Description
Select business or economic problem and try to solve it using a regression model or a time series forecasting model (for example: exponential smoothing). The problem can be related with forecasting, asset pricing or relationship assessment between different variables.
Project Structures:
Introduction, Methods, Results, Conclusion and Excel file + Word file.
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Explanation & Answer

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Area (sq m) Selling Price (in million)
35
13
37
17
39
15
40
16
43
18
48
19
50
20
55
25
60
35
65
37
70
38
75
40
80
43
85
44
90
50
95
54
100
60 predicted Price (in millions)=
135
73
137
77
139
75 Area (sq m)=
140
76
143
78
148
79
150
80
155
85
160
95
165
97
170
98
175
100
180
103
185
104
190
110
195
114
200
120
235
133
237
137
239
135
240
136
243
138
We will be using Linear Regression along with Gra
Using that relation we can predict others house pr
Let Theta0 and Theta1 be initial weights
Now, initially linear equation be
h(x) = theta0 + theta1 * x
Now, updating linear equation by using gradient d
Now, after training dataset
theta0 =
-6.17366
theta1 =
0.602182
so, final linear equation will be
h(x) = -6.17366081 + 0.60218233 * x
so, for prediction
114.2628
200 change value here to get predicted val
r Regression along with Gradient Descent Algorithm to find final linear Equation which is relation between area and price of house
can predict others house prices
be initial weights
Gradient Descent Algorithm
theta0 = theta0 - (alpha/m) * Σ(h(x(i) - y(i)))
theta1 = thet...
