https://github.com/monish-nallagondalla/positional-salaries
This project implements a Decision Tree Regression model to predict salaries based on position levels using the dataset Position_Salaries.csv. The dataset contains position levels and their corresponding salaries. The model uses a decision tree to learn the relationships between position levels and salaries and predict salaries for any given positi
https://github.com/monish-nallagondalla/positional-salaries
data-preprocessing decision-tree-algorithm decision-tree-regression feature-engineering ml-for-salary-estimation model-evaluation position-level-prediction regression-models salary-prediction supervised-learning
Last synced: 5 months ago
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This project implements a Decision Tree Regression model to predict salaries based on position levels using the dataset Position_Salaries.csv. The dataset contains position levels and their corresponding salaries. The model uses a decision tree to learn the relationships between position levels and salaries and predict salaries for any given positi
- Host: GitHub
- URL: https://github.com/monish-nallagondalla/positional-salaries
- Owner: Monish-Nallagondalla
- License: mit
- Created: 2022-03-11T05:56:05.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-17T06:08:24.000Z (11 months ago)
- Last Synced: 2025-04-05T13:30:25.951Z (8 months ago)
- Topics: data-preprocessing, decision-tree-algorithm, decision-tree-regression, feature-engineering, ml-for-salary-estimation, model-evaluation, position-level-prediction, regression-models, salary-prediction, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 33.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Position-Salaries
## **Decision Tree Regression**
### **Overview**
This project implements a **Decision Tree Regression** model to predict salaries based on position levels using the dataset **Position_Salaries.csv**. The dataset contains position levels and their corresponding salaries.
The model uses a decision tree to learn the relationships between position levels and salaries and predict salaries for any given position level.
---
### **Files**
- **Position_Salaries.csv**: This CSV file contains data on position levels (from level 1 to level 10) and their corresponding salaries. It is the dataset used for training the Decision Tree model.
- **decision_tree_regression.py**: This Python script contains the implementation of the Decision Tree Regressor model. It includes steps for importing the dataset, training the model, and visualizing the results.
---
### **Setup**
1. **Clone the Repository**:
```bash
git clone https://github.com/yourusername/Position-Salaries.git
cd Position-Salaries
2. **Install Dependencies**: Ensure you have Python and pip installed, then run:
```bash
pip install numpy pandas matplotlib scikit-learn
```
---
### **Usage**
Load the Dataset: Import the dataset from Position_Salaries.csv using Pandas to load the data for training.
Train the Model: Use the DecisionTreeRegressor from sklearn.tree to fit the model to the data. This will allow the model to learn the relationship between position levels and salaries.
---
### **Predict and Visualize**:
Predictions: Use the trained model to predict the salary for any given position level.
Visualization: Visualize the regression results using Matplotlib, which includes:
-A scatter plot of the original data points.
-A regression curve showing the model's predictions.
-A smoothed curve for better visualization of the model's performance.
---
### **Results**
The project visualizes the Decision Tree Regression results in two ways:
Scatter Plot: Displays the original data points to show the relationship between position levels and salaries.
Regression Curve: Displays the model's predictions, helping visualize how well the decision tree model has fit the data.
The curve is smoothed to enhance visualization of the model's predictions.
---
### **License**
This project is licensed under the MIT License. See the LICENSE file for details.
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