https://github.com/moindalvs/simple_linear_regression_2

Building a prediction model for Salary hike using Years of Experience
https://github.com/moindalvs/simple_linear_regression_2

data-transformation log-transformation ols-regression ordinary-least-squares prediction-model scipy-stats simple-linear-regression sklearn-library

Last synced: about 8 hours ago
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Building a prediction model for Salary hike using Years of Experience

• Host: GitHub
• URL: https://github.com/moindalvs/simple_linear_regression_2
• Owner: MoinDalvs
• Created: 2022-03-19T10:49:52.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-21T18:01:01.000Z (almost 3 years ago)
• Last Synced: 2024-11-17T05:27:59.936Z (2 months ago)
• Topics: data-transformation, log-transformation, ols-regression, ordinary-least-squares, prediction-model, scipy-stats, simple-linear-regression, sklearn-library
• Language: Jupyter Notebook
• Homepage:
• Size: 1.37 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Simple_Linear_regression_2

## Building a prediction model for Salary hike

### Building a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.

### Step 1 Importing Data

### Step 2 Performing EDA On Data

#### a.) Checking Datatype

#### b.) Checking for Null Values

#### c.) 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