https://github.com/shwetapardhi/assignment-04-simple-linear-regression-1

Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regressi
https://github.com/shwetapardhi/assignment-04-simple-linear-regression-1

correlation-analysis data-visualization distplot eda feature-engineering model-building model-prediction model-testing numpy ols-regression p-value pandas python regression-plot rsquare-values seaborn simple-linear-regression smf statsmodel t-score

Last synced: about 2 months ago
JSON representation

Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regressi

• Host: GitHub
• URL: https://github.com/shwetapardhi/assignment-04-simple-linear-regression-1
• Owner: shwetapardhi
• Created: 2024-05-05T23:42:46.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-05T23:43:39.000Z (8 months ago)
• Last Synced: 2024-05-06T00:30:00.202Z (8 months ago)
• Topics: correlation-analysis, data-visualization, distplot, eda, feature-engineering, model-building, model-prediction, model-testing, numpy, ols-regression, p-value, pandas, python, regression-plot, rsquare-values, seaborn, simple-linear-regression, smf, statsmodel, t-score
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Assignment-04-Simple-Linear-Regression-1

Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.

EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regression.