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():
data={"data":pd.read_csv("dataset.csv").head(500).to_json()}
return jsonify(data)
############################################################### PreProcessing ###################################################
@app.route("/prep",methods=["GET","POST"])
def prp():
df=prep.cleaning(pd.read_csv("dataset.csv"))["data"]
data={"data":df.head(500).to_json()}
