def naiv_pred():
flag = int(request.args.get('flag'))
li = []
te = pd.read_csv("test.csv")
if (flag):
li.append(request.args.get('customerID'))
li.append(request.args.get('Gender'))
li.append(request.args.get('SeniorCitizen'))
li.append(request.args.get('Partner'))
li.append(request.args.get('Dependents'))
li.append(request.args.get('tenure'))
li.append(request.args.get('PhoneService'))
li.append(request.args.get('MultipleLines'))
li.append(request.args.get('InternetService'))
li.append(request.args.get('OnlineSecurity'))
li.append(request.args.get('OnlineBackup'))
li.append(request.args.get('DeviceProtection'))
li.append(request.args.get('TechSupport'))
li.append(request.args.get('StreamingTV'))
li.append(request.args.get('StreamingMovies'))
li.append(request.args.get('Contract'))
li.append(request.args.get('PaperlessBilling'))
li.append(request.args.get('BaymentMrthod'))
li.append(request.args.get('MonthlyCharges'))
li.append(request.args.get('TotalCharges'))
dic = {}
c = 0
for i in X.columns:
dic[i] = [li[c]]
c += 1
te = pd.DataFrame(data=dic)
te_c = prep.cleaning(te.copy())["data"]
te["Churn"] = df["d"]["Churn"].inverse_transform(clf_naiv.get_pred(te_c))
data = {"pred": te.head(500).to_json()}
return jsonify(data)
def prp():
df = prep.cleaning(pd.read_csv("dataset.csv"))["data"]
data = {"data": df.head(500).to_json()}
return jsonify(data)
import py_files.prep as prep
import py_files.LR as LR
import py_files.LOGR as LOGR
import py_files.RF as RF
import py_files.KNN as KNN
import py_files.SVM as SVM
import py_files.Dtree as Dtree
import py_files.naiv as naiv
import pandas as pd
import json
from flask import Flask, jsonify, request, render_template
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
df = prep.cleaning(pd.read_csv("dataset.csv")) #load the dataset
lda = LDA(n_components=8) #select 8 feature
X = df["data"].drop("Churn", axis=1)
Y = df["data"]["Churn"]
lda.fit(X, Y) #fit the data to select the best feature
clf_log = LOGR.LR(df["data"].copy(), "Churn",
lda) #intiate object from logisticRegression Class
clf_KNN = KNN.KNN(df["data"].copy(), "Churn",
lda) #intiate object from K-NN Class
clf_RF = RF.RF(df["data"].copy(), "Churn",
lda) #intiate object from RandomForest Class
clf_SVM = SVM.SV(df["data"].copy(), "Churn",
lda) #intiate object from SVM Class
clf_Dt = Dtree.DT(df["data"].copy(), "Churn",
lda) #intiate object from Decision Tree Class
clf_naiv = naiv.RF(df["data"].copy(), "Churn",