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function.py
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function.py
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import base64
import io
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder, StandardScaler
from lightgbm import LGBMClassifier
# Girilen Input Verisinin Tahminini Bir Dataframe Olarak Döndüren Fonksiyon
def pred_data(df, df_input):
def label_encoder(dataframe, cat_cols):
le = LabelEncoder()
for col in cat_cols:
dataframe[col] = le.fit_transform(dataframe[col])
return dataframe
df['Arrival Delay in Minutes'].fillna(df['Arrival Delay in Minutes'].median(), inplace=True)
df['satisfaction'].replace({'neutral or dissatisfied': 0, 'satisfied': 1}, inplace=True)
df = df.drop(columns=["Unnamed: 0"])
df = df.drop(columns=["id"])
df = pd.concat([df, df_input], ignore_index=True)
def grab_col_names(dataframe, cat_th=20, car_th=40):
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
return cat_cols, num_cols, cat_but_car
# Gender Loyality
df.loc[(df['Gender'] == "Male") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Male Loyal"
df.loc[(df['Gender'] == "Female") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Female Loyal"
df.loc[(df['Gender'] == "Male") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Male Unloyal"
df.loc[(df['Gender'] == "Female") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Female Unloyal"
# Age Categorization
df.loc[(df['Age'] >= 7) & (df['Age'] < 25), 'NEW_AGE_CAT'] = "young"
df.loc[(df['Age'] >= 25) & (df['Age'] < 40), 'NEW_AGE_CAT'] = "mature"
df.loc[(df['Age'] >= 40) & (df['Age'] < 65), 'NEW_AGE_CAT'] = "middle_age"
df.loc[(df['Age'] >= 65) & (df['Age'] < 95), 'NEW_AGE_CAT'] = "old_age"
# Age x Gender
df.loc[(df['NEW_AGE_CAT'] == "young") & (df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "young Male"
df.loc[(df['NEW_AGE_CAT'] == "young") & (df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "young Female"
df.loc[(df['NEW_AGE_CAT'] == "mature") & (df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "mature Male"
df.loc[(df['NEW_AGE_CAT'] == "mature") & (df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "mature Female"
df.loc[(df['NEW_AGE_CAT'] == "middle_age") & (
df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "middle_age Male"
df.loc[(df['NEW_AGE_CAT'] == "middle_age") & (
df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "middle_age Female"
df.loc[(df['NEW_AGE_CAT'] == "old_age") & (
df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "old_age Male"
df.loc[(df['NEW_AGE_CAT'] == "old_age") & (
df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "old_age Female"
# Travel Type x Lotality
df.loc[(df['Type of Travel'] == "Personal Travel") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Personal Loyal"
df.loc[(df['Type of Travel'] == "Business travel") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Business Loyal"
df.loc[(df['Type of Travel'] == "Personal Travel") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Personal Unloyal"
df.loc[(df['Type of Travel'] == "Business travel") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Business Unloyal"
# New Customer Travel Type x Gender
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Loyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Loyal Female"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Loyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Loyal Female"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Unloyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Unloyal Female"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Unloyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Unloyal Female"
# New Customer Travel Type x Class
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Loyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Loyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Loyal Business"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Loyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Loyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Loyal Business"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Unloyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Unloyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Unloyal Business"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Unloyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Unloyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Unloyal Business"
