
- 29th Jun 2024
- 20:06 pm
In this assignment select only ONE “optimal” model designed to predict housing prices of real estate assets in a specific area given predefined asset and environmental characteristics (modified data is provided). Specifically, your FinTech team members are interested to see the effect of “age” variable in addition to other variables on the mean observed price per unit. You are free to choose one relevant model (we have covered few ML variations starting from a base simple OLS). Your task is to explain your steps and results and show model coefficients and model accuracy, for example in mean square error (MSE) and/or R^2.
Model choices
- Linear Regression
- RANSAC Regressor
- LASSO
- Polynomials
- Decision Tree
- Random Forest
Your team members are keen to learn about python and would like to see and read the python script with simple explanations of your steps. This is in light of relevant FinTech news that ZILLOW is exiting home buying business. As a result, delivery is in one python.py script format as with ‘’’’’’ explanations of your steps. The aim is to describe your detailed steps from input variables to model parameters and provide final results in a critical, concise and logical way.
FinTech Use Case - Free Assignment Solution
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Free Assignment Solution - FinTech Use Case
'''
Steps:
1. We will first load the provided dataset
2. We will remove the unwanted column such as "No"
3. We will then split the dataset as X (independent variable) and y (dependent variable, house_price_per_unit)
4. We will then fit the data into Linear Regression model (using statsmodels.regression.linear_model.OLS)
5. We will finally print the summary of the model and interpret the result
'''
# START CODE HERE
import pandas as pd
import statsmodels.api as sm
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv("housing.csv")
df.drop(columns=["No"], inplace=True)
X = df.drop(columns=["house_price_per_unit"])
y = df["house_price_per_unit"]
X = sm.add_constant(X)
model = sm.OLS(y,X)
results = model.fit()
print("Printing the bias (const) and the model coefficients\n")
print(results.params)
print("\nMSE value:", results.mse_total, "\n")
print(results.summary())
# END CODE HERE
'''
Results:
Model coefficients are:
const -14437.100802
trade_date 5.146227
age -0.269695
distance_to_MTR -0.004487
number_of_stores 1.133277
latitude 225.472976
longitude -12.423601
MSE value: 185.14
R^2 value: 0.582
On looking at the coefficients of the features, we find that
trade_date, number_of_stores, and latitude have positive coefficient value
which indicates that the house_price_per_unit increases with an increase
in any of the variables described.
On the contrary, the coefficients of the features age, distance_to_MTR and logitude
have negative coefficient value which indicates that the house_price_per_unit
decreases with an increase in any of the variables described for negative coefficient.
But going deeper into the results, we find that variable longitude have a
p-value (0.798) > significant level (0.05) which means that the coefficient
of longitude is not significant and we can discard this feature.
'''
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About The Author - Taylor Reed
He is an experienced Python developer with a specialization in financial technology (FinTech), will guide you through this assignment. Taylor's expertise lies in creating robust and efficient solutions for complex financial problems using Python. In this project, Taylor will help you understand and implement key FinTech concepts, ensuring you gain valuable insights and practical skills. With clear explanations and hands-on examples, Taylor's guidance will make learning Python for FinTech applications accessible and engaging.