Multivariate_Linear_Regression.py

import numpy as np
import pandas as pd
import math
import xlrd
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn import tree
from sklearn.preprocessing import PolynomialFeatures

#prediction accuracy of dataframe
def prediction_accuracy(input_data):

    variable_list = []
    result_list = []
    
    for item in input_data:
        variable_list.append(item)
        result_list.append(item)

    variable_list = variable_list[:-1]
    result_list = result_list[-1]
    
    variables = input_data[variable_list]
    results = input_data[result_list]

    #accuracy of prediction (splittig dataframe in train and test)
    var_train, var_test, res_train, res_test = train_test_split(variables, results, test_size = 0.3, random_state = 4)

    regression = linear_model.LinearRegression() #making linear model
    model = regression.fit(var_train, res_train) #fitting data in linear model

    #calculating accuracy score
    score = regression.score(var_test, res_test)
    score = round(score*100, 2)

    info = "Strength prediction accuracy for this dataset: " + str(score) + " %"

    return info, regression, variables, results

#print(prediction_accuracy(data)[0])
#print(prediction_accuracy(data_less_than_3days)[0])
#print(prediction_accuracy(data_less_than_28days)[0])


#prediction of concrete strength with multivariate linear regression
def linear_prediction_of_future_strength(input_data, cement, blast_fur_slug,fly_ash,
                                      water, superpl, coarse_aggr, fine_aggr, days):

    accuracy_info = prediction_accuracy(input_data)[0]

    regression = prediction_accuracy(input_data)[1]
    variables = prediction_accuracy(input_data)[2]
    results = prediction_accuracy(input_data)[3]

    input_values = [cement, blast_fur_slug, fly_ash, water, superpl, coarse_aggr, fine_aggr, days]
    
    predicted_strength = regression.predict([input_values]) #adding values for prediction
    predicted_strength = round(predicted_strength[0], 2)

    prediction_info = "\nStrength prediction: " + str(predicted_strength) + " MPa"

    full_info = "\n" + accuracy_info + prediction_info
    
    return full_info

#print(linear_prediction_of_future_strength(data, 260.9, 100.5, 78.3, 200.6, 8.6, 864.5, 761.5, 28)) #true value affter 28 days: 32.40 MPa
#print(linear_prediction_of_future_strength(data_less_than_3days, 260.9, 100.5, 78.3, 200.6, 8.6, 864.5, 761.5, 3)) #true value affter 28 days: 32.40 MPa
#print(linear_prediction_of_future_strength(data_less_than_28days, 214.9 , 53.8, 121.9, 155.6, 9.6, 1014.3, 780.6, 3)) #true value affter 3 days: 18.02 MPa