(1%) Describing and motivating the real-world problem:
In the marketsĀ of venture capital and growth equity, information regarding investment prospects is frequently scarce. This is especially difficult for institutional investors, wealth managers, and high-net-worth individuals seeking to invest substantial quantities of money.
The suggested challenge entails the need for a dataset that delivers comprehensive and up-to-date information about venture capital and growth equity brokerage to institutional investors, wealth managers, and high-net-worth individuals. Among other things, the data will contain bid-offer spread pricing, industry trend graphs, and sector performance trend graphs. The significance of this dataset is that it will offer investors with the knowledge they need to make informed investment decisions. Data analytics, according to a Deloitte analysis, is altering the financial services business, and organizations that use data to deliver greater consumer insights and services will have a competitive advantage.
(1%) Loading and showing the dataset:
It is of the utmost importance to supply a dataset that is both thorough and up to date. Investors will benefit from having the significance of each attribute explained in the data dictionary since this will help them better grasp the data. A data visualization would also be beneficial in displaying the patterns and trends that are present in the data. This may be done by using the data.
The below example shows the trend of META's stock in the year of 2013.
import pandas as pd
# Load the historical stock price data
data = pd.read_csv('META.csv')
# Convert the date column to a datetime object
data['Date'] = pd.to_datetime(data['Date'])
# Show the data in a table
print(data[['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']])
Date Open High Low Close Adj Close \
0 2012-05-18 42.049999 45.000000 38.000000 38.230000 38.230000
1 2012-05-21 36.529999 36.660000 33.000000 34.029999 34.029999
2 2012-05-22 32.610001 33.590000 30.940001 31.000000 31.000000
3 2012-05-23 31.370001 32.500000 31.360001 32.000000 32.000000
4 2012-05-24 32.950001 33.209999 31.770000 33.029999 33.029999
... ... ... ... ... ... ...
2683 2023-01-18 135.809998 137.250000 132.800003 133.020004 133.020004
2684 2023-01-19 132.490005 137.449997 132.139999 136.149994 136.149994
2685 2023-01-20 135.889999 139.940002 134.610001 139.369995 139.369995
2686 2023-01-23 139.289993 143.759995 138.660004 143.270004 143.270004
2687 2023-01-24 141.690002 145.000000 141.360001 143.139999 143.139999
Volume
0 573576400
1 168192700
2 101786600
3 73600000
4 50237200
... ...
2683 20215500
2684 28625200
2685 28643100
2686 27470100
2687 21835300
[2688 rows x 7 columns]
import pandas as pd
import matplotlib.pyplot as plt
# Load the historical stock price data
data = pd.read_csv('META.csv')
# Convert the date column to a datetime object
data['Date'] = pd.to_datetime(data['Date'])
# Plot the closing prices over time
plt.plot(data['Date'], data['Close'])
plt.xlabel('Date')
plt.ylabel('Closing Price')
plt.title('Closing Prices of Stock over Time')
plt.show()
(1%) How the data will be used to solve the problem:
The dataset might be utilized, in the early phases of the project, to find patterns and trends in the data by applying fundamental data analysis techniques. In the future, the data may be put to use in the development of predictive models that may be used to forecast market trends, bid-offer spread pricing, and sector performance. The dataset might be used to construct prediction models, which would then be used to make judgments regarding investments. These predictive models could be developed using machine learning techniques such as regression analysis, clustering, and neural networks.
In general, the project that has been offered offers a solution to a problem that already exists in the real world and has the ability to offer insightful information to investors. The success of the project is going to be directly proportional to the quality and completeness of the dataset.