Python data science project from Anglea Yu's course 100 Days of Code: Python on Udemy. This is a professional portfolio project to showcase what I learned from the 100 day challenge.
This analysis digs into the Determinants of Earnings Datasets from Kaggle. Loads data in, cleans as necessary for NaN or null values, and explores the data via a series of questions.
- Python
- Excel
- numpy
- pandas
- plotly
- matplotlib
- seaborn
Below are some code snippets I'm proud of from this project:
Answers the questions: How good our regression is also depends on the residuals - the difference between the model's predictions ( 𝑦̂ 𝑖 ) and the true values ( 𝑦𝑖 ) inside y_train. Do you see any patterns in the distribution of the residuals?
plt.figure(dpi=100)
plt.scatter(x=predicted_vals,
y=residuals,
c='indigo',
alpha=0.6)
plt.title('Residuals vs. Predicted Values (Simple Linear Regression)', fontsize=17)
plt.xlabel('Predicted Earnings $\hat y _i$', fontsize=14)
plt.ylabel('Residuals', fontsize=14)
plt.axhline(y=0, color= 'r', ls='--')
plt.show()Analyze the Estimated Values & Regression Residuals
# Original Regression of Actual vs. Predicted Prices
plt.figure(dpi=100)
plt.scatter(x=y_train, y=predicted_vals, c='indigo', alpha=0.6)
plt.plot(y_train, y_train, color='cyan')
plt.title(f'Actual vs Predicted Earnings: $y _i$ vs $\hat y_i$', fontsize=17)
plt.xlabel('Actual Earnings 000s $y _i$', fontsize=14)
plt.ylabel('Prediced Earnings 000s $\hat y _i$', fontsize=14)
plt.show()
# Residuals vs Predicted values
plt.figure(dpi=100)
plt.scatter(x=predicted_vals, y=residuals, c='indigo', alpha=0.6)
plt.title('Residuals vs Predicted Values', fontsize=17)
plt.xlabel('Predicted Earnings $\hat y _i$', fontsize=14)
plt.ylabel('Residuals', fontsize=14)
plt.show()