Hello Folks, Anupama here, Presenting on behalf of my team for our internship project - Forecasting Gold Prices. for that, we use python and machine learning algorithms and models. with Exploratory data analysis, modelling, model building, model evaluation, deployment, and publishing applications. #machinelearning #datascience #forecasting #predection #timeseries #python #project

1. Presented By:
1.Anupama
2.Laiba
3.Somanath
4.Sunny
5.Rohan
6.Yoginee

2. Contents
• Business Objective
• Project Architecture
• Data Collection & Details
• Exploratory Data Analysis
• Visualizations
• Modeling
• Evaluation
• Deployment

3. Business Objective
• Data provided is related to gold prices. The objective is to understand
the underlying structure in your dataset and come up with a suitable
forecasting model which can effectively forecast gold prices for next
30 days.
• This forecast model will be used by gold exporting and gold importing
companies to understand the metal price movements and accordingly
set their revenue expectations.

4. Significance of GOLD
GOLD
Global
Currency
Investment
Tool
Hedging
against
inflation
Stable
Commodity

5. Project Architecture / Project Flow
Business
Understanding
Data
Collection
Data
Preparation
Exploratory
Data Analysis
Model
Evaluation
Model
Deployment

6. Data Collection & Details
 2182 rows & 2 column
 Year range 01-01-2016 to 21-12-2021
 Unique Date 2182 and Price 1876
These are the explanations for variables.
1) Date (object) : Daily entry date
2) Price (Float64) : Gold Price

7. Importing libraries and Data Sets

8. Exploratory Data Analysis (EDA)

9. Exploratory Data Analysis (EDA)
Information Head
Shape
Is Null present?
Find the Unique values
Duplicates
Describe the data

10. EDA (Visualization)
1.We can see that there is an increasing Trend. So, Trend is not constant.
2.Variance is also not constant.
The drastic increase in gold price after the year 2020 and there is intermediate fluctuation in 2021.
EDA Visualizations

11. Time series decomposition
•Trend - Slow moving changes in a
time series, Responsible for making
series gradually increase or decrease
over time.
•Seasonality - Seasonal Patterns in the
series. The cycles occur repeatedly
over a fixed period of time.
•Residuals - The behavior of the time
series that cannot be explained by the
trend and seasonality components.
Also called random errors/white
noise.

12. Visualizing changes in mean over 365 days
From the above plot, we can see that
there is no constant direction of the
mean (increase/decrease) which is
understandable as there might be
many external factors involved in
price fluctuation.

13. To Check Normality In The Data

15. Bar Plot

16. Pie Chart

17. Trend & Seasonality in Months

18. Trend & Seasonality in Weeks

19. Correlation Plot - EDA

20. Split The Data

23. EDA in HTML using Sweetviz

24. Modelling

25. Model Building

26. ARIMA Model

28. Forecast for the 30 Days

29. SARIMA Model

31. Forecast for next 30 Days

32. Holt Method
single & Multiple exponential smoothing

33. Fit the model tend=‘add', season=‘add'

34. EMA Model

35. Model Evaluation

36. Compering plots
ARIMA
SARIMA
Single exponential smoothing (Hot Encoding)
Multiple exponential smoothing ( Hot Encoding)

37. Compering values
EMA Model
Multiple exponential smoothing ( Hot Encoding)
Single exponential smoothing (Hot Encoding)
ARIMA
SARIMA

38. Deployment

40. Deployment App

41. Challenges faced
• Research on history of gold prices
• Deciding model building technique

42. How Challenges Overcome
• As we are not much aware of the gold price we have done a lot of
research to find out the gold price history.
• We as a team worked so hard to get the knowledge on the previous
year gold price data.
• Which helps us to do the project more effectively.

43. Deciding Model Building Technique
• As we tried many model building techniques every model don’t have
such a significant difference in the output
• We are little bit worried about the output results that we got.
• But we again overcame this as a team, Everyone has worked really
hard on this part and we finally build a model that best suits the data