https://github.com/moindalvs/simple_linear_regression_1

Predicting Delivery Time Using Sorting Time
https://github.com/moindalvs/simple_linear_regression_1

aic bic data-transformation f-statistics likelihood log-transformation matplotlib numpy ols-regression ordinary-least-squares pandas-dataframe pandas-library prediction predictive-modeling residuals rmse-score simple-linear-regression sklearn sklearn-library

Last synced: about 2 months ago
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Predicting Delivery Time Using Sorting Time

• Host: GitHub
• URL: https://github.com/moindalvs/simple_linear_regression_1
• Owner: MoinDalvs
• Created: 2022-03-19T10:44:56.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-24T13:26:19.000Z (about 3 years ago)
• Last Synced: 2025-01-18T00:43:54.136Z (4 months ago)
• Topics: aic, bic, data-transformation, f-statistics, likelihood, log-transformation, matplotlib, numpy, ols-regression, ordinary-least-squares, pandas-dataframe, pandas-library, prediction, predictive-modeling, residuals, rmse-score, simple-linear-regression, sklearn, sklearn-library
• Language: Jupyter Notebook
• Homepage:
• Size: 1.76 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Simple_Linear_Regression_1

## Predicting Delivery Time Using Sorting Time

### Step 1 Importing Data

### Step 2 Performing EDA On Data

#### a.) Renaming columns

#### b.) Checking Datatype

#### c.) Checking for Null Values

#### d.) Checking for Duplicate Values

### Step 3 Plotting the data to check for outliers

### Step 4 Checking the Correlation between variables

### Step 5 Checking for Homoscedasticity or Hetroscedasticity

### Step 6 Feature Engineering

#### a.) Trying different transformation of data to estimate normal distribution and to remove any skewness

### Step 7 Fitting a Linear Regression Model

#### a.) Using Ordinary least squares (OLS) regression

#### b.) Square Root transformation on data

#### c.) Cube Root transformation on Data

#### d.) Log transformation on Data

### Step 8 Residual Analysis

#### a.) Test for Normality of Residuals (Q-Q Plot)

#### b.) Residual Plot to check Homoscedasticity or Hetroscedasticity

### Step 9 Model Validation

#### a.) Comparing different models with respect to their Root Mean Squared Errors

### Step 10 Predicting values from Model with Log Transformation on the Data