regression analysis

1. DATA
SCIENCE &
ANALYTICS
PROJECT
GROUP-6
Abhishekh Rai
Devvrat
Parag Garg
Sarthak Das
Vinayak

2. PROBLEM
To identify the correlation
between advertising
promotion, sales promotion
and sales then predicting
sales on the basis of these
variables.

3. Predictive
analytics
(Marketing
Sales data)
Quarter Adv_exp Sales_pro Sales
1 24 19 724
2 28 21 756
3 32 24 782
4 39 32 831
5 44 36 853
6 45 35 860
7 54 36 896
8 58 41 914
9 62 48 924
10 65 52 938
11 68 54 947
12 76 64 971
Regression Problem (Supervised learning)

4. VARIABLES
Predictor Variable:- 1) Advertisement expenses (Adv_exp)
2) Sales Promotion (Sales_pro)
Target variable:- Sales of the product (Sales)

5. NORMALITY OF DATA
Skewness of Adv_exp -0.0548292021929824
Kurtosis of Adv_exp -1.2135317283851366
Skewness of Sales -0.4681147462504608
Kurtosis of Sales -1.0053087366685975
Skewness of Sales_pro 0.2625732569531058
Kurtosis of Sales_pro -0.8727115194027548
Adjusted R Squared for linear regression 0.9785669877635923
Adjusted R Squared for linear regression 0.9785669877635923
Adjusted R Squared for linear regression 0.9785669877635923

6. PLOT TARGET
VARIABLES
Adv_exp Sales_pro

7. DATA VISUALIZATION
INDEPENDENT VS DEPENDENT
VARIABLE

8. JOINT PLOT BTW INDEPENDENT
VARIABLE

9. ML LINEAR
REGRESSION
PREDICTION
RESULTS
Quarter Adv_exp Sales_pro Sales Sales_prediction
1 24 19 724 [746.45362067],
2 28 21 756 [765.19702851],
3 32 24 782 [783.94043635],
4 39 32 831 [816.74140008],
5 44 36 853 [840.17065988],
6 45 35 860 [844.85651185],
7 54 36 896 [887.02917949],
8 58 41 914 [905.77258734],
9 62 48 924 [924.51599518],
10 65 52 938 [938.57355106],
11 68 54 947 [952.63110695],
12 76 64 971 [990.11792263]]

10. Quarter Adv_exp Sales_pro Sales Sales_prediction Sales_pre
1 24 19 724 [746.45362067], 744.3608
2 28 21 756 [765.19702851], 765.1795
3 32 24 782 [783.94043635], 784.3316
4 39 32 831 [816.74140008], 813.2643
5 44 36 853 [840.17065988], 836.7877
6 45 35 860 [844.85651185], 844.4924
7 54 36 896 [887.02917949], 897.1681
8 58 41 914 [905.77258734], 912.9868
9 62 48 924 [924.51599518], 925.472
10 65 52 938 [938.57355106], 936.9193
11 68 54 947 [952.63110695], 951.7001
12 76 64 971 [990.11792263]] 983.3374
Multiple linear regression prediction