Skip to content
Projects
Groups
Snippets
Help
Loading...
Help
Support
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in / Register
Toggle navigation
2
22_23-J 56
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Issues
0
Issues
0
List
Boards
Labels
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Analytics
Analytics
CI / CD
Repository
Value Stream
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
22_23-J 56
22_23-J 56
Commits
a6f93cf1
Commit
a6f93cf1
authored
Feb 17, 2023
by
IT19954806 Devindi L.A.P.S
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Add new file of Linear regression model
parent
96b63182
Changes
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
36 additions
and
0 deletions
+36
-0
Linear Regression Model
Linear Regression Model
+36
-0
No files found.
Linear Regression Model
0 → 100644
View file @
a6f93cf1
# To train linear regression we are passing the training data
linreg_model = LinearRegression()
linreg_model.fit(x_train, y_train)
# Using the predict function to get the output via test data
linreg_pred = linreg_model.predict(x_test)
# Transform the prediction back to it's original state
linreg_pred = linreg_pred.reshape(-1,1)
linreg_pred_test_set = np.concatenate([linreg_pred,x_test], axis=1)
linreg_pred_test_set = scaler.inverse_transform(linreg_pred_test_set)
# calculating the predicted exports values from the difference values and append that to the predicted data frame
result_list = []
for index in range(0, len(linreg_pred_test_set)):
result_list.append(linreg_pred_test_set[index][0] + act_exports[index])
linreg_pred_series = pd.Series(result_list,name='linreg_pred')
predict_df = predict_df.merge(linreg_pred_series, left_index=True, right_index=True)
# Now that we have calculated the predicted data and now we can evaluate it with varius matrix to test it's accuracy
linreg_rmse = np.sqrt(mean_squared_error(predict_df['linreg_pred'], monthly_exports['Quantity'][-12:]))
linreg_mae = mean_absolute_error(predict_df['linreg_pred'], monthly_exports['Quantity'][-12:])
linreg_r2 = r2_score(predict_df['linreg_pred'], monthly_exports['Quantity'][-12:])
print('Linear Regression RMSE: ', linreg_rmse)
print('Linear Regression MAE: ', linreg_mae)
print('Linear Regression R2 Score: ', linreg_r2)
# Visualising the data with the original values
plt.figure(figsize=(15,7))
plt.plot(monthly_exports['Date'], monthly_exports['Quantity'])
plt.plot(predict_df['Date'], predict_df['linreg_pred'])
plt.title("Export Forecast using Linear Regression")
plt.xlabel("Date")
plt.ylabel("Exports")
plt.legend(["Original exports", "Predicted exports"])
plt.show()
\ No newline at end of file
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment