Step 1 : import library

import pandas as pd

Step 2 : import data

icecream = pd.read_csv('https://github.com/ybifoundation/Dataset/raw/main/Ice%20Cream.csv')

Step 3 : define target (y) and features (X)

icecream.columns

Index(['Temperature', 'Revenue'], dtype='object')

y = icecream['Revenue']

X = icecream[['Temperature']]

Step 4 : train test split

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7, random_state=2529)

check shape of train and test sample

X_train.shape, X_test.shape, y_train.shape, y_test.shape

((350, 1), (150, 1), (350,), (150,))

Step 5 : select model

from sklearn.linear_model import LinearRegression model = LinearRegression()

Step 6 : train or fit model

model.fit(X_train,y_train)

LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

model.intercept_

42.444772590839705

model.coef_

array([21.54587147])

Step 7 : predict model

y_pred = model.predict(X_test)

y_pred

array([645.7291738 , 380.7149547 , 180.33835001, 247.13055157, 415.18834905, 283.75853308, 372.09660611, 671.58421957, 675.89339386, 684.51174245, 531.536055 , 613.41036659, 361.32367037, 303.1498174 , 158.79247854, 473.36220203, 611.25577945, 615.56495374, 512.14477068, 805.16862269, 268.67642305, 441.04339482, 436.73422053, 861.18788852, 531.536055 , 216.96633151, 725.44889825, 311.76816599, 505.68100924, 466.89844059, 684.51174245, 960.29889729, 550.92733933, 615.56495374, 367.78743182, 404.41541332, 413.03376191, 662.96587098, 876.26999855, 544.46357788, 811.63238414, 686.6663296 , 486.28972491, 322.54110172, 637.11082521, 798.70486125, 438.88880767, 790.08651266, 684.51174245, 497.06266065, 380.7149547 , 479.82596347, 479.82596347, 563.85486221, 665.12045813, 453.9709177 , 688.82091675, 647.88376095, 391.48789043, 662.96587098, 507.83559638, 309.61357884, 397.95165188, 199.72963434, 387.17871614, 658.65669669, 441.04339482, 395.79706473, 652.19293524, 841.7966042 , 581.09155939, 352.70532179, 734.06724684, 350.55073464, 253.59431301, 449.66174341, 637.11082521, 135.09201992, 594.01908227, 669.42963242, 699.59385248, 598.32825656, 469.05302773, 912.89798005, 697.43926533, 835.33284275, 527.22688071, 902.12504432, 352.70532179, 423.80669764, 591.86449512, 656.50210954, 548.77275218, 188.9566986 , 641.41999951, 641.41999951, 572.4732108 , 486.28972491, 469.05302773, 632.80165092, 512.14477068, 292.37688166, 729.75807254, 477.67137632, 63.99064406, 395.79706473, 591.86449512, 469.05302773, 568.1640365 , 602.63743086, 736.22183398, 492.75348635, 337.62321175, 656.50210954, 423.80669764, 313.92275314, 458.280092 , 419.49752335, 456.12550485, 559.54568791, 522.91770641, 206.19339578, 742.68559543, 673.73880672, 367.78743182, 690.97550389, 712.52137536, 520.76311927, 309.61357884, 626.33788948, 803.01403555, 576.78238509, 738.37642113, 645.7291738 , 453.9709177 , 578.93697224, 566.00944936, 589.70990798, 447.50715626, 434.57963338, 555.23651362, 509.99018353, 641.41999951, 475.51678917, 441.04339482, 703.90302678, 410.87917476, 462.58926629, 410.87917476, 305.30440455])

Step 8 : model accuracy

from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error

mean_absolute_error(y_test,y_pred)

19.138687444270737

mean_absolute_percentage_error(y_test,y_pred)

0.042214848219420134

mean_squared_error(y_test,y_pred)

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