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 prep
import LR
import LOGR
import RF
import KNN
import SVM
import Dtree
import 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 RandomForest Class
clf_Dt=Dtree.DT(df["data"].copy(),"Churn",lda) #intiate object from RandomForest Class
clf_naiv=naiv.RF(df["data"].copy(),"Churn",lda) #intiate object from RandomForest Class
app = Flask(__name__)
@app.route("/",methods=["GET","POST"])
def hello():