https://github.com/shwetapardhi/assignment-05-multiple-linear-regression-2

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t
https://github.com/shwetapardhi/assignment-05-multiple-linear-regression-2

collinearity-diagnostics cooks-distance correlation-analysis eda heteroscedasticity homoscedasticity leverage-score multi-linear-regression numpy ols-regression p-value pair-plot python r-square-values regress-exog residual-analysis smf statsmodels vif

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Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t

• Host: GitHub
• URL: https://github.com/shwetapardhi/assignment-05-multiple-linear-regression-2
• Owner: shwetapardhi
• Created: 2024-05-06T00:23:52.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-06T00:25:19.000Z (over 1 year ago)
• Last Synced: 2025-01-10T09:17:53.548Z (9 months ago)
• Topics: collinearity-diagnostics, cooks-distance, correlation-analysis, eda, heteroscedasticity, homoscedasticity, leverage-score, multi-linear-regression, numpy, ols-regression, p-value, pair-plot, python, r-square-values, regress-exog, residual-analysis, smf, statsmodels, vif
• Language: Jupyter Notebook
• Homepage:
• Size: 670 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Assignment-05-Multiple-Linear-Regression-2

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in the past few years State -- states from which data is collected Profit -- profit of each state in the past few years.

EDA

Correlation Analysis

Model Building

Model Testing

Model Validation Two Techniques: 1. Collinearity Check & 2. Residual Analysis.

Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Leverage value

Improving the Model

Model Deletion Diagnostics and Final Model

Model Predictions

table containing R^2 value for each prepared model