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app.py
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app.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import MinMaxScaler
from flask import Flask, render_template, request, jsonify
# Create a Flask application instance
app = Flask(__name__)
# Load the dataset from a CSV file
df = pd.read_csv('FuelConsumption2023.csv', encoding='latin-1')
# Select specific columns of interest
df = df[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY', 'CO2EMISSIONS']]
# Data preprocessing
df.dropna(inplace=True)
scaler = MinMaxScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
# Define route to handle the root URL
@app.route('/')
def home():
return render_template('home.html')
# Define route to handle the predict URL
@app.route('/predict', methods=['POST'])
def predict():
try:
enginesize = float(request.form['enginesize'])
fuelconsumption_city = float(request.form['fuelconsumption_city'])
cylinders = int(request.form['cylinders'])
# Create the input data
input_data = pd.DataFrame([[enginesize, cylinders, fuelconsumption_city]], columns=['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY'])
# Scale the input data
input_data_scaled = pd.DataFrame(scaler.transform(input_data), columns=input_data.columns)
# Predict CO2 emission
predictions = model.predict(input_data_scaled)
# Inverse transform the predicted values
predicted_co2 = scaler.inverse_transform(predictions)[0]
return render_template('index.html', predicted_co2=predicted_co2)
except Exception as e:
error = str(e)
return render_template('index.html', error=error)
if __name__ == '__main__':
# Load the pre-trained model
model = LinearRegression()
X = df_scaled[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY']]
y = df_scaled['CO2EMISSIONS']
model.fit(X, y)
# Run the Flask application
app.run(debug=True)