# New Delay Absolute
df["NEW_DELAY_GAP"] = abs(
df["Departure Delay in Minutes"] - df["Arrival Delay in Minutes"])
# Flight Distance Segmentation
df.loc[(df['Flight Distance'] <= 1500), 'NEW_DISTANCE_SEGMENTATION'] = "kısa mesafe"
df.loc[(df['Flight Distance'] > 1500), 'NEW_DISTANCE_SEGMENTATION'] = "uzun mesafe"
# Based Service Score
df["NEW_FLIGHT_SITUATION"] = (df["Inflight wifi service"] + df["Food and drink"] +
df["Seat comfort"] + df["Inflight entertainment"] +
df["Leg room service"]) / 25
df["NEW_OPERATIONAL"] = (df["Departure/Arrival time convenient"] + df["Cleanliness"] +
df["Baggage handling"] + df["Gate location"]) / 20
df["NEW_ONLINE"] = (df["Ease of Online booking"] + df["Online boarding"] + df[
"Checkin service"]) / 15
df["NEW_PERSONAL_BEHAVIOR"] = (df["On-board service"] + df["Inflight service"]) / 10
cat_cols, num_cols, cat_but_car = grab_col_names(df, cat_th=20, car_th=40)
df = label_encoder(df, cat_cols)
X_scaled = StandardScaler().fit_transform(df[num_cols])
df[num_cols] = pd.DataFrame(X_scaled, columns=df[num_cols].columns)
X = df.drop(["satisfaction"], axis=1)
# Inputun Ölçeklenmiş ve Encode Edilmiş Halinin Modele Hazırlanması
df1 = X.iloc[-1]
l1 = [float(df1[0]), float(df1[1]), float(df1[2]), float(df1[3]), int(df1[4]), int(df1[5]),
int(df1[6]), int(df1[7]), int(df1[8]), int(df1[9]), int(df1[10]), int(df1[11]), int(df1[12]),
int(df1[13]), int(df1[14]), int(df1[15]), int(df1[16]), int(df1[17]), int(df1[18]), int(df1[19]),
int(df1[20]), int(df1[21]), int(df1[22]), int(df1[23]), int(df1[24]), int(df1[25]), int(df1[26]),
int(df1[27]), int(df1[28]), int(df1[29]), int(df1[30]), int(df1[31]), int(df1[32]), int(df1[33])]
# Input Verilerinden Tek Satırlık DataFrame Oluşturma
l2 = np.array(l1).reshape(1, -1)
input_df = pd.DataFrame(l2)
return input_df
# Arka Plan Yükleme Fonksiyonu
def get_img_as_base64(file):
with open(file, "rb") as f:
data = f.read()
return base64.b64encode(data).decode()
# Toplu Veri Girişinin Ölçeklenmesi ve Encode Edilmesini Sağlayan Fonksiyon
def save(bigData):
def label_encoder(dataframe, cat_cols):
le = LabelEncoder()
for col in cat_cols:
dataframe[col] = le.fit_transform(dataframe[col])
return dataframe
df = pd.read_csv("data/data.csv")
df['Arrival Delay in Minutes'].fillna(df['Arrival Delay in Minutes'].median(), inplace=True)
df['satisfaction'].replace({'neutral or dissatisfied': 0, 'satisfied': 1}, inplace=True)
df = df.drop(columns=["Unnamed: 0"])
df = df.drop(columns=["id"])
df = pd.concat([df, bigData], ignore_index=True)
def grab_col_names(dataframe, cat_th=20, car_th=40):
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
return cat_cols, num_cols, cat_but_car
# Gender Loyality
df.loc[(df['Gender'] == "Male") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Male Loyal"
df.loc[(df['Gender'] == "Female") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Female Loyal"
df.loc[(df['Gender'] == "Male") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Male Unloyal"
df.loc[(df['Gender'] == "Female") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_GENDER'] = "Female Unloyal"
# Age Categorization
df.loc[(df['Age'] >= 7) & (df['Age'] < 25), 'NEW_AGE_CAT'] = "young"
df.loc[(df['Age'] >= 25) & (df['Age'] < 40), 'NEW_AGE_CAT'] = "mature"
df.loc[(df['Age'] >= 40) & (df['Age'] < 65), 'NEW_AGE_CAT'] = "middle_age"
df.loc[(df['Age'] >= 65) & (df['Age'] < 95), 'NEW_AGE_CAT'] = "old_age"
# Age x Gender
df.loc[(df['NEW_AGE_CAT'] == "young") & (df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "young Male"
df.loc[(df['NEW_AGE_CAT'] == "young") & (df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "young Female"
df.loc[(df['NEW_AGE_CAT'] == "mature") & (df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "mature Male"
df.loc[(df['NEW_AGE_CAT'] == "mature") & (df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "mature Female"
df.loc[(df['NEW_AGE_CAT'] == "middle_age") & (
df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "middle_age Male"
df.loc[(df['NEW_AGE_CAT'] == "middle_age") & (
df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "middle_age Female"
df.loc[(df['NEW_AGE_CAT'] == "old_age") & (
df['Gender'] == "Male"), 'NEW_AGE_Gender'] = "old_age Male"
df.loc[(df['NEW_AGE_CAT'] == "old_age") & (
