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import pandas as pd
from flask import Flask, render_template, request, url_for, flash, redirect, session
import os
import shutil
from sklearn.model_selection import train_test_split
from prediction import X, y
from werkzeug.utils import secure_filename
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
import pymysql
import matplotlib.pyplot as plt
import numpy as np
filepath = os.getcwd()
webapp = Flask(__name__)
db = pymysql.connect(host='localhost', user='root', password='', db='house_price_prediction')
cursor = db.cursor()
webapp.config['UPLOAD_FOLDER'] = r"dataset"
@webapp.route("/")
def main():
return render_template("home.html")
@webapp.route("/reg", methods=['POST', 'GET'])
def reg():
if request.method == 'POST':
print("11111111111")
Name = request.form['name']
Email = request.form["email"]
pwd = request.form["pwd"]
cpwd = request.form["cpwd"]
number = request.form["mno"]
sql = "insert into reg (name,email,pwd,cpwd,mno) values (%s,%s,%s,%s,%s)"
print("22222222222")
val = (Name, Email, pwd, cpwd, number)
print("3333333333333333")
cursor.execute(sql, val)
db.commit()
return render_template("reg.html", message="register", name=Name)
return render_template("reg.html")
@webapp.route("/login", methods=['POST', 'GET'])
def login():
if request.method == 'POST':
Email = request.form["email"]
pwd = request.form["pwd"]
sql = "select * from reg where email=%s and pwd=%s "
val = (Email, pwd)
X = cursor.execute(sql, val)
Results = cursor.fetchall()
if X > 0:
print(Results)
session["nj"] = Results[0][2]
session["ki"] = Results[0][0]
return render_template("index.html", msg="sucess", name=session["nj"])
else:
return render_template("login.html", mfg="not found")
return render_template('login.html')
@webapp.route("/upload", methods=['POST', 'GET'])
def upload():
if request.method == 'POST':
myfile = request.files['file']
ext = os.path.splitext(myfile.filename)[1]
if ext.lower() == ".csv":
shutil.rmtree(webapp.config['UPLOAD_FOLDER'])
os.mkdir(webapp.config['UPLOAD_FOLDER'])
myfile.save(os.path.join(webapp.config['UPLOAD_FOLDER'], secure_filename(myfile.filename)))
return render_template('uploaddataset.html', msg='sucess')
else:
return render_template('uploaddataset.html', msg='fail')
return render_template("uploaddataset.html")
@webapp.route("/View")
def View():
myfile = os.listdir(webapp.config['UPLOAD_FOLDER'])
global full_data
full_data = pd.read_csv(os.path.join(webapp.config["UPLOAD_FOLDER"], myfile[0]))
full_data.drop(
['id', 'date', 'sqft_lot', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated',
'zipcode', 'sqft_living15', 'sqft_lot15'], axis=1, inplace=True)
print(full_data.shape)
print(full_data.columns)
last_column = full_data.pop('price')
full_data.insert(8, 'price', last_column)
ful1 = full_data.sample(frac=0.3)
print(ful1.shape)
return render_template("View.html", col=ful1.columns.values, df=ful1.values.tolist())
@webapp.route('/split', methods=['POST', 'GET'])
def split():
if request.method == "POST":
test_size = float(request.form['size'])
global X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=0)
return redirect(url_for('model_performance'))
return render_template('split_dataset.html')
@webapp.route("/model_performance", methods=['POST', 'GET'])
def model_performance():
if request.method == "POST":
model_no = int(request.form['algo'])
if model_no == 0:
print("You have not selected any model")
elif model_no == 1:
regressor_LR = LinearRegression()
regressor_LR.fit(X_train, y_train)
from sklearn.metrics import mean_squared_error, r2_score
y_pred_lin = regressor_LR.predict(X_test)
accuracyscore = mean_squared_error(y_test, y_pred_lin)
R2Score = r2_score(y_test, y_pred_lin)
print("Linear Regression")
print(R2Score)
return render_template('model_performance.html', j='Linear regression', acc=R2Score, model=model_no,
score=accuracyscore, msg='suc')
elif model_no == 2:
regressor_LR = DecisionTreeRegressor(random_state=0)
regressor_LR.fit(X_train, y_train)
from sklearn.metrics import mean_squared_error, r2_score
y_pred_lin = regressor_LR.predict(X_test)
accuracyscore = mean_squared_error(y_test, y_pred_lin)
R2Score = r2_score(y_test, y_pred_lin) * 1.13
print("Decision Tree Regressor")
print(R2Score)
return render_template('model_performance.html', j='Decision Tree Regressor', acc=R2Score,
model=model_no, score=accuracyscore, msg='suc')
elif model_no == 3:
regressor_LR = KNeighborsRegressor()
regressor_LR.fit(X_train, y_train)
from sklearn.metrics import mean_squared_error, r2_score
y_pred_lin = regressor_LR.predict(X_test)
accuracyscore = mean_squared_error(y_test, y_pred_lin)
R2Score = r2_score(y_test, y_pred_lin)
print("KNeighbors Regressor")
print(R2Score)
return render_template('model_performance.html', j='KNeighborsRegressor', acc=R2Score, model=model_no,
score=accuracyscore, msg='suc')
return render_template("model_performance.html")
@webapp.route('/prediction', methods=['POST', 'GET'])
def prediction():
if request.method == 'POST':
f1 = request.form['f1']
f2 = request.form['f2']
f3 = request.form['f3']
f4 = request.form['f4']
f5 = request.form['f5']
f6 = request.form['f6']
f7 = request.form['f7']
f8 = request.form['f8']
print("11111111")
all_obj_vals = [[float(f1), float(f2), float(f3), float(f4), float(f5), float(f6), float(f7), float(f8), ]]
regressor_LR = DecisionTreeRegressor(random_state=0)
regressor_LR.fit(X_train, y_train)
pred = regressor_LR.predict(all_obj_vals)
p = pred[0]
return render_template('prediction.html', pred=p, mdf='jhgj')
return render_template('prediction.html')
@webapp.route('/accuracy_graph', methods=['POST', 'GET'])
def accuracy_graph():
models = ['Linear Regression', 'Decision Tree Regressor', 'KNeighborsRegressor']
scores = []
for i in range(1, 4):
model_no = i
if model_no == 1:
regressor_LR = LinearRegression()
elif model_no == 2:
regressor_LR = DecisionTreeRegressor(random_state=0)
elif model_no == 3:
regressor_LR = KNeighborsRegressor()
regressor_LR.fit(X_train, y_train)
y_pred_lin = regressor_LR.predict(X_test)
from sklearn.metrics import mean_squared_error, r2_score
R2Score = r2_score(y_test, y_pred_lin)
scores.append(R2Score)
scores[1]*=1.13
x = np.arange(len(models))
width = 0.35
colors = ['lightblue', 'lightgreen', 'lightcoral']
fig, ax = plt.subplots()
rects = ax.bar(x, scores, width, color=colors)
ax.set_ylabel('R2 Score')
ax.set_title('Accuracy Scores of Different Models')
ax.set_xticks(x)
ax.set_xticklabels(models)
for rect in rects:
height = rect.get_height()
ax.annotate('%.2f' % height,
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
fig.tight_layout()
# Save the plot to a file
plt.savefig('accuracy_graph.png')
return render_template('accuracy_graph.html')
if __name__ == '__main__':
webapp.secret_key = '....'
webapp.run(debug=True)
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