['d_profit'][i]
else:
#new_data['d_MRP'][i]=new_data['d_inventory'][i]-new_data['d_inventory'][i]*discount
#new_data['d_profit'][i]=(new_data['d_MRP'][i]-new_data['d_inventory'][i])*new_data['d_quantity'][i]
l_worst_low[(
new_data['d_product'][i])] = new_data['d_profit'][i]
else:
#new_dict[new_data['d_product'][i]]= 'M'
#new_data['d_MRP'][i]=new_data['d_inventory'][i]+new_data['d_inventory'][i]*discount
#new_data['d_profit'][i]=(new_data['d_MRP'][i]-new_data['d_inventory'][i])*new_data['d_quantity'][i]
l_moderate[(new_data['d_product'][i])] = new_data['d_profit'][i]
string = "best"
string2 = "l_" + string
final = eval(string2)
final_list = list(final.values())
for i in range(0, len(final)):
final_list[i] *= discount
for i in range(0, len(new_list)):
flag = 0
for j in range(0, len(new_data)):
if new_list[i] == new_data['d_product'][j]:
flag = 1
break
if flag == 0:
not_sold[(new_list[i])] = "Not Sold"
import pickle
pickle_out = open("Pricing_data.p", "wb")
pickle.dump(new_list, pickle_out)
pickle_out.close()
s = pickle.load(open("Pricing_data.p", "rb"))
pickle_out = open("Pricing_BestProduct.p", "wb")
pickle.dump(l_best, pickle_out)
pickle_out.close()
t = pickle.load(open("Pricing_BestProduct.p", "rb"))
pickle_out = open("Pricing_ModerateProduct.p", "wb")
pickle.dump(l_moderate, pickle_out)
pickle_out.close()
u = pickle.load(open("Pricing_ModerateProduct.p", "rb"))
pickle_out = open("Pricing_WorstHighProduct.p", "wb")
pickle.dump(l_worst_high, pickle_out)
pickle_out.close()
v = pickle.load(open("Pricing_WorstHighProduct.p", "rb"))
pickle_out = open("Pricing_WorstLowProduct.p", "wb")
pickle.dump(l_worst_low, pickle_out)
pickle_out.close()
v = pickle.load(open("Pricing_WorstLowProduct.p", "rb"))
pickle_out = open("not_sold_Product.p", "wb")
pickle.dump(not_sold, pickle_out)
pickle_out.close()
w = pickle.load(open("not_sold_Product.p", "rb"))
category = tk.StringVar(root)
category.set('Select Category')
Category = [
'Select Category', 'Best', 'Moderate', 'Worst Low', 'Worst High',
'Not Sold'
]
categoryOM = tk.OptionMenu(root, category, *Category)
categoryLabel = tk.Label(root, text='Choose Category')
categoryLabel.pack()
categoryOM.pack()
def showData():
#productList = tk.Listbox(root,width = 60, height = 10)
#profitList = tk.Listbox(root,width = 60, height = 10)
if (category.get() == 'Best'):
a = []
#b = []
l_best_keys = list(l_best.keys())
l_best_values = list(l_best.values())
a = [
str(i) + str(l_best_keys[i]) + ':' + str(l_best_values[i])
for i in range(len(l_best))
]
#b = [i for i in list(l_best.values())]
elif (category.get() == 'Moderate'):
a = []
#b = []
l_moderate_keys = list(l_moderate.keys())
l_moderate_values = list(l_moderate.values())
a = [
str(i) + str(l_moderate_keys[i]) + ':' +
str(l_moderate_values[i]) for i in range(len(l_moderate))
]
#b = [i for i in list(l_best.values())]
elif (category.get() == 'Worst High'):
a = []
#b = []
l_worst_high_keys = list(l_worst_high.keys())
l_worst_high_values = list(l_worst_high.values())
a = [
str(i) + str(l_worst_high_keys[i]) + ':' +
str(l_worst_high_values[i]) for i in range(len(l_worst_high))
]
#b = [i for i in list(l_best.values())]
elif (category.get() == 'Worst Low'):
a = []
#b = []
l_worst_low_keys = list(l_worst_low.keys())
l_worst_low_values = list(l_worst_low.values())
a = [
str(i) + str(l_worst_low_keys[i]) + ':' +
str(l_worst_low_values[i]) for i in range(len(l_worst_low))
]
#b = [i for i in list(l_best.values())]
elif (category.get() == 'Not Sold'):
a = []
#b = []
l_not_sold_keys = list(l_not_sold.keys())
l_not_sold_values = list(l_not_sold.values())
a = [
str(i) + str(l_not_sold_keys[i]) + ':' +
str(l_not_sold_values[i]) for i in range(len(l_not_sold))
]
#b = [i for i in list(l_best.values())]
productList.delete(
0,
max(len(l_best), len(l_moderate), len(l_worst_high),
len(l_worst_low), len(not_sold)))
for i in range(len(a)):
productList.insert(i, a[i])
#profitList.insert(i, b[i])
# =============================================================================
# scrollbar = tk.Scrollbar(productList, orient="vertical")
# scrollbar.pack(side="right", fill="y")
# =============================================================================
productList = tk.Listbox(root, width=60, height=10)
#profitList = tk.Listbox(root,width = 60, height = 10)
productList.place(relx=0.05, rely=0.28)
#profitList.pack()
getData = tk.Button(root, text='Get Data', command=showData)
getData.pack()
l = list(dataset2['Product Name'].unique())
inv_dict = {}
inv_dict = lis_inv[inv_mon_name[((x - 2014) * 12 + y) - 1]]
# =============================================================================
# for i in l:
# inv_dict[i] = 500
# =============================================================================
# sold, remaining % sold, adding inventory
sold_percent = {}
if (x == 2017):
if (y == 12):
data1 = lastdata1
else:
data1 = sorted_profit(x, y)
#full dat is dataset1
# =============================================================================
# m = list(dataset1['Product Name'].unique())
# #Pricing
# import operator
# new_l=[]
# dic ={}
# for i in range(len(m)):
# dic[m[i]]=0
#
# for i in range(0,len(m)):
# new_l=[]
# new_l = predicted_sales(x, y, m[i] )
# #new_l= np.array(new_l).reshape(1, -1)
# h=np.array(range(0,len(new_l)))
dataset2 = dataset2.append(dataset1.iloc[i,:])
dataset2 = dataset2.reset_index(drop=True)
# setting inventory size
l = list(dataset2['Product Name'].unique())
inv_dict = {}
inv_dict = lis_inv[inv_mon_name[((year_enter - first_year)*12 + mon_enter)-1]]
# sold, remaining % sold, adding inventory
sold_percent = {}
if(year_enter==2017):
if(mon_enter==12):
data1=lastdata1
else:
data1= sorted_profit(year_enter,mon_enter)
#full data is dataset1
list_unique_products = list(dataset1['Product Name'].unique())
#Predicting Sales
list_prev_sales=[]
list_predicted_sales ={}
for i in range(len(list_unique_products)):
list_predicted_sales[list_unique_products[i]]=0
for i in range(0,len(list_unique_products)):
list_prev_sales=[]
list_prev_sales = predicted_sales(year_enter, mon_enter, list_unique_products[i] )
h=np.array(range(0,len(list_prev_sales)))