df['Gender'] == "Female"), 'NEW_AGE_Gender'] = "old_age Female"
# Travel Type x Lotality
df.loc[(df['Type of Travel'] == "Personal Travel") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Personal Loyal"
df.loc[(df['Type of Travel'] == "Business travel") & (
df['Customer Type'] == "Loyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Business Loyal"
df.loc[(df['Type of Travel'] == "Personal Travel") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Personal Unloyal"
df.loc[(df['Type of Travel'] == "Business travel") & (
df['Customer Type'] == "disloyal Customer"), 'NEW_CUSTOMER_TRAVEL_TYPE'] = "Business Unloyal"
# New Customer Travel Type x Gender
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Loyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Loyal Female"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Loyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Loyal Female"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Unloyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Personal Unloyal Female"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Gender'] == "Male"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Unloyal Male"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Gender'] == "Female"), 'NEW_CUSTOMER-TRAVEL_GENDER'] = "Business Unloyal Female"
# New Customer Travel Type x Class
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Loyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Loyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Loyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Loyal Business"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Loyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Loyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Loyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Loyal Business"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Unloyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Unloyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Personal Unloyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Personal Unloyal Business"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Class'] == "Eco"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Unloyal Eco"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Class'] == "Eco Plus"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Unloyal Eco Plus"
df.loc[(df['NEW_CUSTOMER_TRAVEL_TYPE'] == "Business Unloyal") & (
df['Class'] == "Business"), 'NEW_TRAVEL_TYPE_CLASS'] = "Business Unloyal Business"
# New Delay Absolute
df["NEW_DELAY_GAP"] = abs(
df["Departure Delay in Minutes"] - df["Arrival Delay in Minutes"])
# Flight Distance Segmentation
df.loc[(df['Flight Distance'] <= 1500), 'NEW_DISTANCE_SEGMENTATION'] = "kısa mesafe"
df.loc[(df['Flight Distance'] > 1500), 'NEW_DISTANCE_SEGMENTATION'] = "uzun mesafe"
# Based Service Score
df["NEW_FLIGHT_SITUATION"] = (df["Inflight wifi service"] + df["Food and drink"] +
df["Seat comfort"] + df["Inflight entertainment"] +
df["Leg room service"]) / 25
df["NEW_OPERATIONAL"] = (df["Departure/Arrival time convenient"] + df["Cleanliness"] +
df["Baggage handling"] + df["Gate location"]) / 20
df["NEW_ONLINE"] = (df["Ease of Online booking"] + df["Online boarding"] + df[
"Checkin service"]) / 15
df["NEW_PERSONAL_BEHAVIOR"] = (df["On-board service"] + df["Inflight service"]) / 10
bh = len(bigData)
cat_cols, num_cols, cat_but_car = grab_col_names(df, cat_th=20, car_th=40)
df = label_encoder(df, cat_cols)
X_scaled = StandardScaler().fit_transform(df[num_cols])
df[num_cols] = pd.DataFrame(X_scaled, columns=df[num_cols].columns)
X = df.drop(["satisfaction"], axis=1)
bigDataPred = X.iloc[-bh:]
return bigDataPred
# Toplu Veri Yükleme Fonksiyonu
def bigdats(uploaded):
if uploaded is None:
return None
all_data = []
for uploaded_file in uploaded:
file_bytes = uploaded_file.read()
if not file_bytes:
continue
bigData = pd.read_csv(io.BytesIO(file_bytes))
all_data.append(bigData)
if all_data:
bigData = pd.concat(all_data, ignore_index=True)
return bigData
# Kullanıcıdan alınan datanın excel formatında indirilmesi için buton oluştuma
def download_excel(lastdata):
excel_buffer = io.BytesIO()
lastdata.to_excel(excel_buffer, index=False)
excel_buffer.seek(0)
b64 = base64.b64encode(excel_buffer.read()).decode()
href = f'data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}'
return href